A networking method and system for unmanned aerial vehicles based on BeiDou positioning

Abstract

This invention discloses a networking method and system for unmanned aerial vehicles (UAVs) based on BeiDou positioning, specifically relating to the field of UAV networking technology. By constructing a dynamic adjacency graph for UAVs, and further combining the remaining power, communication load, and trajectory stability of nodes, a multi-factor relay evaluation function is formed to screen relay nodes. Two key indicators are introduced: an energy state heterogeneity imbalance coefficient and a relay node instability propagation chain break coefficient, enabling real-time monitoring of the relay node's operating status and prediction of risk trends. Based on these two coefficients, a communication cascading failure analysis model is constructed to assess the cascading communication collapse risk faced by the entire network in real time. When the index exceeds a threshold, a dynamic relay reconstruction mechanism is immediately triggered, selecting the best candidate nodes to replace high-risk nodes, and completing structural optimization without interrupting the mission communication chain.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) networking technology, and more specifically, to a networking method and system for UAVs based on BeiDou positioning. Background Technology

[0002] With the widespread application of multi-UAV collaborative systems in complex mission scenarios such as emergency communication, disaster relief, and border patrol, building a networking mechanism with high reliability, self-organization capabilities, and wide-area coverage has become a key technology. Traditional multi-UAV networking schemes mostly rely on Wi-Fi Mesh, LTE relay, or short-range communication protocols to achieve mission coordination and data transmission. However, in dynamic formation flight and cross-regional deployment, limitations such as position errors, link instability, and insufficient communication link redundancy can easily lead to mission interruptions. The BeiDou Navigation Satellite System, as a positioning system with centimeter-level positioning accuracy and two-way short message communication capabilities, provides a technological foundation for high-precision collaborative networking of multi-UAVs. However, existing networking methods based on BeiDou positioning generally fail to adequately address the systemic risks caused by heterogeneous node energy states and relay node instability and propagation link breaks.

[0003] Specifically, in multi-node heterogeneous networks, significant differences in drone loads, task densities, and relay responsibilities lead to rapid energy consumption in some nodes, especially critical nodes that have long served as network relays, which are more prone to "energy overload." If the scheduling mechanism lacks dynamic awareness and policy adaptation to the remaining energy of nodes, relay nodes may suddenly crash during operation, causing network topology breaks. As a result, the task nodes they originally covered will become "collaboration islands," losing communication capabilities and task continuation paths, leading to the failure of original routing links, interruption of collaborative task mechanisms, and systemic communication cascading failures, ultimately causing systemic network collapse and task collaboration failure. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, embodiments of the present invention provide a networking method and system for unmanned aerial vehicles based on BeiDou positioning, so as to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] A networking method for unmanned aerial vehicles (UAVs) based on BeiDou positioning includes the following steps:

[0007] Step S1: Each UAV activates the BeiDou high-precision positioning module to obtain the local position vector, calculates the Euclidean distance between UAV nodes based on the local position vector, defines the adjacency relationship within the communication radius, and constructs a dynamic adjacency graph of UAV nodes.

[0008] Step S2: Obtain the remaining battery power, communication load, and trajectory stability of each UAV node to construct a relay evaluation function and filter relay nodes from the UAV node dynamic adjacency graph.

[0009] Step S3: Obtain the energy state heterogeneity imbalance information of the relay node, and obtain the energy state heterogeneity imbalance coefficient based on the energy state heterogeneity imbalance information;

[0010] Step S4: Obtain relay node instability propagation link break information, and obtain relay node instability propagation link break coefficient based on relay node instability propagation link break information;

[0011] Step S5: Construct a communication cascade failure analysis model based on the energy state heterogeneity imbalance coefficient and the relay node instability propagation link breakage coefficient, obtain the communication cascade failure analysis index, and assess the communication cascade failure risk of the current network system.

[0012] Step S6: Compare the communication cascade failure analysis index with the preset communication cascade failure analysis index threshold, and dynamically schedule the relay nodes.

[0013] In a preferred embodiment, the communication load is measured by a forwarding ratio, and the specific formula for calculating the forwarding ratio is as follows: ,in Forwarding ratio, This refers to the amount of data relayed by the drone within the time window CK. This represents the total amount of data processed by the drone within the time window CK.

[0014] The track stability is measured by the heading deviation difference. The heading angle measurement of the UAV is recorded N times within the time window CK, and the heading deviation difference is calculated. ,in This is the difference in heading. This is the (n+1)th heading angle measurement value. This is the value of the nth heading angle measurement.

[0015] In a preferred embodiment, the remaining battery power of the drone node is obtained at the end of the time window CK to construct the relay evaluation function, as follows: ,in This is the relay evaluation value. Remaining battery power Forwarding ratio, This is the difference in heading. These represent preset proportional coefficients for remaining battery power, forwarding ratio, and heading deviation difference, respectively. All are greater than 0.

[0016] In a preferred embodiment, the relay evaluation value is compared with a preset relay evaluation value threshold, and relay nodes are selected from the UAV node dynamic adjacency graph, as follows:

[0017] If the relay evaluation value is greater than the relay evaluation value threshold, the currently evaluated drone node will be added to the candidate relay node set.

[0018] If the relay evaluation value is less than or equal to the relay evaluation value threshold, then there is no need to add the currently evaluated drone node to the candidate relay node set.

[0019] The drone nodes in the candidate relay node set are sorted from largest to smallest according to their relay evaluation values ​​and assigned a sorting value. Drone nodes are selected from the candidate relay node set as relay nodes in order of the required number of relay nodes. The remaining relay nodes are added to the candidate relay node set and sorted from largest to smallest according to their relay evaluation values.

[0020] In a preferred embodiment, by acquiring the energy state heterogeneity imbalance information of the relay node, the energy state heterogeneity imbalance of the relay node is analyzed, and the energy state heterogeneity imbalance coefficient is calculated to measure the degree of energy state heterogeneity imbalance of the relay node.

[0021] The logic for obtaining the energy state heterogeneity imbalance coefficient is as follows:

[0022] Get the normalized remaining battery power array of all current relay nodes: , , Let x be the remaining power of the x-th relay node. This represents the number of relay nodes;

[0023] The total energy is obtained by summing the remaining energy of all relay nodes in the remaining energy array. And obtain the energy percentage of each relay node based on the total energy. ;

[0024] Calculate the energy distribution value based on the energy percentage. ;

[0025] Calculate the energy distribution index based on the energy distribution value. ;

[0026] Analyze the energy differences between relay nodes and construct an energy difference coefficient. The energy difference coefficient is then normalized to the average energy difference. ;

[0027] Calculate the energy imbalance coefficient based on the average energy difference. ;

[0028] Calculate the energy state heterogeneity imbalance coefficient based on the energy imbalance coefficient and the energy distribution index. : ,in This is a very small constant used to prevent division by zero.

[0029] In a preferred embodiment, by acquiring relay node instability propagation link breakage information of relay nodes, the risk of propagation link breakage caused by relay node instability is analyzed, and the relay node instability propagation link breakage coefficient is calculated to measure the degree of risk of propagation link breakage caused by relay node instability.

[0030] The logic for obtaining the link breakage coefficient during relay node instability propagation is as follows:

[0031] Obtain the link quality time series for each relay node ;

[0032] For each relay node, in the sliding window Internally, calculate the discrete difference of link quality. ;

[0033] Calculate the standard deviation of discrete differences ;

[0034] Dynamic adjacency graph of drone nodes Non-relay nodes are removed, and a relay node subgraph is constructed. This subgraph is then mapped to a relay adjacency matrix, and a Laplace matrix is ​​constructed. The second smallest eigenvalue is obtained by solving the eigenvalue problem of the Laplace matrix and is used as the algebraic connectivity of the relay nodes. ;

[0035] For each relay node x, perform a simulated removal operation, and then recalculate the algebraic connectivity of the relay nodes in the remaining relay node subgraph. ;

[0036] Calculate the toughness loss factor of relay nodes : ;

[0037] For each pair of neighbors Calculate its propagation probability ;

[0038] Map all propagation probabilities to local propagation probability matrices, and normalize these local propagation probability matrices to obtain a normalized transition matrix. The elements of the normalized transition matrix are: ;

[0039] Calculate the propagation strength of the cascaded nodes : ;

[0040] Calculate the link breakage coefficient of relay node instability propagation : ,in This represents the number of relay nodes. This is a very small constant used to prevent division by zero.

[0041] In a preferred embodiment, a communication cascade failure analysis model is constructed based on the energy state heterogeneity imbalance coefficient and the relay node instability propagation link breakage coefficient to obtain the communication cascade failure analysis index. The formula used in the communication cascade failure analysis model is as follows: In the formula This is a communication cascade failure analysis index. The energy state heterogeneity imbalance coefficient. The link breakage coefficient during relay node instability propagation. These represent preset proportional coefficients for the energy state heterogeneity imbalance coefficient and the relay node instability propagation link breakage coefficient, respectively. All are greater than 0.

[0042] In a preferred embodiment, the communication cascading failure analysis index is compared with a preset communication cascading failure analysis index threshold to dynamically schedule relay nodes, as follows:

[0043] If the communication cascade failure analysis index is greater than the communication cascade failure analysis index threshold, the relay reconstruction mechanism will be immediately activated to replace the existing high-risk relay nodes one by one from the candidate relay node set according to priority.

[0044] If the communication cascade failure analysis index is less than or equal to the communication cascade failure analysis index threshold, then the current relay node distribution and scheduling status remain unchanged.

[0045] In a preferred embodiment, a networking system for unmanned aerial vehicles (UAVs) based on BeiDou positioning includes an adjacency graph construction module, a relay node screening module, an energy imbalance module, an instability propagation module, a communication cascade failure analysis module, and a dynamic scheduling module.

[0046] The adjacency graph construction module is used to obtain the local position vector by activating the Beidou high-precision positioning module of each UAV, calculate the Euclidean distance between UAV nodes based on the local position vector, and define the adjacency relationship within the communication radius to construct a dynamic adjacency graph of UAV nodes.

[0047] The relay node filtering module is used to obtain the remaining power, communication load, and trajectory stability of each UAV node to construct a relay evaluation function and filter relay nodes from the UAV node dynamic adjacency graph.

[0048] The energy imbalance module is used to obtain the energy state heterogeneity imbalance information of the relay node and obtain the energy state heterogeneity imbalance coefficient based on the energy state heterogeneity imbalance information.

[0049] The instability propagation module is used to obtain relay node instability propagation link break information and obtain the relay node instability propagation link break coefficient based on the relay node instability propagation link break information.

[0050] The communication cascade failure analysis module is used to construct a communication cascade failure analysis model based on the energy state heterogeneity imbalance coefficient and the relay node instability propagation link breakage coefficient, obtain the communication cascade failure analysis index, and assess the communication cascade failure risk of the current network system.

[0051] The dynamic scheduling module is used to compare the communication cascade failure analysis index with the preset communication cascade failure analysis index threshold and to dynamically schedule relay nodes.

[0052] The technical effects and advantages of this invention are as follows:

[0053] 1. This invention constructs a dynamic adjacency graph for UAVs and further combines the remaining power, communication load, and trajectory stability of nodes to form a multi-factor relay evaluation function to screen relay nodes. It introduces two key indicators: the energy state heterogeneity imbalance coefficient and the relay node instability propagation link breakage coefficient. This enables real-time monitoring and risk trend prediction of relay node operation status. The energy state heterogeneity imbalance coefficient accurately characterizes the uneven distribution of remaining energy among relay nodes, identifying potential energy bottlenecks that may cause overload and subsequent shutdowns. The relay node instability propagation link breakage coefficient quantifies the risk of node failure disrupting the network propagation path through time-series analysis of link quality and resilience assessment of the propagation structure. Based on these two coefficients, a communication cascading failure analysis model is constructed to assess the risk of cascading communication collapse faced by the entire network in real time. When the index exceeds a threshold, a dynamic relay reconstruction mechanism is immediately triggered, selecting the best candidate nodes to replace high-risk nodes and completing structural optimization without interrupting the mission communication chain. Attached Figure Description

[0054] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings;

[0055] Figure 1 This is a flowchart of the method in Embodiment 1 of the present invention;

[0056] Figure 2 This is a structural diagram of the system in Embodiment 2 of the present invention. Detailed Implementation

[0057] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. 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.

[0058] Example 1: Figure 1 This invention presents a networking method for unmanned aerial vehicles (UAVs) based on BeiDou positioning, comprising the following steps:

[0059] Step S1: Each UAV activates the BeiDou high-precision positioning module to obtain the local position vector, calculates the Euclidean distance between UAV nodes based on the local position vector, defines the adjacency relationship within the communication radius, and constructs a dynamic adjacency graph of UAV nodes.

[0060] Step S2: Obtain the remaining battery power, communication load, and trajectory stability of each UAV node to construct a relay evaluation function and filter relay nodes from the UAV node dynamic adjacency graph.

[0061] Step S3: Obtain the energy state heterogeneity imbalance information of the relay node, and obtain the energy state heterogeneity imbalance coefficient based on the energy state heterogeneity imbalance information;

[0062] Step S4: Obtain relay node instability propagation link break information, and obtain relay node instability propagation link break coefficient based on relay node instability propagation link break information;

[0063] Step S5: Construct a communication cascade failure analysis model based on the energy state heterogeneity imbalance coefficient and the relay node instability propagation link breakage coefficient, obtain the communication cascade failure analysis index, and assess the communication cascade failure risk of the current network system.

[0064] Step S6: Compare the communication cascade failure analysis index with the preset communication cascade failure analysis index threshold, and dynamically schedule the relay nodes.

[0065] In step S1, each UAV activates its BeiDou high-precision positioning module to obtain its local position vector in a unified coordinate system: ,in This represents the latitude value of the i-th drone node. This represents the longitude value of the i-th drone node. This represents the altitude of the i-th drone node. The Euclidean distance between drone nodes is calculated based on the local location vector. : ,in Let be the local location vector of the j-th UAV node. These represent the latitude, longitude, and altitude of the j-th UAV node, respectively.

[0066] Within the communication radius, adjacency relationships are defined to construct a dynamic adjacency graph for UAV nodes, as follows:

[0067] Set the communication radius of the drone ,like Then, an adjacency relationship is established between drone node i and drone node j, and an adjacency matrix is ​​constructed. : ,in These are the elements in the adjacency matrix;

[0068] Adjacency matrix Mapped as a dynamic adjacency graph of drone nodes ,in This is the set of ID numbers (nodes) for drones. The set of edges that satisfy the communication radius condition, i.e., the adjacent pairs (i,j);

[0069] Step S2: Obtain the remaining battery power, communication load, and trajectory stability of each UAV node to construct a relay evaluation function and filter relay nodes from the UAV node dynamic adjacency graph.

[0070] The communication load is measured by the forwarding ratio, which is an important indicator of the communication load level when a UAV node acts as a network relay. The specific formula for calculating the forwarding ratio is as follows: ,in Forwarding ratio, This refers to the amount of data relayed by the drone within the time window CK. This represents the total amount of data processed by the drone within the time window CK.

[0071] The track stability is measured by the heading deviation difference and is used to represent the stability of the UAV's flight track;

[0072] Record the UAV's heading angle measurement N times within the time window CK, and calculate the heading offset difference: ,in This is the difference in heading. This is the (n+1)th heading angle measurement value. This is the value of the nth heading angle measurement;

[0073] At the end of the time window CK, obtain the remaining battery power of the drone node to construct the relay evaluation function, as follows: ,in This is the relay evaluation value. Remaining battery power Forwarding ratio, This is the difference in heading. These represent preset proportional coefficients for remaining battery power, forwarding ratio, and heading deviation difference, respectively. All are greater than 0;

[0074] It should be noted that the above formulas are all dimensionless calculations. Commonly used methods for removing dimensions include Min-Max normalization and Z-Score standardization, which will not be elaborated here. The settings should be tailored to the specific circumstances. For example, an expert-empowered approach could be adopted, where experts in relevant fields are invited to determine the pre-defined proportions for each indicator through professional opinion surveys and comprehensive evaluations. It can be 0.3, 0.5, or 0.4;

[0075] The relay evaluation value is compared with a preset relay evaluation value threshold, and relay nodes are selected from the UAV node dynamic adjacency graph, as follows:

[0076] If the relay evaluation value is greater than the relay evaluation value threshold, the currently evaluated drone node will be added to the candidate relay node set.

[0077] If the relay evaluation value is less than or equal to the relay evaluation value threshold, then there is no need to add the currently evaluated drone node to the candidate relay node set.

[0078] The drone nodes in the candidate relay node set are sorted from largest to smallest according to their relay evaluation values ​​and assigned a sorting value. Drone nodes are selected from the candidate relay node set as relay nodes in order of the required number of relay nodes. The remaining relay nodes are added to the candidate relay node set and sorted from largest to smallest according to their relay evaluation values.

[0079] Step S3: Obtain the energy state heterogeneity imbalance information of the relay node, and obtain the energy state heterogeneity imbalance coefficient based on the energy state heterogeneity imbalance information;

[0080] The energy state heterogeneity imbalance coefficient in this invention is used to quantify the degree of energy state heterogeneity imbalance in relay nodes, aiming to dynamically perceive the energy heterogeneity and potential imbalance of relay nodes in UAV networking. This coefficient not only reflects the "balance" of the overall energy state of relay nodes, but also provides a basis for subsequent communication stability control, routing adjustment, and risk prediction. Its initial design intention is to reveal the problem of energy gradient asymmetry in relay nodes caused by different node energy consumption rates, uneven task loads, or heterogeneous deployment environments, and thus capture the potential impact of this feature on the stability of the communication network structure.

[0081] Specifically, a large energy state heterogeneity imbalance coefficient indicates that some relay nodes in the system have significantly less remaining energy than other nodes, i.e., there is a cluster of "high-load, low-power" nodes. This energy state difference significantly increases the risk of critical relays being decommissioned. Once such nodes fail, it may trigger a sudden break in the network topology, causing multiple ordinary task nodes to become communication islands, severely interfering with task coordination and command issuance. At the same time, because data traffic must be rerouted to other high-energy relay nodes, the new relay nodes will bear additional forwarding pressure in a short period of time, thereby accelerating their energy consumption, forming a vicious cycle of "load migration - energy overdraft," ultimately causing multi-point failures and chain-like collapses in communication cascading failures.

[0082] Conversely, when the energy state heterogeneity imbalance coefficient is small, it indicates that the remaining power among relay nodes is relatively balanced, and the network's carrying structure is in a relatively stable state. In this case, even if any node is decommissioned, it is easier to be smoothly replaced by a neighboring node, and the communication path has better redundancy and recoverability, thus effectively curbing the probability of chain-reaction breakdowns. Therefore, by calculating and tracking the changing trend of this coefficient in real time, the system can dynamically identify potentially high-risk topologies, provide early warnings and schedule node resources, delay the arrival of relay instability points, and maximize network lifetime and mission duration.

[0083] This invention constructs a relay stability assessment mechanism centered on the energy state heterogeneity imbalance coefficient, enabling quantitative prediction and intervention decision-making for communication cascade failure risks in a distributed UAV network environment. Compared to traditional methods that use single-node power or link strength as scheduling criteria, this scheme considers the non-uniformity of the overall energy state of the relay system more macroscopically, making network scheduling more forward-looking and structurally sensitive. It not only improves the network's resilience in the face of sudden load migrations but also enhances stable operation in complex environments such as high-density communication and high-frequency scheduling. Ultimately, this method achieves a leap from "node-level monitoring" to "network-level prediction," providing more robust and intelligent control support for BeiDou UAV networking systems under complex terrain and dynamic missions.

[0084] Therefore, by acquiring the energy state heterogeneity imbalance information of relay nodes, the energy state heterogeneity imbalance of relay nodes is analyzed, and the energy state heterogeneity imbalance coefficient is calculated to measure the degree of energy state heterogeneity imbalance of relay nodes.

[0085] The logic for obtaining the energy state heterogeneity imbalance coefficient is as follows:

[0086] Get the normalized remaining battery power array of all current relay nodes: , , Let x be the remaining power of the x-th relay node. This represents the number of relay nodes;

[0087] The total energy is obtained by summing the remaining energy of all relay nodes in the remaining energy array. And obtain the energy percentage of each relay node based on the total energy. : ;

[0088] Calculate the energy distribution value based on the energy percentage. : ;

[0089] Calculate the energy distribution index based on the energy distribution value. : ;

[0090] Analyze the energy differences between relay nodes and construct an energy difference coefficient. : The energy difference coefficient is then normalized to the average energy difference. : ;

[0091] Calculate the energy imbalance coefficient based on the average energy difference. : ;

[0092] Calculate the energy state heterogeneity imbalance coefficient based on the energy imbalance coefficient and the energy distribution index. : ,in This is to prevent division by zero by a very small constant (generally taken as...). );

[0093] It should be noted that the above formulas are all dimensionless calculations. Commonly used methods for removing dimensions include Min-Max normalization and Z-Score standardization, which will not be elaborated here.

[0094] Step S4: Obtain relay node instability propagation link break information, and obtain relay node instability propagation link break coefficient based on relay node instability propagation link break information;

[0095] In this invention, the relay node instability propagation link breakage coefficient is an important indicator used to measure the risk of link breakage that may be caused by the instability of a relay node under a specific operating state. It reflects the degree of coupling between the node's structural criticality and stability state in the network. A large relay node instability propagation link breakage coefficient indicates that the node is in a highly structurally sensitive position or its state is unstable, such as a core node in a multi-hop forwarding path, connecting multiple critical task nodes, or experiencing severe track jitter and communication load fluctuations. When such a node fails, it is very easy to cause multi-path cascading breakage or network partitioning, which not only leads to the loss of communication between some task units, but may also cause the interruption of the flow of instructions, data, status and other information in the system, thereby destroying the entire distributed task coordination structure and forming a cascading risk channel of "propagation interruption - task disorder - scheduling imbalance". Conversely, a small relay node instability propagation link breakage coefficient indicates that the node is in a network position with high redundancy or its state is stable. Even if a short-term failure occurs, its impact on the overall communication structure is relatively limited. In this scenario, the system can quickly initiate path reconstruction or node migration operations to achieve communication self-healing, effectively preventing potential link cascading collapses and ensuring the stability of the network topology and the continuity of the task flow. By introducing a relay node instability propagation link breakage coefficient into the system, the UAV network can possess higher fault prediction and response capabilities. Specifically, this coefficient can be combined with an energy state heterogeneous imbalance coefficient to construct a communication cascading failure analysis model. Starting from the complete link of "node instability—link breakage—task loss," it quantifies the evolution trend of system communication risks and dynamically assesses the steady-state degree of the entire UAV swarm's communication capabilities. Ultimately, this invention, by dynamically sensing and quantifying the instability propagation risk of relay nodes, achieves a leap from a "static routing reliability" to a "dynamic link cascading fragility" assessment model. This significantly enhances the robustness and adaptability of the UAV network under BeiDou positioning assistance to uncertainties such as environmental disturbances and sudden changes in task load, and greatly improves system-level communication security, task continuity, and the intelligent resilience of self-organizing networks.

[0096] Therefore, by obtaining relay node instability propagation link breakage information, we can analyze the risk of propagation link breakage caused by relay node instability and calculate the relay node instability propagation link breakage coefficient to measure the degree of risk of propagation link breakage caused by relay node instability.

[0097] The logic for obtaining the link breakage coefficient during relay node instability propagation is as follows:

[0098] Obtain the link quality time series for each relay node ,in For the first Each relay node in time Link quality data (such as SNR, packet loss rate, latency jitter, etc.). This refers to the time window for collecting link quality data.

[0099] For each relay node, in the sliding window Internally, calculate the discrete difference of link quality. : , ;

[0100] Calculate the standard deviation of discrete differences : ,in The average of the discrete differences. ;

[0101] Dynamic adjacency graph of drone nodes Non-relay nodes are removed, and a relay node subgraph is constructed. This subgraph is mapped to a relay adjacency matrix, and a Laplace matrix is ​​constructed (the Laplace matrix is ​​obtained by subtracting a degree matrix from the relay adjacency matrix; the degree matrix is ​​a diagonal matrix used to represent the connectivity degree of each relay node in the relay node subgraph, i.e., the number of edges connected to it). The second smallest eigenvalue is obtained by solving the eigenvalue problem of the Laplace matrix and is used as the algebraic connectivity degree of the relay nodes. ;

[0102] For each relay node x, perform a simulated removal operation (i.e., temporarily remove the node and its edges), and then recalculate the algebraic connectivity of the remaining relay node subgraphs. ;

[0103] Calculate the toughness loss factor of relay nodes : ;

[0104] For each pair of neighbors (i.e., a pair of relay nodes with an element of 1 in the relay adjacency matrix), calculate their propagation probability. : ,in The standard deviation represents the discrete difference of relay node x. The standard deviation represents the discrete difference of relay node v;

[0105] Map all propagation probabilities to a local propagation probability matrix (setting all other non-neighbor positions to 0), and normalize the local propagation probability matrix to obtain a normalized transition matrix. The elements of the normalized transition matrix are: ;

[0106] Calculate the propagation strength of the cascaded nodes : ;

[0107] Calculate the link breakage coefficient of relay node instability propagation : ,in This represents the number of relay nodes. This is to prevent division by zero by a very small constant (generally taken as...). );

[0108] It should be noted that the above formulas are all dimensionless calculations. Commonly used methods for removing dimensions include Min-Max normalization and Z-Score standardization, which will not be elaborated here.

[0109] Step S5: Construct a communication cascade failure analysis model based on the energy state heterogeneity imbalance coefficient and the relay node instability propagation link breakage coefficient, obtain the communication cascade failure analysis index, and assess the communication cascade failure risk of the current network system.

[0110] A communication cascading failure analysis model is constructed based on the energy state heterogeneity imbalance coefficient and the relay node instability propagation link breakage coefficient. The communication cascading failure analysis index is obtained, and the formula used in the model is as follows: In the formula This is a communication cascade failure analysis index. The energy state heterogeneity imbalance coefficient. The link breakage coefficient during relay node instability propagation. These represent preset proportional coefficients for the energy state heterogeneity imbalance coefficient and the relay node instability propagation link breakage coefficient, respectively. All are greater than 0;

[0111] It should be noted that the above formulas are all dimensionless calculations. Commonly used methods for removing dimensions include Min-Max normalization and Z-Score standardization, which will not be elaborated here. The settings should be tailored to the specific circumstances. For example, an expert-empowered approach could be adopted, where experts in relevant fields are invited to determine the pre-defined proportions for each indicator through professional opinion surveys and comprehensive evaluations. It can be 0.5 or 0.5;

[0112] As can be seen from the above calculation expressions, the larger the energy state heterogeneity imbalance coefficient and the larger the relay node instability propagation link break coefficient, the larger the communication cascade failure analysis index. This indicates that the current UAV networking system faces a higher risk of communication cascade failure, and the overall structural stability and collaborative communication capability of the network are in a highly vulnerable state, easily affected by the amplification of local node failures or link mutations. Conversely, the smaller the energy state heterogeneity imbalance coefficient and the smaller the relay node instability propagation link break coefficient, the smaller the communication cascade failure analysis index. This indicates that the current UAV networking system has a low risk of communication cascade failure, and the network has good resilience, load balancing capability, and communication stability.

[0113] Step S6: Compare the communication cascade failure analysis index with the preset communication cascade failure analysis index threshold, and dynamically schedule the relay nodes, as follows:

[0114] If the communication cascade failure analysis index is greater than the communication cascade failure analysis index threshold, it indicates that there is a significant imbalance in energy state or fragile propagation stability among relay nodes. This may lead to problems such as concentrated energy depletion of relay nodes, topology disconnection due to multiple link breaks, and a sharp decline in the transmission efficiency of collaborative tasks. The relay reconstruction mechanism should be activated immediately to replace the existing high-risk relay nodes one by one from the candidate relay node set (i.e., ordinary nodes within the communication radius that have forwarding capabilities but have not yet been selected as relays) according to priority.

[0115] If the communication cascade failure analysis index is less than or equal to the communication cascade failure analysis index threshold, it indicates that the relay nodes are in good condition in terms of energy consumption distribution, link stability, and structural connectivity. The current relay node distribution and scheduling status should be kept unchanged to avoid introducing new load fluctuations or topology disturbances due to unnecessary scheduling.

[0116] It should be noted that the threshold for the communication cascading failure analysis index can be set according to specific application requirements, task fault tolerance, security level, and network scale. The main setting methods include the following:

[0117] Empirical threshold method: This method is set based on a large amount of experimental or simulation experience, such as using the peak value of the communication cascade failure analysis index before system failure as a reference.

[0118] Expert empowerment method: determined through joint evaluation by experts in fields such as communication networks, security assurance, and mission strategy;

[0119] Application scenario method: For high-reliability scenarios such as disaster emergency response and tactical communication, a lower threshold can be set, while a higher threshold can be tolerated for low-risk scenarios.

[0120] For example, in routine communication tasks, the threshold for the communication cascade failure analysis index can be set to 0.4, while in tasks requiring high stability, the threshold can be set to 0.25 or lower.

[0121] This invention constructs a dynamic adjacency graph for UAVs and further combines the remaining power, communication load, and trajectory stability of nodes to form a multi-factor relay evaluation function to screen relay nodes. It introduces two key indicators: the energy state heterogeneity imbalance coefficient and the relay node instability propagation link breakage coefficient, to achieve real-time monitoring and risk trend prediction of relay node operation status. The energy state heterogeneity imbalance coefficient accurately characterizes the uneven distribution of remaining energy among relay nodes, identifying potential energy bottlenecks that may cause downtime due to overload. The relay node instability propagation link breakage coefficient quantifies the risk of node failure disrupting the network propagation path through time-series analysis of link quality and resilience assessment of the propagation structure. Based on these two coefficients, a communication cascading failure analysis model is constructed to assess the risk of cascading communication collapse faced by the entire network in real time. When the index exceeds a threshold, a dynamic relay reconstruction mechanism is immediately triggered, selecting the best candidate nodes to replace high-risk nodes and completing structural optimization without interrupting the mission communication chain.

[0122] Example 2: This example introduces a networking system for unmanned aerial vehicles (UAVs) based on BeiDou positioning. Figure 2 As shown, it includes an adjacency graph construction module, a relay node selection module, an energy imbalance module, an instability propagation module, a communication cascade failure analysis module, and a dynamic scheduling module;

[0123] The adjacency graph construction module is used to obtain the local position vector by activating the Beidou high-precision positioning module of each UAV, calculate the Euclidean distance between UAV nodes based on the local position vector, and define the adjacency relationship within the communication radius to construct a dynamic adjacency graph of UAV nodes.

[0124] The relay node filtering module is used to obtain the remaining power, communication load, and trajectory stability of each UAV node to construct a relay evaluation function and filter relay nodes from the UAV node dynamic adjacency graph.

[0125] The energy imbalance module is used to obtain the energy state heterogeneity imbalance information of the relay node and obtain the energy state heterogeneity imbalance coefficient based on the energy state heterogeneity imbalance information.

[0126] The instability propagation module is used to obtain relay node instability propagation link break information and obtain the relay node instability propagation link break coefficient based on the relay node instability propagation link break information.

[0127] The communication cascade failure analysis module is used to construct a communication cascade failure analysis model based on the energy state heterogeneity imbalance coefficient and the relay node instability propagation link breakage coefficient, obtain the communication cascade failure analysis index, and assess the communication cascade failure risk of the current network system.

[0128] The dynamic scheduling module is used to compare the communication cascade failure analysis index with the preset communication cascade failure analysis index threshold and to dynamically schedule the relay nodes.

[0129] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0130] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0131] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0132] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process of the system and method described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0133] In the several embodiments provided in this application, it should be understood that the disclosed systems and methods can be implemented in other ways.

[0134] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1.A method for networking unmanned aerial vehicles (UAVs) based on Beidou positioning, characterized in that: Comprise the following steps: Step S1, each unmanned vehicle starts the Beidou high-precision positioning module to obtain the local position vector, calculates the Euclidean distance between the unmanned vehicle nodes according to the local position vector, and defines the adjacent relationship within the communication radius to construct the dynamic adjacent graph of the unmanned vehicle nodes; Step S2, the residual capacity, communication load, and track stability of each unmanned vehicle node are obtained to construct a relay evaluation function, and the relay nodes are screened out in the dynamic adjacent graph of the unmanned vehicle nodes; Step S3, the energy state heterogeneous imbalance information of the relay node is obtained, and the energy state heterogeneous imbalance coefficient is obtained according to the energy state heterogeneous imbalance information; Step S4, the relay node instability propagation disconnection information of the relay node is obtained, and the relay node instability propagation disconnection coefficient is obtained according to the relay node instability propagation disconnection information; Step S5, a communication cascade failure analysis model is constructed according to the energy state heterogeneous imbalance coefficient and the relay node instability propagation disconnection coefficient, a communication cascade failure analysis index is obtained, and the communication cascade failure risk of the current networking system is evaluated; Step S6, the communication cascade failure analysis index is compared with the preset communication cascade failure analysis index threshold, and the relay node is dynamically scheduled; The communication load is measured by a forwarding ratio, which is an important indicator of the communication load level when the UAV node relays as a network relay, and the specific calculation formula of the forwarding ratio is as follows: Wherein is the forwarding ratio, is the amount of data forwarded by the UAV in the time window CK, is the total amount of data processed by the UAV in the time window CK; The track stability is measured by the heading deviation, which is used to represent the stability of the flight track of the unmanned vehicle; Record the heading angle measurement value of the unmanned aerial vehicle for N times within the time window CK, and calculate the heading deviation difference: wherein is the heading deviation difference, is the n+1th heading angle measurement value, is the nth heading angle measurement value; At the end time of the time window CK, the residual power of the UAV node is acquired to construct a relay evaluation function, specifically as follows: wherein is a relay evaluation value, is a residual power, is a forwarding ratio, is a heading offset difference, respectively represent preset proportion coefficients of the residual power, the forwarding ratio, and the heading offset difference, and are all greater than 0. The relay evaluation value is compared with the preset relay evaluation value threshold, and the relay nodes are screened out in the dynamic adjacent graph of the unmanned vehicle nodes, as follows: If the relay evaluation value is greater than the relay evaluation value threshold, the currently evaluated unmanned vehicle node is added to the candidate relay node set; If the relay evaluation value is less than or equal to the relay evaluation value threshold, the currently evaluated unmanned vehicle node is not added to the candidate relay node set; The unmanned vehicle nodes in the candidate relay node set are sorted according to the relay evaluation value from large to small and are assigned with sorting values, and the unmanned vehicle nodes are selected from the candidate relay node set as relay nodes according to the number of requirements of the relay nodes, and the remaining relay nodes are added to the candidate relay node set while being sorted according to the relay evaluation value from large to small; The energy state heterogeneous imbalance information of the relay node is obtained, the energy state heterogeneous imbalance of the relay node is analyzed, and the energy state heterogeneous imbalance coefficient is calculated to measure the degree of energy state heterogeneous imbalance of the relay node; The logic of obtaining the energy state heterogeneous imbalance coefficient is as follows: obtaining a normalized remaining power array of all relay nodes currently, , , remaining power of the xth relay node, number of relay nodes; The residual energy of all relay nodes in the residual energy array is accumulated to obtain total energy , and the energy proportion of each relay node is obtained according to the total energy : ; According to the energy proportion, an energy distribution value is calculated : ; calculating an energy distribution index from the energy distribution values : ; An energy difference coefficient is constructed by analyzing the energy difference between relay nodes : , and normalizing the energy difference coefficient to the average energy difference : ; An energy imbalance coefficient is calculated from the average energy difference : ; According to the energy imbalance coefficient and the energy distribution index, an energy state isomerism imbalance coefficient is calculated : wherein is a minimum constant to prevent zero division; The relay node instability propagation disconnection information of the relay node is obtained, the risk of instability-induced propagation disconnection of the relay node is analyzed, and the relay node instability propagation disconnection coefficient is calculated to measure the risk degree of instability-induced propagation disconnection of the relay node; The logic of obtaining the relay node instability propagation disconnection coefficient is as follows: acquiring a time series of link quality for each relay node wherein link quality data for a first relay node at a time t, is a collection period window for the link quality data. For each relay node, within a sliding window the discrete difference of link quality is computed : , ; standard deviation of the discrete differences : where is the mean value of the discrete differences, ; Dynamic adjacency graph of unmanned aerial vehicle nodes The non-relay nodes are eliminated in the unmanned aerial vehicle node dynamic adjacency graph, a relay node subgraph is constructed, the relay node subgraph is mapped into a relay adjacency matrix, a Laplacian matrix is constructed, the second smallest eigenvalue is obtained by solving the eigenvalue of the Laplacian matrix, and the second smallest eigenvalue is taken as the algebraic connectivity of the relay nodes ; For each relay node x, perform a simulated pruning operation, and recompute the algebraic connectivity of the remaining relay node subgraph ; Computing a resilience loss factor for a relay node : ; For each pair of neighbors , compute its propagation probability : where denotes the standard deviation of the discrete difference of the relay node x, denotes the standard deviation of the discrete difference of the relay node v; All the propagation probability is mapped to the local propagation probability matrix, and the local propagation probability matrix is normalized to obtain the normalized transition matrix, and the elements of the normalized transition matrix are: ; Computing node cascading propagation strength : ; Computing a relaying node instability propagation breakage coefficient : where is the number of relaying nodes, is a very small constant that prevents division by zero; According to the energy state isomerism imbalance coefficient, the relay node instability propagation chain breaking coefficient, a communication cascade failure analysis model is constructed, and a communication cascade failure analysis index is obtained. The formula on which the communication cascade failure analysis model is based is as follows , wherein is the communication cascade failure analysis index, is the energy state isomerism imbalance coefficient, is the relay node instability propagation chain breaking coefficient, respectively represent preset proportion coefficients of the energy state isomerism imbalance coefficient and the relay node instability propagation chain breaking coefficient, and are both greater than 0. 2.The method of claim 1, wherein: The communication cascade failure analysis index is compared with the preset communication cascade failure analysis index threshold, and the relay node is dynamically scheduled, as follows: If the communication cascade failure analysis index is greater than the communication cascade failure analysis index threshold, the relay reconstruction mechanism is immediately started, and the existing high-risk relay nodes are replaced one by one from the candidate relay node set according to the priority. If the communication cascade failure analysis index is less than or equal to the communication cascade failure analysis index threshold value, the current relay node distribution and scheduling state are kept unchanged. 3.A network system of unmanned aerial vehicles based on Beidou positioning, used to implement the network method of unmanned aerial vehicles based on Beidou positioning in any one of claims 1-2, characterized in that: The method comprises an adjacency graph construction module, a relay node screening module, an energy imbalance module, an instability propagation module, a communication cascade failure analysis module, and a dynamic scheduling module. The adjacency graph construction module is configured to obtain a local position vector by starting a Beidou high-precision positioning module of each unmanned aerial vehicle, calculate the Euclidean distance between unmanned aerial vehicle nodes according to the local position vector, and define an adjacency relationship within a communication radius to construct a dynamic adjacency graph of the unmanned aerial vehicle nodes. The relay node screening module is configured to obtain the residual power, communication load and track stability of each unmanned aerial vehicle node, construct a relay evaluation function, and screen out relay nodes in the dynamic adjacency graph of the unmanned aerial vehicle nodes. The energy imbalance module is configured to obtain energy state heterogeneous imbalance information of the relay nodes, and obtain an energy state heterogeneous imbalance coefficient according to the energy state heterogeneous imbalance information. The instability propagation module is configured to obtain relay node instability propagation link breakage information of the relay nodes, and obtain a relay node instability propagation link breakage coefficient according to the relay node instability propagation link breakage information. The communication cascade failure analysis module is configured to construct a communication cascade failure analysis model according to the energy state heterogeneous imbalance coefficient and the relay node instability propagation link breakage coefficient, obtain a communication cascade failure analysis index, and evaluate the communication cascade failure risk of the current networking system. The dynamic scheduling module is configured to compare the communication cascade failure analysis index with a preset communication cascade failure analysis index threshold value, and dynamically schedule the relay nodes.

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