Ad hoc network communication system without signal area

Abstract

The invention discloses an ad hoc network communication system without a signal area, particularly relates to the technical field of wireless communication, and is used for solving the problem that a connectivity critical state caused by node density or mobility change in a low-density network is not fully considered in an existing dynamic routing protocol. The network connectivity critical state is pre-judged by acquiring node position coordinates and communication radiuses in real time and combining the average node spacing and the dispersion in the moving direction; screening a high-priority detection node based on an obstacle avoidance cooperative angle variance and a virtual pheromone concentration in a critical state, and adaptively adjusting a detection message sending frequency according to a node moving speed; constructing a path stability evaluation model by fusing the link survival duration, the node residual energy and the signal intensity fluctuation rate, dynamically screening a high-stability path as a main transmission link, and taking the rest lists as standby links; and monitoring the signal fluctuation characteristics of the main link in real time, and quickly switching to the path with the optimal comprehensive stability in the standby link in an abnormal state.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technologies, and more specifically, to a self-organizing network communication system for signal-free areas. Background Art

[0002] In emergency communications in signal-free areas (such as remote mountainous areas and disaster sites), self-organizing network technologies rely on devices to autonomously form a network for information transmission, and their core depends on dynamic routing protocols (such as AODV, OLSR) to maintain the connectivity of the network topology in a changing environment; existing technologies usually establish and maintain a routing table based on neighbor discovery and shortest path selection mechanisms, by periodically broadcasting beacons or on-demand routing requests; however, in scenarios with sparse node distribution and high dynamics (such as wide-area search and rescue, field monitoring), the network topology often exhibits intermittent connectivity characteristics. Traditional methods need to frequently send detection packets to find potential paths, resulting in a sharp increase in control overhead, reduced energy consumption efficiency, and difficulty in ensuring the continuous stability of end-to-end communication.

[0003] The current dynamic routing protocols do not fully consider the critical connectivity state problems caused by changes in node density or mobility in low-density networks; when the number of available paths approaches the threshold of network connectivity phase transition, traditional routing strategies may encounter sudden path interruptions, data flooding retransmission, and energy waste and other problems. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a self-organizing network communication system for signal-free areas to solve the problems proposed in the above background art.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A self-organizing network communication system for signal-free areas, comprising:

[0007] A node parameter acquisition module: periodically acquires node parameters of network nodes, and the node parameters include position coordinates and communication radius;

[0008] A connectivity criticality determination module: calculates the average node spacing and the dispersion of movement directions according to the node parameters to determine whether the network is in a critical connectivity state;

[0009] A detection node screening module: screens high-priority detection nodes in a critical connectivity state;

[0010] A dynamic detection control module: adjusts the detection packet sending frequency according to the moving speed of high-priority detection nodes;

[0011] Multipath Generation Module: Calculate the stability weights of each path according to the detection results, select the path with a stability weight higher than the preset weight threshold as the main transmission link, and the rest as the backup links;

[0012] Data Shunting Execution Module: When the signal strength fluctuation of the main transmission link is abnormal, switch to the path with the highest stability weight among the backup links.

[0013] In a preferred embodiment, screening high-priority detection nodes specifically includes:

[0014] Select the nodes with an obstacle avoidance cooperation angle variance lower than the preset cooperation threshold and a virtual pheromone concentration higher than the population mean as high-priority detection nodes;

[0015] Among them, the population mean is the arithmetic mean of the virtual pheromone concentrations of all nodes.

[0016] In a preferred embodiment, periodically collect the node parameters of network nodes, including:

[0017] Trigger an active collection period according to a preset time interval. During the active collection period, obtain the position coordinates through beacon interaction between nodes, and infer the communication radius by signal strength;

[0018] When the instantaneous change rate of the signal strength received by a node exceeds the preset change threshold, trigger an event-driven collection period. During the event-driven collection period, update the position coordinates through multi-hop cooperative positioning, and dynamically calibrate the communication radius according to the maximum effective communication distance of adjacent nodes.

[0019] In a preferred embodiment, calculate the average node spacing and the mobility direction dispersion degree according to the node parameters to determine whether the network is in a connectivity critical state, including:

[0020] Traverse the position coordinates of all nodes, calculate the Euclidean distance between each pair of nodes, remove the node pairs with a Euclidean distance greater than the communication radius, and take the arithmetic mean of the distances of the remaining node pairs as the average node spacing;

[0021] Extract the change amount of the position coordinates of each node in two adjacent collection periods to generate a mobility direction vector, and calculate the mobility direction dispersion degree of the node group based on the azimuth angle of the mobility direction vector;

[0022] If the average node spacing exceeds a preset multiple of the communication radius and the mobility direction dispersion degree is higher than the preset dispersion threshold, determine that the network is in a connectivity critical state.

[0023] In a preferred embodiment, the method for obtaining the obstacle avoidance cooperation angle variance is:

[0024] Based on the generated movement direction vectors, for each node, all adjacent nodes within the corresponding communication radius are selected;

[0025] Calculate the geometric angle between the movement direction vector of the node and those of each adjacent node. The geometric angle is converted into an angle value through the vector dot product formula;

[0026] Statistically analyze the angle values of the geometric angles of all adjacent node pairs, and calculate the variance of the geometric angle values as the obstacle avoidance cooperation angle variance.

[0027] In a preferred embodiment, the method for obtaining the virtual pheromone concentration is as follows:

[0028] Statistically analyze the number of detection messages successfully forwarded by the node within a preset period. Each successful forwarding increases the virtual pheromone concentration by a preset increment value;

[0029] Weight the virtual pheromone concentration according to the link survival duration maintained by the node. For links whose survival duration exceeds the preset stability threshold, an additional concentration value is increased according to the timeout ratio;

[0030] At the end of each collection period, apply a preset decay rate to the virtual pheromone concentration to generate the updated virtual pheromone concentration.

[0031] In a preferred embodiment, adjusting the detection message sending frequency according to the movement speed of high-priority detection nodes includes:

[0032] Extract the movement speed of the high-priority detection node. When the movement speed exceeds the preset speed threshold, increase the detection message sending frequency to the first preset multiple of the reference frequency;

[0033] Extract the movement direction dispersion degree of the high-priority detection node. When the movement direction dispersion degree exceeds the preset dispersion threshold, increase the detection message sending frequency to the second preset multiple of the reference frequency;

[0034] If the movement speed exceeds the preset speed threshold and the movement direction dispersion degree exceeds the preset dispersion threshold, adjust the detection message sending frequency by superimposing the first preset multiple and the second preset multiple;

[0035] If the movement speed does not exceed the preset speed threshold and the movement direction dispersion degree does not exceed the preset dispersion threshold, restore the detection message sending frequency to the reference frequency.

[0036] In a preferred embodiment, calculating the stability weights of each path according to the detection results, and selecting the path with a stability weight higher than the preset weight threshold as the main transmission link, and the rest as backup links, includes:

[0037] Statistically calculate the link survival duration of the path. The link survival duration is the duration from the establishment of the path to its interruption or deletion, and it is normalized according to the ratio of the survival duration to the total detection duration;

[0038] Obtain the remaining energy of all nodes in the path and calculate the average value of the remaining energy of the nodes;

[0039] Extract the signal strength volatility of the path. The signal strength volatility is the ratio of the standard deviation to the mean of the received signal strength during the path transmission;

[0040] Perform a weighted product of the link survival duration ratio, the average value of the remaining energy of the nodes, and the signal strength volatility to generate the stability weight of the path;

[0041] Select the path with a stability weight higher than the preset weight threshold as the main transmission link, and the remaining paths as the backup links.

[0042] In a preferred embodiment, when the signal strength fluctuation of the main transmission link is abnormal, switch to the path with the highest stability weight among the backup links, including:

[0043] Continuously monitor the signal strength volatility of the main transmission link, and determine it as abnormal when the signal strength volatility exceeds the preset fluctuation threshold;

[0044] Obtain the stability weights of all paths in the backup link list, and screen the path with the highest stability weight as the target switching path;

[0045] Verify whether the current signal strength volatility of the target switching path is lower than that of the main transmission link. If it is satisfied, perform the switch;

[0046] After the switch is completed, downgrade the original main transmission link to a backup link and update the stability weight.

[0047] Compared with the prior art, the present invention has the following beneficial effects:

[0048] 1. By introducing a connectivity critical state prediction mechanism and a multi-dimensional collaborative screening strategy, the present invention effectively solves the problems of excessive control overhead and insufficient communication reliability caused by unstable paths in traditional routing protocols in low-density dynamic network scenarios. First, based on the dynamic perception of node density distribution and group movement trends, the system can identify the connectivity critical state of the network in advance. By optimizing the screening logic of detection nodes and adaptively regulating the detection frequency, the transmission volume of redundant detection messages is significantly reduced. Compared with the fixed-period or random detection mechanisms in traditional methods, the present invention quantifies the node movement coordination through the variance of obstacle avoidance coordination angles, and combines the virtual pheromone concentration to represent the contribution degree of node path maintenance. It realizes the precise screening and resource allocation of detection nodes in high-dynamic scenarios, avoiding both the risk of path failure caused by node movement conflicts and the energy waste caused by inefficient detection, thus improving the overall energy efficiency ratio while ensuring network connectivity.

[0049] 2. Through the dynamic evaluation of multi-path stability weights and the primary and backup link switching mechanism, the present invention breaks through the limitations of traditional routing protocols that rely on single-link or static path selection. Based on the multi-dimensional parameter fusion calculation of link survival duration, node energy status, and signal volatility, the system can real-time screen out the transmission path with the optimal comprehensive stability, and achieve a rapid response to link switching through the volatility threshold trigger mechanism. Compared with the defect of passive retransmission after path breakage in the prior art, the present invention greatly improves the continuity and anti-interference ability of data transmission through predictive path maintenance and dynamic shunt execution. Especially in scenarios with sparse node distribution and frequent topology changes, it can effectively suppress the data flooding phenomenon, reduce the communication delay and energy consumption fluctuation caused by repeated path reconstruction, and provide a highly robust communication guarantee for scenarios such as emergency rescue and field monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 It is a schematic structural diagram of a self-organizing communication system without signal area of the present invention;

[0051] Figure 2 It is a flowchart of the method for obtaining the variance of obstacle avoidance coordination angles in the present invention;

[0052] Figure 3 It is a flowchart of the method for obtaining the virtual pheromone concentration in the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0053] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0054] Example 1: Figure 1 The structural schematic diagram of a self-organizing network communication system in a signal-free area according to the present invention is given. A self-organizing network communication system in a signal-free area includes:

[0055] Node parameter acquisition module: Periodically acquire the node parameters of network nodes. The node parameters include position coordinates and communication radius;

[0056] Connectivity critical determination module: Calculate the average node spacing and the dispersion degree of the moving direction according to the node parameters to determine whether the network is in a connectivity critical state;

[0057] Probe node screening module: Screen high-priority probe nodes in the connectivity critical state;

[0058] Dynamic detection control module: Adjust the detection message sending frequency according to the moving speed of the high-priority probe nodes;

[0059] Multipath generation module: Calculate the stability weights of each path according to the detection results, and select the path with a stability weight higher than the preset weight threshold as the main transmission link, and the rest as the backup links;

[0060] Data splitting execution module: When the signal strength of the main transmission link fluctuates abnormally, switch to the path with the highest stability weight among the backup links.

[0061] Periodically acquire the node parameters of network nodes, including:

[0062] In the self-organizing network communication in a signal-free area, the specific implementation process of periodically acquiring the node parameters of network nodes is as follows:

[0063] Set the preset time interval for periodic acquisition to any value between 5 seconds and 30 seconds. The setting basis of this time interval is the ratio of the average moving speed of network nodes to the communication radius. When the average moving speed of network nodes increases, shorten the preset time interval to improve the acquisition frequency; within the active acquisition period triggered by the preset time interval, each node periodically broadcasts a beacon data packet containing its own position coordinates. The adjacent nodes receive the beacon data packet and extract the position coordinates of the sending node, and at the same time, inversely deduce the communication radius of the sending node according to the received signal strength indication value. The calculation method of inversely deducing the communication radius according to the received signal strength indication value is: Wherein, R is the communication radius, P t is the transmission power, P r is the received power, and L is the environmental attenuation factor, which is obtained by fitting historical communication data.

[0064] When any node detects that the instantaneous change rate of the received signal strength indication value exceeds a preset change threshold (for example, the change rate exceeds 20%) within two consecutive sampling periods, an event-driven acquisition period is triggered.

[0065] During the event-driven acquisition period, the node sends a cooperative positioning request to neighboring nodes, and the neighboring nodes update the position coordinates of the node based on the multi-hop cooperative positioning method. Specifically: the node calculates its own position through trilateration, selects the position coordinates of more than three neighboring nodes as reference points, and corrects its own position coordinates according to the combined solution result of the time difference of arrival and the received signal strength indication value; at the same time, the communication radius of the node is dynamically calibrated according to the maximum effective communication distance among adjacent nodes. The acquisition method of the maximum effective communication distance is: statistically calculate the farthest distance at which all adjacent nodes successfully establish links in historical communications, and take the moving average of the farthest distances as the calibration reference.

[0066] After completing the active acquisition period or the event-driven acquisition period, the node stores the collected position coordinates and communication radius in the local routing table, and periodically broadcasts update information through the routing table.

[0067] Calculate the average node spacing and the dispersion of the moving direction according to the node parameters to determine whether the network is in a critical state of connectivity, including:

[0068] Traverse the position coordinates of all nodes in the network, and calculate the Euclidean distance between each pair of nodes formed by every two nodes. The calculation method of the Euclidean distance is: according to the horizontal and vertical coordinate values of node A and the horizontal and vertical coordinate values of node B, calculate the sum of the squares of the differences in the abscissas and the sum of the squares of the differences in the ordinates respectively, and take the square root of the sum of the squares to obtain the straight-line distance between the two nodes.

[0069] If the calculated Euclidean distance is greater than the communication radius of the node, then exclude this pair of nodes, and only retain the pairs of nodes with Euclidean distances less than or equal to the communication radius; take the arithmetic mean of the Euclidean distances of all retained pairs of nodes to obtain the average node spacing.

[0070] For example, when the network contains nodes 1 to 5, calculate the distances between all pairs of nodes such as between node 1 and node 2, between node 1 and node 3, between node 1 and node 4, between node 1 and node 5, between node 2 and node 3, etc. After excluding the pairs of nodes with distances exceeding the communication radius, add up the distances of the remaining valid pairs of nodes and divide by the number of valid pairs of nodes to finally obtain the average node spacing.

[0071] For each node, extract the change amount of the position coordinates in two adjacent acquisition periods. The change amount of the position coordinates is obtained by subtracting the position coordinate value recorded in the previous acquisition period from the position coordinate value recorded in the current acquisition period.

[0072] Generate a moving direction vector based on the change in position coordinates. The direction of the moving direction vector is determined by the ratio of the horizontal direction change amount to the vertical direction change amount, and is converted into an azimuth angle value through the arctangent function.

[0073] For example, if the position coordinates of a node in the previous cycle are (10 meters, 20 meters) and the current cycle position coordinates are (15 meters, 25 meters), then the horizontal direction change amount is 5 meters, the vertical direction change amount is 5 meters, and the azimuth angle of the moving direction vector is 45 degrees.

[0074] Statistically analyze the azimuth angle values of all nodes, and calculate the standard deviation of the azimuth angle values as the moving direction dispersion.

[0075] The calculation method of the standard deviation of the azimuth angle value is as follows: First, calculate the arithmetic mean of all azimuth angle values, then calculate the square of the difference between each azimuth angle value and the arithmetic mean, take the average of all the squares of the differences and then take the square root to finally obtain the standard deviation of the azimuth angle value.

[0076] Compare the average node spacing with a preset multiple of the communication radius, and at the same time compare the moving direction dispersion with a preset dispersion threshold; if the average node spacing exceeds the preset multiple of the communication radius and the moving direction dispersion is higher than the preset dispersion threshold, it is determined that the network is in a critical connectivity state.

[0077] The preset multiple of the communication radius is set according to the wireless signal attenuation model. For example: in the free space propagation model, when the communication distance is expanded to 1.5 times the original communication radius, the received signal strength will decay to the lowest sensitivity threshold of the receiving device, and at this time the preset multiple is set to 1.5 times; the preset dispersion threshold is set through historical data statistics. For example: in the historical operation data of the network, when the moving direction dispersion of the node group exceeds 30 degrees, the probability of network connectivity breakage exceeds 90%, so the preset dispersion threshold is set to 30 degrees.

[0078] In a specific implementation, if the network nodes are deployed in a wild search and rescue scenario, the initial communication radius of the nodes is 100 meters. When the calculated average node spacing is 160 meters (exceeding 1.5 times of 100 meters) and the moving direction dispersion is 35 degrees (exceeding the 30 - degree threshold), it is determined that the network enters the critical connectivity state and triggers the subsequent path maintenance mechanism. If the node group moves dispersedly at the disaster site, the moving direction dispersion of the nodes rapidly rises to 40 degrees, and at the same time the average node spacing reaches 1.6 times the communication radius, it is also determined to be in the critical state.

[0079] Figure 2 The flowchart of the method for obtaining the obstacle - avoidance cooperation angle variance in the present invention is given. The method for obtaining the obstacle - avoidance cooperation angle variance is as follows:

[0080] Based on the generated movement direction vectors, for each node, all adjacent nodes within its communication radius are selected.

[0081] The way to obtain the communication radius is as follows: The communication radius value collected periodically by the node parameter acquisition module. If the node has completed the dynamic calibration of the communication radius within the event-driven acquisition period, the calibrated communication radius value is preferentially used.

[0082] For example, if the communication radius of node A is 100 meters, then all nodes with a straight-line distance from node A less than or equal to 100 meters are selected as adjacent nodes, and the position coordinates of the adjacent nodes are obtained through the node parameter acquisition module.

[0083] Calculate the geometric angle between the movement direction vector of the node and those of each adjacent node.

[0084] The movement direction vector is generated from the change in position coordinates between two adjacent acquisition periods. Specifically: Subtract the position coordinates of the previous acquisition period from those of the current acquisition period to obtain the horizontal direction change amount and the vertical direction change amount. The horizontal direction change amount and the vertical direction change amount together constitute the movement direction vector.

[0085] For example, if the position coordinates of node B in the previous period were 10 meters east longitude and 20 meters north latitude, and the position coordinates in the current period are 15 meters east longitude and 25 meters north latitude, then the horizontal direction change amount is 5 meters in the east longitude direction, and the vertical direction change amount is 5 meters in the north latitude direction. The movement direction vector indicates a movement in the direction of 45 degrees north of east.

[0086] The calculation process of the geometric angle is as follows: For the current node and a certain adjacent node, obtain the movement direction vectors of both, calculate the dot product of the two vectors. The dot product is equal to the product of the horizontal direction change amount of the current node and the horizontal direction change amount of the adjacent node, plus the product of the vertical direction change amount of the current node and the vertical direction change amount of the adjacent node.

[0087] Calculate the modulus length of the movement direction vector of the current node. The modulus length is the square root of the sum of the squares of the horizontal direction change amount and the vertical direction change amount of the current node; calculate the modulus length of the movement direction vector of the adjacent node, and the calculation method of the modulus length is the same as that of the current node; divide the dot product by the product of the modulus lengths of the current node and the adjacent node to obtain the cosine value; convert the cosine value to an angle value through the inverse cosine function to obtain the geometric angle between the two movement direction vectors.

[0088] Statistically analyze the geometric angle values of all adjacent node pairs, and calculate the variance of the angle values as the obstacle avoidance cooperation angle variance. The calculation process of the variance is as follows: for a certain node, collect the geometric angle values between it and all adjacent nodes, and calculate the arithmetic mean of these angle values; for each angle value, calculate the difference between it and the arithmetic mean, square the difference and sum them up, and then divide by the number of adjacent nodes to obtain the obstacle avoidance cooperation angle variance.

[0089] If the nodes move randomly and dispersedly, the geometric angle differences are significant, and the variance increases accordingly.

[0090] For example, if the node group moves in the same direction during a wide-area search and rescue mission, the geometric angles between the moving direction vectors of each node are generally less than 10 degrees, and the variance is lower than 5, indicating that the group movement is highly cooperative and suitable as a high-priority detection node; if the node group operates dispersedly during field monitoring, the geometric angle differences can reach more than 60 degrees, and the variance exceeds 50, indicating that the group movement is disorderly, and it is necessary to reduce its detection priority to avoid wasting resources.

[0091] Figure 3 The flowchart of the method for obtaining the virtual pheromone concentration in the present invention is given. The method for obtaining the virtual pheromone concentration is as follows:

[0092] Each node statistically analyzes the number of successfully forwarded detection packets within a preset period; the preset period is set according to the network dynamics. For example, when the average moving speed of the node is 2 meters per second, the preset period is set to 30 seconds; when the average moving speed of the node is 5 meters per second, the preset period is shortened to 10 seconds.

[0093] The number of successfully forwarded detection packets is statistically analyzed in the following way: after receiving a detection packet, if the node forwards it to the next-hop node within the preset timeout period (such as 500 milliseconds) and there is no packet loss, it is counted as a successful forward.

[0094] Each time a detection packet is successfully forwarded, the virtual pheromone concentration increases by a preset increment value. The preset increment value is set according to the network load balancing requirements. For example, it is set to 0.1 in a low-load scenario and 0.05 in a high-load scenario to avoid overloading the detection task due to too high a concentration value of a single node.

[0095] The statistical method for the link survival duration is: the duration from the link establishment moment to the link interruption or timeout deletion moment. If the link survival duration exceeds the preset stability threshold (such as 30 seconds), the virtual pheromone concentration is additionally increased according to the timeout ratio.

[0096] The calculation method of the timeout ratio is as follows: Subtract the preset stability threshold from the link survival duration, and then divide by the preset stability threshold to obtain the timeout ratio coefficient. For example, if the preset stability threshold is 30 seconds and the survival duration of a certain link is 50 seconds, the timeout ratio coefficient is (50 - 30) / 30 ≈ 0.67, and the additional increased concentration value is 0.67 × the preset increment value. If the link survival duration is less than 30 seconds, no additional weighting is triggered.

[0097] At the end of each collection cycle, apply a preset attenuation rate to the virtual pheromone concentration to generate the updated virtual pheromone concentration. The preset attenuation rate is set according to the network topology change frequency. For example, in a scenario where the average node movement speed is 2 meters per second, the attenuation rate is set to 5%; in a high-dynamic scenario where the average node movement speed is 5 meters per second, the attenuation rate is increased to 10%.

[0098] The attenuation calculation method is: Multiply the current virtual pheromone concentration by (1 - attenuation rate) to obtain the updated concentration value. For example, if the current concentration of node A is 3.0 and the attenuation rate is 5%, the updated concentration is 3.0 × 0.95 = 2.85.

[0099] In a specific implementation scenario, if node B successfully forwards the detection message 20 times within a preset period of 30 seconds and the preset increment value is 0.1, the basic concentration increment is 20 × 0.1 = 2.0; if the survival duration of a link it maintains is 50 seconds (exceeding the preset stability threshold of 30 seconds), the timeout ratio coefficient is (50 - 30) / 30 ≈ 0.67, and the additional concentration increment is 0.67 × 0.1 = 0.067, and the total increment is 2.0 + 0.067 = 2.067; if the attenuation rate is 5%, the updated concentration is 2.067 × 0.95 ≈ 1.96.

[0100] If node C successfully forwards the detection message 15 times within the same period and the link survival durations are all 25 seconds (not exceeding the preset stability threshold), the basic concentration increment is 15 × 0.1 = 1.5, there is no additional increment, and the updated concentration is 1.5 × 0.95 = 1.425.

[0101] For example, in a disaster rescue scenario, if a node group stably forwards detection messages and maintains long-lived links during a collaborative search and rescue mission, its virtual pheromone concentration is continuously higher than the threshold, and it is suitable as a high-priority detection node; if the node group moves frequently in a field monitoring scenario, resulting in link interruptions, and the virtual pheromone concentration is lower than the threshold due to the attenuation rate and low forwarding volume, then its detection priority is reduced.

[0102] It should be noted that the virtual pheromone concentration here is a quantization index generated by simulating the pheromone diffusion mechanism of the ant colony. It is calculated by the weighted cumulative value of the number of probe packets successfully forwarded by the node and the link survival duration, and characterizes the contribution degree and stability of the node in path maintenance. Its value increases dynamically with the forwarding behavior and decreases periodically according to the preset decay rate.

[0103] Screening high-priority probe nodes in the critical state of connectivity is specifically as follows:

[0104] Calculate the virtual pheromone concentration of all nodes in the current network to generate a virtual pheromone concentration set, which contains the virtual pheromone concentration of each node.

[0105] Calculate the population mean of the virtual pheromone concentration set. The population mean is the arithmetic mean of the virtual pheromone concentrations of all nodes, and the calculation method is: add all the values in the concentration set and then divide by the total number of nodes.

[0106] For example, if there are 10 nodes in the network and the total virtual pheromone concentration is 25.0, then the population mean is 25.0 / 10 = 2.5. If the virtual pheromone concentration of node D is 3.0 (higher than the mean 2.5) and that of node E is 2.0 (lower than the mean 2.5), then node D meets the concentration screening condition and node E does not.

[0107] At the same time, obtain the variance of the obstacle avoidance cooperation angle of each node.

[0108] Finally, screen the nodes that meet the following two conditions simultaneously as high-priority probe nodes:

[0109] The variance of the obstacle avoidance cooperation angle is lower than the preset cooperation threshold: indicating that the node movement direction is consistent with the group cooperation;

[0110] The virtual pheromone concentration is higher than the population mean: indicating that the node's contribution degree in historical path maintenance is higher than the average level.

[0111] The setting basis of the preset cooperation threshold is: in the network historical operation data, when the variance of the obstacle avoidance cooperation angle is lower than a specific threshold, the path breakage probability of the node group is significantly reduced. By statistically analyzing the variance distribution law of network splitting events and dynamically adjusting this threshold in combination with node density and mobility characteristics, ensure that the screening conditions adapt to the group cooperation requirements of different scenarios.

[0112] For example, in the network historical operation data, when the variance of the obstacle avoidance cooperation angle is lower than 20 degrees, the path breakage probability of the node group decreases by 60% compared with the scenario where the variance is higher than 20 degrees. Therefore, by statistically analyzing the variance distribution data of at least 100 network splitting events, the threshold is set to 20 degrees and dynamically adjusted according to the number of nodes per square kilometer in different node density scenarios.

[0113] For example, in a disaster relief scenario, if the variance of the obstacle avoidance cooperation angle of node H is 18 (lower than 20) and the virtual pheromone concentration is 3.2 (higher than the group average of 2.5), it is marked as a high-priority detection node; for node I, the variance is 22 (higher than 20), and although its concentration is 3.0 (higher than the average), it is still excluded. In a field monitoring scenario, for node J, the variance is 15 (lower than 20), but the concentration is 2.3 (lower than the average of 2.5), so it is not considered a high-priority node either.

[0114] Adjust the detection message sending frequency according to the moving speed of high-priority detection nodes, including:

[0115] Extract the moving speed of high-priority detection nodes. The moving speed is calculated based on the change in the position coordinates of the node in two adjacent collection cycles. Specifically: divide the difference between the position coordinates in the current cycle and the previous cycle by the time interval of the collection cycle. For example, if node A moves 50 meters in two cycles with an interval of 10 seconds, the moving speed is 50 meters / 10 seconds = 5 meters per second.

[0116] The preset speed threshold is set according to the network dynamics. For example, in a scenario where the average moving speed of nodes is 3 meters per second, the preset speed threshold is set to 4 meters per second. When the moving speed of a node exceeds 4 meters per second, it is determined to be in a high-speed moving state.

[0117] When the moving speed of a high-priority detection node exceeds the preset speed threshold, increase the detection message sending frequency to the first preset multiple of the reference frequency. The reference frequency is the message sending frequency set for the initial detection task, for example, 1 time per second; the first preset multiple is set according to the speed exceeding ratio. For example, when the speed exceeds the threshold by 20%, the multiple is 1.5 times, and when it exceeds 50%, the multiple is 2 times.

[0118] For example, the moving speed of node B is 6 meters per second (exceeding the 4-meter per second threshold), the reference frequency is 1 time per second, and the first preset multiple is 2 times. Then the adjusted frequency is 2 times per second.

[0119] At the same time, extract the moving direction dispersion degree of high-priority detection nodes. The moving direction dispersion degree is calculated based on the variance of the azimuth angles of the moving direction vectors of the node group. Specifically: count the azimuth angles of the moving directions of all nodes and calculate the square value of its standard deviation. The preset dispersion threshold is set according to the group cooperation requirements. For example, in a cooperative search and rescue scenario, it is set to 20 degrees. When the dispersion degree exceeds 20 degrees, it is determined that the moving directions of the node group are dispersed. For example, if the moving direction dispersion degree of the node group is 25 degrees (exceeding the 20-degree threshold), then trigger the frequency adjustment.

[0120] When the dispersion degree of the moving direction exceeds the preset dispersion threshold, the sending frequency of the detection message is increased to the second preset multiple of the reference frequency. The second preset multiple is set according to the exceeding ratio of the dispersion degree. For example, when the dispersion degree exceeds the threshold by 10 degrees, the multiple is 1.2 times; when it exceeds 20 degrees, the multiple is 1.5 times. For example, if the dispersion degree of the node group is 30 degrees (exceeding the 20-degree threshold), the reference frequency is 1 time per second, and the second preset multiple is 1.5 times, then the adjusted frequency is 1.5 times per second.

[0121] If the high-priority detection node simultaneously satisfies that the moving speed exceeds the preset speed threshold and the dispersion degree of the moving direction exceeds the preset dispersion threshold, the sending frequency of the detection message is adjusted by superimposing the first preset multiple and the second preset multiple. For example, for node C, the moving speed triggers the first multiple of 2 times, and the dispersion degree triggers the second multiple of 1.5 times. After superimposition, the total multiple is 3 times. When the reference frequency is 1 time per second, it is adjusted to 3 times per second.

[0122] When the moving speed does not exceed the preset speed threshold and the dispersion degree of the moving direction does not exceed the preset dispersion threshold, the sending frequency of the detection message is restored to the reference frequency. For example, the moving speed of node D is 3 m / s (not exceeding the 4 m / s threshold), and the dispersion degree is 15 degrees (not exceeding the 20-degree threshold), then the reference frequency of 1 time per second is maintained.

[0123] Calculate the stability weights of each path according to the detection results, and select the path with a stability weight higher than the preset weight threshold as the main transmission link, and the rest as the backup links, including:

[0124] First, count the link survival duration of the path. The link survival duration is the duration from the establishment moment of the path to the interruption or deletion moment, and is normalized according to the proportion of the survival duration in the total detection duration. The total detection duration is the cumulative duration of all detection tasks within the preset period. For example, within a 30-minute period, if the survival duration of a certain link is 15 minutes, then the survival duration ratio is 15 / 30 = 0.5. If the path is deleted due to node movement or signal interruption, the survival duration statistics are terminated.

[0125] Second, obtain the remaining energy of all nodes in the path. The remaining energy is periodically collected by the built-in power sensor of the node, and the arithmetic mean of the remaining energy of all nodes on the path is taken. For example, a certain path includes node A (remaining energy 80%), node B (remaining energy 70%), and node C (remaining energy 60%), then the arithmetic mean is (80 + 70 + 60) / 3 = 70%. If the node energy is lower than the critical value (such as 20%), a low-energy warning is triggered, but it still participates in the average value calculation.

[0126] Next, extract the signal strength volatility of the path. The signal strength volatility is calculated as follows: During the path survival period, continuously collect the received signal strength values, and calculate the ratio of the standard deviation to the mean of these strength values. For example, if the mean of the received signal strength of a certain path during the survival period is -75 dBm and the standard deviation is 5 dBm, then the volatility is 5 / 75 ≈ 0.067. If the path signal strength fluctuates violently (such as the standard deviation exceeds 30% of the mean), the volatility is marked as 0.3.

[0127] After dimensionless processing of the link survival duration ratio, the average remaining energy of the node, and the signal strength volatility, perform a weighted product to generate the stability weight of the path. The calculation method of the weighted product is: W = L α ×E β ×(1 - S) γ ; where, W represents the stability weight of the path; L represents the link survival duration ratio, with a value range of 0 to 1; E represents the average remaining energy of the node, expressed as a percentage (e.g., 70% is converted to 0.7); S represents the signal strength volatility, with a value range of 0 to 1; α, β, and γ are the weight exponents of the link survival duration ratio, the average remaining energy of the node, and the signal strength volatility, respectively.

[0128] Among them, the setting of the weight exponents (α, β, and γ) is based on the network operation requirements and historical path stability data. By analyzing the influence degree of different parameters on the path break probability, the primary and secondary relationships are assigned: The link survival duration ratio reflects the time stability of the path and is given a higher weight; the average remaining energy of the node represents the energy sustainability and is given a medium weight; the signal strength volatility measures the signal reliability and is given a lower weight. The specific index values are determined by statistically analyzing the correlation ratio between each parameter and the path survival rate through simulation experiments, and are periodically optimized and adjusted according to the network dynamic characteristics.

[0129] Finally, select the path with a stability weight higher than the preset weight threshold as the main transmission link. The preset weight threshold is set according to the historical path success rate. For example, when the weight threshold is set to 0.6, the path with a weight of 0.80 is used as the main link, and the path with a weight of 0.46 is downgraded to the backup link.

[0130] For example, in a field monitoring scenario, if the weight of a certain path is 0.55 (slightly lower than the threshold of 0.6) due to frequent node movement, it is still used as the backup link and is only enabled when the main link is interrupted.

[0131] When the signal strength of the main transmission link fluctuates abnormally, switch to the path with the highest stability weight among the backup links, including:

[0132] Continuously monitor the signal strength volatility of the main transmission link. If the signal strength volatility of the main transmission link exceeds the preset fluctuation threshold for 3 consecutive sampling periods, trigger the switching mechanism.

[0133] The preset fluctuation threshold is set according to historical communication quality data. For example, when the volatility exceeds 0.1, it is determined as abnormal.

[0134] Obtain the stability weights of all paths in the backup link list. For example, if the backup link list contains path X (stability weight 0.85), path Y (0.72), and path Z (0.68), then filter path X as the target switching path. If multiple paths have the same weight, the path with a higher average remaining energy of nodes is preferentially selected.

[0135] Verify whether the current signal strength volatility of the target switching path is lower than that of the main transmission link. The verification process includes: at the moment of handover decision, real-time collect the received signal strength value of the target switching path and calculate its current signal strength volatility. For example, if the signal strength volatility of the main transmission link is 0.12 (exceeding the threshold 0.1), and the current signal strength volatility of the target switching path X is 0.08 (lower than 0.1 and lower than the main link 0.12), then the handover condition is met. If the current signal strength volatility of the target switching path is abnormal (such as reaching 0.15), then skip this path and select the path Y with the second highest weight to re-verify.

[0136] When performing the handover, migrate the data stream from the main transmission link to the target switching path and interrupt the transmission task of the original main link. For example, in a disaster relief scenario, when the volatility of the main transmission link rises to 0.13 due to the rapid movement of nodes, after switching to the backup link X, the data stream continues to be transmitted through path X.

[0137] After the handover is completed, downgrade the original main transmission link to a backup link and update its stability weight. The update method is: recalculate the weight according to the remaining survival time of the link after downgrading, the remaining energy of the nodes, and the latest signal strength volatility.

[0138] In a field monitoring scenario, if the volatility of the main transmission link rises to 0.11 (exceeding the threshold 0.1) due to environmental interference, and the stability weight of the backup link Y is 0.75 and the current volatility is 0.09, then switch to link Y. If the volatility of link Y rises to 0.12 in the next cycle after the handover, then trigger the handover mechanism again and select the path Z with the highest weight among the remaining backup links for verification.

[0139] The basis for setting the preset fluctuation threshold is: in the network historical operation data, when the signal strength volatility exceeds 0.1, the packet loss rate increases by 50% compared to the scenario where the volatility is lower than 0.1. Therefore, by statistically analyzing at least 100 communication interruption events, the threshold is set to 0.1. The update frequency of the stability weight is synchronized with the detection period to ensure that the backup link list reflects the path status in real time.

[0140] It should be noted that in specific implementations, if the main link volatility of the node group becomes abnormally high due to sudden obstacles during the collaborative search and rescue mission, the system quickly switches to the backup link to ensure the continuous transmission of rescue instructions. If the volatility of multiple backup links exceeds the standard due to the scattered movement of the node group in the wild, the path reconstruction mechanism is activated instead of forced switching.

[0141] The above formulas are all dimensionless and take their numerical values for calculation. The formulas are obtained by collecting a large amount of data for software simulation to obtain a formula closest to the actual situation. The preset parameters and threshold selection in the formulas are set by those skilled in the art according to the actual situation.

[0142] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wired (such as infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that the computer can access or a data storage device such as a server or data center that contains one or more collections of available media. The available medium can be a magnetic medium (such as a floppy disk, hard disk, magnetic tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0143] Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the above-described systems, devices, and modules can refer to the corresponding processes in the foregoing method embodiments and will not be repeated here.

[0144] In several embodiments provided in the present application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the modules is only a logical function division. In actual implementation, there can be other division methods. For example, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of the devices or modules can be in electrical, mechanical, or other forms.

[0145] The modules described as separate components may or may not be physically separated. The components shown as modules may or may not be physical modules. They can be located in one place or distributed to multiple network modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0146] In addition, in each embodiment of the present application, the functional modules can be integrated in a processing module, or each module can exist physically alone, or two or more modules can be integrated in one module.

[0147] If the above functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of the present application. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0148] As described above, it is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed in the present application can easily think of changes or substitutions, which should all be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0149] Finally, the above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. A self-organizing network communication system in a no-signal area, characterized in that: include: Node parameter collection module: periodically collects node parameters of network nodes, including location coordinates and communication radius; Connectivity criticality judgment module: calculates the average node distance and movement direction dispersion according to the node parameters to determine whether the network is in a critical connectivity state; Detection node screening module: screening high-priority detection nodes under critical connectivity status; Dynamic detection control module: adjusts the detection message sending frequency according to the moving speed of the high-priority detection node; Multi-path generation module: calculates the stability weight of each path based on the detection results, selects the path with a stability weight higher than the preset weight threshold as the main transmission link, and the rest as backup links; Data diversion execution module: When the signal strength of the main transmission link fluctuates abnormally, it switches to the path with the highest stability weight in the backup link.

2. The self-organizing network communication system in a no-signal area according to claim 1, characterized in that: The specific methods for filtering high priority detection nodes are: The nodes whose obstacle avoidance coordination angle variance is lower than the preset coordination threshold and whose virtual pheromone concentration is higher than the group mean are regarded as high-priority detection nodes; Among them, the group mean is the arithmetic mean of the virtual pheromone concentrations of all nodes.

3. The self-organizing network communication system in a no-signal area according to claim 1, characterized in that: Periodically collect node parameters of network nodes, including: The active collection cycle is triggered according to the preset time interval. During the active collection cycle, the location coordinates are obtained through beacon interaction between nodes, and the communication radius is inferred from the signal strength. When the instantaneous change rate of the signal strength received by the node exceeds the preset change threshold, an event-driven acquisition cycle is triggered. During the event-driven acquisition cycle, the position coordinates are updated through multi-hop collaborative positioning, and the communication radius is dynamically calibrated according to the maximum effective communication distance of adjacent nodes.

4. The self-organizing network communication system in a no-signal area according to claim 1, characterized in that: The average node spacing and movement direction dispersion are calculated based on the node parameters to determine whether the network is in a critical state of connectivity, including: Traverse the location coordinates of all nodes, calculate the Euclidean distance between each pair of nodes, remove the node pairs whose Euclidean distance is greater than the communication radius, and take the arithmetic mean of the distances of the remaining node pairs as the average node spacing; The position coordinate change of each node in two adjacent acquisition cycles is extracted to generate a moving direction vector, and the moving direction dispersion of the node group is calculated based on the azimuth of the moving direction vector; If the average node distance exceeds a preset multiple of the communication radius and the dispersion of the moving direction is higher than a preset discrete threshold, the network is determined to be in a critical connectivity state.

5. The self-organizing network communication system in a no-signal area according to claim 2, characterized in that: The method for obtaining the obstacle avoidance coordination angle variance is: Based on the generated moving direction vector, all adjacent nodes within the corresponding communication radius are selected for each node; The geometric angle between the moving direction vectors of the calculated node and each adjacent node is converted into an angle value by using the vector dot product formula; The geometric angle values ​​of all adjacent node pairs are counted, and the variance of the geometric angle values ​​is calculated as the obstacle avoidance coordination angle variance.

6. The self-organizing network communication system in a no-signal area according to claim 2, characterized in that: The method for obtaining the virtual pheromone concentration is: The number of detection messages successfully forwarded by the counting node within the preset period is counted, and the virtual pheromone concentration is increased by a preset increment value each time it is successfully forwarded; The virtual pheromone concentration is weighted according to the link survival time maintained by the node. The link whose survival time exceeds the preset stability threshold has its concentration value increased additionally according to the timeout ratio; At the end of each acquisition cycle, a preset decay rate is applied to the virtual pheromone concentration to generate an updated virtual pheromone concentration.

7. The self-organizing network communication system in a no-signal area according to claim 1, characterized in that: Adjust the detection message sending frequency according to the moving speed of the high-priority detection node, including: Extract the moving speed of the high-priority detection node, and when the moving speed exceeds a preset speed threshold, increase the detection message sending frequency to a first preset multiple of the reference frequency; Extract the moving direction discreteness of the high priority detection node, and when the moving direction discreteness exceeds a preset discrete threshold, increase the detection message sending frequency to a second preset multiple of the reference frequency; If the moving speed exceeds the preset speed threshold and the moving direction discreteness exceeds the preset discrete threshold, the first preset multiple and the second preset multiple are superimposed to adjust the detection message sending frequency; If the moving speed does not exceed the preset speed threshold and the moving direction discreteness does not exceed the preset discrete threshold, the detection message sending frequency is restored to the reference frequency.

8. The self-organizing network communication system in a no-signal area according to claim 1, characterized in that: The stability weight of each path is calculated based on the detection results, and the path with a stability weight higher than the preset weight threshold is selected as the main transmission link, and the rest are used as backup links, including: The link survival time of the path is calculated. The link survival time is the duration from the establishment to the interruption or deletion of the path. It is normalized by the ratio of the survival time to the total detection time. Obtain the residual energy of all nodes in the path and calculate the average residual energy of the nodes; Extract the signal strength fluctuation rate of the path, where the signal strength fluctuation rate is the ratio of the standard deviation of the received signal strength to the mean during the path transmission process; The link survival time ratio, the average node remaining energy and the signal strength fluctuation rate are weighted to generate the path stability weight. The path with a stability weight higher than a preset weight threshold is selected as the main transmission link, and the remaining paths are used as backup links.

9. The self-organizing network communication system in a no-signal area according to claim 1, characterized in that: When the signal strength of the primary transmission link fluctuates abnormally, the backup link is switched to the path with the highest stability weight, including: Continuously monitor the signal strength fluctuation rate of the main transmission link, and determine it as abnormal when the signal strength fluctuation rate exceeds the preset fluctuation threshold; Obtain the stability weights of all paths in the backup link list, and select the path with the highest stability weight as the target switching path; Verify whether the current signal strength fluctuation rate of the target switching path is lower than that of the main transmission link. If so, perform the switching. After the switch is completed, the original primary transmission link is downgraded to a backup link and the stability weight is updated.

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