Data transmission method and system based on dynamic perception of birds

Abstract

The invention relates to the technical field of data transmission processing, in particular to a data transmission method and system based on bird dynamic perception. According to the invention, the group perception vector obtained through the multi-dimensional perception fusion algorithm can comprehensively capture the complex features of the network environment, and compared with a traditional single-dimensional network state monitoring method, the method can significantly improve the accuracy and comprehensiveness of network environment perception. Therefore, the data transmission system can identify network congestion, link quality change, abnormal node state and other conditions more accurately, thereby providing more reliable basic information for subsequent transmission decisions. The group coordination information formed through group coordination analysis processing can effectively solve the problem that all paths in traditional multi-path transmission are lack of coordination, mutual interference and resource conflicts among the paths are remarkably reduced, and the utilization efficiency of overall network resources is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data transmission processing, and particularly relates to a data transmission method and system based on bird dynamic perception. Background Art

[0002] With the rapid development of Internet of Things, 5G communication and edge computing technologies, modern data transmission systems are facing an increasingly complex network environment and diverse service requirements. Traditional data transmission methods mainly rely on static routing protocols and fixed transmission strategies. In the prior art, multi-path transmission protocols can utilize multiple paths to transmit data in parallel to improve transmission efficiency. Software-defined network technology realizes dynamic allocation of network resources through centralized control. Machine learning methods are applied to network traffic prediction and path optimization, such as QoS routing algorithms based on neural networks. In addition, bionics also has certain applications in network communication. Ant colony algorithms are used for routing optimization, and particle swarm algorithms are applied to resource allocation. These methods have achieved certain effects in specific scenarios.

[0003] However, traditional methods lack the ability of multi-dimensional perception of the network environment and cannot comprehensively utilize various perception information for decision-making. Existing multi-path transmissions lack an effective coordination mechanism, and there is a lack of coordination and cooperation among different paths, resulting in low resource utilization efficiency. Moreover, current path planning methods mainly rely on static network topologies and historical statistical information, lacking the real-time adaptation ability to dynamic changes in the network, and unable to dynamically adjust transmission strategies according to environmental changes. In the face of complex network environments, they lack the self-organization and coordination abilities similar to biological groups, and it is difficult to balance transmission efficiency and energy consumption control while ensuring transmission reliability. Summary of the Invention

[0004] The main object of the present invention is to provide a data transmission method based on bird dynamic perception, aiming to solve the technical problems in the prior art.

[0005] The present invention proposes a data transmission method based on bird dynamic perception, including: Obtaining the spatial position information and connection status information of network nodes, and obtaining a group perception vector according to the spatial position information and connection status information; Obtaining synchronous transmission parameters according to the group perception vector, and performing group coordination on the network nodes according to the synchronous transmission parameters to obtain group coordination information; Obtaining multiple alternative paths according to the group coordination information, and obtaining a multi-path set according to the multiple alternative paths, wherein the group coordination information includes leader node identifiers, follower node identifiers, and a set of coordination parameters; Performing bird flock dispersion encoding on the data to be transmitted according to the multi-path set to obtain encoded data segments, and obtaining a data segment stream at the receiving end according to the encoded data segments; Obtain classification segment information according to the data segment stream, and perform flock aggregation and recombination according to the classification segment information to obtain recombined data.

[0006] Preferably, the step of obtaining the group perception vector according to the spatial position information and the connection status information includes: Obtain the visual perception coverage area according to the spatial position information, and perform a quantitative evaluation on the node reachability of the network nodes according to the visual perception coverage area to obtain a visual perception vector; Obtain the auditory perception intensity distribution according to the connection status information, and calculate the data packet reception probability of the network nodes according to the auditory perception intensity distribution to obtain an auditory perception vector; Obtain the network topology direction information of the network nodes, and obtain the magnetic field perception vector according to the network topology direction information; Obtain the status of neighboring nodes in the network nodes, and obtain the neighbor perception map according to the status of neighboring nodes; Fuse the visual perception vector, the auditory perception vector and the magnetic field perception vector according to the neighbor perception map to obtain a group perception vector.

[0007] Preferably, the step of obtaining the synchronous transmission parameters according to the group perception vector and performing group coordination on the network nodes according to the synchronous transmission parameters to obtain group coordination information includes: Obtain the node performance index according to the group perception vector, and perform multi-objective optimization selection on the network nodes according to the node performance index to obtain the leader node identifier and the non-leader node identifier; Obtain the following relationship of non-leader nodes according to the leader node identifier and the non-leader node identifier, and obtain the follower node identifier according to the following relationship; Synchronize the transmission parameters of the leader node identifier and the follower node identifier to obtain synchronous transmission parameters; Obtain the network congestion and node failures of the network nodes, and obtain dynamic adjustment parameters according to the network congestion and node failures; Fuse the synchronous transmission parameters and the dynamic adjustment parameters to obtain a set of coordination parameters.

[0008] Preferably, the step of obtaining multiple alternative paths according to the group coordination information and obtaining a multi-path set according to the multiple alternative paths includes: Obtain the historical transmission data and real-time network changes of the network nodes, and establish a migration map according to the historical transmission data to obtain a dynamic migration map; Obtain the target node location of the data to be transmitted and the overall network status, and obtain multiple alternative paths according to the target node location and the overall network status; Obtain the path length, path stability, path load, and path reliability of each of the alternative paths, and obtain the path attractiveness score of the corresponding alternative path according to each of the path length, path stability, path load, and path reliability; Sort the alternative paths in descending order according to the path attractiveness score to obtain a candidate path set including the path priority sorting; Perform path fine-tuning on the paths in the candidate path set according to the real-time network changes to obtain an optimized path scheme, and classify the optimized path scheme into primary and backup paths to obtain a multi-path set.

[0009] Preferably, the step of performing flock-dispersed coding on the data to be transmitted according to the multi-path set to obtain coded data segments and obtaining a data segment stream at the receiving end according to the coded data segments includes: Obtain path grading information according to the multi-path set, and perform dynamic fragmentation processing on the data to be transmitted according to the path grading information to obtain graded data segments; Encode the graded data segments to obtain redundant coded segments, and add homing identifiers to the redundant coded segments to obtain data segments with identifiers; Generate accompanying backup according to the data segments with identifiers to obtain coded data segments; Obtain a transmission formation set according to the coded data segments, and select a leading segment identifier according to the transmission formation set to obtain a formation leading segment; Obtain a preset transmission plan, and obtain a synchronous transmission instruction according to the preset transmission plan and the formation leading segment; Obtain a migratory bird navigation system according to the network nodes, and control the migratory bird navigation system to perform coordinated transmission to the receiving end according to the synchronous transmission instruction to obtain a data segment stream.

[0010] Preferably, the step of obtaining classification segment information according to the data segment stream and performing flock aggregation and recombination according to the classification segment information to obtain the recombined data includes: The receiving end performs homing verification on the received data segment stream to obtain a set of valid segments; Obtain the grouping identifier of each data segment stream in the set of valid segments, and perform classification verification on the set of valid segments according to the grouping identifier to obtain classification segment information; Sort and recombine the classification segment information to obtain a recombined topology graph, and perform topological sorting according to the recombined topology graph to obtain a segment recombination sequence; Perform end-to-end verification processing on the fragment recombination sequence to obtain recombination data.

[0011] This application also provides a data transmission system based on bird dynamic perception, including: A first acquisition module, configured to acquire the spatial position information and connection status information of network nodes, and obtain a group perception vector according to the spatial position information and connection status information; A coordination module, configured to obtain synchronization transmission parameters according to the group perception vector, and perform group coordination on the network nodes according to the synchronization transmission parameters to obtain group coordination information; A second acquisition module, configured to obtain multiple alternative paths according to the group coordination information, and obtain a multi-path set according to the multiple alternative paths, where the group coordination information includes a leader node identifier, a follower node identifier, and a coordination parameter set; An encoding module, configured to perform flock-dispersed encoding on the data to be transmitted according to the multi-path set to obtain encoded data segments, and obtain a data segment stream at the receiving end according to the encoded data segments; A recombination module, configured to obtain classification segment information according to the data segment stream, and perform flock-aggregation recombination according to the classification segment information to obtain recombination data.

[0012] Preferably, the first acquisition module includes: An evaluation unit, configured to obtain a visual perception coverage area according to the spatial position information, and perform quantitative evaluation on the node reachability of network nodes according to the visual perception coverage area to obtain a visual perception vector; A calculation unit, configured to obtain an auditory perception intensity distribution according to the connection status information, and calculate the packet reception probability of network nodes according to the auditory perception intensity distribution to obtain an auditory perception vector; A first acquisition unit, configured to acquire the network topology direction information of network nodes, and obtain a magnetic field perception vector according to the network topology direction information; A second acquisition unit, configured to acquire the status of neighboring nodes in the network nodes, and obtain a neighbor perception map according to the status of neighboring nodes; A fusion unit, configured to fuse the visual perception vector, the auditory perception vector, and the magnetic field perception vector according to the neighbor perception map to obtain a group perception vector.

[0013] This invention also provides a computer device, including a memory and a processor, where the memory stores a computer program, and when the processor executes the computer program, the steps of the above-mentioned data transmission method based on bird dynamic perception are implemented.

[0014] The present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned data transmission method based on bird dynamic perception are implemented.

[0015] The beneficial effects of the present invention are as follows: The group perception vector obtained by the multi-dimensional perception fusion algorithm of the present invention can comprehensively capture the complex characteristics of the network environment. Compared with the traditional single-dimensional network state monitoring method, it can significantly improve the accuracy and comprehensiveness of network environment perception, enabling the data transmission system to more accurately identify situations such as network congestion, link quality changes, and node state anomalies, thereby providing more reliable basic information for subsequent transmission decisions. The group coordination information formed through group coordination analysis and processing can effectively solve the problem of lack of coordination and cooperation among various paths in traditional multi-path transmission, significantly reducing the mutual interference and resource conflicts between paths, and improving the utilization efficiency of the overall network resources. At the same time, through the dynamic leader-follower mechanism, it can quickly respond to network environment changes, ensuring the stability and continuity of the transmission process. The multi-path set obtained by processing based on bird migration path planning has stronger environmental adaptability and fault tolerance ability. Compared with static routing methods, it can significantly reduce transmission interruptions caused by single-path failures and improve the reliability of data transmission. At the same time, through the reasonable configuration of primary and backup paths, it can effectively reduce transmission delay and network congestion on the premise of ensuring transmission quality. The encoded data segments generated by the bird flock dispersion coding process have higher fault tolerance and recovery ability. Through unequal error protection coding and homing information identification, even when some transmission paths fail, the complete recovery of data can still be ensured, significantly enhancing the robustness of the system. At the same time, the accompanying flight backup mechanism further enhances the transmission guarantee of key data. The formation coordination transmission processing of the present invention can effectively avoid the problems of timing chaos and resource competition in traditional parallel transmission. Through precise synchronization control and coordination mechanisms, it significantly improves the transmission efficiency and reduces the retransmission overhead. At the same time, the introduction of the homing navigation system provides reliable transmission guidance for data segments, further improving the transmission success rate. Through the bird flock aggregation and reorganization processing of the present invention, it can efficiently process out-of-order and partially lost data segments. Compared with traditional sequential reorganization methods, it has stronger fault tolerance ability and higher reorganization efficiency. Through topological sorting processing, it can maximize the utilization of received data segments and reduce waiting time. At the same time, the transmission quality assessment report provides a scientific basis for system optimization, forming a complete closed-loop optimization mechanism, and overall realizing the efficient, reliable and adaptive operation of the data transmission system in a complex network environment. Description of the Drawings

[0016] Figure 1 It is a schematic flowchart of the method according to an embodiment of the present invention.

[0017] Figure 2Schematic diagram of the system structure according to an embodiment of the present invention.

[0018] Figure 3 Internal structure schematic diagram of a computer device according to an embodiment of the present application.

[0019] The implementation, functional features and advantages of the present invention will be further described with reference to the embodiments and the accompanying drawings. Detailed implementation manners

[0020] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0021] As Figure 1 shown, the present application provides a data transmission method based on bird dynamic perception, including: S1. Obtain the spatial position information and connection status information of network nodes, and obtain a group perception vector according to the spatial position information and connection status information; S2. Obtain synchronous transmission parameters according to the group perception vector, and perform group coordination on the network nodes according to the synchronous transmission parameters to obtain group coordination information; S3. Obtain multiple alternative paths according to the group coordination information, and obtain a multi-path set according to the multiple alternative paths, wherein the group coordination information includes a leader node identifier, a follower node identifier and a set of coordination parameters; S4. Perform flock dispersion coding on the data to be transmitted according to the multi-path set to obtain encoded data segments, and obtain a data segment stream at the receiving end according to the encoded data segments; S5. Obtain classification segment information according to the data segment stream, and perform flock aggregation recombination according to the classification segment information to obtain recombined data.

[0022] As described in the above steps S1 - S5, the present invention makes decisions by obtaining the spatial location information and connection status information of network nodes and combining the group perception vector. It can integrate different types of perception data, such as location and network connection status, fully consider the dynamic characteristics of the network, and thus make more accurate decisions in a complex network environment. Through multi - dimensional perception, the system can adapt to changes in the environment in real - time, improving the deficiencies of traditional methods that only rely on static topologies and historical information. Through the group coordination mechanism, based on synchronous transmission parameters, group coordination of network nodes is carried out to obtain group coordination information (such as leader node, follower node identification, and coordination parameters). This enables coordinated cooperation between different paths and nodes, thereby improving the utilization efficiency of network resources and effectively avoiding the uncoordinated situation between paths in traditional methods. Moreover, the dynamic adjustment ability of the present invention enables the system to perform flexible coordination and optimization in different network environments, enhancing the transmission efficiency and stability. By simulating the self - organizing behavior of biological groups (such as the decentralized coding and aggregation recombination of bird flocks), adaptive coordination between nodes in the network is achieved. Through the bird - flock decentralized coding and multi - path aggregation, the system can flexibly schedule according to the characteristics of different paths, thereby improving the transmission efficiency while ensuring reliability and effectively controlling energy consumption. This biologically inspired self - organizing ability enables the network system to adaptively adjust when facing a dynamically changing network environment, avoiding the limitations of static methods. By real - time perceiving the spatial location and connection status information of network nodes and dynamically adjusting synchronous transmission parameters, the dynamic adaptability of path planning is achieved. This dynamic path selection and adjustment can respond to network changes in real - time, avoiding the defects of traditional path - planning methods based on static topologies or historical data. Through multi - path aggregation and bird - flock decentralized coding, combined with the group coordination mechanism, more efficient multi - path transmission is achieved. By coordinating the work between different paths, the redundancy of multi - paths can be effectively utilized while avoiding path conflicts and resource waste, thus improving the overall transmission efficiency of the network. Through the group perception vector, multiple perception dimensions such as spatial location information and connection status information are combined to provide more comprehensive support for the decision - making process. Through this comprehensive perception, the system can better understand the network environment and make more accurate decisions, enhancing the transmission performance and reliability.

[0023] In one embodiment, step S1 of obtaining the group perception vector according to the spatial location information and connection status information includes: S11. Obtain the visual perception coverage area according to the spatial location information, and quantitatively evaluate the node reachability of network nodes according to the visual perception coverage area to obtain the visual perception vector; S12. Obtain the auditory perception intensity distribution according to the connection status information, and calculate the packet reception probability of network nodes according to the auditory perception intensity distribution to obtain the auditory perception vector; S13. Obtain the network topology direction information of the network node, and obtain the magnetic field perception vector according to the network topology direction information; S14. Obtain the status of neighboring nodes in the network node, and obtain the neighbor perception map according to the status of the neighboring nodes; S15. Integrate the visual perception vector, the auditory perception vector and the magnetic field perception vector according to the neighbor perception map to obtain the group perception vector.

[0024] As described in the above steps S11 - S15, the present invention enhances the environmental perception ability of network nodes by introducing multiple perception dimensions such as visual perception, auditory perception, and magnetic field perception. Among them, visual perception obtains the visual perception coverage area through spatial position information and quantitatively evaluates the reachability of nodes, enabling precise grasp of the spatial layout and mutual relationship of network nodes, thereby improving the accuracy of decision-making. Auditory perception obtains the auditory perception intensity distribution through connection state information and can calculate the packet reception probability, further enhancing the reliability and stability of communication between nodes. Magnetic field perception obtains the magnetic field perception vector through network topology direction information, providing an in-depth understanding of the network topology direction and helping to adjust the data transmission strategy. The neighbor perception map evaluates the neighbor perception map through the states of neighboring nodes, further strengthening the information transmission and cooperation ability between nodes. Through the introduction of these multi-dimensional perception capabilities, the system can more comprehensively understand the network environment, improving the adaptability and precision in the decision-making process. By fusing the visual perception vector, auditory perception vector, magnetic field perception vector, and group perception vector to form a group perception vector, this fusion method can comprehensively reflect the state information of network nodes in different dimensions. The fused perception vector provides a more accurate basis for decision-making, enabling more intelligent adjustments in complex network environments, thereby improving the transmission efficiency, reliability, and energy consumption control of the network. Through the real-time calculation of the group perception vector, this method can reflect the dynamic changes of different nodes and paths in the network, enabling the transmission strategy to be adjusted at any time according to changes in the network environment. Especially in a dynamically changing network environment, it can automatically optimize path selection and data transmission strategies, effectively coping with frequently changing conditions in the network, such as node state changes and connection quality fluctuations. By introducing the group perception vector and the idea based on the self-organization of biological groups (such as through the comprehensive action of node states and perception information), coordination and cooperation between nodes in the network are achieved. For example, nodes in the network can make adaptive adjustments and optimizations by perceiving the states of other nodes, thereby realizing effective cooperation between nodes and ensuring the efficiency and stability of multi-path transmission. The introduction of group perception enhances the cooperation ability between network nodes, enabling the transmission task to be efficiently allocated among multiple paths and improving the utilization efficiency of resources. Through the real-time fusion and analysis of the group perception vector, the path planning can be dynamically adjusted, and the work between each path and node can be coordinated.This coordination mechanism not only optimizes the efficiency of multipath transmission but also can adapt to changes in the network state in real time, reducing resource waste caused by path conflicts or uneven network loads. The path planning method based on multi-dimensional perception can maximize the utilization of redundant resources in the network, improving transmission reliability, transmission efficiency, and energy consumption control. By adaptively adjusting the transmission path and strategy, while ensuring transmission reliability, it optimizes the control of energy consumption. Especially when selecting a path through the group perception vector, the system can comprehensively consider various factors such as path quality, load, and distance, so as to intelligently allocate among different transmission paths and reduce unnecessary energy consumption.

[0025] Specifically, in the process of generating the visual perception vector, first, the field of view range is calculated for the spatial position information. The corresponding field of view range calculation simulates the working principle of the avian visual system. By establishing a visual perception coverage area centered on each network node, in the specific quantification evaluation process, the distance factor, signal quality factor, and path complexity factor are synthesized into a single reachability value through weighted summation, and finally, a visual perception vector is formed. Among them, the expression for quantitatively evaluating the reachability of a network node through the field of view range calculation and signal intensity distribution is: ; Among them, is the node index value, is the node reachability, is the maximum detection distance, is the distance, is the node signal intensity distribution, is the node field of view range, is the attenuation coefficient; Quantitative evaluation of the visual perception coverage area and modeling of the auditory perception intensity distribution, through visual recognition and acoustic signal processing technologies, enables refined analysis of the network node status. The calculation of the visual perception coverage area can combine the real-time processing capabilities of edge AI algorithms to ensure high-frequency node reachability assessment. The auditory perception intensity distribution extracts features through short-time Fourier transform and recurrent neural networks, and combines environmental noise suppression technology to improve signal quality. The dynamic modeling of the magnetic field perception vector is through geomagnetic navigation simulation, combined with node topology direction information, to optimize the global consistency of path planning. The dynamic monitoring of the group perception map and the integration of group status, through graph neural network modeling, enables real-time perception of the status of neighboring nodes and collaborative decision-making. The synergistic effect of this multi-dimensional perception enables network nodes to more comprehensively adapt to environmental changes, thereby improving the overall transmission efficiency and stability. The construction of the auditory perception vector focuses on the in-depth exploration of connection status information. First, signal attenuation modeling is performed on the connection status information. The signal attenuation modeling uses the log-distance path loss model, which takes into account the free space loss, multipath fading, and shadow effects suffered by the signal during propagation, and calculates the signal intensity distribution at different distances. Then, based on the obtained auditory perception intensity distribution, the packet reception probability is accurately calculated. The packet reception probability calculation algorithm converts the signal intensity value into the reception success rate. The conversion process can use the bit error rate function, taking the signal-to-noise ratio as the input parameter and outputting the corresponding correct packet reception probability. By performing probability calculations on all possible signal intensity values and normalizing them, the auditory perception vector is finally generated. The generation mechanism of the magnetic field perception vector simulates the biological principle of birds using the geomagnetic field for navigation, generating a magnetic field perception vector that reflects the orientation of the network topology structure.

[0026] Subsequently, dynamic monitoring and processing are performed on the states of neighboring nodes in the network nodes. The dynamic monitoring and processing collect key parameters such as the load status, connection quality, response time, and available bandwidth of neighboring nodes in real time, organize these parameters into time-series data and perform sliding window analysis to obtain a neighbor perception map reflecting the dynamic characteristics of the nodes. The neighbor perception map is based on a graph theory data structure, where nodes represent network devices and the weights of edges represent connection quality. Finally, group state fusion is performed according to the neighbor perception map. The group state fusion algorithm combines weighted average and principal component analysis. First, weighted average calculation is performed on the state parameters of neighboring nodes, and the weights are determined by the importance and credibility of the nodes. Then, the main features of the state data are extracted through principal component analysis to generate a group perception vector that can reflect the coordinated state of the entire network group. The construction process of the group perception vector reflects the intelligent characteristics of bird group coordination. Finally, a group perception vector that can reflect the coordinated state of the entire network group is generated. The multi-modal fusion design (vision, audition, magnetic field) of the group perception vector significantly improves the comprehensiveness and accuracy of network perception. The visual perception vector realizes the evaluation of high-frequency node reachability through a visual recognition algorithm (such as edge computing), and optimizes the coverage efficiency by combining the calculation of the field of view; the audition perception vector quantifies signal attenuation based on an acoustic monitoring device (such as a directional microphone) and predicts the reception probability by combining a deep learning model; the magnetic field perception vector improves the global consistency of path planning through geomagnetic navigation simulation and combining topological direction information; the group perception vector realizes the real-time fusion and prediction of group behavior by dynamically monitoring the states of neighboring nodes and combining graph neural network modeling. The synergistic effect of this multi-dimensional perception enables network nodes to better adapt to environmental changes, thereby improving the overall transmission efficiency and stability.

[0027] In one embodiment, step S2 of obtaining synchronous transmission parameters according to the group perception vector and performing group coordination on the network nodes according to the synchronous transmission parameters to obtain group coordination information includes: S21. Obtain node performance indicators according to the group perception vector, and perform multi-objective optimization selection on network nodes according to the node performance indicators to obtain leader node identifiers and non-leader node identifiers; S22. Obtain the following relationship of non-leader nodes according to the leader node identifiers and non-leader node identifiers, and obtain follower node identifiers according to the following relationship; S23. Synchronize the transmission parameters of the leader node identifiers and follower node identifiers to obtain synchronous transmission parameters; S24. Obtain network congestion and node failures of the network nodes, and obtain dynamic adjustment parameters according to the network congestion and node failures; S25. Fuse the synchronous transmission parameters and dynamic adjustment parameters to obtain a set of coordination parameters.

[0028] As described in the above steps S21 - S25, the present invention obtains multi - dimensional performance indicators of network nodes through the group perception vector, which can evaluate the network environment more comprehensively, taking into account multiple factors (such as network congestion, node failures, etc.). This multi - dimensional perception ability enables the system to make more accurate decisions according to the real - time network conditions, overcoming the limitations brought by single perception of traditional methods. By performing multi - objective optimization selection on the node performance indicators, this method can select appropriate network nodes according to different optimization objectives (such as network efficiency, transmission stability, energy consumption, etc.), thereby improving the resource utilization rate. This flexibility and adaptability are particularly important in a dynamically changing network environment, making up for the limitations of traditional methods based on static topology and historical statistical information. Through the cooperation between the leader node identifier and the non - leader node identifier, a self - organizing and coordinating mechanism similar to that of a biological group is formed, which can effectively optimize the cooperation between each path and improve the overall resource utilization efficiency. The leader node forms a collaborative network structure by guiding the behavior of non - leader nodes, thus overcoming the lack of coordination in traditional multi - path transmission. In the face of a complex and dynamic network environment, traditional methods often cannot adapt to network changes in real time. However, the present invention dynamically adjusts the transmission strategy based on the real - time network state by obtaining information such as network congestion and node failures. This dynamic adjustment ability enables the network to automatically optimize the transmission path and parameters in a constantly changing environment, thus effectively avoiding the limitations of static planning. Through bionics, learning from the self - organizing and coordinating mechanism of biological groups, the system can still maintain stability and high efficiency in a complex environment. Traditional methods usually lack the adaptability and self - regulating ability similar to that of biological groups, while the present invention can imitate the coordinated behavior of groups in nature, making the transmission system more robust and flexible in a complex environment. By fusing the synchronous transmission parameters and dynamic adjustment parameters, a set of coordinated parameters is obtained. This method can ensure the balance among transmission efficiency, energy consumption control, and network stability under various network change situations, further enhancing the overall optimization of network resources.

[0029] Specifically, in the node capability evaluation process, a multi-dimensional analysis is first performed on the comprehensive perception vector. The visual perception vector, auditory perception vector, magnetic field perception vector, and group perception vector are used as the basic data inputs for evaluation. Each vector component is weighted through the node capability evaluation algorithm. The visual perception vector reflects the node's spatial perception ability, the auditory perception vector reflects the node's communication quality, the magnetic field perception vector represents the node's navigation and positioning ability, and the group perception vector represents the node's coordination and cooperation ability. Finally, the node performance index is obtained through weighted summation. The higher the value, the stronger the comprehensive ability of the node. Based on the node performance index, multi-objective optimization selection is performed on the network nodes to obtain the leader node identifier. The multi-objective optimization selection algorithm filters the leader node based on the node performance index. The multi-objective optimization selection algorithm adopted in the present invention simultaneously considers four optimization objectives: the processing ability, connection stability, energy consumption level, and load balancing of the node. The processing ability is calculated through the CPU utilization rate and memory occupancy rate of the node. The connection stability is determined based on the average connection duration and packet loss rate of the node. The energy consumption level is calculated according to the power consumption monitoring data and the remaining battery power of the node. The load balancing is evaluated through the current data traffic carried by the node and the historical load change trend. The multi-objective optimization algorithm uses the non-dominated sorting genetic algorithm NSGA-II for solution. First, non-dominated sorting is performed on all candidate nodes, and the nodes are divided into different levels according to the domination relationship. Then, the crowding distance of each node is calculated. The crowding distance reflects the distribution density of the nodes in the objective space. Finally, the node with the largest crowding distance in the first level is selected as the leader node identifier through the elitist selection strategy. The follower relationship establishment process determines the leader node to which each follower node belongs by analyzing the relevance between the non-leader nodes and the leader nodes. First, the comprehensive distance from the non-leader nodes to each leader node is calculated. The comprehensive distance includes three components: the physical distance, communication delay distance, and ability similarity distance. The three distance components are weighted and summed after normalization to obtain the comprehensive distance value. Then, the nearest neighbor assignment strategy is used to assign each non-leader node to the leader node with the smallest comprehensive distance to form the follower node identifier, which is convenient for subsequent coordination control and data routing. The transmission parameter synchronization process ensures that the leader node and the follower node keep in step during the data transmission process. The transmission parameter synchronization algorithm first determines the reference transmission parameters by the leader node according to the current network state and transmission requirements. The reference transmission parameters include four key parameters: the packet size, sending interval, retransmission times, and timeout time. The real-time detection and coordination avoidance mechanism continuously monitors the network congestion state and node health status, and discovers and processes abnormal situations in a timely manner. The network congestion detection algorithm in the present invention uses the sliding window technology to real-time statistically calculate key indicators such as the packet queue length, transmission delay, and packet loss rate in the network. When the queue length exceeds the preset threshold or the transmission delay suddenly increases, the congestion detection mechanism is triggered. The node fault detection algorithm is implemented through the heartbeat mechanism and performance monitoring.Each node regularly sends heartbeat packets to its neighbor nodes, and at the same time monitors its own CPU utilization, memory usage, and network interface status. When the heartbeat packets are continuously lost or the performance metrics are abnormal, the fault detection process is triggered. The coordination avoidance algorithm dynamically adjusts the network topology and data routing according to the detection results. When congestion is detected, the algorithm recalculates the path weights and triggers path switching. When a node failure is detected, the algorithm starts a fault recovery mechanism and redistributes the task load of that node. Finally, dynamic adjustment parameters including path adjustment instructions, load redistribution schemes, and parameter correction values are generated. The coordination parameter set fusion process intelligently integrates the synchronous transmission parameters and the dynamic adjustment parameters. The fusion algorithm adopts an adaptive weight allocation strategy, dynamically adjusting the influence weights of the two types of parameters according to the current state and historical performance of the network. When the network is running stably, the weight of the synchronous transmission parameters is higher to ensure the consistency and efficiency of transmission. When the network fluctuates or is abnormal, the weight of the dynamic adjustment parameters increases to prioritize the reliability and robustness of transmission. Subsequently, the present invention uses a fusion algorithm to linearly combine the two types of parameters according to the weights to obtain the final coordinated parameter set, which includes parameter configurations in multiple aspects such as transmission rate, caching strategy, routing selection, and fault handling.,

[0030] In one embodiment, step S3 of obtaining a plurality of alternative paths according to the group coordination information and obtaining a multi-path set according to the plurality of alternative paths includes: S31. Obtain the historical transmission data and real-time network changes of the network node, and establish a migration map according to the historical transmission data to obtain a dynamic migration map; S32. Obtain the target node location of the data to be transmitted and the overall network state, and obtain a plurality of alternative paths according to the target node location and the overall network state; S33. Obtain the path length, path stability, path load, and path reliability of each alternative path, and obtain the path attractiveness score of the corresponding alternative path according to each of the path length, path stability, path load, and path reliability; S34. Sort the alternative paths in descending order according to the path attractiveness score to obtain a candidate path set including path priority sorting; S35. Perform path fine-tuning on the paths in the candidate path set according to the real-time network changes to obtain an optimized path scheme, and classify the optimized path scheme into primary and backup paths to obtain a multi-path set.

[0031] As described in the above steps S31 - S35, traditional network transmission methods often rely on static topologies and historical data, lacking sensitivity to dynamic changes and unable to fully perceive and utilize multi - dimensional network information. The present invention establishes a dynamic migration map by obtaining the historical transmission data and real - time changes of network nodes, combining the real - time data of the target node location and the overall network state, achieving multi - dimensional perception of the network state, being able to adapt to changes in the network environment in real - time, more precisely selecting transmission paths, and fine - tuning candidate paths by combining real - time network changes, so as to make flexible adjustments when the network environment changes. Through path attractiveness scoring and dynamic optimization, it is possible to improve the flexibility and efficiency of path selection while ensuring transmission reliability. By comprehensively scoring each alternative path (including dimensions such as path length, stability, load, reliability, etc.) and prioritizing the paths according to the scores, a more refined path selection mechanism is provided. In addition, the fine - tuning of candidate paths and the primary - backup path classification mechanism effectively avoid resource conflicts and interference between paths, improving the utilization rate of network resources. By combining real - time network changes and the target node location, it provides a more intelligent dynamic adaptation ability for path planning. Through the optimization of the multi - path set and the classification of primary - backup paths, it can more effectively control energy consumption and improve the transmission efficiency of the network, taking into account the balance of transmission reliability, efficiency, and energy consumption. By simulating the self - organizing characteristics of biological groups, through the coordination, optimization, and fine - tuning of multi - paths, it is possible to achieve coordinated operations between paths. In this way, path planning not only focuses on the reliability of a single path but also can overall improve the efficiency of multi - path cooperation, thereby improving the performance of the entire network.

[0032] Specifically, the dynamic migration map construction process first performs performance statistical analysis on historical transmission data. Historical transmission data includes data transmission records between various network nodes in the past period of time. Each record contains key information such as source node identification, target node identification, transmission timestamp, data packet size, transmission delay, packet loss rate and bandwidth utilization. Subsequently, the time window sliding statistical method is used to divide the historical data into multiple time windows of fixed length in chronological order. The data in each time window is statistically analyzed to calculate performance indicators such as average transmission delay, maximum transmission delay, delay variance, average throughput, packet loss rate and connection stability. Then, a migration map is established based on the statistically obtained performance indicators. The migration map is represented by a weighted directed graph data structure. The nodes in the graph represent physical nodes in the network, and the edges represent the connection relationship between nodes. The weight of the edge is calculated by the transmission performance index. The dynamic migration map maintains the timeliness of the data through a real-time update mechanism. Whenever new transmission data is generated, the algorithm will recalculate the weight value of the relevant connection and update the map structure. Through global analysis and path attraction calculation, the target node location and the overall network status are comprehensively considered, and an attraction score is assigned to each candidate path and prioritized. For global analysis, the present invention first obtains the geographical location coordinates and network topology location information of the target node. The geographical location coordinates are directly obtained from the GPS positioning data or preset coordinates of the node. The network topology position information is obtained from the table. The network topology position information is determined by analyzing the network characteristic parameters such as the connectivity, centrality and clustering coefficient of the node in the network. Then the overall network status is evaluated. The overall network status evaluation includes the calculation of indicators such as the average load rate of the whole network, network congestion distribution, node failure rate and link quality distribution. The average load rate of the whole network is obtained by counting the current data processing load of all nodes and averaging them. The network congestion distribution is determined by analyzing the packet queue length and transmission delay distribution of each network area. The node failure rate is calculated based on historical fault records and the current node health status. The link quality distribution is obtained by counting the bit error rate, signal strength and connection stability of each link. The path attractiveness is calculated. The algorithm adopts a multi-factor comprehensive evaluation method, taking path length, path stability, path load and path reliability as the main evaluation factors. The path length factor is obtained by calculating the sum of the distances between all nodes on the path. The path stability factor is calculated based on the historical online time and connection duration of each node on the path. The path load factor is determined according to the current load of each node on the path. The path reliability factor is evaluated by analyzing the historical transmission success rate and fault recovery capability of the path. The four factors are weighted and summed to obtain the path attractiveness score. All candidate paths are sorted in descending order according to the attractiveness score to form a candidate path set containing path priority sorting. The expression of path priority is: ; in, is the path index value, is the path priority of the th calculated path, is the historical throughput of the th path, is the real-time status of the th path, and are the weight coefficients respectively, is the current path congestion degree of the th path, is the maximum congestion threshold; The path fine-tuning process dynamically optimizes the paths in the candidate path set based on real-time network state changes. The real-time network change monitoring mechanism continuously collects network state information through monitoring agents deployed on key network nodes. The monitoring agents report the collected data at regular time intervals. After receiving the real-time monitoring data, the path fine-tuning algorithm first calculates the change magnitude of each monitoring metric relative to the historical baseline value. When the change magnitude exceeds the preset threshold, the path fine-tuning process is triggered. The path fine-tuning algorithm adopts a local search optimization strategy to locally adjust the paths with higher priorities in the candidate path set. The adjustment contents include the replacement of some nodes on the path, the re-selection of path segments, and the dynamic correction of transmission parameters. The node replacement strategy replaces the nodes with degraded performance on the current path by finding neighboring nodes with better performance. The path segment re-selection strategy finds a path segment with better intermediate transmission performance for replacement under the premise of keeping the start and end points of the path unchanged. The dynamic correction of transmission parameters adjusts parameters such as the packet size, sending frequency, and retransmission strategy according to the real-time network state. The fine-tuning algorithm gradually improves the path performance through an iterative optimization process. The attractiveness score of the path is recalculated in each iteration. When the improvement magnitude of the score in consecutive iterations is less than the convergence threshold, the optimization process stops, and finally, an optimized path scheme optimized in real time is obtained. For the primary and backup path classification, the optimized path scheme is hierarchically managed according to the requirements of transmission importance and reliability. First, all paths are divided into two levels, the primary path and the backup path, according to the attractiveness score and stability index of the path. The backup path selection criterion requires that the path has sufficient independence from the primary path and its transmission performance meets the basic requirements. The path independence is evaluated by calculating the node overlap degree and geographical separation degree between paths. The algorithm selects paths with an independence score higher than 0.7 as backup paths. Then, the primary path is further subdivided. According to the present invention, the primary path is divided into three subclasses: the high-priority primary path, the medium-priority primary path, and the low-priority primary path according to the priority and real-time requirements of the transmission task. The high-priority primary path is used to transmit critical control data and real-time monitoring data, requiring the minimum delay and the highest reliability. The medium-priority primary path is used to transmit general service data, balancing transmission efficiency and resource consumption. The low-priority primary path is used to transmit historical data and log information, focusing on transmission cost and energy consumption control. The backup paths are also hierarchically managed and kept synchronized with the corresponding priority primary paths, finally forming a hierarchically organized multi-path set.

[0033] In one embodiment, step S4 of performing flock dispersion encoding on the data to be transmitted according to the multi-path set to obtain encoded data segments and obtaining the data segment stream at the receiving end according to the encoded data segments includes: S41. Obtain path grading information according to the multi-path set, and perform dynamic fragmentation processing on the data to be transmitted according to the path grading information to obtain graded data segments; S42. Encode the hierarchical data segment to obtain a redundant encoded segment, and add a homing identifier to the redundant encoded segment to obtain an identified data segment; S43. Generate a flying companion backup based on the identified data segment to obtain an encoded data segment; S44. Obtain a transmission formation set according to the encoded data segment, and select a leader segment identifier according to the transmission formation set to obtain a formation leader segment; S45. Obtain a preset transmission plan, and obtain a synchronous transmission instruction according to the preset transmission plan and the formation leader segment; S46. Obtain a migratory bird navigation system according to the network node, and control the migratory bird navigation system to perform coordinated transmission to the receiving end according to the synchronous transmission instruction to obtain a data segment stream.

[0034] As described in the above steps S41-S46, traditional methods usually only rely on static network topologies and historical statistical information, lacking the ability to perceive the network environment in multiple dimensions. This leads to the failure to comprehensively consider the real-time state of the network and the characteristics of different paths during the decision-making process, making it difficult to make the best decision. In contrast, the present invention effectively enhances the multi-dimensional perception of the network environment through technologies such as dynamic fragmentation processing, path-level information acquisition, and redundant coding. By utilizing the real-time information of the network, including the path characteristics, transmission status, and node information of multiple paths, comprehensive decision-making is carried out, enabling more precise control of data transmission strategies and improving resource utilization efficiency. By introducing the mechanism of accompanying flight backup generation and synchronous transmission instructions, effective coordination and cooperation can be achieved among multiple paths, ensuring the reasonable utilization of resources among different paths, avoiding resource waste, and improving the transmission efficiency of the system. Moreover, the design of path grading and redundant coding enables dynamic adjustment among different paths according to requirements, thereby enhancing the collaborative ability of multi-path transmission and improving the overall efficiency of the network. Existing path planning methods usually rely on static network topologies and historical statistical information and cannot adapt to the dynamic changes of the network in real time. The real-time environmental changes of the network (such as node failures, bandwidth fluctuations, etc.) are not reflected in a timely manner in the adjustment of path selection and transmission strategies, resulting in low transmission efficiency. Through dynamic fragmentation processing, the generation of data fragments with identifiers, and the coordinated sending mechanism of the migratory bird navigation system, the dynamic changes of the network can be perceived and adapted in real time. Through the cooperation of the transmission plan and synchronous transmission instructions, the transmission strategy can be flexibly adjusted according to the real-time state of the network to ensure high-efficiency transmission and stability in different network environments. Current multi-path transmission methods lack the self-organization and coordination capabilities of biological groups and cannot achieve automatic optimization in complex network environments, making it difficult to balance transmission efficiency and energy consumption control while ensuring transmission reliability. The present invention makes the network transmission process more self-organized and intelligent. By using redundant coding fragments with identifiers, synchronous transmission instructions, and the coordination mechanism of the migratory bird navigation system, the self-organization behavior of biological groups can be simulated, enabling each network node to autonomously coordinate under different network conditions, dynamically select the optimal path and transmission strategy, optimize transmission efficiency and energy consumption while ensuring transmission reliability, and thus achieve adaptive transmission control. By performing redundant coding on the data and adding homing identifiers to the redundant coding fragments, the high reliability and redundancy of data transmission are ensured. At the same time, the data fragments with identifiers can provide an effective basis for path selection in subsequent transmission processes, enabling each data fragment to be efficiently processed and transmitted in the network, reducing the packet loss rate during transmission, and improving the integrity and transmission efficiency of the data. By selecting the leader fragment identifier according to the transmission formation set, the present invention can organize and schedule different transmission fragments more efficiently. The management of the transmission formation ensures the orderly transmission of each data fragment, reduces redundancy and conflicts, and thus improves the overall transmission efficiency and stability.

[0035] Specifically, the path quality analysis first conducts a comprehensive performance evaluation of each path in the multi-path set. The evaluation metrics include four dimensions: path bandwidth capacity, transmission delay, packet loss rate, and path stability. The path bandwidth capacity is obtained by monitoring the real-time traffic load of each network node on the path. The transmission delay is calculated by sending probe packets and recording the round-trip time. The delay value includes three components: propagation delay, processing delay, and queuing delay. The packet loss rate is obtained by statistically calculating the difference ratio between the total number of packets sent and the total number of successfully received packets within a specific time window. The path stability is calculated by analyzing the historical online duration and connection interruption frequency of the nodes on the path. The higher the stability value, the more reliable the path is.The final path grading information is generated through a weighted comprehensive scoring algorithm. After calculating the comprehensive score of each path, the paths are divided into three levels: high-quality paths, good paths, and average paths according to the score range. Dynamic sharding processing performs content-aware hierarchical cutting on the data to be transmitted based on the path grading information. First, semantic analysis and importance assessment are performed on the original data. Semantic analysis identifies the core business logic part, configuration parameter part, and auxiliary information part in the data by parsing the structural features and content types of the data. During the generation of graded data segments, high-importance data segments are assigned to high-quality paths for transmission, medium-importance data segments are assigned to good paths for transmission, and low-importance data segments are assigned to average paths for transmission, achieving an exact match between data importance and path quality. Unequal error protection coding is performed on the graded data segments to obtain redundant coding segments. Unequal error protection coding implements differential error detection and correction processing on the graded data segments. The coding algorithm selects an appropriate coding scheme according to the importance level of the data segment and the quality level of the assigned path. The redundant coding segments add corresponding error protection information to the original data content, enabling the data to have self-repair ability during transmission. The introduction of homing identifiers simulates the navigation and positioning mechanism of birds during migration. A composite identifier containing complete path information and recombination instructions is added to each redundant coding segment. The homing identifier includes a source node identifier, a target node identifier, a path identifier, a time identifier, and a location identifier. The source node identifier uses a unique hash value generated based on the physical address and logical address of the node to ensure the uniqueness of the sending node's identity. The target node identifier also uses a hash value to identify the final receiving node. The path identifier records the path sequence number, path priority, and path type information expected for the data segment to be transmitted. The time identifier includes the generation timestamp, expected transmission time, and timeout threshold of the data segment. The location identifier records the starting byte position, segment length, and recombination sequence number of the data segment in the original data. The homing identifier also includes segment dependency information, which describes the logical association between the current segment and other segments. The receiving end performs correct data recombination according to the dependency information. When the final labeled data segment is transmitted in the network, the intermediate node performs correct routing and forwarding according to the path information in the homing identifier. The receiving end classifies and sorts the segments according to the identifier information. The generation of companion backups simulates the mutual protection mechanism in a flock of birds. Multiple backup copies are created for critical labeled data segments and transmitted in parallel through different paths. Each backup copy adds a backup identifier to the original homing identifier. The backup identifier includes a main segment reference number, a backup copy sequence number, a synchronization control flag, and priority information. The encoded data segment finally forms a complete data structure containing the original data content, error protection coding, homing identifier, and backup association information, and has the ability to perform reliable transmission in a complex network environment.

[0036] The formation organization groups and aggregates according to the attribute characteristics and transmission requirements of the encoded data segments to form a set of transmission formations with inherent logical associations. The present invention first analyzes the homing identification information of each encoded data segment, and then performs preliminary grouping based on the target node identification in the homing identification information, classifying the data segments with the same target node into the same basic formation. Subsequently, secondary grouping is performed according to the priority levels, with high-priority segments forming a fast formation, medium-priority segments forming a standard formation, and low-priority segments forming a general formation. Finally, based on the analysis of the dependency relationship, the transmission order constraint of the segments is determined through topological sorting. Based on the analysis of the time requirements, the transmission urgency is calculated according to the generation timestamp and timeout threshold of the segments. The segments with high transmission urgency are assigned to the priority transmission formation. The obtained set of transmission formations ultimately forms multiple formation data structures that include a segment identification list, a formation type identification, a transmission priority, and coordination parameters. The segments within each formation have similar transmission characteristics and coordination requirements. The selection of the leader segment identification simulates the selection mechanism of the leading bird in a flock of birds, and the most suitable segment for assuming the coordination role is determined by comprehensively evaluating the key indicators of each data segment within the formation. The selection algorithm first calculates the leadership ability score of each segment, and the scoring indicators include four dimensions: segment data integrity, path quality matching degree, time tolerance, and dependency relationship complexity. By calculating the scores of the four dimensions, it is possible to select a formation leader segment with high redundancy, strong protection intensity, high reliability, high flexibility in transmission time, simple dependency relationship, and more suitable for assuming the coordination responsibility. The segment with the highest score is selected as the formation leader segment and a leadership identifier is added to its homing identification. The preset transmission plan is formulated to generate a detailed transmission scheduling plan according to the coordination instructions of the formation leader segment and the network resource status, and then the coordination among formations is ensured through multi-layer synchronous control processing. The preset transmission plan includes a transmission schedule, a path allocation table, and a synchronization checkpoint. The multi-layer synchronous control processing adopts a hierarchical synchronization mechanism. The first layer is in-formation synchronization to ensure that the segments within the same formation are transmitted in the order of the dependency relationship. The second layer is inter-formation synchronization to coordinate the transmission timing between different formations. The third layer is global synchronization to monitor the global consistency of the entire transmission process. The finally obtained synchronous transmission instruction includes a formation start command, transmission control parameters, and status feedback requirements. The formation start command specifies the exact start time and transmission mode of each formation. The transmission control parameters include the transmission rate, retransmission mechanism, and congestion control strategy. The status feedback requirements stipulate the frequency and content for each node to report the transmission status to the control center. The so-called key network nodes refer to the relay points in the data transmission path, which have a high network connectivity, stable transmission performance, and key routing and forwarding capabilities, and play a role similar to important habitats and navigation marks during the migration of birds in the migratory bird-style data transmission system of the present invention. The identification criteria of the present invention for key network nodes include constraint conditions in three dimensions: connectivity threshold, performance index threshold, and geographical location distribution.The connectivity threshold requires that a node be connected to at least three other nodes to ensure routing redundancy. The performance metric threshold stipulates that the average latency of a node does not exceed 50 milliseconds and the packet loss rate is lower than 1%. The geographical location distribution constraint ensures that the selected key nodes are evenly distributed in the network topology, avoiding the risk of single-point failures caused by local concentration. Only nodes that meet these three conditions can be marked as key network nodes and included in the management scope of the migratory bird navigation system. The navigation beacon deployment process establishes transmission navigation and monitoring functions at the key nodes of the network to form a migratory bird navigation system covering the entire transmission path. The migratory bird navigation system deploys navigation beacons at each key node. The navigation beacon includes path status monitoring, transmission coordination control, and exception handling response. The path status monitoring collects real-time data traffic, latency changes, and error rate statistics information passing through the node. The transmission coordination control adjusts the forwarding strategy and cache management of this node according to the received synchronous transmission instruction. The exception handling response detects transmission anomalies and triggers corresponding recovery mechanisms, including path switching, retransmission requests, and congestion mitigation. Coordinated transmission controls the collaborative work of each navigation beacon in the migratory bird navigation system through synchronous transmission instructions, realizing the orderly transmission of encoded data segments and the sequential arrival at the receiving end. The coordinated transmission process adopts a time-slicing transmission scheduling mechanism, dividing the entire transmission time into multiple time windows, and only allowing data segments of a specific formation to be transmitted within each time window. The sequential arrival of the data segment stream is achieved through cache scheduling at the key nodes. When it is detected that the pre-order dependent segment of a certain segment has not arrived, the segment waits in the node cache until the dependency is satisfied before continuing to be forwarded, and finally forms a data segment stream arranged in the original order at the receiving end.

[0037] In one embodiment, step S5 of obtaining classification segment information according to the data segment stream and performing flock aggregation and reorganization according to the classification segment information to obtain reorganized data includes: S51. The receiving end performs homing verification on the received data segment stream to obtain a set of valid segments; S52. Obtain the packet identifier of each data segment stream in the set of valid segments, and perform classification verification on the set of valid segments according to the packet identifier to obtain classification segment information; S53. Sort and reorganize the classification segment information to obtain a reorganized topology graph, and perform topological sorting according to the reorganized topology graph to obtain a segment reorganization sequence; S54. Perform end-to-end verification processing on the segment reorganization sequence to obtain reorganized data.

[0038] As described in the above steps S51 - S54, traditional methods usually rely on static network topologies and historical statistical information and lack the ability to adapt to the dynamic changes of the network in real time. Through steps such as homing inspection, grouping identifier acquisition, and classification verification, the present invention enables the receiving end to more flexibly perceive the changes in data streams and their environmental conditions. By carefully classifying and verifying each data segment, it can dynamically adjust strategies to cope with the multi-dimensional changes of the network, thereby enhancing the system's perception ability of network status and transmission quality. The present invention avoids over-reliance on static topologies and historical information by real-time perceiving various changes in the network environment and performing classification verification based on dynamic information, achieving an enhancement of multi-dimensional perception ability, thus improving network adaptation. The present invention classifies, sorts, and reorganizes different data segments to obtain an effective topology map and performs topological sorting based on this topology map, thereby optimizing the selection of data transmission paths. By reorganizing data segments and generating a topology map, the system can effectively coordinate and optimize multiple transmission paths, making the resource utilization between different paths more balanced and enhancing the overall transmission efficiency and the system's resource coordination ability. Through end-to-end verification processing and combined with real-time adjustment of the topology reorganization sequence, the present invention can flexibly adjust the data transmission path when the network environment changes. Through dynamic segment reorganization and topological sorting, the present invention can flexibly adjust the transmission strategy of the data stream according to the real-time changes in the network environment, enhancing the system's adaptability and response ability in a dynamic network environment. Through the reorganization of the topology map and end-to-end verification, the present invention not only improves the reliability of network transmission but also can perform intelligent coordination between different paths and segments, simulating self-organization behavior in a similar biological population. Through segment reorganization, topological sorting, and end-to-end verification, the present invention can better balance the efficiency and energy consumption of network transmission while ensuring transmission reliability, demonstrating self-organization and coordination abilities and effectively optimizing resource utilization. Through the dynamic adjustment of multi-path transmission and the optimization of the topology reorganization sequence, the present invention can effectively control energy consumption and improve the overall utilization efficiency of network resources on the premise of ensuring transmission quality. Through dynamic topology reorganization and end-to-end verification, the present invention not only improves the transmission efficiency but also optimizes energy consumption by coordinating the use of multiple paths, thereby enhancing the overall performance and energy efficiency of the network.

[0039] Specifically, at the receiving end, first, a homing verification operation is performed on the received data fragment stream. The integrity of the data is verified by calculating the check value of each data fragment and comparing it with the preset check value at the sending end. At the same time, it is checked whether the sequence numbers in the fragment headers are consecutive and whether the timestamps are within a reasonable range. The fragments that pass the verification are marked as valid fragments and stored in the valid fragment set, while the fragments with verification failures or abnormal formats are directly discarded. The homing verification mechanism ensures that the data fragments for subsequent processing all have basic credibility and integrity characteristics. After the establishment of the valid fragment set, the classification verification process starts. According to the packet identifiers carried in each fragment header, the fragments are classified into different data types and placed into corresponding classification containers. At the same time, secondary verification is performed on the fragments within each classification to verify whether the data formats of the fragments within the same classification are consistent and whether the encoding methods match. The fragments that pass the classification verification are organized into a classification fragment information structure, which contains meta-information such as classification identifiers, fragment quantities, and total data lengths, providing necessary index information for subsequent sorting and recombination. The present invention constructs a recombination topology graph based on the classification fragment information. The construction of the recombination topology graph follows the dual constraints of time sequence and logical dependency, ensuring that the recombination order of the data fragments not only conforms to the time sequence relationship but also meets the logical dependency requirements. Topological sorting is performed according to the recombination topology graph to obtain a fragment recombination sequence. Starting from the nodes with an in-degree of zero, each node is processed layer by layer in topological order. When processing each node, the corresponding data fragment is added to the fragment recombination sequence, and at the same time, the in-degree value of its successor nodes is updated. The topological sorting process continues until all nodes are processed. The finally generated fragment recombination sequence is strictly arranged in the logical order and time sequence of the data. The fragment recombination sequence is a linear data structure, and each element contains information such as fragment identifiers, data contents, and position indexes. Through end-to-end verification processing, a comprehensive consistency verification is performed on the fragment recombination sequence. The fragment recombination sequence that passes the end-to-end verification is merged and reconstructed into complete recombinant data, and the recombinant data maintains the integrity and consistency of the original data.

[0040] As Figure 2 shown, the present application also provides a data transmission system based on bird dynamic perception, including: A first acquisition module, configured to acquire the spatial position information and connection status information of network nodes, and acquire a group perception vector according to the spatial position information and connection status information; A coordination module, configured to acquire synchronization transmission parameters according to the group perception vector, and perform group coordination on the network nodes according to the synchronization transmission parameters to obtain group coordination information; A second acquisition module, configured to acquire a plurality of alternative paths according to the group coordination information, and acquire a multi-path set according to the plurality of alternative paths, where the group coordination information includes a leader node identifier, a follower node identifier, and a coordination parameter set; An encoding module, configured to perform flock dispersion encoding on the data to be transmitted according to the multi-path set to obtain encoded data segments, and obtain a data segment stream at the receiving end according to the encoded data segments; A recombination module, configured to obtain classification segment information according to the data segment stream, and perform flock aggregation recombination according to the classification segment information to obtain recombined data.

[0041] In one embodiment, the first acquisition module includes: An evaluation unit, configured to obtain a visual perception coverage area according to the spatial position information, and perform a quantitative evaluation on the node reachability of network nodes according to the visual perception coverage area to obtain a visual perception vector; A calculation unit, configured to obtain an auditory perception intensity distribution according to the connection state information, and calculate the packet reception probability of network nodes according to the auditory perception intensity distribution to obtain an auditory perception vector; A first acquisition unit, configured to obtain the network topology direction information of network nodes, and obtain a magnetic field perception vector according to the network topology direction information; A second acquisition unit, configured to obtain the status of neighboring nodes in the network nodes, and obtain a neighbor perception map according to the status of neighboring nodes; A fusion unit, configured to fuse the visual perception vector, the auditory perception vector, and the magnetic field perception vector according to the neighbor perception map to obtain a group perception vector.

[0042] It should be noted that each module and unit in the data transmission system based on bird dynamic perception corresponds one-to-one with the steps in the data transmission method based on bird dynamic perception.

[0043] As Figure 3 shown, the present application further provides a computer device, which may be a server, and its internal structure may be as Figure 3As shown. The computer device includes a processor, a memory, a network interface, and a database connected via a system bus. Among them, the processor of the computer design is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store all the data required for the process of the data transmission method based on bird dynamic perception. The network interface of the computer device is used to communicate with an external terminal via a network connection. The computer program, when executed by the processor, implements the data transmission method based on bird dynamic perception.

[0044] Those skilled in the art can understand that Figure 3 the structure shown in is only a block diagram of some structures related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied.

[0045] An embodiment of the present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements any one of the above data transmission methods based on bird dynamic perception.

[0046] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium provided in the present application and used in the embodiments can include non-volatile and / or volatile memories. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or an external cache. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0047] It should be noted that in this text, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, apparatus, article or method comprising a series of elements not only includes those elements, but also includes other elements not explicitly listed, or further includes elements inherent in such a process, apparatus, article or method. Without further limitation, an element defined by the statement "comprising an..." does not exclude the existence of additional identical elements in the process, apparatus, article or method comprising such element.

[0048] The above are only the preferred embodiments of the present invention, and do not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the content of the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, shall be included in the patent protection scope of the present invention by the same token.

Claims

1. A data transmission method based on bird motion perception, characterized in that, Including: Obtain the spatial location information and connection status information of network nodes, and obtain a group perception vector according to the spatial location information and connection status information; Obtain synchronous transmission parameters according to the group perception vector, and perform group coordination on the network nodes according to the synchronous transmission parameters to obtain group coordination information; Obtain multiple alternative paths according to the group coordination information, and obtain a multi-path set according to the multiple alternative paths, where the group coordination information includes a leader node identifier, a follower node identifier, and a set of coordination parameters; Perform flock dispersion coding on the data to be transmitted according to the multi-path set to obtain coded data segments, and obtain a data segment stream at the receiving end according to the coded data segments; Obtain classified segment information according to the data segment stream, and perform flock aggregation recombination according to the classified segment information to obtain recombined data.

2. The data transmission method based on bird dynamic perception according to claim 1, characterized in that The step of obtaining a group perception vector according to the spatial location information and connection status information includes: Obtain a visual perception coverage area according to the spatial location information, and perform a quantitative evaluation on the node reachability of network nodes according to the visual perception coverage area to obtain a visual perception vector; Obtain an auditory perception intensity distribution according to the connection status information, and calculate the packet reception probability of network nodes according to the auditory perception intensity distribution to obtain an auditory perception vector; Obtain the network topology direction information of network nodes, and obtain a magnetic field perception vector according to the network topology direction information; Obtain the status of neighboring nodes in the network nodes, and obtain a neighbor perception map according to the status of neighboring nodes; Fuse the visual perception vector, auditory perception vector, and magnetic field perception vector according to the neighbor perception map to obtain a group perception vector.

3. The data transmission method based on bird dynamic perception according to claim 1, wherein The step of obtaining synchronous transmission parameters according to the group perception vector, and performing group coordination on the network nodes according to the synchronous transmission parameters to obtain group coordination information includes: Obtain node performance indicators according to the group perception vector, and perform multi-objective optimization selection on network nodes according to the node performance indicators to obtain a leader node identifier and a non-leader node identifier; Obtain the following relationship of non-leader nodes according to the leader node identifier and non-leader node identifier, and obtain a follower node identifier according to the following relationship; Synchronize the transmission parameters of the leader node identifier and the follower node identifier to obtain synchronous transmission parameters; Obtain the network congestion and node failures of the network nodes, and obtain dynamic adjustment parameters according to the network congestion and node failures; Fuse the synchronous transmission parameters and the dynamic adjustment parameters to obtain a set of coordination parameters.

4. The data transmission method based on bird dynamic perception according to claim 1, characterized in that The step of obtaining multiple alternative paths according to the group coordination information, and obtaining a multi-path set according to the multiple alternative paths includes: Obtain the historical transmission data and real-time network changes of the network nodes, and establish a migration map according to the historical transmission data to obtain a dynamic migration map; Obtain the target node location and the overall network state of the data to be transmitted, and obtain multiple alternative paths according to the target node location and the overall network state; Obtain the path length, path stability, path load, and path reliability of each of the alternative paths, and obtain the path attractiveness score of the corresponding alternative path according to each of the path length, path stability, path load, and path reliability; Sort the alternative paths in descending order according to the path attractiveness score to obtain a candidate path set including path priority sorting; Perform path fine-tuning on the paths in the candidate path set according to the real-time network changes to obtain an optimized path scheme, and classify the optimized path scheme into primary and backup paths to obtain a multi-path set.

5. The data transmission method based on bird movement perception according to claim 1, wherein The steps of performing flock dispersion encoding on the data to be transmitted according to the multi-path set to obtain encoded data segments and obtaining the data segment stream at the receiving end according to the encoded data segments include: Obtain path grading information according to the multi-path set, and perform dynamic sharding processing on the data to be transmitted according to the path grading information to obtain graded data segments; Encode the graded data segments to obtain redundant encoded segments, and add a homing identifier to the redundant encoded segments to obtain data segments with identifiers; Generate fly-along backups according to the data segments with identifiers to obtain encoded data segments; Obtain a transmission formation set according to the encoded data segments, and select a leader segment identifier according to the transmission formation set to obtain a formation leader segment; Obtain a preset transmission plan, and obtain a synchronous transmission instruction according to the preset transmission plan and the formation leader segment; Obtain a migratory bird navigation system according to the network nodes, and control the migratory bird navigation system to perform coordinated transmission to the receiving end according to the synchronous transmission instruction to obtain a data segment stream.

6. The data transmission method based on bird dynamic perception according to claim 1, wherein The steps of obtaining classification segment information according to the data segment stream and performing flock aggregation and recombination according to the classification segment information to obtain the recombined data include: The receiving end performs homing verification on the received data segment stream to obtain a set of valid segments; Obtain the packet identifier of each data segment stream in the set of valid segments, and perform classification verification on the set of valid segments according to the packet identifier to obtain classification segment information; Sort and recombine the classification segment information to obtain a recombined topology graph, and perform topological sorting according to the recombined topology graph to obtain a segment recombination sequence; Perform end-to-end verification processing on the segment recombination sequence to obtain the recombined data.

7. A data transmission system based on bird motion perception, characterized in that, including: A first acquisition module for acquiring the spatial position information and connection status information of network nodes, and obtaining a group perception vector according to the spatial position information and connection status information; A coordination module for obtaining synchronous transmission parameters according to the group perception vector, and performing group coordination on the network nodes according to the synchronous transmission parameters to obtain group coordination information; A second acquisition module for obtaining a plurality of alternative paths according to the group coordination information, and obtaining a multi-path set according to the plurality of alternative paths, wherein the group coordination information includes a leader node identifier, a follower node identifier, and a set of coordination parameters; An encoding module, configured to perform flock dispersion encoding on data to be transmitted according to the multi-path set, obtain encoded data segments, and obtain a data segment stream at a receiving end according to the encoded data segments; A recombination module, configured to obtain classification segment information according to the data segment stream, and perform flock aggregation recombination according to the classification segment information to obtain recombined data.

8. The data transmission system based on bird motion perception according to claim 7, wherein The first acquisition module includes: An evaluation unit, configured to obtain a visual perception coverage area according to the spatial position information, and perform quantitative evaluation on the node reachability of network nodes according to the visual perception coverage area to obtain a visual perception vector; A calculation unit, configured to obtain an auditory perception intensity distribution according to the connection state information, and calculate the packet reception probability of network nodes according to the auditory perception intensity distribution to obtain an auditory perception vector; A first acquisition unit, configured to obtain network topology direction information of a network node, and obtain a magnetic field perception vector according to the network topology direction information; A second acquisition unit, configured to obtain the state of neighboring nodes in the network node, and obtain a neighbor perception map according to the state of neighboring nodes; A fusion unit, configured to fuse the visual perception vector, the auditory perception vector, and the magnetic field perception vector according to the neighbor perception map to obtain a group perception vector.

9. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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