A multi-hop communication node intelligent scheduling method and system

Abstract

The present invention discloses a multi-hop communication node intelligent scheduling method and system, which relates to the field of communication network technology. It is used to solve the scheduling and switching problems of multi-hop relay nodes in disaster emergency communications. By collecting the residual energy, channel fading parameters and link load data of the UAV and the ground terminal, the communication potential index is calculated in combination with dynamic weight fusion to accurately evaluate the relay capability of the node in a complex environment. The relay node selection threshold is set according to the disaster type, and nodes with a communication potential index higher than the disaster adaptation threshold are given priority to construct an initial hybrid topology structure with anti-destruction priority to improve network stability and anti-destruction. An autonomous game agent model is adopted to design a multi-objective reward function based on local channel quality, residual energy and task urgency to ensure that under the dynamic topology and node failure conditions, the network achieves energy consumption balance, transmission real-time performance and coordinated optimization of anti-destruction performance, and realizes efficient and continuous optimization of the disaster emergency communication network.

Description

Technical Field

[0001] The present invention relates to the technical field of communication networks, and in particular to a multi-hop communication node intelligent scheduling method and system. Background Art

[0002] During sudden disasters such as earthquakes, floods, and typhoons, traditional emergency communication networks often suffer from infrastructure damage or inability to restore them in a timely manner, hindering emergency response and rescue operations. This is especially true in remote areas or complex disaster zones after a disaster, where communication links are susceptible to factors such as channel fading, network congestion, and energy consumption. To ensure smooth communication during disasters and provide timely and effective rescue information, intelligent multi-hop communication networks have become a crucial means of emergency communication. With the increasing popularity and development of drones and ground-based emergency terminals, combined with wireless communication technology and intelligent scheduling methods, self-organizing networks can be rapidly established after a disaster, providing flexible and efficient communication support.

[0003] Currently, the application of traditional multi-hop communication methods in emergency communications still faces multiple challenges. First, existing methods often ignore the changes in the remaining energy of nodes and channel quality in disaster environments, resulting in the network being easily interrupted due to energy depletion or channel instability in sudden situations. Second, traditional scheduling strategies often rely on static routing or simple load balancing algorithms, which fail to fully consider the differences in disaster types and the real-time status of nodes. Furthermore, existing methods lack adaptive mechanisms to cope with changes in network topology and node failures, especially in dynamic and complex post-disaster environments. Relay nodes or communication links cannot be adjusted in a timely manner, affecting the stability and real-time performance of communications. Finally, traditional methods are mostly limited to the optimization of a single objective, such as energy or delay, and ignore the collaborative optimization of multiple objectives. It is difficult to achieve a balance between energy efficiency, transmission timeliness and network survivability in complex environments. Summary of the Invention

[0004] In view of the deficiencies in the prior art, the present invention provides a multi-hop communication node intelligent scheduling method and system, which solves the problems of the above-mentioned background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a multi-hop communication node intelligent scheduling method, comprising the following steps: S1. Real-time collection of residual energy, channel fading parameters and link load data of UAV and ground emergency terminal communication nodes, and calculation of communication potential index through dynamic weight fusion, which is used to comprehensively quantify the multi-hop relay capability of nodes under energy limitation and channel mutation; S2. According to the communication potential index, a relay node selection threshold is set for different disaster types, and communication nodes with a communication potential index higher than the disaster adaptation threshold are preferentially selected as relay nodes, an initial hybrid topology with anti-destruction priority is constructed, and the load of key rescue data streams is pre-allocated in combination with the disaster heat distribution map; S3. When a relay node is detected When the communication potential index drops below the dynamically updated safety threshold due to a sudden drop in energy or channel disturbance, the role migration operation is triggered, and a migration request carrying an emergency level identifier is broadcast to neighboring nodes. Based on the real-time communication potential index ranking results and link survival prediction information, the optimal candidate node is selected to take over the relay function; S4. Each node is modeled as an autonomous game agent, and its action space includes four states: relay, terminal, standby, and emergency communication. A multi-objective reward function is designed based on the local channel quality, remaining energy, and the urgency of the rescue mission. Through the interactive incremental game decision information of the anti-interference beacon, it converges to the Nash equilibrium state under the dynamic topology and partial node failure scenarios, and generates a multi-hop scheduling strategy with coordinated optimization of energy consumption balance, transmission real-time performance, and network anti-destruction performance.

[0006] Furthermore, the specific process of real-time collection of residual energy, channel fading parameters and link load data of the communication nodes of the UAV and the ground emergency terminal is as follows: through the energy monitoring modules deployed on the UAV and the ground terminal, the remaining energy value of the battery is periodically collected, and the energy attenuation trend is predicted in combination with the charge and discharge rate; through the real-time communication feedback mechanism of the link layer, the received signal strength and bit error rate between each pair of communication nodes are counted, the instantaneous channel fading level is calculated, and the fluctuation amplitude in a short time is subjected to sliding analysis to extract the channel quality change gradient to determine whether there is a mutation risk in the channel; combined with the forwarding queue status and actual load situation of each node, the cache queue length, average waiting delay and data packet queuing frequency are monitored in real time to form a load vector reflecting the link load degree, and the load peaks in different time slices are normalized to generate a standardized link pressure characteristic value.

[0007] Furthermore, the specific process of calculating the communication potential index through dynamic weight fusion is as follows: for each communication node, the standardized index values ​​are extracted from the three dimensions of residual energy, channel fading and link load, and their numerical ranges are unified to avoid calculation deviations caused by inconsistent dimensions between dimensions; according to the current disaster type, node role task and task urgency parameters, differentiated dynamic weights are assigned to the three indicators to generate a weight coefficient group that reflects the current scheduling priority requirements; the three weighted indicators are fused to construct a three-dimensional feature vector, and by calculating its mapping function value in a specific scheduling strategy space, a communication potential index representing the multi-hop relay capability of the current node is generated; the distribution of the communication potential index among all candidate nodes is analyzed for deviations to determine whether the node has a significant relay capability advantage, and it serves as an important input signal for subsequent relay selection and migration criteria.

[0008] Furthermore, the specific process of setting the relay node selection threshold for different disaster types based on the communication potential index is as follows: obtain the communication assurance level requirement corresponding to the current disaster type, and based on the distribution range of the communication potential index in historical disaster emergency communication tasks, extract the minimum communication potential index that successfully maintains the stability of the communication link in various disaster scenarios as a reference threshold; combine the current task urgency and node distribution density to set a weighted correction coefficient, dynamically adjust the basic threshold, and form a disaster-adaptive communication potential selection threshold; sort the communication potential indexes of all candidate nodes, and select nodes with a value above the set threshold as a candidate set of relay nodes; based on the candidate node set, further combine the following specific criteria to select target nodes suitable as relays: Geographic location constraint: eliminate nodes located in communication blind spots, severely obstructed by terrain, or located beyond a preset communication radius from the target service area; link connectivity constraint: select nodes whose number of adjacent nodes is higher than the average connectivity threshold through adjacency matrix calculation; energy distribution balance constraint: calculate the energy variance of the candidate node set, give priority to nodes with a medium to high level of power, and eliminate nodes whose current remaining energy is lower than a certain proportion of the network average.

[0009] Furthermore, an initial hybrid topology with survivability priority is constructed, and the specific process of load pre-allocation of key rescue data streams is combined with the disaster heat distribution map as follows: backbone nodes are selected based on the communication potential index. Some backbone nodes serve as cluster head nodes to cover high-priority rescue areas, and the remaining backbone nodes form a mesh redundancy layer to form a communication topology with multi-path connection capabilities; rescue data is managed hierarchically, and different transmission resources and path redundancy levels are allocated according to the importance of the data. High-priority data is configured with fixed time slots and multi-path transmission strategies, while low-priority data adopts dynamic resource scheduling. Backup channels are preset for critical data to enhance link reliability; redundant cluster head nodes are deployed in potential high-risk areas to form a cross-coverage structure, and a load sensing mechanism is set up at key nodes to dynamically migrate part of the traffic to neighboring nodes based on the current node load status to achieve link load balancing; the topology structure drives each node to update local routing information in real time by periodically broadcasting topology status beacons. When a link is interrupted, an alternative node is quickly selected to complete path reconstruction based on the communication potential index.

[0010] Furthermore, based on the real-time communication potential index sorting results and link survival prediction information, the specific process of selecting the optimal candidate node to take over the relay function is as follows: receive the real-time communication potential index broadcast by the neighboring nodes, generate a candidate node list in descending order, and remove nodes that exceed the effective connection range in combination with the geographical location and communication range restrictions; based on the historical channel stability data of the candidate nodes and the link survival prediction model, screen the nodes with a survival probability higher than the set threshold to form a high-reliability candidate set; send migration requests to the nodes in the high-reliability candidate set and verify their current load status to ensure that their communication potential index and load level meet the replacement conditions; realize seamless switching through the double-buffer routing table mechanism, the old table maintains the existing data transmission, and the new table completes the migration and takes effect within the set time; trigger the local topology synchronization beacon broadcast, update the routing information of the nodes in the entire network, and quickly reconstruct the path based on the communication potential index when the link is interrupted.

[0011] Furthermore, a multi-objective reward function is designed according to the local channel quality, residual energy and rescue mission urgency. The specific process of converging to the Nash equilibrium state under dynamic topology and partial node failure scenarios is as follows: according to the local channel quality, residual energy and rescue mission urgency, the channel gain term, energy penalty term and task priority weighted term are designed respectively to construct a multi-objective composite reward function; the local decision information, including node action strategy, reward value increment and link status summary, is compressed and encoded by the anti-interference beacon, and broadcast to neighboring nodes using frequency hopping spread spectrum technology; the node updates the local strategy based on the received game information, and iteratively optimizes the action selection probability distribution through distributed reinforcement learning; when the strategy difference of the entire network nodes is continuously lower than the set threshold and the key performance indicators tend to be stable, it is determined that the system has converged to the Nash equilibrium state.

[0012] Furthermore, the specific process of generating a multi-hop scheduling strategy that coordinates energy consumption balance, transmission real-time performance, and network survivability is as follows: dynamically allocate relay tasks based on the Nash equilibrium strategy, limit the continuous working time of high-load nodes, and trigger low-load nodes to take over part of the forwarding traffic; reserve dedicated low-slot resources for critical data streams such as vital signs and location coordinates, allowing them to preempt non-critical data slots to ensure real-time transmission; deploy redundant paths in potential risk areas of the disaster heat map, and quickly activate the optimal backup link based on the difference in communication potential index when the main path is interrupted; periodically trigger the synchronization of the entire network topology status, dynamically update the node routing table, and suppress the spread of local congestion to ensure network survivability.

[0013] An intelligent scheduling system for multi-hop communication nodes includes the following modules: a communication potential analysis module, a relay node selection module, a role migration trigger module, and an autonomous game decision module; the communication potential analysis module is used to collect the residual energy, channel fading parameters and link load data of the communication nodes of the UAV and the ground emergency terminal in real time, and calculate the communication potential index through dynamic weight fusion to comprehensively quantify the multi-hop relay capability of the node under energy limitation and channel mutation; the relay node selection module is used to set the relay node selection threshold for different disaster types according to the communication potential index, give priority to the communication nodes with the communication potential index higher than the disaster adaptation threshold as relay nodes, construct an initial hybrid topology structure with priority on anti-destruction, and pre-distribute the load of the key rescue data stream in combination with the disaster heat distribution map; the role migration trigger ... ground emergency terminal and calculate the communication potential index of the communication node The migration trigger module is used to trigger the role migration operation when it detects that the communication potential index of the relay node is lower than the dynamically updated safety threshold due to a sudden drop in energy or channel disturbance. The migration request carrying the emergency level identifier is broadcast to the neighboring nodes, and the optimal candidate node is selected to take over the relay function based on the real-time communication potential index ranking result and link survival prediction information; the autonomous game decision module is used to model each node as an autonomous game intelligent agent, whose action space includes four states: relay, terminal, standby and emergency communication. A multi-objective reward function is designed according to the local channel quality, remaining energy and the urgency of the rescue mission. Through the interactive incremental game decision information of the anti-interference beacon, it converges to the Nash equilibrium state under the dynamic topology and partial node failure scenarios, and generates a multi-hop scheduling strategy that collaboratively optimizes energy consumption balance, transmission real-time performance and network anti-destruction performance.

[0014] The present invention has the following beneficial effects:

[0015] (1) A multi-hop communication node intelligent scheduling method, by real-time collection of residual energy, channel fading parameters and link load data of UAVs and ground emergency terminals, combined with the communication potential index calculated by dynamic weight fusion, can comprehensively quantify the multi-hop relay capability of nodes under energy constraints and channel mutation conditions. This enables the network to adaptively select the most suitable relay node in emergency communication scenarios to ensure the stability and reliability of data transmission. At the same time, according to the communication potential index, the relay node selection threshold for different disaster types is set, effectively constructing a hybrid topology structure with anti-destruction priority, and reasonably pre-allocating the load of key rescue data streams, thereby improving the network's disaster response capabilities.

[0016] (2) An intelligent scheduling system for multi-hop communication nodes. By introducing an agent model based on autonomous game theory and designing a multi-objective reward function based on local channel quality, residual energy, and rescue mission urgency, the present invention can ensure that the system responds quickly and converges to a Nash equilibrium state in an environment with dynamic topology and partial node failure, thereby optimizing the energy balance, transmission real-time performance, and network resilience of multi-hop communication. In addition, when a node fails, the system can quickly reconstruct the path and synchronize the network topology, ensuring the continuity and stability of communication and enhancing the disaster recovery capability of the entire network.

[0017] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a flow chart of a multi-hop communication node intelligent scheduling method of the present invention.

[0019] Figure 2 This is a flow chart of a multi-hop communication node intelligent scheduling system of the present invention. DETAILED DESCRIPTION

[0020] The present invention addresses the scheduling and switching issues of multi-hop relay nodes in disaster emergency communications through a method and system for intelligently scheduling multi-hop communication nodes. This system collects node energy, channel status, and link load data in real time and calculates a communication potential index based on dynamic weights. This method prioritizes appropriate relay nodes based on the type of disaster and triggers role migration when node energy or channel status changes, ensuring network stability and efficiency in complex environments.

[0021] The overall idea of ​​the solution in the embodiments of this application is as follows:

[0022] The residual energy, channel fading parameters and link load data of the communication nodes of the UAV and the ground emergency terminal are collected in real time. The communication potential index is calculated through dynamic weight fusion to comprehensively quantify the multi-hop relay capability of the nodes under energy limitation and channel mutation.

[0023] According to the communication potential index, the relay node selection threshold is set for different disaster types. Communication nodes with a communication potential index higher than the disaster adaptation threshold are preferentially selected as relay nodes. An initial hybrid topology structure with anti-destruction priority is constructed, and the load of key rescue data streams is pre-allocated in combination with the disaster heat distribution map.

[0024] When it is detected that the communication potential index of the relay node is lower than the dynamically updated safety threshold due to a sudden drop in energy or channel disturbance, the role migration operation is triggered, and a migration request carrying an emergency level identifier is broadcast to the neighboring nodes. Based on the real-time communication potential index sorting results and link survival prediction information, the optimal candidate node is selected to take over the relay function.

[0025] Each node is modeled as an autonomous game agent, whose action space includes four states: relay, terminal, standby, and emergency communication. A multi-objective reward function is designed according to the local channel quality, remaining energy, and the urgency of the rescue mission. Through the interactive incremental game decision information of anti-interference beacons, it converges to the Nash equilibrium state under dynamic topology and partial node failure scenarios, and generates a multi-hop scheduling strategy with coordinated optimization of energy consumption balance, transmission real-time, and network invulnerability.

[0026] See also Figure 1 , an embodiment of the present invention provides a technical solution: a multi-hop communication node intelligent scheduling method, comprising the following steps: S1. real-time collection of residual energy, channel fading parameters and link load data of UAV and ground emergency terminal communication nodes, and calculation of communication potential index by dynamic weight fusion, which is used to comprehensively quantify the multi-hop relay capability of nodes under energy limitation and channel mutation; S2. according to the communication potential index, setting the relay node selection threshold for different disaster types, giving priority to selecting communication nodes with communication potential index higher than the disaster adaptation threshold as relay nodes, constructing an initial hybrid topology structure with priority on anti-destruction, and pre-allocating the load of key rescue data streams in combination with the disaster heat distribution map; S3. when the communication potential of the relay node is detected, When the energy index drops below the dynamically updated safety threshold due to a sudden drop in energy or channel disturbance, the role migration operation is triggered, and a migration request carrying an emergency level identifier is broadcast to neighboring nodes. The optimal candidate node is selected to take over the relay function based on the real-time communication potential index ranking results and link survival prediction information; S4. Each node is modeled as an autonomous game agent, and its action space includes four states: relay, terminal, standby, and emergency communication. A multi-objective reward function is designed based on the local channel quality, remaining energy, and the urgency of the rescue mission. Through the interactive incremental game decision information of the anti-interference beacon, it converges to the Nash equilibrium state under dynamic topology and partial node failure scenarios, and generates a multi-hop scheduling strategy that coordinates the optimization of energy consumption balance, transmission real-time performance, and network anti-destruction performance.

[0027] In this embodiment, step S1: the system acquires the remaining energy, channel fading parameters, and link load data of the UAV and ground emergency terminal in real time. These data are collected to calculate the communication potential index.

[0028] Remaining energy: This refers to the remaining battery energy of the drone or ground terminal. The higher the energy, the stronger the node's communication capability. Channel fading parameters: This describes the attenuation of wireless communication signals during transmission due to environmental factors (such as weather and obstacles). Poor channel quality can affect communication stability. Link load data: This indicates the current node communication load, indicating whether the node is overloaded. Overloaded nodes may not be able to forward data efficiently. Using this data, the system uses dynamic weight fusion to calculate a communication potential index for each node. This index combines the node's remaining energy, channel quality, and load to measure the node's multi-hop relay capability under energy-constrained and channel-fluctuating conditions. Step S2: Based on the communication potential index calculated in the previous step, the system sets a relay node selection threshold for different disaster types. Disaster type: Different disasters (such as earthquakes and floods) place varying demands on the communication network. The system sets thresholds based on the characteristics of the disaster to ensure the selection of the most suitable nodes for relaying. Nodes with a Communication Potential Index Above the Disaster Adaptation Threshold: During disaster conditions, the system prioritizes nodes with a Communication Potential Index above a set threshold as relay nodes, as these nodes are able to provide more stable communication services. The system then constructs an initial hybrid topology that prioritizes resiliency. This prioritizes nodes with strong resiliency based on their Communication Potential Index, ensuring the stability of the communication network during disasters. Furthermore, the system pre-distributes the load of critical rescue data streams based on the disaster heat map, rationally allocating data traffic based on the severity and distribution of the disaster to avoid communication bottlenecks. Step S3: When the Communication Potential Index of a relay node is detected to fall below a preset safety threshold due to a sudden drop in energy or channel disturbances, the system initiates a role migration operation. This role migration operation involves migrating the relay node's tasks to other communication nodes to maintain the stability and reliability of the communication network. An Emergency Level Flag is included with each migration request, indicating its urgency. Requests with higher priority are prioritized. The system broadcasts a migration request, requesting neighboring nodes to take over the relay function, and selects the optimal candidate node based on the real-time communication potential index and link survival prediction information. This step ensures that when the original node fails, the network can quickly switch to the optimal relay node to avoid communication interruption. Step S4: In this step, the system models each communication node as an autonomous game agent. This means that each node makes independent decisions, and the nodes interact with each other through game to jointly optimize the operation of the network. Autonomous game agent: When making decisions, each node decides whether to act as a relay node, terminal, standby node, or emergency communication node based on local information and environmental factors (such as channel quality, remaining energy, and task urgency).Action space: This refers to the types of actions each node can choose, such as "relay" (forwarding data), "terminal" (receiving or sending data), "standby" (standing on standby), or "emergency communication" (handling emergency tasks). Based on these factors, each node is designed to employ a multi-objective reward function that comprehensively considers the node's local channel quality, remaining energy, and task urgency, ensuring that the network optimizes energy consumption, real-time transmission capabilities, and resilience under different conditions. Nash equilibrium: Through game-based decision information, the system aims to achieve a Nash equilibrium among nodes, where each node's decision remains unchanged given the decisions of other nodes. This allows each node in the network to automatically adjust to an optimized state, maintaining optimal system operation despite changes in network topology and partial node failures. Ultimately, these decisions lead to the coordinated optimization of energy consumption balance, real-time transmission, and resilience, resulting in an efficient and reliable multi-hop scheduling strategy that ensures stable and real-time communication in disaster emergency communication environments.

[0029] Specifically, the specific process of real-time collection of residual energy, channel fading parameters and link load data of UAV and ground emergency terminal communication nodes is as follows: through the energy monitoring modules deployed on the UAV and ground terminals, the remaining battery energy value is periodically collected, and the energy attenuation trend is predicted in combination with the charge and discharge rate; through the real-time communication feedback mechanism of the link layer, the received signal strength and bit error rate between each pair of communication nodes are counted, the instantaneous channel fading level is calculated, and the fluctuation amplitude in a short time is slidingly analyzed to extract the channel quality change gradient to determine whether there is a mutation risk in the channel; combined with the forwarding queue status and actual load situation of each node, the cache queue length, average waiting delay and data packet queuing frequency are monitored in real time to form a load vector reflecting the link load degree, and the load peaks in different time slices are normalized to generate standardized link pressure characteristic values.

[0030] In this implementation, an energy monitoring module is deployed to collect the remaining battery energy. The energy monitoring module is a hardware device installed on drones and ground-based emergency terminals. It periodically collects the remaining battery energy. These devices monitor the battery status through a built-in battery management system. The remaining battery energy value indicates the current amount of power the battery can provide. The remaining battery energy is crucial for determining whether a node can continue to perform communication tasks. Charge and discharge rate: Batteries undergo charging and discharging processes during operation. The monitoring module uses the battery charge and discharge rates to predict the battery degradation trend over the next period of time. For example, a high battery discharge rate can lead to rapid power consumption, affecting the node's operating time. Energy degradation trend prediction: By predicting the battery's energy consumption rate, decisions can be made in advance to prevent nodes from being unable to continue communication relaying due to battery depletion. Channel fading parameters are calculated using the link layer real-time communication feedback mechanism. The link layer real-time communication feedback mechanism is a feedback mechanism in wireless communications. During data transmission, communication nodes provide feedback, such as signal strength and bit error rate, through the link layer. This feedback information allows the system to determine changes in channel quality. Received signal strength: This indicates the signal strength received by the receiving end of a communication link from the transmitting end. A high signal strength indicates good channel quality and high communication stability; a low signal strength may result in data loss or errors. Bit Error Rate (BER): This indicates the rate of errors during data transmission. The lower the BER, the better the channel quality and the more reliable the data transmission. Instantaneous Channel Fading: Channel fading refers to the attenuation of signals during propagation due to factors such as multipath propagation and interference. The instantaneous channel fading level represents the channel's degradation at a given moment. The system can assess the current channel quality by measuring this level. Sliding Analysis of Channel Quality Gradient: To accurately capture the changing trend of channel fading, a sliding window approach is used to analyze changes in signal strength or bit error rate. This approach smooths signal fluctuations, reduces the impact of instantaneous changes, and more accurately reflects long-term trends. Channel Quality Gradient: This indicates the rate or magnitude of change in channel quality over time. For example, a sharp drop in channel quality over a short period of time may indicate a sudden change in the channel. By calculating the channel quality gradient, the system can provide early warning of potential channel sudden change issues. Link load monitoring combined with node forwarding queue status: Forwarding queue status: While processing data, each communication node may need to forward data to other nodes. A node's forwarding queue is used to cache data packets waiting to be forwarded. The queue status reflects the node's current load. Cache queue length refers to the number of data packets currently stored in the cache queue. A long queue length indicates a heavy node load, potentially leading to queuing issues and data forwarding delays. Average latency refers to the average time it takes for a data packet to enter the queue and be forwarded.Long wait times can affect the real-time nature of data, especially in emergency communications, where timely data forwarding is crucial. Packet queuing frequency: This indicates how often packets enter the queue. If the queuing frequency is too high, it can increase node communication latency. Process for generating a standardized link pressure characteristic value: Load vector: Combining the cache queue length, average wait delay, and packet queuing frequency, the system generates a load vector containing multiple parameters representing the node load. These parameters are used to assess whether a node is overloaded. Peak load normalization: Node load levels fluctuate over time, so the peak load for each time slice needs to be normalized. This normalization process converts load data from different time slices to a uniform scale, eliminating bias caused by time differences and making load characteristics more comparable. Link pressure characteristic value: The normalized load vector generates a standardized link pressure characteristic value, which quantifies the current link load pressure. This characteristic value helps the system determine the level of link congestion and decide whether to adjust data forwarding strategies or migrate relay tasks to less loaded nodes.

[0031] Specifically, the specific process of calculating the communication potential index through dynamic weight fusion is as follows: for each communication node, the standardized index values ​​are extracted from the three dimensions of residual energy, channel fading and link load, and their numerical ranges are unified to avoid calculation deviations caused by inconsistent dimensions; according to the current disaster type, node role task and task urgency parameters, differentiated dynamic weights are assigned to the three indicators to generate a weight coefficient group reflecting the current scheduling priority requirements; the three weighted indicators are fused to construct a three-dimensional feature vector, and by calculating its mapping function value in a specific scheduling strategy space, a communication potential index representing the multi-hop relay capability of the current node is generated; the distribution of the communication potential index among all candidate nodes is analyzed for deviations to determine whether the node has a significant relay capability advantage, and it serves as an important input signal for subsequent relay selection and migration criteria.

[0032] In this implementation, the purpose of multidimensional data normalization is to eliminate dimensional differences in the three dimensions of residual energy, channel fading, and link load to ensure data comparability. Each dimension of data is normalized separately and mapped to the interval 0 and 1. The formula is: ; Parameter description: : The original data of the kth dimension ( is the remaining energy, is the channel fading, is the link load); 、 : The historical minimum and maximum values ​​of the kth dimension. Dynamic weight allocation: Dynamically adjust the weights of each dimension based on the disaster type, node role, and task urgency. Define weight allocation rules: Earthquake scenario: Increase energy weight (focus on node survivability); Flood scenario: Increase channel fading weight (suppress the impact of multipath interference); Fire scenario: Increase link load weight (avoid node overload in high-temperature areas). Formula: ; Parameter description: : Weight coefficients of residual energy, channel fading, and link load (satisfying ); : Disaster type code (e.g. earthquake = 1, flood = 2, fire = 3); : Weight distribution function, dynamically outputs weight values ​​based on disaster type. Three-dimensional feature vector fusion: Fusion of weighted multi-dimensional indicators into a single communication potential index (CPI). Weighted summation of standardized indicators is performed, and discrimination is enhanced through nonlinear mapping. Formula: ; Parameter description: :Sigmoid function( ), used to compress the result to the interval [0,1]; : Link load reverse processing (the lower the load, the higher the contribution value). Deviation analysis and relay advantage determination purpose: to screen nodes with significant relay capability advantages. Calculate the CPI average of all candidate nodes ( ) and standard deviation ( ); Determine the relative advantage of nodes through Zscore standardization: ;like , the node is judged to have a significant relay advantage and is selected as a candidate. Disaster scenario adaptability: Through the dynamic weight distribution function ( ), flexibly adapt to the core needs of earthquake, flood, fire and other scenes; anti-bias design: normalization processing ( ) and nonlinear mapping (Sigmoid) suppress extreme value interference; Interpretability: Zscore quantifies the node relay capability advantage and provides a clear criterion for migration decision-making.

[0033] Specifically, the specific process of setting the relay node selection threshold for different disaster types based on the communication potential index is as follows: obtain the communication assurance level requirement corresponding to the current disaster type, and based on the distribution range of the communication potential index in historical disaster emergency communication tasks, extract the minimum communication potential index that successfully maintains the stability of the communication link in various disaster scenarios as a reference threshold; combine the current task urgency and node distribution density to set a weighted correction coefficient, dynamically adjust the basic threshold, and form a disaster-adaptive communication potential selection threshold; sort the communication potential indexes of all candidate nodes, and select nodes with a value above the set threshold as a candidate set of relay nodes; based on the candidate node set, further combine the following specific criteria to select target nodes suitable as relays: Geographic location constraint: eliminate nodes located in communication blind spots, severely obstructed by terrain, or located beyond a preset communication radius from the target service area; link connectivity constraint: select nodes whose number of adjacent nodes exceeds the average connectivity threshold through adjacency matrix calculation; energy distribution balance constraint: calculate the energy variance of the candidate node set, give priority to nodes with a medium-to-high level of power, and eliminate nodes whose current remaining energy is lower than a certain proportion of the network average.

[0034] In this implementation, the type of disaster typically impacts the requirements of the communication network. For example, during natural disasters, the communication network's security level may require different quality of service. For example, the more severe the disaster, the higher the required network reliability and stability. Based on the type of disaster, the system will determine the communication security level requirements for that particular scenario. These requirements will determine the selection criteria for relay nodes in the network. For example, some disaster types may require very high communication link stability, while others may require lower stability. The distribution data of communication potential indices from historical disaster emergency communication missions can be used as a reference to determine the minimum communication potential index required to successfully maintain communication link stability in similar disaster scenarios. Based on data from historical disaster events, the system will calculate the minimum communication potential index of nodes that successfully maintained communication. This value serves as a reference threshold for setting selection criteria for the current mission. After obtaining the basic reference threshold, the system will perform a weighted adjustment based on the current mission urgency and node distribution density. Mission urgency: Tasks with higher urgency may require higher communication node potential, so the basic threshold will be increased to ensure that the selected nodes have sufficient capabilities to support the mission. Node distribution density: The node distribution density may influence the node selection strategy. In densely populated areas, the threshold can be appropriately lowered, while in sparsely populated areas, a higher potential value is required to ensure network stability. Based on these factors, the basic threshold is dynamically adjusted to ensure it adaptively adapts to the current disaster situation and mission requirements, forming a disaster-adaptive communication potential selection threshold. Based on the aforementioned adaptive threshold, the communication potential index of all candidate nodes is ranked. This ranking process sorts the communication potential indexes of all nodes from high to low, selecting nodes with a value above the set threshold as candidate relay nodes. At this point, the candidate nodes have at least met the basic communication potential requirements and become potential relay node candidates. After initially selecting nodes that meet the threshold conditions, several other specific criteria are combined to further select target nodes suitable for relaying. These criteria include: Geographical location constraints: Nodes located in communication blind spots or with severe terrain obstruction are excluded. For example, some nodes may be blocked by mountains or buildings, significantly impacting signal propagation and making them unsuitable as relay nodes. Nodes beyond a preset communication radius from the target service area are also excluded. Nodes too far from the disaster area may not be able to provide stable communication services and are therefore excluded. Link connectivity constraint: The connectivity of each candidate node is calculated using the adjacency matrix. The adjacency matrix represents the connections between nodes. Nodes with strong connections to other nodes are selected to ensure the connectivity of relay nodes in the network. Based on the number of adjacent nodes, nodes with a number of adjacent nodes above the average connectivity threshold are selected. This means that nodes with direct connections to more nodes are selected to improve network robustness. Energy distribution balance constraint: The energy variance of the candidate node set is calculated, that is, the degree of dispersion of node energy.Energy variance analysis can help avoid selecting nodes with excessively dispersed energy, which might not be able to sustain relay tasks for long periods of time. Prioritizing nodes with medium to high battery levels ensures that relay nodes don't run out of energy while performing tasks. By eliminating nodes with a residual energy level below a certain percentage of the network average, we ensure relay node stability, avoid over-reliance on low-battery nodes, and maintain the continuity of the communication network.

[0035] Specifically, an initial hybrid topology prioritizing survivability is constructed, and the specific process of load pre-allocation of key rescue data streams is performed in combination with the disaster heat distribution map as follows: backbone nodes are selected based on the communication potential index. Some backbone nodes serve as cluster head nodes to cover high-priority rescue areas, and the remaining backbone nodes form a mesh redundancy layer to form a communication topology with multi-path connection capabilities; rescue data is managed hierarchically, and different transmission resources and path redundancy levels are allocated according to the importance of the data. High-priority data is configured with fixed time slots and multi-path transmission strategies, while low-priority data adopts dynamic resource scheduling. Backup channels are preset for critical data to enhance link reliability; redundant cluster head nodes are deployed in potential high-risk areas to form a cross-coverage structure, and a load-sensing mechanism is set up at key nodes to dynamically migrate part of the traffic to neighboring nodes based on the current node load status to achieve link load balancing; the topology structure drives each node to update local routing information in real time by periodically broadcasting topology status beacons. When a link is interrupted, an alternative node is quickly selected to complete path reconstruction based on the communication potential index.

[0036] In this implementation, backbone nodes are core nodes in the network, performing critical relay tasks. Backbone nodes are selected based on their communication potential index, which measures the node's comprehensive capabilities in terms of energy, channel quality, and load. Selecting nodes with a higher communication potential index ensures they possess strong relay and routing capabilities. Cluster head nodes are selected as cluster head nodes. Cluster head nodes manage communications within their coverage area, primarily responsible for high-priority rescue operations. Mesh redundancy layer: The remaining backbone nodes form a mesh redundancy layer. These nodes connect to other nodes via multiple paths, creating redundant connections to ensure stable communication even if some nodes or links fail. A mesh topology offers enhanced resilience because even if certain links or nodes fail, data can still be transmitted via alternative paths. Data hierarchical management: Different rescue data is assigned different priorities based on its importance. For high-priority data (such as emergency rescue instructions and vital signs), the system allocates more transmission resources and path redundancy to ensure timely and reliable transmission. Low-priority data (such as routine status reports) utilizes dynamic resource scheduling, flexibly adjusting transmission bandwidth and paths based on current network load. Multipath transmission strategy: For high-priority data, in addition to fixed transmission time slots, a multipath transmission strategy is also employed. This strategy involves transmitting the same data stream over different paths, thereby enhancing data transmission reliability and fault tolerance. Backup channels: Backup channels are provided for critical data streams to enhance link reliability. Even if a channel is interrupted, the system can continue transmitting data through the backup channel, preventing communication interruptions from negatively impacting rescue missions. Redundant cluster head nodes: To enhance resilience in high-risk areas, the system deploys multiple redundant cluster head nodes in potentially high-risk areas (such as the core area of ​​a disaster site). These nodes provide coverage within the area, and through cross-coverage, they ensure that communication links within the area are not interrupted due to node failure. The cross-coverage structure between the redundant cluster head nodes prevents the failure of a single cluster head node from impacting communications across the entire area. Through redundancy and cross-coverage, the network can quickly restore communications in the event of a node or link failure. Load sensing mechanism: Each node monitors its own load status (such as queue length and transmission rate) to determine whether it is overloaded. If a node is overloaded, the system dynamically migrates some traffic to a nearby node with a lower load. This mechanism helps balance load, ensuring that data flows in the network are distributed appropriately across nodes, preventing a single node from overloading and causing network congestion or communication interruptions. Link load balancing: Through load sensing and traffic migration, the system can adjust data flow distribution in real time under dynamically changing network conditions, ensuring stable and efficient communication links.Periodic broadcast of topology status beacons: Each node regularly broadcasts its own topology status information. These signals contain information such as the node's communication status and connection status. Other nodes receive these signals and update their local routing information in real time. This allows nodes in the network to understand changes in the global topology and make routing choices accordingly. Path reconstruction in the event of link interruption: In the event of a link interruption or node failure, the system quickly selects alternative nodes based on communication potential indicators and rebuilds the data transmission path. Through dynamic path reconstruction, the network can quickly recover and resume communication. When selecting alternative nodes, nodes are ranked according to their communication potential index, giving priority to those with higher potential.

[0037] Specifically, based on the real-time communication potential index sorting results and link survival prediction information, the specific process of selecting the optimal candidate node to take over the relay function is as follows: receive the real-time communication potential index broadcast by the neighboring nodes, generate a candidate node list in descending order, and remove nodes that exceed the effective connection range in combination with the geographical location and communication range restrictions; based on the historical channel stability data of the candidate nodes and the link survival prediction model, screen the nodes with a survival probability higher than the set threshold to form a high-reliability candidate set; send migration requests to the nodes in the high-reliability candidate set and verify their current load status to ensure that their communication potential index and load level meet the replacement conditions; realize seamless switching through the double-buffer routing table mechanism, the old table maintains the existing data transmission, and the new table completes the migration and takes effect within the set time; trigger the local topology synchronization beacon broadcast, update the routing information of the nodes in the entire network, and quickly reconstruct the path based on the communication potential index when the link is interrupted.

[0038] In this implementation, real-time communication potential index: Each node periodically broadcasts its communication potential index, which reflects the node's communication capabilities and integrates multiple factors such as energy, channel quality, and load. Candidate node list generation by descending sorting: The communication potential indices of all received neighboring nodes are sorted in descending order, prioritizing nodes with higher communication potential. Geographic location and communication range restrictions: Nodes outside the effective connection range are eliminated based on a combination of geographic location (e.g., distance, terrain, etc.) and communication range (e.g., a preset communication radius) to ensure that subsequently selected nodes are physically connectable. Historical channel stability data: This data records the channel stability of the candidate node, that is, the stability of the communication channel between the node and other nodes, including packet loss rate, latency, and signal strength fluctuations during past communications. Link survival prediction model: This model uses historical node data and current network status to predict the survival probability of the link between the node and other nodes. By analyzing the survival probability of candidate nodes, nodes with survival probabilities above a set threshold are selected. These nodes are considered capable of maintaining stable communication for a certain period of time in the future. High-Reliability Candidate Set: Nodes are screened to form a high-reliability candidate set. These nodes have a higher probability of survival and stronger communication capabilities, making them suitable candidates for relay node replacements. Migration requests are sent to nodes in the high-reliability candidate set, and their current load status is verified to ensure that their communication potential index and load level meet the replacement criteria. Migration Request: Migration requests are sent to nodes in the high-reliability candidate set, asking them if they are willing to take over the relay function. Migration requests consider not only the node's communication potential index but also its current load. Load Status Verification: The candidate node's load (e.g., queue length, processing capacity, latency, etc.) is verified. If the node is overloaded, it is not suitable for taking over the relay task. A node is selected only when both its communication potential index and load level meet the replacement criteria. Replacement Criteria: Generally, a candidate node's communication potential index must meet a certain threshold, and its load level must be below a set value to ensure that it can efficiently assume the relay role. A double-buffered routing table mechanism enables seamless handover. The old table maintains existing data transmission, while the new table completes the migration and takes effect within a set time. Double-buffered routing table mechanism: The double-buffered routing table mechanism maintains two routing tables during data transmission: one is the old table, used to maintain the current communication path, and the other is the new table, used to store new routing information. When the system decides to switch relay nodes, the new routing table is already prepared in advance. Seamless switching: The new routing table is migrated and takes effect within the set time, ensuring that data transmission is not affected by changes in relay nodes. Even during the switching process, data flow is not interrupted. This seamless switching helps improve network stability. It triggers the broadcast of local topology synchronization beacons, updates the routing information of nodes across the entire network, and quickly reconstructs the path based on the communication potential index when the link is interrupted.Local topology synchronization beacon broadcast: When a relay node changes, the system triggers a local topology synchronization beacon broadcast, notifying surrounding nodes to update their local routing information. This is a measure to ensure the consistency of the network topology and prevent communication failures caused by some nodes failing to promptly learn of the new topology. Network-wide node routing information update: Broadcast beacons can help nodes across the entire network update their routing tables in real time, ensuring that every node in the network can understand the current communication path. Rapid path reconstruction in the event of link interruption: In the event of a link interruption, the system will quickly select a new relay node based on the node's communication potential index and reconstruct the path by updating the routing table. The selected node must have strong communication potential to ensure stable data transmission.

[0039] Specifically, a multi-objective reward function is designed according to the local channel quality, residual energy and rescue mission urgency. The specific process of converging to the Nash equilibrium state under dynamic topology and partial node failure scenarios is as follows: according to the local channel quality, residual energy and rescue mission urgency, the channel gain term, energy penalty term and task priority weighted term are designed respectively to construct a multi-objective composite reward function; the local decision information, including node action strategy, reward value increment and link status summary, is compressed and encoded by the anti-interference beacon, and broadcast to neighboring nodes using frequency hopping spread spectrum technology; the node updates the local strategy based on the received game information, and iteratively optimizes the action selection probability distribution through distributed reinforcement learning; when the strategy difference of the entire network nodes is continuously lower than the set threshold and the key performance indicators tend to be stable, it is determined that the system has converged to the Nash equilibrium state.

[0040] In this implementation, a multi-objective composite reward function is constructed by designing channel gain, energy penalty, and task priority weighting based on local channel quality, residual energy, and data task urgency. Channel gain refers to the quality of the channel between nodes. High-quality channels improve the reliability and efficiency of data transmission. Therefore, in the reward function, channel quality has a positive impact on the node selection strategy. The formula can be expressed as: ; is a node The channel gain is , and SNR is the signal-to-noise ratio. Energy penalty: The remaining energy of a node affects its ability to continue to undertake communication and computing tasks. Nodes with low energy should be penalized to prevent them from participating in the task. The energy penalty term can be expressed as follows: ; is a node The energy penalty term, is the current remaining energy of the node, is the maximum energy of the node, is the coefficient of energy penalty. Weighted sum of task priorities: The urgency of the rescue mission determines the priority of different tasks. High-priority tasks should receive more transmission resources. The weighted terms of task priorities can be expressed as: ; It's a task The priority weighted items, It's a task The urgency of Is the weighted coefficient of task priority. Multi-objective composite reward function: The reward function designed based on the above three parts combines the channel gain, energy penalty and task priority weighting terms to form a multi-objective reward function: ; is a node Execute the task The comprehensive reward value at that time. are weight coefficients representing the relative importance of channel gain, energy penalty, and task priority. Local decision information, including the node's action strategy, reward increment, and link status summary, is compressed and encoded using interference-resistant beacons and broadcast to neighboring nodes using frequency hopping spread spectrum technology. Local decision information compression and encoding: Each node generates decision information based on its current strategy and reward. This information includes the node's action strategy (selected action), reward increment (the change in reward after a specific action), and link status (current network status, such as link availability and quality). Interference-resistant beacons: To resist external interference, interference-resistant beacon technology is used to compress and encode this decision information, ensuring stable transmission in complex environments. Frequency hopping spread spectrum technology: Frequency hopping spread spectrum technology randomly changes signal frequency to avoid interference and improve transmission reliability. This technology broadcasts compressed local decision information to neighboring nodes, enabling them to share decision information over a wide range. Nodes update their local policies based on received game information, iteratively optimizing the action selection probability distribution through distributed reinforcement learning. Game information update: After receiving game information from neighboring nodes, each node adjusts its local policy accordingly. Through game theory, nodes can update their own strategies based on the strategies of other nodes to optimize overall network performance. Distributed reinforcement learning: Each node performs iterative optimization using a reinforcement learning algorithm, independent of centralized control, and dynamically adjusts its behavior based on the network environment. After each decision, nodes adjust their strategies by observing rewards to maximize long-term returns. The update process can be expressed as: ; is a node In selecting an action The Q value at , represents the long-term reward of this behavior. It is the learning rate that determines the speed of learning. is the discount factor, which represents the weight of future rewards. The node is selecting the action Instant rewards when. is the other action selected, It is the next state reached by this action. When the strategy difference of the nodes in the entire network continues to be lower than the set threshold and the key performance indicators tend to be stable, the system is judged to have converged to the Nash equilibrium state. Strategy Diversity: The strategy difference of the nodes in the entire network is an indicator to measure the consistency of the decisions of each node in the entire system. If the strategies of most nodes tend to be the same, it indicates that the nodes in the system have reached a certain balance in the game. Key performance indicator stability: Key performance indicators, such as network throughput, latency, link stability, etc., reach the expected stable state, indicating that the resource allocation in the network has reached the optimal state. Convergence judgment: When the strategy difference of the nodes in the entire network is less than the set threshold and the key performance indicators have stabilized, the system is judged to have converged to the Nash equilibrium state, indicating that the behavior between the nodes has reached a stable equilibrium point.

[0041] Specifically, the specific process of generating a multi-hop scheduling strategy that coordinates energy consumption balance, transmission real-time performance, and network survivability is as follows: dynamically allocate relay tasks based on the Nash equilibrium strategy, limit the continuous working time of high-load nodes, and trigger low-load nodes to take over part of the forwarding traffic; reserve dedicated low-slot resources for critical data streams such as vital signs and location coordinates, allowing them to preempt non-critical data slots to ensure real-time transmission; deploy redundant paths in potential risk areas of the disaster heat map, and quickly activate the optimal backup link based on the difference in communication potential index when the main path is interrupted; periodically trigger the synchronization of the entire network topology status, dynamically update the node routing table, and suppress the spread of local congestion to ensure network survivability.

[0042] In this implementation scheme, relay tasks are dynamically allocated based on the Nash equilibrium strategy, the continuous working time of high-load nodes is limited, and low-load nodes are triggered to take over part of the forwarding traffic. The Nash equilibrium strategy dynamically allocates relay tasks: In the network, each node allocates relay tasks based on the current load status and task requirements through the Nash equilibrium strategy in game theory. Specifically, the node dynamically chooses whether to participate in the relay task based on the current network status (such as channel quality, node energy, load, etc.). If the node has a high load, it will avoid carrying relay tasks for a long time. Limit the continuous working time of high-load nodes: In order to avoid the performance degradation of some nodes due to overload, it is necessary to set a working time limit for high-load nodes to prevent them from overworking and affecting the overall network performance. By setting an upper limit on the node working time, the burden on the node can be effectively reduced. The formula is expressed as: ; is the current working time of node i, Is the maximum working time of node i, to prevent high-load nodes from working overtime. Trigger low-load nodes to take over part of the forwarding traffic: When the load of a node is lower than a certain threshold, it will automatically take over some of the forwarding traffic of high-load nodes. The amount of traffic taken over It can be expressed by the following formula: ; is the forwarding traffic taken over by node i. is the current load of node i, is the maximum load capacity of the node, is the adjustment coefficient, is the total traffic. Dedicated low-slot resources are reserved for critical data streams such as vital signs and location coordinates, allowing them to preempt non-critical data slots to ensure real-time transmission. Dedicated low-slot resources: To ensure that critical data (such as vital signs and location coordinate data) can be transmitted in real time, the system will reserve dedicated low-slot resources for these data streams. These low-slot resources ensure the priority of critical data streams and prevent transmission delays or losses. Preempt non-critical data slots: When the transmission demand of critical data streams is small, the system allows them to preempt the slots of non-critical data to ensure real-time transmission. This is achieved through a priority scheduling mechanism. The formula is expressed as: ; is the time slot reserved by node i for critical data flow, is the priority of key data, is the time slot resource allocated to node i. Redundant paths are deployed in potential risk areas of the disaster heat map. When the main path is interrupted, the optimal backup link is quickly activated based on the difference in the communication potential index. Redundant path deployment: In high-risk disaster areas, redundant paths need to be deployed to ensure the reliability and anti-destruction capabilities of communications. Partial deployment of redundant paths can be based on the disaster heat map, selecting areas with low probability of disaster occurrence and strong communication potential for redundant link deployment. Activate backup links based on the difference in the communication potential index: When the main path is interrupted, the system will quickly activate potential backup links based on the communication potential index (CPI). The difference in the communication potential index can be expressed by the following formula: ; is the communication potential index of the main path, is the communication potential index of the backup path, is the difference in potential index. If the difference is greater than the set threshold, the system will activate the backup link. Periodically trigger the synchronization of the topology state of the entire network, dynamically update the node routing table and suppress the spread of local congestion to ensure the network's anti-destruction capabilities. Synchronization of the topology state of the entire network: In order to ensure the dynamic stability of the network, the node will periodically trigger the synchronization of the topology state to ensure that the routing tables and network states of all nodes are consistent. During the synchronization process, the node will broadcast its own routing information and current state to ensure that all nodes in the network have a consistent understanding of the current network status. Dynamically update the node routing table: Based on the topology synchronization information, the node will update the local routing table and adjust the path of the data flow to cope with the current network status and topology changes. Suppress the spread of local congestion: When network congestion occurs in a local area, the system dynamically adjusts the data flow path to prevent congestion from spreading to the entire network. By selecting the optimal path and limiting traffic at congested nodes, the impact of local congestion can be effectively suppressed. The formula is expressed as: ; is a node The degree of congestion, Is with the node The set of adjacent nodes, Is a neighboring node of traffic.

[0043] See also Figure 2, a multi-hop communication node intelligent scheduling system, including the following modules: a communication potential analysis module, a relay node selection module, a role migration trigger module, and an autonomous game decision module; the communication potential analysis module is used to collect the residual energy, channel fading parameters and link load data of the communication nodes of the UAV and the ground emergency terminal in real time, and calculate the communication potential index through dynamic weight fusion to comprehensively quantify the multi-hop relay capability of the node under energy limitation and channel mutation; the relay node selection module is used to set the relay node selection threshold for different disaster types according to the communication potential index, give priority to the communication node with a communication potential index higher than the disaster adaptation threshold as the relay node, construct an initial hybrid topology structure with anti-destruction priority, and pre-distribute the load of the key rescue data stream in combination with the disaster heat distribution map; the role The migration trigger module is used to trigger the role migration operation when it is detected that the communication potential index of the relay node is lower than the dynamically updated safety threshold due to a sudden drop in energy or channel disturbance, and broadcast a migration request carrying an emergency level identifier to the neighboring nodes. According to the real-time communication potential index ranking results and link survival prediction information, the optimal candidate node is selected to take over the relay function; the autonomous game decision module is used to model each node as an autonomous game intelligent agent, whose action space includes four states: relay, terminal, standby and emergency communication. A multi-objective reward function is designed according to the local channel quality, remaining energy and the urgency of the rescue mission. Through the interactive incremental game decision information of the anti-interference beacon, it converges to the Nash equilibrium state under the dynamic topology and partial node failure scenarios, and generates a multi-hop scheduling strategy that collaboratively optimizes energy consumption balance, transmission real-time performance and network anti-destruction performance.

[0044] In this implementation, the Communication Potential Analysis Module dynamically collects and integrates multi-dimensional data, including residual energy, channel fading parameters, and link load, from both drones and ground-based emergency terminal nodes to calculate a communication potential index. This approach differs from traditional single-factor evaluation methods by dynamically calculating the communication potential index based on multiple factors (such as energy, channel quality, and load). This more accurately reflects the multi-hop relay capability of nodes in different network environments, ensuring the stability of system communication performance, especially under energy constraints and sudden channel changes. The Relay Node Selection Module selects relay nodes based on the communication potential index and pre-allocates the load for critical rescue data streams based on the disaster heat map. This strategy sets relay node selection thresholds based on different disaster types, prioritizing nodes with high potential indices as relays and building a hybrid topology that prioritizes resiliency. This not only improves network reliability but also dynamically optimizes resource allocation based on different disaster scenarios, ensuring efficient transmission of rescue data streams. The Role Migration Triggering Module implements a role migration mechanism that monitors a node's communication potential index in real time and triggers migration when it falls below a safety threshold. Node migration selects the optimal candidate node by real-time sorting of communication potential index and link survival prediction information. This role migration mechanism based on dynamic monitoring and prediction can ensure seamless switching of relay tasks when nodes fail or the load is too high, avoiding the communication interruption problem caused by relay node failure in traditional methods. Autonomous game decision module: This module adopts the Nash equilibrium model in game theory, regards each communication node as an autonomous game agent, and designs a multi-objective reward function to balance goals such as energy consumption, transmission real-time, and indestructibility. Nodes optimize their own decisions through distributed reinforcement learning and exchange information through incremental game decision-making through anti-interference beacons. In the case of dynamic topology and partial node failure, the system can adaptively converge to the Nash equilibrium state, thereby achieving coordinated optimization of energy consumption balance and network indestructibility goals.

[0045] In summary, this application has at least the following effects:

[0046] A method and system for intelligently scheduling multi-hop communication nodes. Through multi-dimensional data fusion and dynamic weight calculation within a communication potential analysis module, the system can evaluate and optimize the multi-hop relay capabilities of nodes in real time, ensuring stable and reliable communication links even in disaster environments facing sudden channel changes and energy constraints. The relay node selection process considers factors such as disaster type, node status, and task urgency. Dynamic adjustments can be made based on the real-time communication potential index to ensure the selection of the most suitable node for relaying, thereby improving the network's resilience and adaptability. When a node's communication potential index decreases or a link problem arises, the role migration trigger module can respond quickly, migrating relay tasks to neighboring nodes with greater communication potential in real time. This avoids communication interruptions caused by single node failures and enhances the system's self-healing and fault tolerance. The autonomous game decision module combines game theory models with distributed reinforcement learning to dynamically optimize node behavior decisions, collaboratively optimizing multiple objectives such as energy consumption, transmission real-time, and network resilience. This ensures the system can adaptively adjust in dynamic environments and improves overall network efficiency. By combining reserved resources for critical rescue data streams with disaster heat maps, the system ensures that mission-critical data streams (such as vital signs and location coordinates) are prioritized during transmission, improving the real-time and success rate of critical data transmission. The system optimizes network status and load by updating routing information and adjusting resource allocation in real time, even in dynamic topologies and with partial node failures, ensuring efficient and stable operation in disaster scenarios.

[0047] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0048] The present invention is described with reference to flowcharts and / or block diagrams of systems, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0049] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0050] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0051] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0052] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A multi-hop communication node intelligent scheduling method, characterized in that: The following steps are involved: S1. Real-time collection of residual energy, channel fading parameters, and link load data from UAV and ground emergency terminal communication nodes. Calculation of the communication potential index through dynamic weight fusion to comprehensively quantify the node's multi-hop relay capability under energy constraints and channel mutations. S2. Based on the communication potential index, set relay node selection thresholds for different disaster types. Prioritize communication nodes with a communication potential index higher than the disaster adaptation threshold as relay nodes. Construct an initial hybrid topology that prioritizes survivability. Pre-allocate the load for key rescue data streams based on the disaster heat map. S3. When a relay node's communication potential index is detected to be below a dynamically updated safety threshold due to a sudden drop in energy or channel disturbance, a role migration operation is triggered. A migration request with an emergency level indicator is broadcast to neighboring nodes. Based on the real-time communication potential index ranking results and link survival prediction information, the optimal candidate node is selected to take over the relay function. S4. Each node is modeled as an autonomous game agent, whose action space includes four states: relay, terminal, standby, and emergency communication. A multi-objective reward function is designed based on local channel quality, remaining energy, and the urgency of the rescue mission. By interactively leveraging incremental game decision information from anti-interference beacons, the agent converges to a Nash equilibrium under dynamic topologies and partial node failure scenarios, generating a multi-hop scheduling strategy that collaboratively optimizes energy consumption balance, transmission real-time performance, and network survivability. The specific process of converging to the Nash equilibrium state in dynamic topology and partial node failure scenarios by interactive incremental game decision information with anti-interference beacons is as follows: According to the local channel quality, remaining energy and rescue mission urgency, the channel gain term, energy penalty term and task priority weighting term are designed respectively to construct a multi-objective composite reward function. The local decision information, including node action strategy, reward value increment and link status summary, is compressed and encoded by anti-interference beacon and broadcasted to neighboring nodes using frequency hopping spread spectrum technology. The node updates its local strategy based on the received game information and iteratively optimizes the probability distribution of action selection through distributed reinforcement learning; When the strategy difference of nodes in the entire network continues to be lower than the set threshold and the key performance indicators tend to be stable, the system is judged to have converged to the Nash equilibrium state.

2. The method for intelligent scheduling of multi-hop communication nodes according to claim 1, characterized in that: The specific process of real-time collection of residual energy, channel fading parameters, and link load data between the UAV and the ground emergency terminal communication node is as follows: Energy monitoring modules deployed on drones and ground terminals periodically collect the remaining battery energy value and predict the energy decay trend based on the charge and discharge rate. Through the real-time communication feedback mechanism of the link layer, the received signal strength and bit error rate between each pair of communication nodes are counted, the instantaneous channel fading level is calculated, and the fluctuation amplitude within a short period of time is analyzed by sliding. The channel quality change gradient is extracted to determine whether there is a risk of sudden change in the channel. Combining the forwarding queue status and actual load conditions of each node, the cache queue length, average waiting delay and packet queuing frequency are monitored in real time to form a load vector reflecting the link load level. The load peaks in different time slices are normalized to generate standardized link pressure characteristic values.

3. The method for intelligent scheduling of multi-hop communication nodes according to claim 2, characterized in that: The specific process of calculating the communication potential index through dynamic weight fusion is as follows: For each communication node, the standardized index values ​​are extracted from the three dimensions of residual energy, channel fading, and link load, and their numerical ranges are unified; According to the current disaster type, node role task and task urgency parameters, the three indicators are assigned differentiated dynamic weights to generate a weight coefficient group that reflects the current scheduling priority requirements; The three weighted indicators are fused to construct a three-dimensional feature vector, and by calculating its mapping function value in a specific scheduling strategy space, a communication potential index representing the multi-hop relay capability of the current node is generated.

4. The method for intelligent scheduling of multi-hop communication nodes according to claim 3, characterized in that: The specific process of setting the relay node selection threshold for different disaster types based on the communication potential index is as follows: Obtain the communication assurance level requirements corresponding to the current disaster type. Based on the distribution range of the communication potential index in historical disaster emergency communication tasks, extract the minimum value of the communication potential index that successfully maintains the stability of the communication link in various disaster scenarios as a reference threshold; Combining the current task urgency and node distribution density, a weighted correction coefficient is set to dynamically adjust the basic threshold to form a disaster-adaptive communication potential selection threshold. Sort the communication potential indexes of all candidate nodes and select nodes with a value higher than the set threshold as the candidate set of relay nodes; Based on the candidate node set, the target node suitable for relay is further selected based on the following specific criteria: Geographical location constraints: Eliminate nodes located in communication blind spots, severely blocked by terrain, or beyond the preset communication radius from the target service area; Link connectivity constraint: By calculating the adjacency matrix, nodes with a number of adjacent nodes higher than the average connectivity threshold are selected; Energy distribution balance constraint: Calculate the energy variance of the candidate node set, give priority to nodes with medium to high power levels, and eliminate nodes whose current remaining energy is lower than a certain percentage of the network average.

5. The method for intelligent scheduling of multi-hop communication nodes according to claim 4, characterized in that: The specific process of constructing an initial hybrid topology that prioritizes survivability and pre-distributing the load of key rescue data streams based on the disaster heat distribution map is as follows: Backbone nodes are selected based on their communication potential index. Some of these backbone nodes serve as cluster head nodes, covering high-priority rescue areas, while the remaining backbone nodes form a mesh redundancy layer, forming a communication topology with multi-path connectivity. Rescue data is managed hierarchically, with different transmission resources and path redundancy levels allocated based on the importance of the data. High-priority data is configured with fixed time slots and multi-path transmission strategies, while low-priority data uses dynamic resource scheduling. Backup channels are also pre-set for critical data to enhance link reliability. Redundant cluster head nodes are deployed in potential high-risk areas to form a cross-coverage structure. Load sensing mechanisms are set up at key nodes to dynamically migrate some traffic to adjacent nodes based on the current load status of the nodes to achieve link load balancing. The topology structure periodically broadcasts topology status beacons to drive each node to update local routing information in real time. When a link is interrupted, it quickly selects an alternative node based on the communication potential indicator to complete the path reconstruction.

6. The method for intelligent scheduling of multi-hop communication nodes according to claim 5, characterized in that: The specific process of selecting the best candidate node to take over the relay function based on the real-time communication potential index ranking results and link survival prediction information is as follows: Receive the real-time communication potential index broadcast by neighboring nodes, generate a list of candidate nodes in descending order, and remove nodes that are beyond the effective connection range by combining geographical location and communication range restrictions; Based on the historical channel stability data of candidate nodes and the link survival prediction model, nodes with survival probability higher than the set threshold are screened to form a highly reliable candidate set; Send migration requests to nodes in the high-reliability candidate set and verify their current load status; After the migration verification is passed, the local topology synchronization beacon broadcast is triggered to update the routing table of all network nodes. If the link is interrupted, the network path is quickly reconstructed based on the communication potential index and topology status to ensure uninterrupted data transmission.

7. The method for intelligent scheduling of multi-hop communication nodes according to claim 6, characterized in that: The specific process of generating a multi-hop scheduling strategy that coordinates optimization of energy consumption balance, transmission real-time performance, and network invulnerability is as follows: Dynamically allocate relay tasks based on the Nash equilibrium strategy, limit the continuous working time of high-load nodes, and trigger low-load nodes to take over some forwarding traffic; Reserve dedicated low-slot resources for preset critical data streams, allowing them to preempt non-critical data slots to ensure real-time transmission; Deploy redundant paths in potential risk areas on the disaster heat map. When the primary path is interrupted, quickly activate the optimal backup link based on the difference in communication potential index. Periodically trigger the synchronization of the entire network topology status, dynamically update the node routing table and suppress the spread of local congestion to ensure network anti-destruction capabilities.

8. A multi-hop communication node intelligent scheduling system, applied to a multi-hop communication node intelligent scheduling method according to any one of claims 1 to 7, characterized in that: It includes the following modules: communication potential analysis module, relay node selection module, role migration trigger module, and autonomous game decision module; The communication potential analysis module is used to collect the residual energy, channel fading parameters and link load data of the communication nodes of the UAV and the ground emergency terminal in real time, and calculate the communication potential index through dynamic weight fusion to comprehensively quantify the multi-hop relay capability of the node under energy limitation and channel mutation; The relay node selection module is used to set relay node selection thresholds for different disaster types based on the communication potential index, preferentially select communication nodes with a communication potential index higher than the disaster adaptation threshold as relay nodes, build an initial hybrid topology structure that prioritizes survivability, and pre-distribute the load of key rescue data streams based on the disaster heat distribution map; The role migration triggering module is used to trigger the role migration operation when it detects that the communication potential index of the relay node is lower than the dynamically updated safety threshold due to a sudden drop in energy or channel disturbance. The migration request with the emergency level identifier is broadcast to the neighboring nodes, and the optimal candidate node is selected to take over the relay function based on the real-time communication potential index ranking result and link survival prediction information; The autonomous game decision module is used to model each node as an autonomous game agent. Its action space includes four states: relay, terminal, standby, and emergency communication. A multi-objective reward function is designed based on the local channel quality, remaining energy, and the urgency of the rescue mission. Through the interactive incremental game decision information of anti-interference beacons, it converges to the Nash equilibrium state under dynamic topology and partial node failure scenarios, and generates a multi-hop scheduling strategy that coordinates the optimization of energy consumption balance, transmission real-time performance, and network invulnerability.

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