A data transmission method and system based on bird dynamic perception

Abstract

The present application relates to the technical field of data transmission processing, in particular to a data transmission method and system based on bird dynamic perception. The group perception vector obtained by the multi-dimensional perception fusion algorithm can comprehensively capture the complex characteristics of the network environment. Compared with the traditional single-dimensional network state monitoring method, the accuracy and comprehensiveness of network environment perception can be significantly improved, so that the data transmission system can more accurately identify network congestion, link quality change and node state anomaly and the like, thereby providing more reliable basic information for subsequent transmission decision. The group coordination information formed by group coordination analysis and processing can effectively solve the problem of lack of coordination among paths in traditional multi-path transmission, significantly reduce the mutual interference and resource conflict among paths, and improve the utilization efficiency of overall network resources.

Description

TECHNICAL FIELD

[0001] The present application 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

[0002] With the rapid development of Internet of Things, 5G communication and edge computing technology, modern data transmission systems are facing increasingly complex network environments and diversified business demands. Traditional data transmission methods are mainly based on static routing protocols and fixed transmission strategies. In existing technologies, multi-path transmission protocols can utilize multiple paths for parallel data transmission 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 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 results in specific scenarios.

[0003] However, traditional methods lack multi-dimensional perception ability of network environment and cannot comprehensively utilize multiple perception information for decision-making. Existing multi-path transmission lacks effective coordination mechanism, and each path lacks coordination and cooperation, resulting in low resource utilization efficiency. Current path planning methods are mainly based on static network topology and historical statistical information, lack real-time adaptability to network dynamic changes, and cannot dynamically adjust transmission strategies according to environmental changes. In the face of complex network environments, they lack self-organization and coordination ability similar to biological groups, and it is difficult to balance transmission efficiency and energy consumption control while ensuring transmission reliability. SUMMARY

[0004] The main purpose of the present application 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 application provides a data transmission method based on bird dynamic perception, comprising:

[0006] obtaining spatial position information and connection state information of network nodes, and obtaining group perception vectors according to the spatial position information and connection state information;

[0007] obtaining synchronization transmission parameters according to the group perception vectors, and performing group coordination on the network nodes according to the synchronization transmission parameters to obtain group coordination information;

[0008] obtaining multiple candidate paths according to the group coordination information, and obtaining a multi-path set according to the multiple candidate paths, wherein the group coordination information includes leader node identifier, follower node identifier and coordination parameter set;

[0009] According to the multi-path set, the data to be transmitted is flock dispersively encoded to obtain encoded data segments, and a data segment stream of a receiving end is obtained according to the encoded data segments;

[0010] Classification segment information is obtained according to the data segment stream, and flock aggregation reorganization is performed according to the classification segment information to obtain reorganized data.

[0011] Preferably, the step of obtaining a group perception vector according to the spatial position information and the connection state information comprises:

[0012] A visual perception coverage area is obtained according to the spatial position information, and node reachability of a network node is quantitatively evaluated according to the visual perception coverage area to obtain a visual perception vector;

[0013] An auditory perception intensity distribution is obtained according to the connection state information, and a data packet reception probability of the network node is calculated according to the auditory perception intensity distribution to obtain an auditory perception vector;

[0014] Network topology direction information of the network node is obtained, and a magnetic field perception vector is obtained according to the network topology direction information;

[0015] A state of a neighboring node in the network node is obtained, and a neighbor perception atlas is obtained according to the state of the neighboring node;

[0016] The visual perception vector, the auditory perception vector and the magnetic field perception vector are fused according to the neighbor perception atlas to obtain the group perception vector.

[0017] Preferably, the step of obtaining a synchronization transmission parameter according to the group perception vector, and performing group coordination on the network node according to the synchronization transmission parameter to obtain group coordination information comprises:

[0018] A node performance index is obtained according to the group perception vector, and a multi-objective optimization selection is performed on the network node according to the node performance index to obtain a leader node identifier and a non-leader node identifier;

[0019] A following relationship of the non-leader node is obtained according to the leader node identifier and the non-leader node identifier, and a follower node identifier is obtained according to the following relationship;

[0020] The leader node identifier and the follower node identifier are synchronized in transmission parameters to obtain a synchronization transmission parameter;

[0021] Network congestion and node failure of the network node are obtained, and a dynamic adjustment parameter is obtained according to the network congestion and the node failure;

[0022] The synchronization transmission parameter and the dynamic adjustment parameter are fused to obtain a coordinated parameter set.

[0023] Preferably, the step 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 comprises:

[0024] The historical transmission data of the network node and real-time network changes are obtained, and a migration map is established according to the historical transmission data to obtain a dynamic migration map.

[0025] The target node position of the data to be transmitted and the overall state of the network are obtained, and a plurality of alternative paths are obtained according to the target node position and the overall state of the network.

[0026] The path length, path stability, path load and path reliability of each alternative path are obtained, and the path attraction score of the corresponding alternative path is obtained according to each path length, path stability, path load and path reliability.

[0027] The alternative paths are sorted in descending order according to the path attraction score to obtain a candidate path set containing path priority sorting.

[0028] The paths in the candidate path set are fine-tuned according to the real-time network changes to obtain an optimized path scheme, and the optimized path scheme is classified into primary and backup paths to obtain a multi-path set.

[0029] Preferably, the step of performing bird swarm dispersion coding on the data to be transmitted according to the multi-path set to obtain encoded data segments, and obtaining a data segment stream of a receiving end according to the encoded data segments comprises:

[0030] Path hierarchical information is obtained according to the multi-path set, and the data to be transmitted is dynamically fragmented according to the path hierarchical information to obtain hierarchical data segments.

[0031] The hierarchical data segments are encoded to obtain redundant encoded segments, and a homing identifier is added to the redundant encoded segments to obtain an identified data segment.

[0032] Companion backup generation is performed according to the identified data segment to obtain an encoded data segment.

[0033] A transmission formation set is obtained according to the encoded data segment, and a leader segment identifier is selected according to the transmission formation set to obtain a formation leader segment.

[0034] A preset transmission plan is obtained, and a synchronization transmission instruction is obtained according to the preset transmission plan and the formation leader segment.

[0035] According to the network node, a bird navigation system is acquired, and the bird navigation system is controlled to perform coordinated sending to a receiving end according to the synchronization transmission instruction, so as to obtain a data segment stream.

[0036] As preferred, the step of acquiring classification segment information according to the data segment stream and performing bird flock aggregation reorganization according to the classification segment information to obtain reorganized data comprises:

[0037] The receiving end performs homing verification on the received data segment stream to obtain an effective segment set.

[0038] A packet identifier of each data segment stream in the effective segment set is acquired, and classification verification is performed on the effective segment set according to the packet identifier to obtain classification segment information.

[0039] The classification segment information is sorted and reorganized to obtain a reorganized topology graph, and topology sorting is performed according to the reorganized topology graph to obtain a segment reorganization sequence.

[0040] End-to-end verification processing is performed on the segment reorganization sequence to obtain reorganized data.

[0041] The application also provides a data transmission system based on bird dynamic perception, comprising:

[0042] A first acquisition module is configured to acquire spatial position information and connection state information of a network node, and acquire a group perception vector according to the spatial position information and the connection state information.

[0043] A coordination module is configured to acquire synchronization transmission parameters according to the group perception vector, and perform group coordination on the network node according to the synchronization transmission parameters to obtain group coordination information.

[0044] A second acquisition module is 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, wherein the group coordination information comprises a leader node identifier, a follower node identifier, and a coordination parameter set.

[0045] An encoding module is configured to perform bird flock dispersion encoding on to-be-transmitted data according to the multi-path set to obtain encoded data segments, and acquire a data segment stream of a receiving end according to the encoded data segments.

[0046] A reorganization module is configured to acquire classification segment information according to the data segment stream, and perform bird flock aggregation reorganization according to the classification segment information to obtain reorganized data.

[0047] As preferred, the first acquisition module comprises:

[0048] An evaluation unit is configured to acquire a visual perception coverage area according to the spatial position information, and quantitatively evaluate node reachability of the network node according to the visual perception coverage area to obtain a visual perception vector.

[0049] A calculation unit is configured to acquire an auditory perception intensity distribution according to the connection state information, and calculate a data packet receiving probability of the network node according to the auditory perception intensity distribution to obtain an auditory perception vector.

[0050] A first acquisition unit is configured to acquire network topology direction information of the network node, and acquire a magnetic field perception vector according to the network topology direction information.

[0051] A second acquisition unit is configured to acquire a state of a neighboring node in the network node, and acquire a neighbor perception atlas according to the state of the neighboring node.

[0052] A fusion unit is configured to fuse the visual perception vector, the auditory perception vector and the magnetic field perception vector according to the neighbor perception atlas to obtain a group perception vector.

[0053] The application further provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the data transmission method based on bird dynamic perception when executing the computer program.

[0054] The application further provides a computer readable storage medium, which stores a computer program, and the computer program implements the steps of the data transmission method based on bird dynamic perception when executed by a processor.

[0055] The application has the advantages that: the group perception vector obtained by the multi-dimensional perception fusion algorithm can comprehensively capture the complex characteristics of the network environment, compared with the traditional single-dimensional network state monitoring method, the accuracy and comprehensiveness of network environment perception can be significantly improved, so that the data transmission system can more accurately identify network congestion, link quality change and node state anomaly and the like, thereby providing more reliable basic information for subsequent transmission decision, the group coordination information formed through group coordination analysis and processing can effectively solve the problem that each path lacks coordination in the traditional multi-path transmission, significantly reduce the mutual interference and resource conflict between paths, and improve the utilization efficiency of overall network resources, at the same time, through the dynamic leader-follower mechanism, the network environment change can be quickly responded, and the stability and continuity of the transmission process are ensured, the multi-path set obtained based on the bird migration path planning processing has stronger environmental adaptability and fault tolerance, compared with the static routing method, the transmission interruption caused by single path failure can be significantly reduced, and the reliability of data transmission is improved, at the same time, through reasonable configuration of the primary and backup paths, the transmission delay and network congestion can be effectively reduced on the premise of ensuring transmission quality, the coded data segments generated through bird swarm dispersion coding processing have higher fault tolerance and recovery ability, through unequal error protection coding and homing information identification, even in the case that part of the transmission paths fail, the complete recovery of data can still be ensured, and the robustness of the system is significantly improved, at the same time, the accompanying flying backup mechanism further enhances the transmission guarantee of key data, the formation coordination transmission processing of the application can effectively avoid the timing confusion and resource competition problem in the traditional parallel transmission, through accurate synchronization control and coordination mechanism, the transmission efficiency is significantly improved, and the retransmission overhead is reduced, at the same time, the introduction of the migratory bird navigation system provides reliable transmission guidance for the data segments, and the transmission success rate is further improved, through the bird swarm aggregation and recombination processing, the out-of-order arrival and partially lost data segments can be efficiently processed, compared with the traditional sequential recombination method, the fault tolerance and recombination efficiency are higher, through the topological sorting processing, the received data segments can be maximized, the waiting time is reduced, at the same time, the transmission quality evaluation report provides a scientific basis for system optimization, a complete closed-loop optimization mechanism is formed, and the efficient, reliable and adaptive operation of the data transmission system in the complex network environment is realized as a whole. BRIEF DESCRIPTION OF DRAWINGS

[0056] Figure 1 The method flowchart of an embodiment of the application.

[0057] Figure 2 The system structure schematic diagram of an embodiment of the application.

[0058] Figure 3 The internal structure schematic diagram of a computer device of an embodiment of the application.

[0059] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0060] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0061] like Figure 1 As shown, the present application provides a data transmission method based on bird dynamic perception, comprising:

[0062] S1. Acquire spatial location information and connection status information of network nodes, and acquire a group perception vector based on the spatial location information and connection status information;

[0063] S2. Acquire synchronous transmission parameters according to the group sensing vector, and perform group coordination on the network nodes according to the synchronous transmission parameters to obtain group coordination information;

[0064] S3. Acquire multiple candidate paths according to the group coordination information, and acquire a multi-path set according to the multiple candidate paths, wherein the group coordination information includes a leader node identifier, a follower node identifier, and a coordination parameter set;

[0065] S4. Performing flock dispersion coding on the data to be transmitted according to the multipath set to obtain coded data segments, and obtaining a data segment stream at a receiving end according to the coded data segments;

[0066] S5. Obtain classification segment information according to the data segment stream, and perform flock aggregation and reorganization according to the classification segment information to obtain reorganized data.

[0067] As described in steps S1-S5 above, the application can integrate different types of perception data such as location, network connection state, etc. by obtaining the spatial position information and connection state information of the network nodes, making decisions in combination with the group perception vector, fully considering the dynamic characteristics of the network, and making more accurate decisions in complex network environments. Through multi-dimensional perception, the system can adapt in real time according to environmental changes, improving the shortcomings of traditional methods that rely only on static topology and historical information. Through the group coordination mechanism, the network nodes are coordinated based on synchronous transmission parameters to obtain group coordination information (such as leader node, follower node identifier and coordination parameter), which enables different paths and nodes to coordinate with each other, thereby improving the utilization efficiency of network resources and effectively avoiding the uncoordinated situation between paths in traditional methods. The dynamic adjustment capability of the application enables the system to flexibly coordinate and optimize in different network environments, improving transmission efficiency and stability. By simulating the self-organizing behavior of biological groups (such as bird flock dispersion coding and aggregation recombination), adaptive coordination between nodes in the network is achieved. Through bird flock dispersion coding and multi-path set, the system can flexibly schedule according to the characteristics of different paths, thereby improving transmission efficiency while ensuring reliability and effectively controlling energy consumption. This biological-inspired self-organizing capability enables the network system to adaptively adjust when facing dynamic changes in the network environment, avoiding the limitations of static methods. By dynamically adjusting the synchronous transmission parameters based on real-time perception of the spatial position and connection state information of the network nodes, 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 topology or historical data. Through multi-path set and bird flock dispersion coding, in combination with the group coordination mechanism, more efficient multi-path transmission is achieved. By coordinating the work between different paths, the redundancy of multi-path can be effectively utilized while avoiding path conflicts and resource waste, thereby improving the overall transmission efficiency of the network. Through the group perception vector, spatial position information, connection state information and other multi-dimensional perception 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 to improve transmission performance and reliability.

[0068] In one embodiment, the step S1 of obtaining a group perception vector according to the spatial position information and connection state information comprises:

[0069] S11, obtaining a visual perception coverage area according to the spatial position information, and quantitatively evaluating the node reachability of the network nodes according to the visual perception coverage area to obtain a visual perception vector;

[0070] S12, acquire the auditory perception intensity distribution according to the connection state information, and calculate the data packet receiving probability of the network node according to the auditory perception intensity distribution to obtain an auditory perception vector;

[0071] S13, acquire network topology direction information of the network node, and acquire a magnetic field perception vector according to the network topology direction information;

[0072] S14, acquire a neighboring node state in the network node, and acquire a neighbor perception atlas according to the neighboring node state;

[0073] S15, fuse the visual perception vector, the auditory perception vector and the magnetic field perception vector according to the neighbor perception atlas to obtain a group perception vector.

[0074] As described in steps S11-S15, the present application enhances the network node's perception of the environment by introducing multiple perception dimensions such as visual perception, auditory perception, magnetic field perception, etc. Visual perception obtains a visual perception coverage area through spatial position information and quantitatively evaluates the accessibility of nodes, accurately grasping the spatial layout and mutual relationship of network nodes, thereby improving the accuracy of decision-making. Auditory perception obtains an auditory perception intensity distribution through connection state information, enabling the calculation of packet reception probability and further enhancing the reliability and stability of inter-node communication. Magnetic field perception obtains a magnetic field perception vector through network topology direction information, providing an in-depth understanding of network topology direction and helping to adjust data transmission strategies. Neighbor perception map evaluates the neighbor perception map through the state of neighboring nodes, further strengthening the information transmission and collaboration capabilities between nodes. Through the introduction of these multi-dimensional perception capabilities, the system can more comprehensively understand the network environment, improving adaptability and accuracy in the decision-making process. By fusing the visual perception vector, auditory perception vector, magnetic field perception vector, and group perception vector into a group perception vector, this fusion method can comprehensively reflect the state information of network nodes in different dimensions, and the fused perception vector provides a more accurate basis for decision-making, enabling more intelligent adjustments in complex network environments, thereby improving network transmission efficiency, reliability, and energy consumption control. Through real-time calculation of the group perception vector, this method can reflect the dynamic changes of different nodes and paths in the network, allowing transmission strategies to be adjusted in real time according to changes in the network environment. Especially in dynamically changing network environments, it can automatically optimize path selection and data transmission strategies, effectively dealing with frequent changes in the network, such as node state changes and connection quality fluctuations. By introducing the group perception vector and the idea of biological group self-organization (such as through the comprehensive action of node state and perception information), the coordination between nodes in the network is achieved. For example, nodes in the network can adaptively adjust and optimize by perceiving the state of other nodes, thereby realizing effective collaboration between nodes and ensuring the efficiency and stability of multi-path transmission. The introduction of group perception enhances the collaboration capabilities between network nodes, enabling efficient allocation of transmission tasks among multiple paths and improving resource utilization efficiency. Through real-time fusion and analysis of the group perception vector, path planning can be dynamically adjusted, and the work between various paths and nodes can be coordinated.This coordination mechanism not only optimizes the efficiency of multi-path transmission, but also adapts to changes in network status 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 use of redundant resources in the network, improve transmission reliability, transmission efficiency and energy consumption control, and optimize energy consumption control while ensuring transmission reliability by adaptively adjusting transmission paths and strategies. Especially when selecting paths through group perception vectors, the system can comprehensively consider multiple factors such as path quality, load, distance, etc., thereby making intelligent allocations between different transmission paths and reducing unnecessary energy consumption.

[0075] Specifically, the visual perception vector generation process first calculates the field of view of the spatial position information. The corresponding field of view calculation simulates the working principle of the bird's visual system. By establishing a visual perception coverage area centered on each network node, the specific quantitative evaluation process synthesizes the distance factor, signal quality factor, and path complexity factor into a single reachability value through weighted summation, and finally forms a visual perception vector. Among them, the expression for quantitatively evaluating the reachability of the network node through field of view calculation and signal strength distribution is:

[0076] ;

[0077] in, is the node index value, For nodes accessibility, is the maximum detection distance, For distance, For nodes The signal strength distribution, For nodes The field of view, is the attenuation coefficient;

[0078] The quantitative evaluation of visual perception coverage area and the modeling of auditory perception intensity distribution realize the fine analysis of network node state through visual recognition and acoustic signal processing technology. The calculation of visual perception coverage area can combine the real-time processing capability of edge AI algorithm to ensure high-frequency node accessibility evaluation. The auditory perception intensity distribution extracts features through short-time Fourier transform and recurrent neural network, and combines environmental noise suppression technology to improve signal quality. The dynamic modeling of magnetic field perception vector is simulated through geomagnetic navigation, combined with node topology direction information to optimize the global consistency of path planning. The dynamic monitoring of group perception map and group state fusion realize real-time perception and cooperative decision of adjacent node state through graph neural network modeling. The synergistic effect of multi-dimensional perception makes the network node more adaptable to environmental changes, thereby improving the overall transmission efficiency and stability. The construction of auditory perception vector focuses on the deep mining of connection state information. First, the connection state information is modeled by signal attenuation. The signal attenuation model adopts a logarithmic distance path loss model that considers the free space loss, multipath fading and shadowing effect of the signal during propagation. The signal intensity distribution at different distances is calculated. Then, the data packet reception probability is accurately calculated based on the obtained auditory perception intensity distribution. The data packet reception probability calculation algorithm converts the signal strength value into the success rate of receiving. The conversion process can use the bit error rate function, taking the signal-to-noise ratio as the input parameter and outputting the corresponding data packet correct reception probability. Through probability calculation and normalization processing of all possible signal strength values, the auditory perception vector is finally generated. The generation mechanism of the magnetic field perception vector simulates the biological principle of birds using geomagnetic field for navigation to generate a magnetic field perception vector reflecting the directionality of network topology structure.

[0079] Subsequent to the dynamic monitoring of the state of the neighboring nodes in the network node, the dynamic monitoring processes the key parameters such as the load state, connection quality, response time and available bandwidth of the neighboring nodes in real time, organizes the parameters into time series data and performs sliding window analysis to obtain a neighbor perception graph reflecting the dynamic characteristics of the nodes, the neighbor perception graph is based on a graph theory data structure, the nodes represent network devices, and the weight of the edge represents the connection quality, and finally, the group state fusion is performed according to the neighbor perception graph, the group state fusion algorithm adopts a method combining weighted average and principal component analysis, first, the state parameters of the neighboring nodes are calculated by weighted average, the weight is determined by the importance and reliability of the nodes, then the main features of the state data are extracted by principal component analysis to generate a group perception vector reflecting the coordination state of the entire network group, the construction process of the group perception vector embodies the intelligent characteristics of the bird group coordination, and finally, a group perception vector reflecting the coordination state of the entire network group is generated, the multi-modal fusion design (vision, hearing, magnetic field) of the group perception vector significantly improves the comprehensiveness and accuracy of network perception, the visual perception vector realizes high-frequency node accessibility evaluation through a visual recognition algorithm (such as edge computing), and the coverage efficiency is optimized in combination with the field of view range; the hearing perception vector quantifies signal attenuation based on acoustic monitoring equipment (such as a directional microphone), and predicts the reception probability in combination with a deep learning model; the magnetic field perception vector simulates geomagnetic navigation in combination with topological direction information to improve the global consistency of path planning; the group perception vector realizes real-time fusion and prediction of group behavior through dynamic monitoring of the state of the neighboring nodes in combination with graph neural network modeling, and the synergistic effect of multi-dimensional perception enables the network nodes to adapt to environmental changes more comprehensively, thereby improving the overall transmission efficiency and stability.

[0080] In one embodiment, the step S2 of obtaining synchronization transmission parameters according to the group perception vector and performing group coordination on the network nodes according to the synchronization transmission parameters to obtain group coordination information comprises:

[0081] S21, obtaining a node performance index according to the group perception vector, and performing multi-objective optimization selection on the network nodes according to the node performance index to obtain a leader node identifier and a non-leader node identifier;

[0082] S22, obtaining a following relationship of the non-leader node according to the leader node identifier and the non-leader node identifier, and obtaining a follower node identifier according to the following relationship;

[0083] S23, synchronizing transmission parameters of the leader node identifier and the follower node identifier to obtain synchronization transmission parameters;

[0084] S24, obtaining network congestion and node failure of the network nodes, and obtaining a dynamic adjustment parameter according to the network congestion and the node failure;

[0085] S25, fuse the synchronization transmission parameters and dynamic adjustment parameters to obtain a coordinated parameter set.

[0086] As described in steps S21-S25, the application obtains multi-dimensional performance indicators of network nodes through group perception vectors, which can more comprehensively evaluate the network environment and comprehensively consider multiple factors (such as network congestion, node failure, etc.). This multi-dimensional perception capability enables the system to make more accurate decisions based on real-time network conditions, overcoming the limitations of traditional methods with single perception. Through multi-objective optimization selection of node performance indicators, this method can select appropriate network nodes according to different optimization targets (such as network efficiency, transmission stability, energy consumption, etc.), thereby improving resource utilization. This flexibility and adaptability is particularly important in dynamically changing network environments, making up for the limitations of traditional methods based on static topology and historical statistical information. Through the cooperation between leader node identifiers and non-leader node identifiers, a self-organizing and coordinating mechanism similar to biological groups is formed, which can effectively optimize the coordination between paths and improve overall resource utilization efficiency. The leader node guides the behavior of the non-leader node, forming a collaborative network structure, thereby overcoming the lack of coordination in traditional multi-path transmission. In the face of complex and dynamic network environments, traditional methods often cannot adapt to network changes in real time, while the application obtains information such as network congestion and node failure, and dynamically adjusts the transmission strategy based on real-time network state. This dynamic adjustment capability enables the network to automatically optimize transmission paths and parameters in a changing environment, thereby effectively avoiding the limitations of static planning. Through bionics, the application learns from the self-organizing and coordinating mechanism of biological groups, and can still maintain the stability and efficiency of the system in complex environments. Traditional methods often lack the adaptability and self-regulation ability of biological groups, while the application can mimic the coordinating behavior of groups in nature, making the transmission system more robust and flexible in complex environments. By fusing synchronization transmission parameters and dynamic adjustment parameters to obtain a coordinated parameter set, this method can ensure that the balance between transmission efficiency, energy consumption control, and network stability is maintained under various network changes, further improving the overall optimization of network resources.

[0087] Specifically, the node capability evaluation process first performs multi-dimensional analysis on the comprehensive perception vector, takes the visual perception vector, the auditory perception vector, the magnetic field perception vector and the group perception vector as the basic data input for evaluation, performs weighted processing on each vector component through the node capability evaluation algorithm, the visual perception vector reflects the spatial perception capability of the node, the auditory perception vector embodies the communication quality of the node, the magnetic field perception vector represents the navigation positioning capability of the node, and the group perception vector represents the coordination capability of the node, and finally the node performance index is obtained through weighted summation, and the higher the value is, the stronger the comprehensive capability of the node is; based on the node performance index, the network node is selected by multi-objective optimization, and the leader node identifier is obtained; the multi-objective optimization selection algorithm selects the leader node based on the node performance index, and the multi-objective optimization selection algorithm of the application considers four optimization targets of processing capability, connection stability, energy consumption level and load balancing at the same time, the processing capability is calculated through the CPU utilization rate and the memory occupation rate of the node, the connection stability is determined based on the average connection time and the packet loss rate of the node, the energy consumption level is calculated according to the power consumption monitoring data and the remaining battery capacity of the node, and the load balancing is evaluated through the current data flow and the historical load change trend of the node; the multi-objective optimization algorithm is solved by using the non-dominated sorting genetic algorithm NSGA-II, first, all candidate nodes are non-dominated sorted, and the nodes are divided into different levels according to the dominance relationship, then the crowding distance of each node is calculated, the crowding distance reflects the distribution density of the node in the target space, and finally the node with the largest crowding distance is selected as the leader node identifier from the first level through the elite selection strategy, the following relationship establishment process determines the leader node to which each follower node belongs by analyzing the relevance between the non-leader node and the leader node, first, the comprehensive distance of the non-leader node to each leader node is calculated, the comprehensive distance includes three components of physical distance, communication delay distance and capability similarity distance, the three distance components are weighted and summed after normalization to obtain the comprehensive distance value, then each non-leader node is assigned to the leader node with the minimum comprehensive distance by using the nearest neighbor allocation strategy, forming 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 pace in the data transmission process, the transmission parameter synchronization algorithm first determines the reference transmission parameter by the leader node according to the current network state and the transmission demand, the reference transmission parameter includes four key parameters of data packet size, sending interval, retransmission times and timeout, the real-time detection and coordination avoidance mechanism detects and processes abnormal situations in time by continuously monitoring the network congestion state and the node health condition, the network congestion detection algorithm in the application adopts the sliding window technology to realize real-time statistics of key indicators such as data 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, and the node fault detection algorithm is realized through the heartbeat mechanism and performance monitoring,Each node periodically sends heartbeat packets to its neighboring nodes and monitors its own CPU utilization, memory usage, and network interface status. When heartbeat packets are continuously lost or performance indicators are abnormal, a fault detection process is triggered. The coordinated avoidance algorithm dynamically adjusts the network topology and data routing based on the detection results. When congestion is detected, the algorithm recalculates path weights and triggers path switching. When a node failure is detected, the algorithm initiates a fault recovery mechanism and redistributes the node's task load, ultimately generating dynamic adjustment parameters that include path adjustment instructions, load redistribution plans, and parameter correction values. The coordinated parameter set fusion process intelligently integrates synchronous transmission parameters and dynamic adjustment parameters. The fusion algorithm uses an adaptive weight allocation strategy to dynamically adjust the influence weights of the two parameters based on the current state and historical performance of the network. When the network is stable, the synchronous transmission parameters are given a higher weight to ensure transmission consistency and efficiency. When the network experiences fluctuations or anomalies, the dynamic adjustment parameters are given an increased weight to prioritize transmission reliability and robustness. Subsequently, the present invention uses a fusion algorithm to linearly combine the two parameters according to their weights to obtain the final coordinated parameter set, which includes parameter configurations for multiple aspects such as transmission rate, caching strategy, routing selection, and fault handling.

[0088] In one embodiment, the step S3 of acquiring multiple candidate paths according to the group coordination information and acquiring a multi-path set according to the multiple candidate paths includes:

[0089] S31, obtaining historical transmission data and real-time network changes of the network nodes, and establishing a migration map based on the historical transmission data to obtain a dynamic migration map;

[0090] S32, obtaining a target node location and an overall network status of the data to be transmitted, and obtaining multiple alternative paths based on the target node location and the overall network status;

[0091] S33. Obtaining the path length, path stability, path load, and path reliability of each candidate path, and obtaining a path attractiveness score of the corresponding candidate path based on each path length, path stability, path load, and path reliability;

[0092] S34. Sort the candidate paths in descending order according to the path attractiveness scores to obtain a candidate path set including a path priority ranking;

[0093] S35 . Fine-tune the paths in the candidate path set according to the real-time network change to obtain an optimized path solution, and classify the optimized path solution into primary and backup paths to obtain a multi-path set.

[0094] The traditional network transmission method as described in steps S31-S35 is often based on static topology and historical data, lacks sensitivity to dynamic changes, and cannot fully perceive and utilize multi-dimensional network information. The present application realizes multi-dimensional perception of network state by obtaining historical transmission data and real-time changes of network nodes, combining real-time data of target node position and overall network state, and establishing a dynamic migration map. It can adapt to changes in network environment in real time, more accurately select a transmission path, and make flexible adjustments when the network environment changes. Through path attraction scoring and dynamic optimization, the flexibility and efficiency of path selection can be improved while ensuring transmission reliability. By comprehensively scoring each candidate path (including path length, stability, load, reliability, etc.), and prioritizing paths according to the score, a more refined path selection mechanism is provided. In addition, the candidate path fine-tuning and primary and backup path classification mechanism effectively avoids resource conflicts and interference between paths, improving network resource utilization. By combining real-time network changes and target node position, the path planning provides more intelligent dynamic adaptation. Through optimization of the multi-path set and classification of primary and backup paths, energy consumption can be more effectively controlled and network transmission efficiency can be improved, balancing transmission reliability, efficiency, and energy consumption. By simulating the self-organizing characteristics of biological populations, through coordination, optimization, and fine-tuning of multiple paths, coordinated combat between paths can be achieved. In this way, path planning not only focuses on single path reliability, but also improves the efficiency of multi-path cooperation as a whole, thereby improving the performance of the entire network.

[0095] Specifically, the dynamic migration map construction process first performs performance statistical analysis on historical transmission data, which includes data transmission records between various network nodes in the past period of time, each record containing source node identification, target node identification, transmission timestamp, data packet size, transmission delay, packet loss rate, and bandwidth utilization, etc. Subsequently, a time window sliding statistical method is used to divide the historical data into multiple fixed-length time windows 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 statistical performance indicators. The migration map uses a weighted directed graph data structure, where nodes represent physical nodes in the network, and edges represent the connection relationship between nodes. The weight of the edge is calculated based on the transmission performance indicators. The dynamic migration map maintains the timeliness of the data through a real-time updating mechanism. Whenever new transmission data is generated, the algorithm recalculates the weight values of the relevant connections and updates the map structure. Through global analysis and path attraction force calculation, the target node position and the overall network state are considered to assign attraction scores to each candidate path and prioritize them. For global analysis, the geographical location coordinates and network topology position information of the target node are first obtained. The geographical location coordinates are directly obtained from the GPS positioning data or a pre-set coordinate table of the node, and the network topology position information is determined by analyzing the connection degree, centrality, and clustering coefficient of the node in the network. Then, the overall network state is evaluated, including the calculation of indicators such as the overall network average load rate, network congestion distribution, node failure rate, and link quality distribution. The overall network average load rate is obtained by statistically averaging the current data processing load of all nodes. The network congestion distribution is determined by analyzing the data packet queue length and transmission delay distribution in each network region. The node failure rate is calculated based on historical failure records and the current node health status. The link quality distribution is obtained by statistically analyzing the bit error rate, signal strength, and connection stability of each link. The path attraction force calculation algorithm uses 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 failure recovery capability of the path. The four factors are summed by weighting to obtain the path attraction score. All candidate paths are sorted in descending order according to the attraction score to form a candidate path set containing path priority ranking. The expression of path priority is:

[0096] ;

[0097] wherein, is a path index value, is a calculated path priority of the th path, is a historical throughput of the th path, is a real-time status of the th path, and are weight coefficients, respectively, is a current path congestion degree of the th path, is a maximum congestion threshold value;

[0098] The path fine-tuning process is based on real-time network state changes to dynamically optimize the paths in the candidate path set. The real-time network change monitoring mechanism continuously collects network state information through the monitoring agents deployed on key network nodes. The monitoring agents report the collected data at fixed time intervals. After receiving the real-time monitoring data, the path fine-tuning algorithm first calculates the change amplitude of each monitoring indicator relative to the historical baseline value. When the change amplitude exceeds the preset threshold, the path fine-tuning process is triggered. The path fine-tuning algorithm uses a local search optimization strategy to locally adjust the paths with higher priority in the candidate path set. The adjustment content includes the replacement of part of the nodes on the path, the reselection of the path segment, and the dynamic correction of the transmission parameters. The node replacement strategy replaces the nodes with poor performance on the current path with adjacent nodes with better performance. The path segment reselection strategy replaces the path segment with better performance while keeping the starting point and the ending point of the path unchanged. The transmission parameter dynamic correction adjusts 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 the iterative optimization process. The attraction score of the path is recalculated in each iteration. When the improvement amplitude of the score in consecutive iterations is less than the convergence threshold, the optimization process is stopped. Finally, the optimized path scheme is obtained through real-time optimization. The main and backup paths are classified and managed in layers according to the transmission importance and reliability requirements. First, all paths are divided into main paths and backup paths according to the attraction score and stability indicators. The backup path selection standard requires that the path has sufficient independence from the main path and the transmission performance meets the basic requirements. The path independence is evaluated by calculating the node overlap degree and geographical separation degree between paths. Paths with an independence score higher than 0.7 are selected as backup paths. Then, the main paths are further subdivided. According to the priority and real-time requirements of the transmission task, the main paths are divided into high-priority main paths, medium-priority main paths, and low-priority main paths. The high-priority main paths are used to transmit critical control data and real-time monitoring data, requiring the smallest delay and the highest reliability. The medium-priority main paths are used to transmit general service data, balancing transmission efficiency and resource consumption. The low-priority main paths are used to transmit historical data and log information, focusing on transmission cost and energy consumption control. The backup paths are also managed in layers and synchronized with the main paths of the corresponding priority. Finally, a hierarchical multi-path set is formed.

[0099] In one embodiment, the step S4 of performing bird swarm dispersion coding on the to-be-transmitted data according to the multi-path set to obtain encoded data segments, and obtaining a data segment stream of a receiving end according to the encoded data segments, comprises:

[0100] S41, obtaining path classification information according to the multi-path set, and performing dynamic fragmentation processing on the to-be-transmitted data according to the path classification information to obtain hierarchical data segments;

[0101] 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;

[0102] S43, generate a formation backup according to the identified data segment to obtain an encoded data segment;

[0103] 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;

[0104] S45, obtain a preset transmission plan, and obtain a synchronous transmission instruction according to the preset transmission plan and the formation leader segment;

[0105] S46, obtain a migratory bird navigation system according to the network node, and control the migratory bird navigation system to send to a receiving end in coordination according to the synchronous transmission instruction to obtain a data segment stream.

[0106] As described in steps S41-S46, the conventional method is usually only based on static network topology and historical statistical information, lacking multi-dimensional perception ability of network environment, resulting in failure to comprehensively consider real-time state of network and characteristics of different paths in decision-making process, and it is difficult to make the best decision, and the present application effectively enhances multi-dimensional perception of network environment through dynamic fragmentation processing, path hierarchical information acquisition and redundant coding technology, uses real-time information of network, including path characteristics, transmission state and node information of multiple paths, makes comprehensive decision, can more accurately control data transmission strategy, improves resource utilization efficiency, through introducing accompanying flight backup generation and synchronous transmission instruction mechanism, can effectively coordinate between multiple paths, ensures that the resources of each path are reasonably utilized, avoids waste of resources, improves transmission efficiency of the system, and the design of path hierarchy and redundant coding enables dynamic adjustment between different paths according to demand, thereby enhancing the coordination ability of multi-path transmission and improving the overall efficiency of the network, the existing path planning method is usually based on static network topology and historical statistical information, which cannot adapt to the dynamic changes of the network in real time, and the real-time environmental changes of the network (such as node failure, bandwidth fluctuation, etc.) cannot be reflected in the adjustment of path selection and transmission strategy in time, resulting in low transmission efficiency, through dynamic fragmentation processing, generation of data fragments with identifier and coordination sending mechanism of migratory navigation system, the dynamic changes of the network can be perceived and adapted in real time, through the cooperation of transmission plan and synchronous transmission instruction, the transmission strategy can be flexibly adjusted according to the real-time state of the network, ensuring that high efficiency and stability can be maintained under different network environments, the current multi-path transmission method lacks self-organization and coordination ability like biological groups, and cannot realize automatic optimization in complex network environment, it is difficult to balance transmission efficiency and energy consumption control while ensuring transmission reliability, the present application makes the network transmission process more self-organizing and intelligent, through the use of redundant coding fragments with identifier, synchronous transmission instruction and coordination mechanism of migratory navigation system, the self-organizing behavior of biological groups can be simulated, so that each network node can independently coordinate under different network conditions, dynamically select the optimal path and transmission strategy, optimize transmission efficiency and energy consumption while ensuring transmission reliability, thereby realizing adaptive transmission control, through redundant coding of data and adding homing identifier to redundant coding fragments, the high reliability and redundancy of data transmission are ensured, and the data fragments with identifier can provide effective path selection basis for subsequent transmission process, so that each data fragment can be efficiently processed and transmitted in the network, reducing the packet loss rate in transmission and improving the integrity and transmission efficiency of data, by selecting leader fragment identifier according to transmission formation set, the present application can more efficiently organize and schedule different transmission fragments, management of transmission formation ensures that each data fragment can be transmitted in order, reduces redundancy and conflict, thereby improving the efficiency and stability of the overall transmission.

[0107] Specifically, the path quality analysis first evaluates the comprehensive performance of each path in the multi-path set, and the evaluation indexes include four dimensions of 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 of propagation delay, processing delay and queuing delay, the packet loss rate is obtained by calculating the difference between the total number of sent packets and the total number of successfully received packets within a certain time window, and 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.The final path grading information is generated by a weighted comprehensive score algorithm. After calculating the comprehensive score of each path, the path is divided into three levels of high-quality path, good path and general path according to the score interval. The dynamic fragmentation processing performs content-aware hierarchical cutting on the data to be transmitted according to the path grading information. First, the original data is subjected to semantic analysis and importance evaluation. The semantic analysis identifies the core business logic part, configuration parameter part and auxiliary information part in the data by analyzing the structural features and content types of the data. In the process of generating 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 general paths for transmission, thereby achieving accurate matching of data importance and path quality. The graded data segments are subjected to unequal error protection coding to obtain redundant coding segments. The unequal error protection coding performs differential error detection and correction processing on the graded data segments. The coding algorithm selects a suitable coding scheme according to the importance level of the data segment and the quality level of the assigned path. The redundant coding segments increase the corresponding error protection information on the basis of the original data content, so that the data has self-repairing capability during transmission. The introduction of the homing identifier simulates the navigation 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 node physical address and logical address to ensure the uniqueness of the sending node 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 of the data segment expected to be transmitted. The time identifier contains the data segment generation timestamp, expected transmission time and timeout threshold. 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 contains segment dependency relationship information, which describes the logical association between the current segment and other segments. The receiving end correctly recombines the data according to the dependency relationship information. When the final data segment with identifier is transmitted in the network, the intermediate nodes correctly route and forward according to the path information in the homing identifier. The receiving end classifies and sorts the segments according to the identifier information. The companion backup generation simulates the mutual protection mechanism in bird flocks. Multiple backup copies of the key data segment with identifier are created and transmitted in parallel through different paths. Each backup copy increases a backup identifier on the basis of the original homing identifier. The backup identifier contains the main segment reference number, backup copy sequence number, synchronization control marker 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.

[0108] The formation organization groups and aggregates according to the attribute characteristics and transmission requirements of the coded data segments, forms a transmission formation set with inherent logical association, the application firstly analyzes the homing identification information of each coded data segment, then carries out preliminary grouping based on the target node identification in the homing identification information, puts the data segments with the same target node into the same basic formation, subsequently carries out secondary grouping according to the priority level, puts the high-priority segments into a fast formation, the medium-priority segments into a standard formation, and the low-priority segments into an ordinary formation, finally determines the transmission order constraint of the segments through topological sorting based on the dependence relationship analysis, calculates the transmission urgency according to the generation timestamp and timeout threshold of the segments based on the time requirement analysis, the segments with high urgency are distributed to the priority transmission formation, the obtained transmission formation set finally forms a plurality of formation data structures containing the segment identification list, formation type identification, transmission priority and coordination parameters, the segments in each formation have similar transmission characteristics and coordination requirements, the selection of the leader segment identification simulates the selection mechanism of the head bird in the bird flock, determines the most suitable segment to undertake the coordination role by comprehensively evaluating the key indicators of each data segment in the formation, the selection algorithm firstly calculates the leadership score of each segment, the score indicators include four dimensions of segment data integrity, path quality matching degree, time tolerance and dependence relationship complexity, through the calculation of the scores of the four dimensions, the formation leader segment with high redundancy, strong protection strength, high reliability, high transmission time flexibility, simple dependence relationship is selected, the segment with the highest score is selected as the formation leader segment and the leadership identifier is added in its homing identification, the preset transmission plan is generated according to the coordination instructions of the formation leader segment and the network resource status, then the multi-layer synchronization control processing is used to ensure the coordination consistency among the formations, the preset transmission plan contains the transmission schedule, path allocation table and synchronization checkpoint, the multi-layer synchronization control processing adopts a hierarchical synchronization mechanism, the first layer is intra-formation synchronization, ensures that the segments in the same formation are transmitted in the order of dependence relationship, the second layer is inter-formation synchronization, coordinates the transmission time sequence among different formations, the third layer is global synchronization, monitors the global consistency of the whole transmission process, finally the obtained synchronization transmission instruction contains the formation start command, transmission control parameter and state feedback requirement, the formation start command specifies the accurate start time and transmission mode of each formation, the transmission control parameter includes the transmission rate, retransmission mechanism and congestion control strategy, the state feedback requirement specifies the frequency and content of the transmission state reported by each node to the control center, the key network node refers to the relay point in the data transmission path, has higher network connectivity, stable transmission performance and key routing forwarding capability, plays a role similar to important habitats and navigation marks in the bird migration process in the migratory bird type data transmission system of the application, the identification criteria of the key network node in the application include three-dimensional constraint conditions of connectivity threshold, performance indicator threshold and geographical location distribution,The connectivity threshold requires that a node is connected to at least three other nodes to ensure routing redundancy, the performance threshold specifies that the average delay of a node does not exceed 50 milliseconds and the packet loss rate is less than 1%, and the geographical distribution constraint ensures that the selected key nodes are evenly distributed in the network topology to avoid the risk of single point failure caused by local concentration. A node that meets these three conditions can be marked as a key network node and included in the management range of the migratory bird navigation system. The navigation beacon deployment process establishes transmission navigation and monitoring functions at the key nodes of the network, forming a migratory bird navigation system that covers the entire transmission path. The migratory bird navigation system deploys navigation beacons at each key node, which include path state monitoring, transmission coordination control, and exception handling response. Path state monitoring collects real-time data traffic, delay changes, and error rate statistics information passing through the node. Transmission coordination control adjusts the forwarding strategy and cache management of the node according to the received synchronization transmission instructions. Exception handling response detects transmission exceptions and triggers corresponding recovery mechanisms, including path switching, retransmission request, and congestion relief. The coordination sending process controls the coordinated work of each navigation beacon in the migratory bird navigation system through synchronization transmission instructions, achieving the ordered transmission of encoded data segments and the sequential arrival of the receiving end. The coordination sending process uses a time slicing transmission scheduling mechanism to divide the entire transmission time into multiple time windows, and only allows specific data segments of a certain formation to transmit within each time window. The sequential arrival of data segment streams is achieved through cache scheduling at key nodes. When it is detected that the predecessor dependent segment of a certain segment has not arrived, the segment waits in the node cache until the dependency relationship is satisfied before continuing to forward. Finally, a data segment stream arranged in the original order is formed at the receiving end.

[0109] In one embodiment, the step S5 of obtaining classification segment information according to the data segment stream and performing bird flock aggregation and reorganization according to the classification segment information to obtain reorganized data comprises:

[0110] S51, the receiving end performs homing verification on the received data segment stream to obtain an effective segment set;

[0111] S52, obtain the packet identifier of each data segment stream in the effective segment set, and perform classification verification on the effective segment set according to the packet identifier to obtain classification segment information;

[0112] S53, sort and reorganize the classification segment information to obtain a reorganized topology graph, and perform topology sorting according to the reorganized topology graph to obtain a segment reorganization sequence;

[0113] S54, perform end-to-end verification processing on the segment reorganization sequence to obtain reorganized data.

[0114] As described in steps S51-S54, the conventional method generally relies on static network topology and historical statistical information, lacking real-time adaptability to network dynamic changes, while the present application enables the receiving end to more flexibly perceive changes in data flow and their environmental conditions through homing verification, packet identifier acquisition, and classification verification, etc., and dynamically adjusts strategies to cope with multi-dimensional changes in the network through detailed classification and verification of each data segment, thereby improving the system's perception of network state and transmission quality, the present application perceives various changes in the network environment in real time and performs classification verification based on dynamic information, avoiding excessive reliance on static topology and historical information, achieving enhanced multi-dimensional perception, thereby improving network adaptation, the present application classifies, sorts, and reorganizes different data segments to obtain an effective topology graph, and performs topology sorting based on the topology graph, thereby optimizing the selection of data transmission paths, by reorganizing data segments and generating a topology graph, the system can effectively coordinate and optimize multiple transmission paths, making resource utilization among different paths more balanced, improving overall transmission efficiency and system resource coordination ability, the present application can flexibly adjust data transmission paths when the network environment changes through end-to-end verification processing combined with real-time adjustment of topology reorganization sequences, the present application can flexibly adjust data flow transmission strategies according to real-time changes in the network environment through dynamic segment reorganization and topology sorting, improving the system's adaptability and response ability in dynamic network environments, the present application not only improves network transmission reliability through topology reorganization and end-to-end verification, but also intelligently coordinates between different paths and segments, simulating self-organizing behavior in biological populations, through segment reorganization, topology sorting, and end-to-end verification, the present application can better balance network transmission efficiency and energy consumption while ensuring transmission reliability, demonstrating self-organizing and coordinating ability, effectively optimizing resource use, the present application can effectively control energy consumption and improve overall network resource utilization efficiency under the premise of ensuring transmission quality through dynamic adjustment of multi-path transmission and optimization of topology reorganization sequences, through dynamic topology reorganization and end-to-end verification, the present application not only improves transmission efficiency, but also optimizes energy consumption by coordinating the use of multiple paths, thereby improving overall network performance and energy efficiency.

[0115] Specifically, at the receiving end, first, the received data segment stream is subjected to a homing verification operation, the data integrity is verified by calculating the check value of each data segment and comparing it with the check value preset by the sending end, and the sequence number in the segment header is checked for continuity and the timestamp is checked for being within a reasonable range, the verified segments are marked as valid segments and stored in a valid segment set, and the segments that fail the verification or have abnormal format are discarded directly, the homing verification mechanism ensures that the data segments subjected to subsequent processing all have basic credibility and integrity features, after the valid segment set is established, a classification verification process is executed, the segments are classified according to different data types according to the grouping identifier carried in the header of each segment, the classified segments are put into corresponding classification containers, and the segments in each classification are subjected to secondary verification to verify whether the data format of the segments in the same classification is consistent and the encoding mode is matched, the segments that pass the classification verification are organized into a classified segment information structure, the structure contains classification identifier, segment quantity, total data length and other meta information, which provides necessary index information for subsequent sorting and reorganization, the application constructs a reorganization topology graph based on the classified segment information, the construction of the reorganization topology graph follows the dual constraints of time sequence and logical dependency, ensuring that the reorganization order of the data segments meets both the time sequence and the logical dependency requirements, the reorganization topology graph is subjected to topological sorting to obtain a segment reorganization sequence, starting from the node with an in-degree of zero, each node is processed layer by layer according to the topological sequence, each time a node is processed, the corresponding data segment is added to the segment reorganization sequence, and the in-degree value of the successor node is updated, the topological sorting process continues until all nodes are processed, and finally the generated segment reorganization sequence is arranged strictly according to the logical order and the time sequence of the data, the segment reorganization sequence is a linear data structure, each element contains segment identifier, data content, position index and other information, the segment reorganization sequence is subjected to comprehensive consistency verification through end-to-end check processing, and the segment reorganization sequence that passes the end-to-end check is merged and reconstructed into complete reorganization data, the reorganization data retains the integrity and consistency of the original data.

[0116] As Figure 2 shown, the application also provides a data transmission system based on bird dynamic perception, comprising:

[0117] A first acquisition module is configured to acquire spatial position information and connection state information of network nodes, and acquire a group perception vector according to the spatial position information and the connection state information;

[0118] A coordination module is 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;

[0119] The second obtaining module is configured to obtain a plurality of candidate paths according to the group coordination information, and obtain a multi-path set according to the plurality of candidate paths, wherein the group coordination information comprises a leader node identifier, a follower node identifier and a coordination parameter set;

[0120] The encoding module is configured to perform bird swarm dispersion encoding on the to-be-transmitted data according to the multi-path set to obtain encoded data segments, and obtain a data segment stream of a receiving end according to the encoded data segments.

[0121] The recombination module is configured to obtain classification segment information according to the data segment stream, and perform bird swarm aggregation recombination according to the classification segment information to obtain recombined data.

[0122] In one embodiment, the first obtaining module comprises:

[0123] The evaluation unit is configured to obtain a visual perception coverage area according to the spatial position information, and quantitatively evaluate node reachability of the network node according to the visual perception coverage area to obtain a visual perception vector.

[0124] The calculation unit is configured to obtain an auditory perception intensity distribution according to the connection state information, and calculate a data packet reception probability of the network node according to the auditory perception intensity distribution to obtain an auditory perception vector.

[0125] The first obtaining unit is configured to obtain network topology direction information of the network node, and obtain a magnetic field perception vector according to the network topology direction information.

[0126] The second obtaining unit is configured to obtain a state of a neighboring node in the network node, and obtain a neighbor perception atlas according to the state of the neighboring node.

[0127] The fusion unit is configured to fuse the visual perception vector, the auditory perception vector and the magnetic field perception vector according to the neighbor perception atlas to obtain a group perception vector.

[0128] It should be noted that each module and unit in the data transmission system based on bird dynamic perception corresponds to each step in the data transmission method based on bird dynamic perception.

[0129] As shown in Figure 3 The present application also provides a computer device, which can be a server, and the internal structure thereof can be as shown in Figure 3The computer device includes a processor, a memory, a network interface and a database connected through a system bus. The processor of the computer device is configured 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 internal memory provides an environment for running the operating system and the computer program in the non-volatile storage medium. The database of the computer device is configured to store all data required by the process of the data transmission method based on bird dynamic perception. The network interface of the computer device is configured to communicate with an external terminal through a network connection. The computer program is executed by the processor to implement the data transmission method based on bird dynamic perception.

[0130] Those skilled in the art can understand that, Figure 3 The structure shown in the above embodiment is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied.

[0131] The computer program is executed by the processor to implement the data transmission method based on bird dynamic perception.

[0132] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be completed by a computer program instructing related hardware. 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 above-mentioned embodiments. Any reference to memory, storage, database or other medium provided by the present application and used in the embodiments can include non-volatile and / or volatile memory. 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 external cache memory. As an illustration but 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), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM) and memory bus dynamic RAM (RDRAM) and the like.

[0133] It should be noted that, in this text, the terms "comprising", "containing" or any other variant thereof are intended to cover non-exclusive inclusions, so that a process, device, article or method including a series of elements not only includes those elements, but also includes other elements not explicitly listed, or elements inherent to such a process, device, article or method. Without more limitations, the element defined by the statement "including a" does not exclude the presence of other identical elements in the process, device, article or method including the element.

[0134] The above description is only the preferred embodiment of the present application, and does not limit the patent scope of the present application, and any equivalent structure or equivalent process transformation using the content of the present application specification and drawings, or direct or indirect application in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A data transmission method based on bird dynamic perception, characterized in that, The method comprises the following steps: acquiring spatial position information and connection state information of a network node, acquiring a visual perception coverage area according to the spatial position information, and quantitatively evaluating node reachability of the network node according to the visual perception coverage area to obtain a visual perception vector; acquiring an auditory perception intensity distribution according to the connection state information, and calculating a data packet receiving probability of the network node according to the auditory perception intensity distribution to obtain an auditory perception vector; acquiring network topology direction information of the network node, and acquiring a magnetic field perception vector according to the network topology direction information; acquiring a state of a neighboring node in the network node, and acquiring a neighbor perception graph according to the state of the neighboring node; fusing the visual perception vector, the auditory perception vector, and the magnetic field perception vector according to the neighbor perception graph to obtain a group perception vector; acquiring a synchronous transmission parameter according to the group perception vector, and group coordinating the network node according to the synchronous transmission parameter to obtain group coordination information; acquiring a plurality of alternative paths according to the group coordination information, and acquiring a multi-path set according to the plurality of alternative paths, wherein the group coordination information comprises a leader node identifier, a follower node identifier, and a coordination parameter set; acquiring path hierarchical information according to the multi-path set, and dynamically fragmenting processing the to-be-transmitted data according to the path hierarchical information to obtain hierarchical data fragments; encoding the hierarchical data fragments to obtain redundant encoded fragments, and adding a homing identifier to the redundant encoded fragments to obtain an identified data fragment; generating a companion backup according to the identified data fragment to obtain an encoded data fragment; acquiring a transmission formation set according to the encoded data fragment, and selecting a leader fragment identifier according to the transmission formation set to obtain a formation leader fragment; acquiring a preset transmission plan, and acquiring a synchronous transmission instruction according to the preset transmission plan and the formation leader fragment; acquiring a migrant navigation system according to the network node, and controlling the migrant navigation system to send the synchronous transmission instruction to a receiving end according to the synchronous transmission instruction to obtain a data fragment stream; acquiring classification fragment information according to the data fragment stream, and performing bird group aggregation and reorganization according to the classification fragment information to obtain reorganized data.

2. The data transmission method based on bird dynamic perception according to claim 1, characterized in that, The step of acquiring a synchronous transmission parameter according to the group perception vector, and group coordinating the network node according to the synchronous transmission parameter to obtain group coordination information comprises the following steps: acquiring a node performance index according to the group perception vector, and multi-objective optimization selecting the network node according to the node performance index to obtain a leader node identifier and a non-leader node identifier; acquiring a following relationship of a non-leader node according to the leader node identifier and the non-leader node identifier, and acquiring a follower node identifier according to the following relationship; synchronizing transmission parameters of the leader node identifier and the follower node identifier to obtain the synchronous transmission parameter; acquiring network congestion and node failure of the network node, and acquiring a dynamic adjustment parameter according to the network congestion and the node failure; fusing the synchronous transmission parameter and the dynamic adjustment parameter to obtain the coordination parameter set. 3.The data transmission method based on bird dynamic perception according to claim 1, characterized in that, The step of obtaining a plurality of alternative paths according to the flock coordination information and obtaining a multi-path set according to the plurality of alternative paths comprises: Obtaining historical transmission data and real-time network changes of the network node, and establishing a migration map according to the historical transmission data to obtain a dynamic migration map; Obtaining target node positions and overall network states of the to-be-transmitted data, and obtaining a plurality of alternative paths according to the target node positions and overall network states; Obtaining path length, path stability, path load and path reliability of each alternative path, and obtaining path attraction scores of the corresponding alternative paths according to the path length, path stability, path load and path reliability of each alternative path; According to the path attraction scores, the alternative paths are sorted in descending order to obtain a candidate path set containing path priority sorting; According to the real-time network changes, the paths in the candidate path set are fine-tuned to obtain an optimized path scheme, and the optimized path scheme is classified into primary and backup paths to obtain a multi-path set.

4. The data transmission method based on bird dynamic perception according to claim 1, characterized in that, The step of obtaining classification segment information according to the data segment stream and performing bird flock aggregation and reorganization according to the classification segment information to obtain reorganized data comprises: The receiving end performs homing inspection on the received data segment stream to obtain an effective segment set; Obtaining the grouping identifier of each data segment stream in the effective segment set, and performing classification verification on the effective segment set according to the grouping identifier to obtain classification segment information; The classification segment information is sorted and reorganized to obtain a reorganized topology graph, and the topology is sorted according to the reorganized topology graph to obtain a segment reorganization sequence; The segment reorganization sequence is processed through end-to-end verification to obtain reorganized data.

5. A data transmission system based on bird dynamic perception, characterized by, Comprise: The first obtaining module is configured to obtain spatial position information and connection state information of a network node, obtain a visual perception coverage area according to the spatial position information, and quantitatively evaluate node reachability of the network node according to the visual perception coverage area to obtain a visual perception vector; According to the connection state information, the hearing perception intensity distribution is obtained, and the data packet reception probability of the network node is calculated according to the hearing perception intensity distribution to obtain a hearing perception vector; Obtaining network topology direction information of the network node, and obtaining a magnetic field perception vector according to the network topology direction information; Obtaining the state of the adjacent nodes in the network node, and obtaining a neighbor perception map according to the state of the adjacent nodes; According to the neighbor perception map, the visual perception vector, the hearing perception vector and the magnetic field perception vector are fused to obtain a flock perception vector; The coordination module is configured to obtain synchronization transmission parameters according to the flock perception vector, and perform flock coordination on the network node according to the synchronization transmission parameters to obtain flock coordination information; The second obtaining module is configured to obtain a plurality of alternative paths according to the flock coordination information, and obtain a multi-path set according to the plurality of alternative paths, wherein the flock coordination information comprises leader node identifier, follower node identifier and coordination parameter set. The encoding module is configured to acquire path hierarchy information according to the path set, and perform dynamic fragmentation processing on the to-be-transmitted data according to the path hierarchy information to obtain hierarchical data segments; The hierarchical data segments are encoded to obtain redundant encoded segments, and a homing identifier is added to the redundant encoded segments to obtain identified data segments; The identified data segments are used to generate a formation backup to obtain encoded data segments; A transmission formation set is acquired according to the encoded data segments, and a leader segment identifier is selected according to the transmission formation set to obtain a formation leader segment; A preset transmission plan is acquired, and a synchronous transmission instruction is acquired according to the preset transmission plan and the formation leader segment; A migratory bird navigation system is acquired according to the network node, and the migratory bird navigation system is controlled to perform coordinated sending to a receiving end according to the synchronous transmission instruction to obtain a data segment stream; The recombination module is configured to acquire classified segment information according to the data segment stream, and perform bird flock aggregation recombination according to the classified segment information to obtain recombined data. 6.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is configured to perform the method according to any one of claims 1-5 when the computer program is executed by the processor. The processor executes the computer program to implement the steps of the method in any one of claims 1 to 4.

7. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the method in any one of claims 1 to 4.

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