Dynamic networking rapid deployment method for portable repeater

Abstract

The invention discloses a dynamic networking rapid deployment method for a portable repeater, and relates to the technical field of communication, and the method comprises the steps: S1, collecting a first heterogeneous parameter at a target repeater, and monitoring an initial relay parameter; s2, abnormal nodes are screened out by monitoring routing convergence time, and abnormal heterogeneous parameters and abnormal relay parameters are marked; s3, calculating a first channel index by using the abnormal heterogeneous parameter, and further calculating a corrected relay parameter; and S4, running the corrected relay parameter, collecting a second heterogeneous parameter, calculating a second channel index, comparing the second channel index with the first channel index, and executing networking completion or returning to execute the step S3. According to the method, the channel index and the accurate mapping model are constructed by collecting the heterogeneous parameters of the heterogeneous service state, the self-convergence continuous optimization of the dynamic networking congestion process is realized, and the method has the advantages of improving the data delivery success rate during dynamic networking and improving the overall dynamic networking regulation and control efficiency.

Description

Technical Field

[0001] This invention relates to the field of communication technology, and more specifically to a method for rapid deployment of dynamic networking for portable repeaters. Background Technology

[0002] Portable repeater dynamic networking technology is a core means of achieving rapid, temporary communication coverage in a given area. This technology autonomously constructs a multi-hop, mobile, self-organizing network using multiple portable nodes, without relying on fixed infrastructure, and possesses the core advantages of rapid deployment, self-organization, and self-healing. As application scenarios become more complex, the service flows that the network needs to carry exhibit high heterogeneity, mainly including real-time audio streams, voice and critical commands, and network control signaling. In the actual rapid deployment process of dynamic networking using portable repeaters, the coexistence of multiple heterogeneous data streams can easily lead to severe protocol conflicts, resulting in a sharp increase in routing convergence time—a key network parameter—and a significant decrease in the network's self-healing capability.

[0003] To minimize cost, size, and power consumption, most portable repeaters are equipped with only one or more independent radio frequency systems. All different types of service and control flows must compete for the same shared channel. When using portable repeaters to transmit multiple heterogeneous data streams simultaneously, high-bandwidth audio streams and TCP streams attempting to maintain throughput continuously fill the node's wireless channel and data forwarding buffer. This preemption is indiscriminate, severely crowding out the transmission opportunities required for routing control messages. This causes topology-aware nodes to delay detecting link interruptions, resulting in delayed reporting of topology change information. Due to the severe delay in the transmission and processing of control signaling, the entire network's response to topology changes becomes extremely slow. Nodes perform routing calculations based on incomplete or outdated information, leading to persistent inconsistencies in their routing tables. Route convergence time deteriorates from the ideal hundreds of milliseconds to several seconds or even tens of seconds. During the lengthy convergence period, communication channels can become congested due to multiple heterogeneous data streams, leading to data transmission blockage. This congestion can cause control signaling data transmission to be interrupted, resulting in the interruption or loss of information commands issued by repeaters. Consequently, during the critical initial deployment phase of dynamic networking, a large amount of delivery data cannot be delivered normally. Regarding data congestion caused by excessively long route convergence times, commonly used portable repeaters lack effective self-healing capabilities to mitigate channel data transmission issues. This makes it difficult for portable repeaters to achieve good data transmission performance when rapidly deploying dynamic networks for heterogeneous data streams. Summary of the Invention

[0004] This invention provides a method for rapid deployment of dynamic networking for portable repeaters, which solves the problem that existing portable repeaters suffer from protocol behavior conflicts caused by multiple heterogeneous data streams during dynamic networking deployment, leading to congestion in dynamic network channels and consequently poor data transmission performance of network control signaling.

[0005] This invention is achieved through the following technical solution: A method for rapid deployment of dynamic networking for portable repeaters, the method comprising: Step S1: Preset a reference time range for the routing convergence time of the target repeater, preset the initial network deployment for the target repeater to process heterogeneous data streams, monitor and collect the first heterogeneous parameters composed of heterogeneous data streams in the initial network deployment of all network nodes of the target repeater, and monitor the initial relay parameters used by the target repeater to implement the initial network deployment. Step S2: Send the control signaling for the initial network deployment through the network nodes, monitor the route convergence time, and mark all network nodes whose route convergence time for sending control signaling exceeds the reference time range as abnormal nodes. Mark the first heterogeneous parameter and the initial relay parameter collected by each abnormal node as abnormal heterogeneous parameter and abnormal relay parameter, respectively. Step S3: For each abnormal node, use abnormal heterogeneous parameters to calculate the first channel index representing the network channel congestion, construct a variable mapping model between the initial relay parameters and the first channel index, substitute the first channel index into the variable mapping model, and label the calculation output as the corrected relay parameters representing the adjustment target of the abnormal relay parameters. Step S4: Update and correct the relay parameters and send them to the repeater working point of the corresponding abnormal node. Run the target repeater and collect the second heterogeneous parameter. The collection method and data type of the second heterogeneous parameter are the same as those of the first heterogeneous parameter. Calculate the second channel index using the second heterogeneous parameter. Set the channel threshold based on the first channel index. When the second channel index is less than the channel threshold, it indicates that the channel relief is effective. When the second channel index is greater than the channel threshold, return to step S3.

[0006] Furthermore, both the first heterogeneous parameter and the second heterogeneous parameter include the UDP stream bandwidth ratio, TCP stream buffer ratio, and transmission throughput ratio obtained from each network node; The UDP stream bandwidth ratio represents the ratio of the average rate of the UDP data stream to the physical bandwidth of the network channel; the TCP stream buffer ratio represents the ratio of the buffer occupied by TCP protocol data to the total buffer of the network node; and the transmission throughput ratio represents the ratio of the number of data bits sent to the maximum bit capacity of the network channel.

[0007] Further, the UDP flow bandwidth occupancy ratio is obtained by identifying the UDP flow of the target repeater through the deep packet inspection method. The TCP flow buffer occupancy ratio is obtained by querying the Socket buffer status of the dynamic networking kernel protocol stack. The transmission throughput occupancy ratio is obtained by reading the sending queue status of the networking nodes in the dynamic networking.

[0008] Further, the initial relay parameter is set to the upper limit value of the MAC layer retransmission times of the target repeater, and the upper limit value of the MAC layer retransmission times represents the maximum number of attempts for the data frame to be repeatedly sent in the target repeater.

[0009] Further, the upper limit value of the MAC layer retransmission times is obtained by reading the object value of the management information base supporting the SNMP protocol; the reading process is completed by all networking nodes by sending requests to the SNMP agent.

[0010] Further, a sliding window is used to set the reference duration range, and the process includes: Initialize the sliding window to store the recent several routing convergence times, collect the actual duration values of each network convergence and add them to the tail of the sliding window. When the number of actual duration value data in the sliding window exceeds the carrying capacity, remove the actual duration value data at the head; calculate the duration mean and duration standard deviation of all actual duration values in the window, set the sensitivity coefficient, multiply the duration standard deviation by the sensitivity coefficient, and then set the sum of the product and the duration mean as the upper boundary value of the reference duration range; when the routing convergence time exceeds the upper boundary value, screen out and label the networking node that sends the control signaling at this time as an abnormal node.

[0011] Further, sensitivity coefficients are independently assigned to different networking nodes in the dynamic networking, where the networking nodes located on the core path of the network topology are set to the minimum value, and the edge networking nodes are set to the maximum value.

[0012] Further, the UDP flow bandwidth occupancy ratio, the TCP flow buffer occupancy ratio, and the transmission throughput occupancy ratio are respectively represented as U, Q, and T. Set the first weight α, the second weight β, and the third weight λ; set the percentile function rank_pct, set the channel penalty term ω, and let the first channel exponent be represented as I1. Then the calculation formula of the first channel exponent I1 is expressed as: , where a penalty threshold c is set. When U∙Q < c, the channel penalty term ω = 0; rank_pct (U) in the formula represents the percentile of the risk ranking of the UDP flow bandwidth occupancy ratio among all networking nodes calculated by the percentile function.

[0013] Further, the calculation formula of the channel penalty term ω is set as: , Where, ω max This represents the maximum implementable value of the channel penalty term ω when U∙Q=1.

[0014] Furthermore, within each network node, the product of the UDP flow bandwidth ratio and the TCP flow buffer ratio is obtained, and the penalty threshold c is set to the average of the product values ​​of all network nodes.

[0015] Furthermore, the variable mapping model is constructed based on the Sigmoid function, and its construction includes: The maximum number of MAC retransmissions is set to R, which is the preset maximum number of MAC retransmissions in the target repeater. max Let the slope of the Sigmoid function be denoted as k, and let the center threshold of the Sigmoid function be preset and denoted as I. m Set the mapping penalty term φ. The computational formula for the variable mapping model is then expressed as: , In the formula, R(I1) represents the upper limit of the number of MAC retransmissions corresponding to the first channel index in the variable mapping model.

[0016] Furthermore, in the calculation formula of the variable mapping model, the center weight value is multiplied by the center threshold to dynamically set the center threshold; the initial value of the center weight value is set to 1, and when two or more abnormal nodes appear consecutively in the initial network deployment, the center weight value is linearly increased; when two or more non-abnormal network nodes appear consecutively, the center weight value is linearly decreased.

[0017] Furthermore, critical link nodes and nodes with dense heterogeneous data flows are screened out from all network nodes; when critical link nodes and nodes with dense heterogeneous data flows are marked as abnormal nodes, the center weight value is adjusted first.

[0018] Compared with the prior art, the present invention has the following advantages and beneficial effects: 1. By collecting heterogeneous parameters of heterogeneous service status, the system can proactively sense the congestion impact of heterogeneous services on network channels, quantify the degree of service competition, and use routing convergence timeout as a hard criterion to judge network congestion risks in real time. This enables the system to identify and control the trend of obstructed signaling transmission in advance, giving the repeater autonomous flow control awareness. 2. By constructing a channel index and a precise mapping model, targeted parameter tuning is achieved instead of blind tuning across the entire network. The corrected relay parameters obtained from the mapping are specifically applied to abnormal nodes, and the corresponding nodes and parameters of blocked links are automatically marked. This transforms the network performance degradation from being ambiguous across the entire network to being locally controllable, thereby improving the overall efficiency and accuracy of dynamic network control. 3. Set up automatic judgment of relief effect, build a closed-loop verification mechanism to improve the network self-healing speed, restore the priority of control signaling transmission, form a self-adjusting and self-converging continuous optimization process, significantly improve the data delivery success rate and network connectivity reliability in the critical stage of initial deployment, and ensure the communication continuity and recovery capability of portable repeater real-time tasks. Attached Figure Description

[0019] The accompanying drawings, which are included to provide a further understanding of embodiments of the invention and form part of this application, do not constitute a limitation thereof. In the drawings: Figure 1 This is a structural block diagram of the present invention. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the embodiments and accompanying drawings. The illustrative embodiments and descriptions of the present invention are only used to explain the present invention and are not intended to limit the present invention.

[0021] Example 1, such as Figure 1 As shown in the figure, this embodiment is a method for rapid deployment of dynamic networking for portable repeaters, the method including: Step S1: Preset a reference time range for the routing convergence time of the target repeater, preset the initial network deployment for the target repeater to process heterogeneous data streams, monitor and collect the first heterogeneous parameters composed of heterogeneous data streams in the initial network deployment of all network nodes of the target repeater, and monitor the initial relay parameters used by the target repeater to implement the initial network deployment. Step S2: Send the control signaling for the initial network deployment through the network nodes, monitor the route convergence time, and mark all network nodes whose route convergence time for sending control signaling exceeds the reference time range as abnormal nodes. Mark the first heterogeneous parameter and the initial relay parameter collected by each abnormal node as abnormal heterogeneous parameter and abnormal relay parameter, respectively. Step S3: For each abnormal node, use abnormal heterogeneous parameters to calculate the first channel index representing the network channel congestion, construct a variable mapping model between the initial relay parameters and the first channel index, substitute the first channel index into the variable mapping model, and label the calculation output as the corrected relay parameters representing the adjustment target of the abnormal relay parameters. Step S4: Update and correct the relay parameters and send them to the repeater working point of the corresponding abnormal node. Run the target repeater and collect the second heterogeneous parameter. The collection method and data type of the second heterogeneous parameter are the same as those of the first heterogeneous parameter. Calculate the second channel index using the second heterogeneous parameter. Set the channel threshold based on the first channel index. When the second channel index is less than the channel threshold, it indicates that the channel relief is effective. When the second channel index is greater than the channel threshold, return to step S3.

[0022] The rapid deployment process of dynamic networking using portable repeaters can be summarized as follows: power-on self-test startup, scanning neighbor nodes and establishing links, generating a multi-hop topology network, dynamic convergence of routing protocols, and data delivery for service communication. The reference time range represents an expected normal range for routing convergence time based on the target repeater's processing capacity, protocol mechanism, and typical topology scale. As long as the actual convergence time falls within the reference time range, control signaling transmission and topology updates are considered to be in a healthy state. The initial network deployment refers to the initial network configuration set for all network nodes before the target repeater begins dynamic networking, ensuring it can simultaneously carry multiple different types of data services. This configuration enables it to perform basic forwarding, contention, and scheduling of heterogeneous data streams from the initial stage. In specific implementation, the initial network deployment may include default configurations related to the radio frequency and physical layers, initial settings for MAC layer resource contention and queuing strategies, initial settings for routing protocol parameters, and initial thresholds for traffic management. Specifically, this may include initial settings for parameters such as reference channels, bandwidth, and transmit power; default priorities and contention window sizes for each service flow; configuration for topology discovery and update process startup; and default bandwidth constraints for high-bandwidth audio and TCP streams. All network nodes refer to the set of all node entities that form network connections through wireless links and participate in dynamic network topology maintenance during the initial network deployment. The first heterogeneous parameter represents various heterogeneous service flow data during the initial network deployment process. In practical applications, it can be a single data point or a set of data points; specific parameters can be distinguished according to the service type and source, such as voice stream parameters, real-time audio stream parameters, TCP data stream parameters, and control signaling status parameters. The initial relay parameters refer to the basic operating parameters set by the portable repeater when it first starts dynamic network deployment in order to participate in the network. In practical applications, these parameters include MAC layer window parameters, physical layer channel parameters, network layer control parameters, etc., and can be further set as data frame upload count, CTS trigger threshold, node transmit power, maximum data frame length, relay buffer space capacity, etc. In specific applications, similar to the first heterogeneous parameter, it can be a single data point or a set of data points.

[0023] By monitoring route convergence time to screen for abnormal nodes, the core objective is to identify which network nodes caused channel congestion and slowed route convergence in the early stages of network deployment by observing the transmission behavior of control signaling. During the initial network deployment phase, the target repeater instructs all participating network nodes to send control signaling in the routing protocol. The time taken for the entire network to complete topology convergence—that is, the time from route initialization to the consistency of routing tables across all network nodes—is monitored. If this time is prolonged, it indicates that control signaling is blocked or delayed. If the control signaling sent by a network node causes the convergence time to exceed the reference range, it indicates a risk of congestion on the links or forwarding behavior associated with that node. The parameters corresponding to these network nodes are considered to indicate the location of congestion and will be used for subsequent calculations and relay parameter corrections. The abnormal heterogeneous parameters and abnormal relay parameters are, in essence, special first heterogeneous parameters and initial relay parameters. The first channel index is a single value calculated using abnormal heterogeneous parameters. It quantifies the congestion level of the network channel where the abnormal node resides. It compresses multi-dimensional heterogeneous parameters into a scalar index, facilitating mapping to subsequent relay parameter adjustment models. Steps S2-S3 mean: all abnormal nodes are screened out using routing convergence time, and corresponding abnormal heterogeneous parameters are collected. For each abnormal node, the first channel index, a quantified value representing the channel congestion level, is calculated using the abnormal heterogeneous parameters. This quantified value is then used to deduce the relay parameters required to maintain channel balance, and these relay parameters are represented as corrected relay parameters. The data type of the corrected relay parameters is consistent with the initial relay parameters. They are dynamic adjustment amounts calculated for specific abnormal nodes and specific congestion states, and change according to the congestion state.

[0024] The corrected relay parameters are updated to the relay operating point corresponding to the abnormal node, causing the abnormal node currently performing various data calculations to change its deployment behavior and attempt to alleviate congestion caused by heterogeneous data streams. After updating the parameters, the target relay continues to run for a period of time and re-collects heterogeneous parameters using indicators consistent with the initial collection of the first heterogeneous parameters. This heterogeneous parameter collected after running based on the corrected relay parameters is labeled as the second heterogeneous parameter; that is, the collection and calculation process of the first heterogeneous parameter is the same as that of the second heterogeneous parameter. Similarly, at this time, the first channel index calculation method is used again, and a new channel index is calculated using the second heterogeneous parameter, and labeled as the second channel index. That is, the properties of the second channel index are the same as those of the first channel index, the difference being the numerical value. The numerical value of the channel threshold is smaller than that of the first channel index, and the channel threshold is used as a verification standard for whether the second channel index meets the current network channel congestion relief criteria. Setting the channel threshold based on the first channel index is used to mitigate the impact of a high first channel index, indicating that even if the channel is not completely blocked, there is still a high level of congestion. The channel threshold is directly compared with the second channel index. Since the second channel index indicates that the higher the value, the more congested the channel. When the second channel index is less than the channel threshold, it means that the adjusted repeater parameters have successfully reduced network congestion. The abnormal node does not require further adjustment, the system can continue to operate normally, routing convergence performance improves, and control signaling transmission is restored. When the second channel index is greater than the channel threshold, it means that the adjustment of the repeater parameters has not achieved the expected effect, the channel congestion has worsened, or the adjustment is insufficient to alleviate congestion. In this case, this embodiment chooses to recalculate and correct the repeater parameters. In specific applications, different degrees of adjustment or adjustment strategies can be applied to the calculation process to try to alleviate congestion again and achieve closed-loop feedback. In this embodiment, the system does not rely on a single parameter adjustment, but achieves adaptive optimization of abnormal nodes through a cycle of "measure → judge → modify → measure". In actual engineering, thresholds, minimum step sizes, or multi-window averaging can be combined to avoid over-adjustment or oscillation.

[0025] In practice, the method for setting the channel threshold is not limited to a single approach. For example, based on the routing convergence time when a network node channel becomes congested in existing repeater technology, and combined with the routing convergence time of the current target network node, the congestion level of the current network channel can be assessed. The theoretical maximum value of the first channel index can be estimated using an empirical rule as an equal percentage. The channel threshold can then be set based on this theoretical maximum value as a percentage (e.g., 50%-60%). Alternatively, the channel threshold can be set directly by selecting the value of the first channel index as a percentage.

[0026] In practical implementation, the first channel index can be calculated using a normalization method or an empirical threshold quantification method for anomalous heterogeneous parameters. For example, each heterogeneous parameter can be assigned a high, medium, or low level, and the level scores of each parameter can be accumulated according to different contributions to form the final first channel index. As a specific application, the process can be set as follows: determine the set of indicators and sampling window for heterogeneous parameters; use linear normalization to map each indicator to a uniform scale and then to different discrete levels of indicator scores; assign contribution weights to each indicator score; calculate the weighted score and add a penalty term to obtain the first channel index. The linear normalization method can use a truncation function normalization method, and its calculation formula can be set as follows: Where x represents the original parameter value of each heterogeneous parameter, x min and x max These represent the minimum and maximum values ​​of the sample, respectively. The clip function represents a fraction. The calculation result in the fraction is used to determine the value of x. If the result of the fraction calculation is less than 0, x is set to 0; if it is greater than 1, x is set to 1; otherwise, x is kept at its original value. This is used to prevent extreme values ​​or sampling noise from causing the normalized result to exceed the (0,1) interval and to prevent outliers from going out of bounds.

[0027] The variable mapping model represents mapping the current initial relay parameters of the repeater to the network channel congestion situation represented by the first channel parameter, establishing a computational function relationship. In specific implementations, the variable mapping model can be constructed using empirical simulation formulas, rule mapping tables, regression calculation models, or nonlinear functions. As a specific application, a variable mapping model can be constructed using simulation formulas. The process can be set as follows: Let the first channel index be represented as I1, the initial relay parameter as R0, and the corrected relay parameter as R, then construct a piecewise simulation calculation formula: Where k1 and k2 represent the first and second fitting coefficients, respectively, I t R(I1) represents the fitted preset threshold, and R(I1) represents the corrected relay parameter corresponding to the first channel index I1. The fitted preset threshold represents a quantitative reference value for the point of high channel congestion. When the first channel index is less than the fitted preset threshold, the network is relatively smooth, and the system uses a linear growth law to adjust the retransmission upper limit; when the first channel index is greater than the fitted preset threshold, the network has obvious congestion, and the growth law changes to a logarithmic change, making the adjustment of the retransmission number tend to be moderate or saturated, preventing the system from over-retransmitting in the high congestion range.

[0028] Example 2: In this example, a sliding window is used to set a reference duration range. The process includes: A sliding window is initialized to store the recent routing convergence times. The actual duration of each network convergence is collected and added to the tail of the sliding window. When the number of actual duration data in the sliding window exceeds the capacity, the actual duration data at the head is removed. The mean and standard deviation of all actual duration values ​​in the window are calculated, a sensitivity coefficient is set, the standard deviation is multiplied by the sensitivity coefficient, and the sum of the product and the mean duration is set as the upper boundary value of the reference duration range. When the routing convergence time exceeds the upper boundary value, the network node sending control signaling at this time is identified, filtered out, and marked as an abnormal node. Sensitivity coefficients are independently assigned to different network nodes in the dynamic network, with the minimum value set for network nodes located on the core path of the network topology and the maximum value set for edge network nodes.

[0029] The initialization of the sliding window involves creating a fixed-capacity data structure to store the network routing convergence times of the most recent few times. Each time the network completes routing convergence, the actual time value is added to the tail of the sliding window. Because the sliding window has a fixed capacity, when new data is added, the oldest data is deleted, ensuring that the statistical results are based only on the most recent state. The mean within the sliding window represents the average level of the network convergence times of the most recent few times, representing the center value of the time required for normal network convergence, providing a benchmark reference value. The standard deviation within the sliding window represents the fluctuation range of the routing convergence time; the greater the fluctuation, the more likely there is short-term congestion or network instability, providing a sensitivity reference value. The sensitivity coefficient is used to adjust the system's tolerance to fluctuations. When the sensitivity coefficient is higher, the upper boundary value of the reference time range is higher, and nodes are less likely to be judged as abnormal; when the sensitivity coefficient is lower, the upper boundary value of the reference time range is lower, and nodes are more likely to be judged as abnormal. In practical implementation, for network nodes in the dynamic network other than the core path and edge points, sensitivity coefficients can be allocated using a segmented constant allocation method. For the network topology, the path distance index between each node and the core path is calculated, and segmented intervals are set. For each segmented interval, a fixed sensitivity coefficient is set for the network nodes in a decreasing trend from the core path to the edge point. As a specific implementation, the calculation form of the upper boundary value can be expressed as follows: Let the upper boundary value be ε, the mean and standard deviation of the routing convergence time be γ and ρ respectively, and let the sensitivity coefficient be v. Then the calculation form of the upper boundary value is ε = γ + ρ∙v, where ρ∙v represents the tolerance fluctuation amount adjusted according to the sensitivity coefficient. It should be noted that because the duration data is a scalar that only measures one-sided values, the boundary of the reference duration range usually does not need to be set with a lower boundary. In practical applications, the lower boundary value of the reference duration range can be set to 0 or a small value.

[0030] Example 3: In this example, both the first heterogeneous parameter and the second heterogeneous parameter include the UDP stream bandwidth ratio, TCP stream buffer ratio, and transmission throughput ratio obtained from each network node. The UDP stream bandwidth ratio represents the ratio of the average rate of the UDP data stream to the physical bandwidth of the network channel; the TCP stream buffer ratio represents the ratio of the buffer occupied by TCP protocol data to the total buffer of the network node; and the transmission throughput ratio represents the ratio of the number of data bits sent to the maximum bit capacity of the network channel. The UDP stream bandwidth ratio is obtained by identifying the UDP stream of the target repeater using a deep packet inspection method; the TCP stream buffer ratio is obtained by querying the socket buffer status of the dynamic networking kernel protocol stack; and the transmission throughput ratio is obtained by reading the transmission queue status of the network node in the dynamic networking.

[0031] The UDP stream bandwidth percentage is equal to the ratio of the average UDP stream rate to the physical bandwidth of the network channel. The UDP stream bandwidth percentage is chosen because UDP is typically used for real-time audio, video, or command streams. Its characteristics include high bandwidth, latency sensitivity, and lack of congestion control, accurately reflecting the proportion of channel occupied by the UDP stream. In calculations, it quantifies the resource proportion occupied by the UDP stream in the entire network channel, thereby measuring its potential competitive pressure on other data streams and control signaling. In practical applications, the DPI can be used to identify the average UDP stream rate data of the target repeater.

[0032] The TCP stream buffer ratio represents the ratio of the TCP data-occupied buffer to the total buffer of the abnormal node. The TCP protocol adjusts the data transmission rate through sliding windows and congestion control, and its data occupies the node's socket buffer before transmission. The socket buffer is a network communication endpoint provided by the system for data transmission between processes or devices, and is a memory area in the TCP protocol stack used for temporary data storage. The higher the TCP stream buffer ratio in the abnormal node, the more pressure is placed on the abnormal node's forwarding capacity, which may lead to delays or blockages in control signaling. In portable repeaters, multiple streams share a single channel, and TCP streams are prone to continuously occupying the buffer due to high throughput, affecting the forwarding of low-volume control messages. Incorporating the TCP stream buffer ratio into the index allows the first channel index to reflect a more realistic congestion situation. In specific implementations, the TCP stream buffer ratio can be obtained through embedded traffic monitoring or by querying the socket buffer status of the dynamic networking kernel protocol stack using the Netlink interface.

[0033] The transmission throughput ratio, calculated by comparing the number of bits actually transmitted by a node to the maximum capacity of the physical channel, provides an overall load metric to aid in the calculation of UDP stream bandwidth ratio and TCP stream buffer ratio, thus supplementing the reflection of the node's pressure level across the entire network channel. When the transmission throughput approaches the channel's upper limit, it indicates potential congestion issues such as queue backlog or control signaling obstruction. Quantifying this metric can directly trigger channel index calculations, determining whether relay parameters need optimization. By monitoring the total node load, high-load nodes can be identified promptly, preventing the failure of a single traffic metric and prioritizing control message transmission opportunities, thereby reducing routing convergence latency.

[0034] Furthermore, as a feasible implementation, the initial relay parameter is set to the upper limit of the MAC layer retransmission count of the target repeater, which represents the maximum number of attempts to repeatedly send data frames in the target repeater; the upper limit of the MAC layer retransmission count is obtained by reading the value of a management information base object that supports the SNMP protocol; the reading process is completed by all network nodes by initiating a request to the SNMP agent.

[0035] The MAC layer retransmission mechanism is used to resend data frames when packet loss or collisions occur in the wireless link. Selecting an upper limit for the number of MAC layer retransmissions determines the maximum number of attempts a node can make in the event of a short-term transmission failure, thus ensuring the success rate of critical control signaling and data frame transmission. An initial upper limit for the number of retransmissions ensures that critical routing messages and control commands can still be successfully transmitted under interference from multiple heterogeneous streams, shortening route convergence time. A reasonable upper limit prevents nodes from saturating the channel due to excessive retransmissions, providing transmission opportunities for heterogeneous data streams such as UDP and TCP. When the number of MAC layer retransmissions for network nodes is too low, the packet loss rate of the network channel is high, affecting route convergence and the reliability of control signaling; when the number of MAC layer retransmissions for network nodes is too high, the time data frames occupy in the channel increases, potentially leading to congestion of heterogeneous service streams. When setting initial relay parameters, conventional values ​​based on empirical rules can be used as initial baseline values. The SNMP mentioned is a lightweight, universal, and programmable interface that can remotely obtain the MAC layer parameters of each node without modifying the underlying protocol stack. The process of reading the Management Information Base (MIB) via SNMP is as follows: the network node sends a request to the SNMP agent of the target repeater to read the object value of the MAC retransmission count limit in the MIB, and uses the result as the initial relay parameter for dynamic network deployment.

[0036] Furthermore, as a feasible implementation, the UDP stream bandwidth ratio, TCP stream buffer ratio, and transmission throughput ratio are represented as U, Q, and T, respectively, with a first weight α, a second weight β, and a third weight λ; a percentile function rank_pct is set, a channel penalty term ω is set, and the first channel index is represented as I1. The calculation formula of the first channel index I1 is expressed as: , where a penalty threshold c is set. When U∙Q < c, the channel penalty term ω = 0; rank_pct(U) in the formula represents the percentile rank of the UDP flow bandwidth occupancy ratio among all network nodes calculated through the percentile function.

[0037] The U∙Q represents considering two main loads, namely the UDP flow bandwidth occupancy ratio and the TCP flow buffer occupancy ratio, in abnormal nodes simultaneously. When the U·Q value is high, it indicates that the node has both a high UDP flow bandwidth occupancy ratio and a high TCP flow buffer occupancy ratio, and the abnormal node may be severely congested. When the U·Q value is low, it indicates that the current abnormal node is under light load or occupied by a single flow, and the channel penalty term is not triggered when it is below the penalty threshold.

[0038] The first weight α is used to adjust the contribution of the UDP flow bandwidth occupancy ratio to the channel index, the second weight β is used to adjust the contribution of the TCP flow buffer occupancy ratio to the channel index, and the third weight λ is used to adjust the contribution of the transmission throughput occupancy ratio (i.e., the overall transmission load of the node) to the channel index. The channel penalty term ω represents an additional adjustment weight for the channel index, which is used to strengthen the risk index of high-load nodes and ensure that congested nodes are preferentially identified in the index. The relationship between the UDP flow bandwidth occupancy ratio and channel congestion varies depending on the physical layer performance and service scheduling strategy. It is difficult to unify the risk judgment in heterogeneous devices and multi-bandwidth scenarios using the absolute occupancy ratio. Therefore, in this embodiment, the percentile function rank_pct is used to represent the percentile rank to represent the relative congestion position. Since UDP is usually for real-time services with high priority, its preemption is rigid, short-cycle, and is the most direct source of squeezing for the route. Therefore, in specific implementation, the first weight α can be set as the largest weight ratio. The TCP flow buffer occupancy ratio Q is set in the form of a square term because the risk of the TCP flow buffer occupancy ratio accelerates the deterioration of the network channel congestion condition as the value increases, which belongs to a strongly non-linear parameter, and the channel occupation risk increases with acceleration. Setting it as a square term can increase the data sensitivity and the presentation degree of the risk difference value.

[0039] Furthermore, based on the calculation formula of the above first channel index I1 and each parameter in the formula, in this embodiment, as a feasible specific application, the channel threshold can be further defined. The setting process of the channel threshold includes: setting the channel threshold as Th and setting a reduction coefficient η greater than 1. The calculation formula of the channel threshold Th of the first channel index I1 is expressed as: .

[0040] The reduction coefficient η represents the adjustment compression intensity of the control channel threshold relative to the first channel index I1. The greater the adjustment of the reduction coefficient η, the smaller the channel threshold, and the more aggressive the discrimination of high-congestion channels, that is, it means that a greater improvement amplitude is required to be judged as successful in relieving congestion. In the fractional formula of the calculation formula, when the value of the first channel index I1 is smaller, it means that the channel is relatively unobstructed at this time, and the channel threshold should not limit the second channel index. Therefore, the channel threshold Th approaches the first channel index I1 in this calculation. When the value of the first channel index I1 is larger, the channel threshold Th will also increase accordingly. However, affected by the radical, the channel threshold Th tends to grow sub-linearly, and the growth amplitude slows down significantly. The larger the value of the first channel index I1, the current networked channel may be close to a completely congested state. At the same time, in order to avoid the situation of excessive screening, the channel threshold cannot be set too low. Therefore, the channel threshold is still positively correlated with the first channel index, but an obvious redundant space for the networked channel needs to be reserved, so that the difference between the channel threshold and the first channel index cannot be too low. Therefore, a radical operation is performed in the denominator.

[0041] In specific applications, the form of the percentile function rank_pct can be set by methods such as sorting, discrete distribution, and piecewise mapping. As a specific implementation, if the discrete percentile method is used, there are N abnormal nodes, and the UDP flow bandwidth occupancy ratios are expressed as U1, U2,..., UN according to the abnormal node ordinal numbers. All nodes are arranged in ascending order of the UDP flow bandwidth occupancy ratio. After sorting, the UDP flow bandwidth occupancy ratios of all abnormal nodes are recorded in order as: U(1) < U(2) < … < U(N); assuming that the abnormal node j is ranked at the f-th position after sorting, the value of the percentile function of the abnormal node j is expressed as: rank_pct(Uj) = (f - 1) / (N - 1).

[0042] Further, as a feasible implementation, the calculation formula of the channel penalty term ω is set as: , where ω max represents the maximum enforceable value of the channel penalty term ω when U∙Q = 1; obtain the product value of the UDP flow bandwidth occupancy ratio and the TCP flow buffer occupancy ratio in each networked node, and set the value of the penalty threshold c to the average value of the product values of all networked nodes.

[0043] The ω maxThis represents the maximum theoretically feasible value for the channel penalty term, which can be set based on rules of thumb, historical or real-time data statistics. The (U∙Qc) part in the formula represents the amount by which the load of the current abnormal node exceeds the penalty threshold, measuring the severity of exceeding the threshold. (1-c) is used to ensure that the penalty term does not exceed the set upper limit, maintaining the stability of the channel exponent; when U∙Q approaches 1, it allows the value of ω to approach ω0. max The penalty threshold c is set to the average product of U∙Q of all network nodes. This means that c is set by averaging the products of all network nodes, and the penalty threshold is adaptively adjusted according to network load. When the overall load is high, the penalty threshold c increases, causing the mechanism to penalize only relatively higher-performing abnormal nodes; when the overall load is low, the penalty threshold c decreases, allowing even minor network congestion to trigger a penalty. Using a product rather than a single value emphasizes the risk of double load superposition and avoids misjudgment due to excessive occupancy of a single flow. Using the average product of U∙Q as the threshold prevents slight fluctuations from triggering ω, improving the stability of channel index calculation.

[0044] Furthermore, as a feasible implementation method, the variable mapping model is constructed based on the Sigmoid function, and its construction includes: The maximum number of MAC retransmissions is set to R, which is the preset maximum number of MAC retransmissions in the target repeater. max Let the slope of the Sigmoid function be denoted as k, and let the center threshold of the Sigmoid function be preset and denoted as I. m Set the mapping penalty term φ. The computational formula for the variable mapping model is then expressed as: , In the formula, R(I1) represents the upper limit of the number of MAC retransmissions corresponding to the first channel index in the variable mapping model.

[0045] The center threshold represents the intermediate risk level of the abnormal node load and can be set according to empirical rules. When the first channel index is less than the center threshold, the Sigmoid output is close to 1, and the R value is close to the maximum value R. max At this point, the risk is low, and the number of retransmissions can be increased to ensure the reliability of data transmission in the network channel. When the first channel index is greater than the center threshold, the upper limit value R of the MAC retransmission count decreases significantly, indicating a higher risk. In this case, the number of retransmissions needs to be reduced to alleviate channel pressure. The Sigmoid slope represents the sensitivity of the upper limit value of the MAC retransmission count to changes in the first channel index. The mapping penalty term is used for compensation and adjustment to ensure that the mapped retransmission count R does not become excessively high due to a low first channel index. The maximum value R of the MAC retransmission count... maxThis represents the maximum allowed number of MAC retransmissions in dynamic networking. It defines the upper limit of the mapping, ensuring the reliability of low-load nodes and allowing abnormal nodes to retry data sufficiently even with low channel exponents, thus guaranteeing successful control signaling transmission. The Sigmoid function maps the first channel exponent to the upper limit of MAC retransmissions, achieving non-linear adaptive adjustment of node load. Low-load nodes maintain a high retransmission count to ensure data reliability, while high-load nodes reduce retransmissions to alleviate channel congestion. The Sigmoid's center threshold, slope, and mapping penalty term provide a controllable adjustment space, thereby supporting rapid deployment and closed-loop self-healing optimization in dynamic networking.

[0046] Furthermore, as a feasible implementation method, the calculation formula of the variable mapping model is set to multiply the center weight value by the center threshold to dynamically set the center threshold; the center weight value is initially set to 1, and when two or more abnormal nodes appear consecutively in the initial network deployment, the center weight value is linearly increased; when two or more non-abnormal network nodes appear consecutively, the center weight value is linearly decreased; critical link nodes and heterogeneous data flow dense nodes are screened out from all network nodes; when critical link nodes and heterogeneous data flow dense nodes are marked as abnormal nodes, the center weight value is adjusted first.

[0047] The center threshold of the Sigmoid mapping model is no longer fixed, but dynamically adjusted according to the network's operating status: sensitivity is automatically increased under high load and decreased under low load. Priority is given to critical links and nodes with dense heterogeneous flows to ensure smooth core communication links and reduce overall routing convergence delay and data congestion risks. Critical link nodes and nodes with dense heterogeneous data flows are screened in the network. When these nodes are marked as anomalous, they are prioritized for adjustment to ensure that congestion on core paths and traffic-intensive nodes is alleviated first. Dynamic adjustment through continuous anomalous and non-anomalous node information achieves adaptive adjustment of network congestion identification, avoiding misjudgments or slow response problems that may occur with fixed thresholds. The center weight value is multiplied by the center threshold, making the inflection point of the Sigmoid function dynamic. It is linearly increased or decreased through continuous anomalous or non-anomalous node information, while prioritizing critical links and nodes with dense heterogeneous data flows. This achieves closed-loop optimization of adaptive adjustment of MAC retransmission counts, improving the accuracy of anomalous node identification and network self-healing capabilities, ensuring rapid deployment and stable operation of dynamic networking in multi-heterogeneous flow environments.

[0048] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for rapid deployment of dynamic networking for portable repeaters, characterized in that, The method includes: Step S1: Preset a reference time range for the routing convergence time of the target repeater, preset the initial network deployment for the target repeater to process heterogeneous data streams, monitor and collect the first heterogeneous parameters composed of heterogeneous data streams in the initial network deployment of all network nodes of the target repeater, and monitor the initial relay parameters used by the target repeater to implement the initial network deployment. Step S2: Send the control signaling for the initial network deployment through the network nodes, monitor the route convergence time, and mark all network nodes whose route convergence time for sending control signaling exceeds the reference time range as abnormal nodes. Mark the first heterogeneous parameter and the initial relay parameter collected by each abnormal node as abnormal heterogeneous parameter and abnormal relay parameter, respectively. Step S3: For each abnormal node, use abnormal heterogeneous parameters to calculate the first channel index representing the network channel congestion, construct a variable mapping model between the initial relay parameters and the first channel index, substitute the first channel index into the variable mapping model, and label the calculation output as the corrected relay parameters representing the adjustment target of the abnormal relay parameters. Step S4: Update and correct the relay parameters and send them to the relay working point of the corresponding abnormal node. Run the target relay and collect the second heterogeneous parameter. The collection method and data type of the second heterogeneous parameter are the same as those of the first heterogeneous parameter. Calculate the second channel index using the second heterogeneous parameter. Set the channel threshold based on the first channel index. When the second channel index is less than the channel threshold, it indicates that the channel relief is effective. When the second channel index is greater than the channel threshold, return to step S3.

2. The method for rapid deployment of dynamic networking for portable repeaters according to claim 1, characterized in that, Both the first heterogeneous parameter and the second heterogeneous parameter include the UDP stream bandwidth ratio, TCP stream buffer ratio, and transmission throughput ratio obtained from each network node; The UDP stream bandwidth ratio represents the ratio of the average rate of the UDP data stream to the physical bandwidth of the network channel; the TCP stream buffer ratio represents the ratio of the buffer occupied by TCP protocol data to the total buffer of the network node; and the transmission throughput ratio represents the ratio of the number of data bits sent to the maximum bit capacity of the network channel.

3. The method for rapid deployment of dynamic networking for portable repeaters according to claim 2, characterized in that, The UDP stream bandwidth ratio is obtained by identifying the UDP stream of the target repeater using a deep packet inspection method. The TCP stream buffer ratio is obtained by querying the socket buffer status of the dynamic networking kernel protocol stack. The transmission throughput ratio is obtained by reading the transmission queue status of the network nodes in the dynamic networking.

4. The method for rapid deployment of dynamic networking for portable repeaters according to claim 2, characterized in that, The initial relay parameter is set to the upper limit of the MAC layer retransmission count of the target repeater, which represents the maximum number of attempts to repeatedly send data frames in the target repeater.

5. The method for rapid deployment of dynamic networking for portable repeaters according to claim 4, characterized in that, The upper limit of the number of MAC layer retransmissions is obtained by reading the value of a Management Information Base object that supports the SNMP protocol; the reading process is completed by all network nodes sending a request to the SNMP agent.

6. The method for rapid deployment of dynamic networking for portable repeaters according to claim 1, characterized in that, The process of setting a reference duration range using a sliding window includes: Initialize a sliding window to store the recent routing convergence times. Collect the actual duration values of each network convergence and add them to the tail of the sliding window. When the number of actual duration value data in the sliding window exceeds the carrying capacity, remove the actual duration value data at the head; calculate the duration mean and duration standard deviation of all actual duration values in the window, set the sensitivity coefficient, multiply the duration standard deviation by the sensitivity coefficient, and then set the sum of the product and the duration mean as the upper boundary value of the reference duration range; when the routing convergence time exceeds the upper boundary value, identify and label the networking node that sends the control signaling at this time as an abnormal node.

7. A method for rapid deployment of dynamic networking for portable repeaters according to claim 6, characterized in that, Independently assign sensitivity coefficients to different networking nodes in the dynamic networking, where the networking nodes located on the core path of the network topology are set to the minimum value, and the edge networking nodes are set to the maximum value.

8. A method for rapid deployment of dynamic networking for portable repeaters according to claim 4, characterized in that, Represent the UDP flow bandwidth occupancy ratio, TCP flow buffer occupancy ratio, and transmission throughput occupancy ratio as U, Q, and T respectively, and set the first weight α, the second weight β, and the third weight λ; set the percentile function rank_pct, set the channel penalty term ω, and let the first channel exponent be denoted as I1. The formula for calculating the first channel index I1 is: , Among them, set the penalty threshold c. When U∙Q < c, the channel penalty term ω = 0; rank_pct(U) in the formula represents the risk ranking percentile of the UDP flow bandwidth occupancy ratio calculated through the percentile function among all networking nodes.

9. A method for rapid deployment of dynamic networking for portable repeaters according to claim 8, characterized in that, The formula for calculating the channel penalty term ω is set as follows: , Where, ω max This represents the maximum implementable value of the channel penalty term ω when U∙Q=1.

10. A method for rapid deployment of dynamic networking for portable repeaters according to claim 9, characterized in that, Obtain the product value of the UDP flow bandwidth occupancy ratio and the TCP flow buffer occupancy ratio in each networking node, and set the value of the penalty threshold c as the mean value of the product values of all networking nodes.

11. A method for rapid deployment of dynamic networking for portable repeaters according to claim 8, characterized in that, Construct the variable mapping model based on the Sigmoid function, and its construction content includes: The maximum number of MAC retransmissions is set to R, which is the preset maximum number of MAC retransmissions in the target repeater. max Let the slope of the Sigmoid function be denoted as k, and let the center threshold of the Sigmoid function be preset and denoted as I. m Set the mapping penalty term φ. The computational formula for the variable mapping model is then expressed as: , Among them, R(I1) in the formula represents the upper limit value of the MAC retransmission times corresponding to the first channel exponent in the variable mapping model.

12. A method for rapid deployment of dynamic networking for portable repeaters according to claim 11, characterized in that, Set the product of the central weight value and the central threshold in the calculation formula of the variable mapping model to dynamically set the central threshold; let the initial value of the central weight value be 1. When two or more abnormal nodes continuously appear in the initial network deployment, linearly increase the central weight value; when two or more non-abnormal networking nodes continuously appear, linearly decrease the central weight value.

13. A method for rapid deployment of dynamic networking for portable repeaters according to claim 12, characterized in that, Screen out the key link nodes and heterogeneous data flow intensive nodes among all networking nodes; when the key link nodes and heterogeneous data flow intensive nodes are labeled as abnormal nodes, preferentially adjust the central weight value.

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