A multi-directional communication data forwarding method and system

Abstract

The present invention relates to the field of information technology, and its main purpose is to ensure the technical problem of communication continuity and data integrity in remote mountainous villages. The present application provides a multi-directional communication data forwarding method and system, including: obtaining terrain elevation data and mountain surface reflection characteristics, constructing a three-dimensional terrain simulation environment, calculating the initial visible range between each communication node, and obtaining a preliminary signal propagation interference distribution map; obtaining signal time difference data of each path in the preliminary forwarding path plan, and generating an optimized forwarding path set; real-time monitoring of the changes in the communication node distribution of the forwarding path set, continuously monitoring the changing trend of signal propagation interference, determining the signal loss rate data during the path execution process, and generating a stable communication transmission channel; based on the stable communication transmission channel, analyzing the changing trend of the signal time difference during the path switching process, and determining the switching time interval, signal strength threshold, and node load balancing coefficient of the path switching.

Description

Technical Field

[0001] This invention relates to the field of information technology, and in particular to a multi-directional communication data forwarding method and system. Background Art

[0002] Establishing a stable and reliable multidirectional communication network in complex mountainous terrain, overcoming the adverse effects of terrain obstacles on signal propagation, and ensuring the stability and continuity of data transmission have become crucial issues that urgently need to be addressed in the field of communication technology. Currently, although various communication forwarding strategies have been proposed and applied in mountainous environments, these methods often overlook the deep-seated interference of dynamic terrain changes on signal propagation, especially when facing complex situations such as mountain obstruction and signal reflection, lacking in-depth consideration of environmental adaptability. More significantly, these solutions often focus on fixed path planning or local optimization of a single signal source, failing to effectively address the sudden obstruction and frequent changes in signal propagation paths caused by undulating mountain terrain at the system level. This results in limited communication coverage, low data transmission efficiency, and difficulty in meeting the actual communication needs in complex mountainous environments. The primary challenge lies in the intermittent shortening of the visible range between communication nodes caused by the mountain peak obstruction effect. This terrain obstruction phenomenon makes it difficult to maintain long-term stability of the signal transmission path within the line-of-sight distance, increasing not only the risk of data packet loss but also making the network topology highly dynamic and uncertain. The highly dynamic and uncertain nature of network topology leads to differences in signal arrival times. This multipath propagation phenomenon not only causes signal interference and attenuation but also significantly reduces the accuracy of data synchronization, making it difficult for communication nodes to accurately determine and select the optimal data forwarding path in dynamically changing environments. Therefore, designing a dynamically adjusted data forwarding path selection strategy to address the discontinuity of visibility caused by mountain peaks and the signal time differences caused by mountain reflections in complex mountainous terrain has become a key issue in ensuring the continuity of communication and data integrity in remote mountain villages. Summary of the Invention

[0003] This invention provides a multi-directional communication data forwarding method, mainly comprising:

[0004] The process involves acquiring terrain elevation data and mountain surface reflection characteristics to construct a 3D terrain simulation environment. This allows for the calculation of the initial visible range between communication nodes, resulting in a preliminary signal propagation interference distribution map. Based on this map, regions with discontinuous visible ranges are identified, and reflected and scattered signals from these regions are collected. The location intervals of interfered communication nodes are determined based on signal time differences. The degree of terrain obstruction and signal attenuation within these intervals are then assessed. The main signal propagation interference source is identified based on the degree of terrain obstruction, and a dynamic interference weighting factor is determined based on the distance between nodes and terrain complexity. Finally, a candidate set of data forwarding paths is constructed based on the dynamic interference weighting factor, and feasibility and interference intensity are evaluated. The system sorts and extracts candidate paths that meet communication quality requirements. By calculating the path length change rate and node connectivity change rate of the candidate paths, it selects the path combination with the least signal propagation interference to determine the initial forwarding path scheme. It obtains the signal time difference data of each path in the initial forwarding path scheme to generate an optimized forwarding path set. It monitors the changes in the distribution of communication nodes in the forwarding path set in real time, continuously monitors the changing trend of signal propagation interference, determines the signal loss rate data during path execution, and generates a stable communication transmission channel. Based on the stable communication transmission channel, it analyzes the changing trend of signal time difference during path switching to determine the switching time interval, signal strength threshold, and node load balancing coefficient for path switching.

[0005] Furthermore, the acquisition of terrain elevation data and mountain surface reflection characteristics, the construction of a three-dimensional terrain simulation environment, the calculation of the initial visible range between each communication node, and the obtaining of a preliminary signal propagation interference distribution map include:

[0006] By scanning and acquiring point cloud data of mountain terrain, a digital elevation matrix is ​​generated, the elevation value of each grid cell is recorded, surface material information of the mountain is collected, and the electromagnetic wave reflection coefficient of each grid cell is determined. A three-dimensional terrain database containing elevation information and electromagnetic wave reflection coefficient is constructed. Based on the latitude and longitude coordinates and elevation information of the communication node distribution locations, the spatial position of each node is located in the three-dimensional terrain database, and the terrain elevation profile data on the node connection path is extracted to generate an initial set of visible nodes for each node. Based on the distance between nodes and the transmission power in the initial set of visible nodes, the free space propagation loss is calculated. The electromagnetic wave propagation path is traced by combining the electromagnetic wave reflection coefficient, the reflected wave intensity is calculated, and the direct signal intensity and reflected signal intensity are superimposed to generate the composite signal intensity value of each node. Based on the composite signal intensity value, a preliminary signal propagation interference distribution map covering the entire mountain area is generated through spatial interpolation.

[0007] Furthermore, the step of identifying discontinuous areas within the visible range based on the preliminary signal propagation interference distribution map, collecting reflected and scattered signals from these discontinuous areas, and determining the location range of the interfered communication node based on signal time differences includes:

[0008] Based on the preliminary signal propagation interference distribution map, the signal strength values ​​of adjacent grid cells are scanned, discontinuities where the signal strength difference exceeds a threshold are marked, and continuous discontinuities are connected to form discontinuity boundary lines, generating a set of geographic coordinates for the visible discontinuous area. Sampling points are selected from this set of geographic coordinates to receive signals from communication nodes, and the amplitude variation sequence of signals from communication nodes over time is recorded. The arrival times of direct, reflected, and scattered signals are detected, and the time difference between the reflected and direct signals, as well as the time difference between the scattered and direct signals, is calculated to generate a multipath delay difference table. Based on this multipath delay difference table, combined with electromagnetic wave propagation speed and terrain elevation data, the coordinates of the reflecting mountain surface are determined using positioning principles, and the signal transmission path is traced in reverse to determine the location range of the interfered communication node.

[0009] Furthermore, the step of obtaining the degree of terrain obstruction and signal strength attenuation within the location range of the interfered communication node, and identifying the main signal propagation interference source based on the degree of terrain obstruction, includes:

[0010] Topographic elevation sampling points within the location range of the interfered communication node are extracted. The difference between the node's elevation and the sampling point elevation is calculated, and the proportion of sampling points with positive elevation differences is used as the terrain occlusion coefficient. The actual received signal strength within the location range of the interfered communication node is measured and compared with the theoretical signal strength to generate a signal strength attenuation. Based on the terrain occlusion coefficient and the signal strength attenuation, the space around the node is divided into multiple sectors. The product of the occlusion coefficient and the signal strength attenuation in each sector is calculated. The sector with the largest product value is selected, and the mountain in the direction of that sector is identified as the main source of signal propagation interference, generating interference source azimuth distribution data.

[0011] Furthermore, the step of constructing a candidate set of data forwarding paths based on dynamic interference weighting factors, performing feasibility assessments and interference intensity rankings, and extracting alternative paths that meet communication quality requirements includes:

[0012] Based on the dynamic interference weight factor, adjacent node pairs in the communication network are traversed, and node pairs with weight factors below the threshold are marked as available links. All reachable paths from the starting node to the receiving node are searched, and the path node sequence and link weight factor sequence are recorded to generate a data forwarding path candidate set. Based on the data forwarding path candidate set, the link weight factor of each path is accumulated to generate a total path interference value. Paths with total interference values ​​below the threshold are retained as feasible paths. The paths with the smallest interference values ​​are selected as the candidate path set according to the total interference values.

[0013] Furthermore, after selecting the path with the smallest interference value as the candidate path set, the process includes:

[0014] For each path in the candidate path set, calculate the ratio of the number of link segments contained in the path to the minimum number of link segments to generate the path length change rate; count the number of neighboring nodes that each relay node in the candidate path set can reach after the path is removed, calculate the ratio of this number to the number before removal, and generate the node connectivity change rate; based on the sum of the path length change rate and the node connectivity change rate, select the path with the smallest sum value to generate the path combination with the least signal propagation interference.

[0015] Furthermore, the generation of the optimized forwarding path set includes:

[0016] For each path in the forwarding path scheme, a test signal is sent, and the arrival times of the signal at each relay node and the endpoint are recorded. The difference between the propagation time between adjacent nodes and the theoretical time is calculated to generate signal time difference data. Based on the signal time difference data, the time difference values ​​of all link segments on the path are accumulated to generate the total path delay deviation. A location with high altitude and no obstruction is searched around the midpoint of the link segment with the largest time difference value to generate a replacement relay node location. Based on the replacement relay node location, the path configuration is updated, the signal propagation loss is measured, the power of the transmitting node is adjusted, and an optimized forwarding path set is generated.

[0017] Furthermore, the real-time monitoring of the communication node distribution changes in the forwarding path set, continuous monitoring of the changing trends of signal propagation interference, determination of signal loss rate data during path execution, and generation of a stable communication transmission channel include:

[0018] The system monitors the online status and location coordinates of each node in the optimized forwarding path set in real time, records changes in node distribution, measures the deviation of signal strength and delay, and generates signal propagation interference change trend data. Based on the signal propagation interference change trend data, the system calculates the signal loss rate of the path and selects the candidate path with the highest signal strength as the alternative path. Based on the alternative path, the system updates the transmission configuration, sends a new next-hop node address, and generates a stable communication transmission channel.

[0019] Furthermore, based on a stable communication transmission channel, the analysis of the signal time difference variation trend during path switching, and the determination of the path switching time interval, signal strength threshold, and node load balancing coefficient, includes:

[0020] Extract the time of the path switching event and the signal propagation time in the stable communication transmission channel, calculate the time difference before and after the switching, and generate signal time difference change data; calculate the time difference standard deviation based on the signal time difference change data, determine the switching recovery time, and generate the path switching time interval; extract the signal strength value when the switching is triggered, and generate the signal strength threshold; count the number of data packets forwarded by each node, calculate the ratio of the node forwarding volume to the average forwarding volume, and generate the node load balancing coefficient.

[0021] This invention provides a multi-directional communication data forwarding system, mainly comprising: a terrain data acquisition module, used to acquire terrain elevation data and mountain surface reflection characteristics, construct a three-dimensional terrain simulation environment, calculate the initial visible range between each communication node, and obtain a preliminary signal propagation interference distribution map; an interference area identification module, used to identify areas with discontinuous visible ranges based on the preliminary signal propagation interference distribution map, acquire reflected and scattered signals from these discontinuous areas, and determine the location interval of the interfered communication nodes based on signal time differences; an interference source analysis module, used to acquire the terrain occlusion degree and signal strength attenuation of the location interval of the interfered communication nodes, identify the main signal propagation interference source based on the terrain occlusion degree, and determine a dynamic interference weight factor based on the distance between nodes and terrain complexity; and a path candidate set construction module, used to construct a data forwarding path based on the dynamic interference weight factor. The system generates a candidate path set, performs feasibility assessment and interference intensity ranking, extracts alternative paths that meet communication quality requirements, and selects the path combination with the least signal propagation interference by calculating the path length change rate and node connectivity change rate of the alternative paths, thus determining the preliminary forwarding path scheme. A path optimization module acquires signal time difference data for each path in the preliminary forwarding path scheme and generates an optimized forwarding path set. A communication monitoring module monitors the changes in the distribution of communication nodes in the forwarding path set in real time, continuously monitors the changing trend of signal propagation interference, determines the signal loss rate data during path execution, and generates a stable communication transmission channel. A channel stability analysis module analyzes the changing trend of signal time difference during path switching based on the stable communication transmission channel, and determines the switching time interval, signal strength threshold, and node load balancing coefficient for path switching. The technical solution provided by this embodiment of the invention can include the following beneficial effects:

[0022] This invention discloses a multi-directional communication data forwarding method and system. The method constructs a simulation environment by performing 3D modeling of mountainous terrain, calculating the initial visible range between communication nodes, and obtaining a radio communication signal propagation interference distribution map. Based on the interference distribution, it identifies discontinuous areas in the visible range, analyzes the mountain reflection effect, and determines the location of the interfered nodes. Based on the degree of terrain obstruction and signal attenuation, it identifies the main interference source and determines a dynamic interference weighting factor, constructs and evaluates a candidate set of data forwarding paths, and selects the path combination with the least interference. By monitoring signal time differences in real time, it dynamically adjusts the relay node position and transmission power to optimize the forwarding path. Simultaneously, a path switching mechanism is established to improve environmental adaptability and achieve stable communication transmission. This invention can effectively solve the communication interference problem caused by complex mountainous terrain, improving communication quality and reliability. Attached Figure Description

[0023] Figure 1 This is a flowchart of a multi-directional communication data forwarding method according to the present invention.

[0024] Figure 2 This is a schematic diagram of the structure of a multi-directional communication data forwarding system according to the present invention. Detailed Implementation

[0025] The technical solutions of the embodiments of the present invention will be clearly and thoroughly described below with reference to the accompanying drawings. The described embodiments are merely some embodiments of the present invention.

[0026] like Figures 1-2 As shown, this embodiment of a multi-directional communication data forwarding method and system may specifically include:

[0027] S101: Obtain terrain elevation data and mountain surface reflection characteristics, construct a three-dimensional terrain simulation environment, calculate the initial visible range between each communication node, and obtain a preliminary signal propagation interference distribution map.

[0028] Mountainous point cloud data is acquired through lidar scanning. This data is then rasterized to generate a digital elevation matrix (DEM), where each raster cell records its corresponding elevation value. Simultaneously, information on the mountain surface material is collected. The electromagnetic wave reflection coefficient of each raster cell is determined based on rock, soil, and vegetation cover types, constructing a three-dimensional terrain database containing elevation information and electromagnetic wave reflection coefficients. Based on the latitude and longitude coordinates and elevation information of the communication nodes, the spatial location of each node is located within the three-dimensional terrain database. For each pair of node connections, terrain elevation profile data is extracted along the path. Geometric optics principles are used to determine if there is mountain occlusion between nodes. If the terrain elevation of any point on the connection path exceeds the height of the node connection line, the node pair is determined to be invisible, resulting in an initial set of visible nodes for each pair. For each pair of nodes in the initial set of visible nodes, the free-space propagation loss L = 32.45 + 20log(f) + 20log(d) is calculated based on the distance d between nodes and the transmitted power P, where f is the communication frequency. Combining the electromagnetic wave reflection coefficient along the propagation path, the electromagnetic wave propagation path is traced along the node connection direction. When the electromagnetic wave encounters the terrain surface, the reflected wave intensity is calculated based on the incident angle and reflection coefficient. The direct signal intensity and the reflected signal intensity are superimposed according to the phase relationship to obtain the composite signal intensity value at the receiving point. Based on the composite signal intensity values ​​between each pair of nodes, spatial interpolation is performed on the grid cells of the 3D terrain database. For grid cells at non-node locations, the inverse distance weighting method is used to calculate the estimated signal intensity value of the grid cell based on the signal intensity values ​​of surrounding nodes. The signal intensity values ​​of all grid cells are color-mapped according to intensity levels to generate a preliminary signal propagation interference distribution map covering the entire mountainous area.

[0029] Specifically, in the process of digital modeling mountainous terrain, lidar scanning technology obtains high-precision three-dimensional point cloud data by emitting laser pulses and receiving ground reflection signals.

[0030] Specifically, lidar equipment is mounted on an aircraft and emits laser beams towards the ground at a fixed scanning frequency. When the laser beam encounters the mountain surface and reflects back, the precise location of the ground point is determined by calculating the round-trip time difference. This scanning method can penetrate vegetation canopy to obtain accurate surface elevation information, making it particularly suitable for measuring complex terrain in mountainous areas. Rasterization is a crucial step in converting irregularly distributed point cloud data into a regular grid structure.

[0031] In one possible implementation, the entire mountainous area is divided into 10m x 10m grid cells. Each grid cell may contain multiple laser points, and the average elevation of these points is calculated as the representative elevation of that grid cell. Simultaneously, the surface material type is determined based on the laser reflection intensity information. The reflection intensity of rock surfaces is typically between 0.7 and 0.9, soil surfaces between 0.3 and 0.5, while vegetation-covered areas range from 0.1 to 0.3. These reflection coefficients directly affect the propagation characteristics of electromagnetic waves.

[0032] It should be noted that the application of geometric optics principles is crucial when determining the visibility between nodes.

[0033] For example, when two communication nodes are located on opposite sides of a valley, the terrain elevation needs to be checked point by point along the line connecting the nodes. Assume node A is at an altitude of 1500 meters and node B is at an altitude of 1600 meters, with a horizontal distance of 5 kilometers between them. An elevation sampling point is extracted every 50 meters along the connecting path. If the terrain elevation of a sampling point is 1800 meters, but the line-of-sight height at that point is only 1550 meters, it indicates that a mountain is obstructing the view, and the two nodes are not visible. This method ensures the accuracy of subsequent signal propagation calculations. In signal propagation calculations, the free-space propagation loss formula L = 32.45 + 20log(f) + 20log(d) describes the attenuation law of electromagnetic waves in unobstructed space.

[0034] Preferably, when the communication frequency f is 900MHz and the propagation distance d is 5km, the free space loss is approximately 113dB. However, actual propagation in mountainous environments is more complex. Electromagnetic waves are reflected when they encounter the mountain surface, and the reflected wave and the direct wave may cause constructive or destructive interference when they superimpose at the receiving point. If the intensity of the direct wave is -80dBm, the intensity of the reflected wave attenuates to -85dBm after reflection by the mountain. When the phase difference between the two is 180 degrees, destructive interference will occur, causing the received signal strength to drop below -86dBm. The inverse range weighted interpolation method plays an important role in generating signal distribution maps.

[0035] In one embodiment, for any grid point at a non-node location, its signal strength is obtained by a weighted average of the signal strengths of surrounding known nodes, with the weight inversely proportional to the square of the distance. Closer nodes contribute more to the signal strength at that point. This method can reasonably reflect the impact of mountainous terrain on signal propagation. The generated interference distribution map uses different colors to represent signal strength levels: red areas represent good coverage areas with signal strength above -70dBm, yellow areas represent general coverage areas with signal strength between -70 and -90dBm, and blue areas represent weak coverage areas with signal strength below -90dBm, providing an intuitive reference for subsequent network optimization.

[0036] S102, based on the preliminary signal propagation interference distribution map, identify the areas with discontinuities in the visible range, collect the reflected and scattered signals in the areas with discontinuities in the visible range, and determine the location range of the interfered communication nodes based on the signal time difference.

[0037] Based on the preliminary signal propagation interference distribution map, the signal strength values ​​of adjacent grid cells are scanned. If the signal strength difference between adjacent grid cells exceeds a preset threshold, it is marked as a visible discontinuity. Continuous discontinuities are connected to form discontinuity boundary lines. The spatial range and geographic coordinate set of the visible discontinuity area are obtained by enclosing the boundary lines. Sampling points are selected from the geographic coordinate set of the visible discontinuity area. For each sampling point, signals from the communication node are received, and the amplitude variation sequence of the received signal over time is recorded. Peak detection is used to identify the arrival times of direct signals, mountain reflection signals, and edge scattering signals. The time difference between the reflected signal and the direct signal, and the time difference between the scattering signal and the direct signal are calculated to form a multipath delay difference table for each sampling point. Based on the time difference data in the multipath delay difference table, the distance difference between the reflected path and the direct path is obtained by multiplying the time difference by the electromagnetic wave propagation speed. Combined with the sampling point coordinates and the mountain terrain elevation data, the position coordinates of the mountain surface that produces the reflection are determined by the hyperbolic positioning principle. Based on the mountain reflection position, the sampling point position and the electromagnetic wave incident reflection law, the signal transmission path is traced in reverse to determine the location range of the interfered communication node.

[0038] Specifically, in mountainous communication environments, drastic changes in signal strength often indicate the presence of terrain obstruction or multipath interference.

[0039] Specifically, when scanning the signal propagation interference distribution map, if the signal strength between adjacent 10m × 10m grid cells suddenly drops from -70dBm to -95dBm, this 25dB difference far exceeds the normal attenuation range, indicating the presence of a visible discontinuity. By connecting these discontinuities to form a boundary line, the specific area affected by the mountain's obstruction can be accurately delineated.

[0040] It should be noted that these discontinuous regions are precisely the areas where multipath propagation is most complex.

[0041] In one possible implementation, when multiple sampling points within a discontinuous region are selected for signal measurement, the received signals exhibit significant differences in temporal characteristics. The direct signal, arriving first, has the strongest amplitude and a complete waveform; the subsequent mountain reflection signal, having undergone an additional propagation path, not only suffers from attenuation in intensity but also experiences a delay of several microseconds in arrival time; while the edge-scattered signal manifests as a superposition of multiple small-amplitude pulses, with even longer delays and a more dispersed distribution. Peak detection plays a crucial role in identifying these signals from different paths.

[0042] For example, when the first peak exceeding three times the noise threshold appears in the amplitude sequence of the received signal, this moment is recorded as t1, which is the arrival time of the direct signal; continue to monitor the subsequent signal, and when the second obvious peak appears and the amplitude exceeds twice the noise threshold, it is recorded as t2, which is the arrival time of the reflected signal; the time difference between the two, t2-t1, is the additional propagation delay of the reflected path relative to the direct path.

[0043] Preferably, the application of the hyperbolic positioning principle makes it possible to deduce the node location from time delay information. Considering that electromagnetic waves propagate in the air at a speed of 300,000 kilometers per second, a 1-microsecond delay corresponds to a path difference of 300 meters. If a sampling point measures a delay of 3.5 microseconds between the reflected signal and the direct signal, it means that the reflected path is 1050 meters longer than the direct path. Based on this path difference, combined with the known coordinates of the sampling point, it can be determined that the communication node must be located on a specific hyperbola with the sampling point and the reflection point as its foci.

[0044] In one embodiment, measurement data from multiple sampling points can further narrow down the node's location range. When different delay values ​​are measured at three different sampling points, each sampling point can determine a hyperbolic trajectory. The intersection of the three hyperbolas is the accurate location of the interfered communication node. Even considering measurement errors, the intersection area of ​​these hyperbolas can provide a reliable node location range, typically within 50-100 meters, sufficient for subsequent network optimization and interference cancellation. This multipath delay-based positioning method fully utilizes the reflection phenomenon caused by the complex terrain of mountainous areas, transforming the original interference factors into favorable conditions for positioning.

[0045] S103, obtain the degree of terrain obstruction and signal strength attenuation in the location range of the interfered communication nodes, identify the main signal propagation interference source based on the degree of terrain obstruction, and determine the dynamic interference weight factor based on the distance between nodes and terrain complexity.

[0046] For the location range of the interfered communication node, terrain elevation sampling points are extracted at preset intervals within this range. The difference between the node's elevation and the elevation of each sampling point is calculated. The proportion of sampling points with positive elevation differences to the total number of sampling points is used as the terrain obstruction coefficient. Simultaneously, the actual received signal strength within this range is measured and compared with the theoretical signal strength calculated based on the transmission power and propagation distance. The difference between the measured and contour measurements is the signal strength attenuation. Based on the terrain obstruction coefficient and signal strength attenuation, the space around the node is divided into multiple sectors according to orientation. The product of the obstruction coefficient and signal attenuation is calculated for each sector. If the product value of a certain sector exceeds a preset threshold and is the largest among all sectors, the mountain in the direction of that sector is determined to be the main source of signal propagation interference. The center orientation of that sector is recorded as the orientation of the interference source, thus obtaining the orientation distribution data of the interference source. Based on the azimuth distribution data of the interference source, the straight-line distance between the interfered node and the adjacent communication node is calculated. The terrain elevation sequence is extracted along the node connection line. The terrain complexity index is obtained by dividing the sum of the absolute values ​​of the elevation differences between adjacent points in the elevation sequence by the sequence length. The dynamic interference weight factor with a value between 0 and 1 is determined by multiplying the inverse of the distance by the terrain complexity index and then dividing by the maximum value of the product of all nodes.

[0047] Specifically, a quantitative assessment of the degree of terrain obstruction is a key step in understanding communication interference in mountainous areas.

[0048] In one possible implementation, after determining the location range of the disturbed node, a terrain elevation sampling point is set every 20 meters centered on that node, forming a sampling grid covering a 1-kilometer radius. Assuming the node is located on a hillside at an altitude of 1200 meters, 350 of the 500 extracted sampling points have elevations exceeding 1200 meters. This means that 70% of the surrounding terrain is higher than the node's location, creating a significant terrain shading effect.

[0049] It should be noted that the calculation of theoretical signal strength is based on unobstructed propagation conditions.

[0050] Specifically, when the transmit power is 30dBm and the propagation distance is 3 kilometers, according to the principle of free space path loss, the theoretical received signal strength should be -79dBm. However, actual measurements show that the received signal is only -92dBm, a difference of 13dB. This difference directly reflects the additional attenuation caused by mountain obstruction and reflection. This comparison method can accurately quantify the actual impact of terrain on signal propagation. The sector division method makes the location of interference sources more precise.

[0051] For example, the 360-degree space surrounding the node is divided into 12 sectors, each representing a possible direction of interference. In the northeast sector, the obstruction coefficient reaches 0.8, and the signal attenuation is 15 dB; the product of these two is 12, significantly higher than the values ​​in other sectors. This indicates that a major mountain obstructs the northeast direction, becoming the primary source of interference for signal propagation. This sector-based analysis accurately identifies the terrain features that have the greatest impact on communication quality. Quantifying terrain complexity is crucial for assessing the propagation environment.

[0052] In one embodiment, elevation points are extracted every 100 meters along a line connecting two nodes 5 kilometers apart, resulting in 50 elevation data points. If the elevation differences between adjacent sampling points are 15 meters, 8 meters, 23 meters, etc., the sum of the absolute values ​​of the elevation differences between all adjacent points is calculated to be 420 meters. Dividing this by the number of sampling points (49) yields an average elevation change rate of 8.6 meters. This value reflects the degree of terrain undulation along the propagation path; a typical value in mountainous areas is between 5 and 15 meters, while in plains it is usually less than 2 meters. Normalization of the dynamic interference weighting factor ensures comparability between different node pairs.

[0053] Preferably, when the reciprocal of the distance between a node pair is calculated to be 0.0002 and the terrain complexity index is 8.6, their product is 0.00172. If the maximum value of this product among all node pairs is 0.0025, then the normalized weight factor is 0.688. This value, between 0 and 1, comprehensively reflects the influence of the two key factors, distance and terrain, on the degree of interference. Node pairs that are closer in distance and have more complex terrain will receive a higher weight factor, thus receiving priority in subsequent network optimization.

[0054] S104. Construct a candidate set of data forwarding paths based on the dynamic interference weighting factor, conduct feasibility assessment and interference intensity ranking, extract alternative paths that meet communication quality requirements, and screen out the path combination with the least signal propagation interference by calculating the path length change rate and node connectivity change rate of the alternative paths, and determine the preliminary forwarding path scheme.

[0055] Based on the dynamic interference weighting factor, all adjacent node pairs in the communication network are traversed. Node pairs with weighting factors less than a preset threshold are marked as available links. Starting from the originating node of the data to be transmitted, all reachable paths to the data receiving node are searched. The node sequence traversed by each path and the corresponding link weighting factor sequence are recorded, constructing a candidate set of data forwarding paths containing all possible paths. For each path in the candidate set, the dynamic interference weighting factors of each link segment on the path are accumulated to obtain the total path interference value. The total path interference value is compared with a preset communication quality threshold. Paths with total path interference values ​​lower than the threshold are retained as feasible paths. The paths are sorted from smallest to largest according to their total path interference values, and a preset number of paths with the smallest interference values ​​after sorting are selected as the candidate path set. For each path in the candidate path set, the ratio of the number of link segments contained in the path to the minimum number of link segments from the starting node to the receiving node is calculated as the path length change rate. The number of neighboring nodes that each relay node in the path can still reach after the current path is removed is counted, and the ratio of this number to the number of neighboring nodes that can be reached before removal is calculated. The average of the ratios of all relay nodes is used to obtain the node connectivity change rate. The path with the smallest sum of the path length change rate and the node connectivity change rate is selected as the preliminary forwarding path scheme.

[0056] Specifically, the dynamic interference weight factor plays a crucial filtering role in constructing forwarding paths.

[0057] In one possible implementation, a weight factor of 0.3 between two adjacent nodes indicates that the link is less affected by terrain interference and can be used as a usable link for data forwarding. A link with a weight factor of 0.8, however, indicates severe mountain obstruction or signal attenuation, making it unsuitable for carrying important data transmission. Setting a threshold of 0.5 effectively distinguishes between usable links and highly interfered links, thus laying the foundation for subsequent path search. The path search process embodies a construction approach that moves from the local to the global.

[0058] Specifically, starting from the data transmission originating node A, all neighboring nodes connected to A with weight factors below a threshold are first identified, assuming they are nodes B, C, and D. Then, the process continues outward from these three nodes, searching for their available neighboring nodes, recursively until the data receiving node Z is reached. This process may generate multiple paths, such as ABEZ, ACFGZ, ADHZ, etc., each path recording a complete sequence of nodes and their corresponding weight factor sequence.

[0059] It should be noted that the total interference value of the path is calculated by accumulation, which can comprehensively reflect the communication quality of the entire path.

[0060] For example, the weight factors for the three links in path ABEZ are 0.2, 0.3, and 0.4, respectively, resulting in a total interference value of 0.9. In contrast, path ACFGZ, although traversing four links, has weight factors of only 0.1, 0.2, 0.1, and 0.2 for each link, resulting in a lower total interference value of 0.6. This cumulative evaluation method ensures that the selected path has better overall communication performance. The path length change rate reflects the additional overhead incurred by choosing a detour path.

[0061] In one embodiment, if the shortest path from starting node A to receiving node Z requires only 2 hops, while the actually selected path ACFGZ requires 4 hops, then the path length change rate is 2.0. The closer this ratio is to 1.0, the closer the path is to the optimal path length, and the lower the transmission delay. The evaluation of the node connectivity change rate ensures the robustness of the network.

[0062] Preferably, we examine relay node C in path ACFGZ. Assuming C originally connected to 5 adjacent nodes, and after removing path AC, C still connects to 4 nodes, then C's connectivity ratio is 0.8. Similarly, we calculate the connectivity ratios of F and G, and average these three to obtain the node connectivity change rate for the entire path. A higher change rate indicates that choosing this path will not significantly affect the connectivity of other nodes in the network. A comprehensive evaluation mechanism ensures the rationality of the final scheme. By adding the path length change rate to the node connectivity change rate, we obtain the comprehensive score for each candidate path. Path ABEZ has a length change rate of 1.5 plus a connectivity change rate of 0.7, resulting in a total score of 2.2; while path ACFGZ, although longer, has a score of 2.0 + 0.9 = 2.9. Selecting the path with the lowest score as the initial forwarding scheme achieves a balance between interference avoidance, transmission efficiency, and network stability.

[0063] S105: Obtain signal time difference data for each path in the preliminary forwarding path scheme and generate an optimized forwarding path set.

[0064] For each path in the initial forwarding path scheme, a test signal is sent at the path's starting point, and the actual arrival times of the signal at each relay node and the endpoint are recorded. The propagation time between adjacent nodes is calculated, and this time is compared with the theoretical time obtained by dividing the distance between nodes by the electromagnetic wave propagation speed. The difference between the two is used as the signal time difference data for each link segment. The signal time difference data is monitored in real time, and the total path delay deviation is obtained by accumulating the time difference values ​​of all link segments on each path. If the total path delay deviation exceeds a preset threshold, the link segment with the largest time difference value is selected, and the coordinates of the nodes at both ends of this link segment are obtained. A location with a high altitude and no mountain obstruction is searched around the midpoint of the line connecting the two nodes and determined as the replacement relay node location. The path configuration is updated using the replacement relay node location, and the signal propagation loss of the updated path is remeasured. If the signal strength at the receiving end is lower than a preset receiving sensitivity threshold, the difference between the threshold value and the actual received strength is calculated, and the output power of the transmitting node is increased based on this difference. All paths that have completed local adjustments are summarized to obtain the optimized forwarding path set.

[0065] Specifically, measuring signal time difference is an important means of assessing the quality of communication paths in mountainous areas.

[0066] In one possible implementation, the test signal is recorded at time t0 when it is emitted from the starting node A, and at time t1 when it arrives at the first relay node B. The actual propagation time is t1 - t0. Based on the straight-line distance of 500 meters between nodes A and B, divided by the speed of electromagnetic waves in air (300,000 kilometers per second), the theoretical propagation time should be 1.67 microseconds. If the actual measurement time is 2.1 microseconds, the time difference is 0.43 microseconds. This difference is mainly due to the multipath effect caused by reflection from the mountain.

[0067] It should be noted that the cumulative effect of the total path delay deviation can accurately reflect the transmission quality of the entire path.

[0068] Specifically, a path ABCDE containing four link segments has time differences of 0.43, 0.28, 0.52, and 0.31 microseconds respectively, resulting in a total delay deviation of 1.54 microseconds. When the preset threshold is 1.2 microseconds, this path significantly exceeds the limit and requires optimization. By identifying link segments C and D corresponding to the maximum time difference of 0.52 microseconds, the problem can be accurately located. The optimal selection of relay node locations directly affects signal propagation efficiency.

[0069] For example, the original relay node C was located at the bottom of a valley, and D was located on the opposite mountainside, with a significant ridge obstructing the view between them. During a search within a 1-kilometer radius of the midpoint of the line connecting C and D, a mountaintop platform at an altitude of 1850 meters was discovered. This platform had direct line-of-sight connections to both C and D and no terrain obstructions. Moving the relay node to this mountaintop location, while slightly increasing the physical distance of the new propagation path, avoided complex reflection paths, reducing the time difference to within 0.15 microseconds. Real-time assessment of signal propagation loss ensured the reliability of the communication link.

[0070] In one embodiment, after path adjustment, the total propagation loss from the starting node to the terminal node is 115 dB. Considering a transmit power of 30 dBm, the signal strength at the receiving end is only -85 dBm. The sensitivity threshold of the receiving device is typically set to -90 dBm, which, while theoretically possible, only provides a 5 dB margin, potentially leading to communication interruptions under adverse weather conditions. The implementation of the power adjustment strategy improves system stability.

[0071] Preferably, to ensure a 10dB link margin, the received signal strength needs to reach -80dBm, a difference of 5dB from the current -85dBm. This can be achieved by increasing the transmit power of the starting node by 5dB to 35dBm, or by increasing it by 3dB at the relay node and 2dB at the node before the terminal. This distributed power adjustment method satisfies communication requirements while avoiding interference caused by excessive power at a single point. The formation of the forwarding path set reflects a system-level optimization approach. By conducting latency tests, node optimization, and power adjustments on each path in the initial scheme, each path in the final path set meets both latency and signal strength requirements. This optimized forwarding path set not only improves the reliability of data transmission but also provides high-quality candidate schemes for subsequent dynamic routing selection.

[0072] The coordinates of nodes whose signal time difference exceeds the threshold are obtained, the terrain obstruction angle and altitude difference are analyzed, alternative locations are selected based on line-of-sight conditions, the terrain reflection interference of alternative locations is measured, the deployment coordinates of relay nodes are determined, and the power output level of transmitting nodes is adjusted based on the signal intensity attenuation of mountain reflection. The signal propagation quality of the adjusted path is verified, and an optimized path configuration scheme including new relay coordinates and power parameters is formed.

[0073] After acquiring the coordinates of nodes whose signal time difference exceeds a threshold, the relative position data between the node and the surrounding mountains are extracted. The angle between the line connecting the top of the mountain and the node and the horizontal plane is calculated as the terrain occlusion angle. The difference between the node's altitude and the average altitude of the surrounding terrain is calculated. If the occlusion angle is less than a preset angle and the node's altitude is higher than the average altitude of the surrounding terrain, a set of candidate location coordinates that meet the direct path conditions is searched around the node. For each location in the candidate location coordinate set, a test signal is transmitted and the reflected echo from the mountain is received. The ratio of the reflected signal power to the direct signal power is measured as an indicator of the terrain reflection interference level. The location with the lowest reflection interference level is selected as the relay node deployment coordinate, and the surrounding terrain undulation change data of this coordinate point is acquired. Based on the relay node deployment coordinates and surrounding terrain undulation data, the total attenuation of the signal propagation path from the transmitting node through the relay node to the receiving node is recalculated. The difference between the preset received signal strength threshold and the actual received signal strength is used as the required compensation power. The compensation power is allocated to the corresponding transmitting node according to the proportion of terrain undulation in each path segment. After adjusting the power output level of each node, the signal strength and propagation delay values ​​at both ends of the path are measured. The relay node coordinates, the adjusted power parameters of each node, the signal strength value, and the delay value are combined to form an optimized path configuration scheme.

[0074] Specifically, the calculation of terrain obstruction angle is a key indicator for assessing communication conditions in mountainous areas.

[0075] In one possible implementation, a communication node is located on a mountainside at an altitude of 1500 meters, and there is a 2100-meter-high peak 2 kilometers to its northeast. By calculating the angle between the line connecting the peak's summit and the node and the horizontal plane, the obstruction angle is determined to be 17 degrees. This angle reflects the degree to which the terrain obstructs signal propagation; a larger angle indicates more severe obstruction. When the preset acceptable obstruction angle is 15 degrees, the node clearly does not meet the line-of-sight propagation requirements.

[0076] It should be noted that determining the altitude difference is crucial for selecting a suitable relay location.

[0077] Specifically, if the average elevation of the terrain within a 1-kilometer radius of a node is 1450 meters, while the node itself is at an elevation of 1500 meters, then the node is 50 meters higher than the surrounding terrain. This positive elevation difference means the node's location is relatively high, which is beneficial for long-distance signal propagation. Searching for alternative locations at higher elevations, such as ridgelines above 1800 meters, can yield better line-of-sight conditions. The measurement of terrain reflection interference directly affects the final location selection of the relay node.

[0078] For example, when testing was conducted at three candidate locations, the reflected signal power received at the first location was -75dBm, and the direct signal power was -60dBm, with a ratio of 0.032; the ratio at the second location was 0.1; and at the third location, due to its open area, the reflected signal was extremely weak, with a ratio of only 0.01. Selecting the third location with the smallest ratio as the relay node deployment point can minimize the impact of multipath interference. Obtaining data on the surrounding terrain undulations provided a basis for subsequent power allocation.

[0079] In one embodiment, analysis of terrain data around the relay node revealed that the path from the node to the transmitter traverses two valleys with dramatic terrain undulations, while the path to the receiver is relatively flat. This asymmetrical terrain feature directly affects the distribution of signal attenuation. The power compensation allocation strategy reflects a refined network optimization approach.

[0080] Preferably, assuming a total compensation power requirement of 9dB, the power is allocated according to the proportion of terrain undulation in each path segment: the path from the transmitting node to the relay node accounts for 70% of the total undulation, receiving a 6dB power boost; the relay node itself receives 2dB; the path from the relay to the receiving end accounts for 30%, with the last hop node receiving 1dB. This allocation method based on terrain complexity ensures the rational use of power resources. The formation of the optimized path configuration scheme marks the completion of the entire optimization process. By integrating the precise coordinates of the relay nodes, the adjusted power levels of each node, the measured end-to-end signal strength values, and propagation delay data, a complete set of configuration parameters is formed. Actual measurements show that the optimized path signal strength increased from -92dBm to -78dBm, and the propagation delay decreased from 2.1 microseconds to 1.8 microseconds, significantly improving communication quality in mountainous areas. This optimization scheme based on actual terrain characteristics provides a reliable guarantee for the stable operation of communication networks in mountainous areas.

[0081] S106, monitors the changes in the distribution of communication nodes in the forwarding path set in real time, continuously monitors the changing trend of signal propagation interference, determines the signal loss rate data during the path execution process, and generates a stable communication transmission channel.

[0082] The system monitors the online status of each communication node in the forwarding path set in real time, periodically acquires node location coordinates and working status data, compares the current node distribution with the initial distribution in the optimized path configuration scheme, identifies changes in node offline status or location offset, and records real-time measurements of signal strength and latency on each path. The deviation of these measurements from the optimized configuration is calculated as trend data of signal propagation interference. Based on the trend data of signal propagation interference and changes in node distribution, the total number of data packets sent and the number of acknowledgment packets received for each path within a fixed time window are statistically analyzed. The proportion of unreceived acknowledgment packets to the total number of sent packets is calculated as the signal loss rate. If the signal loss rate of a path exceeds a preset threshold or a key node goes offline, the currently available path with the highest signal strength is selected from the pre-built alternative paths as the replacement path. The current transmission configuration is updated using the alternative path. A switching instruction containing the new next-hop node address is sent to each node on the path. After receiving the instruction, the node updates the next-hop forwarding address stored locally and transmits subsequent data packets according to the new forwarding address. The signal loss rate and transmission delay of the path after the switch are continuously monitored. When the loss rate remains below the threshold and the delay is stable for multiple consecutive monitoring periods, a stable communication transmission channel is confirmed.

[0083] Specifically, real-time monitoring mechanisms play a crucial role in communication networks in mountainous areas.

[0084] In one possible implementation, a heartbeat detection packet is sent to each communication node every 30 seconds. Upon receiving the packet, each node immediately returns a response packet containing its own GPS coordinates and battery level. If a node on the hillside fails to respond due to equipment failure caused by heavy rain, that node is immediately marked as offline. Meanwhile, another node, although responding normally, will have its coordinates shifted by 50 meters from its original location. This shift could be due to a landslide or human movement, both of which directly affect the communication quality along the original path.

[0085] It should be noted that the changing trend of signal propagation interference can indicate potential communication problems.

[0086] Specifically, during the initial network optimization configuration, the signal strength of a certain path was -75 dBm, and the transmission latency was 1.5 microseconds; these values ​​were recorded as a baseline. During real-time monitoring, it was found that the signal strength of this path had dropped to -82 dBm, and the latency had increased to 2.1 microseconds. The deteriorating trend of these two indicators suggests the possible presence of new interference sources or environmental changes, such as additional attenuation caused by seasonal vegetation growth. The calculation of the signal loss rate provides a quantitative quality assessment standard.

[0087] For example, within a 5-minute monitoring window, a certain path transmitted 1000 data packets, of which 920 received acknowledgments from the receiver, and 80 packets did not receive acknowledgments, resulting in a loss rate of 8%. When the preset acceptable loss rate threshold is 5%, this path clearly no longer meets the communication quality requirements. This evaluation method based on actual transmission performance is more reliable than simply relying on signal strength. The selection of alternative paths reflects the network's redundancy design philosophy.

[0088] In one embodiment, when the primary path encounters a problem, the system selects from three pre-built alternative paths: the first alternative path has a current signal strength of -79dBm, but one of its relay nodes is low on power; the second path has a signal strength of -77dBm, and all nodes are functioning normally; the third path has the strongest signal at -73dBm, but one of its critical nodes is offline. After comprehensive consideration, the second path is selected as the alternative. The path switching process requires precise coordination and control.

[0089] Preferably, the switching command includes new forwarding rules: data that was originally forwarded from node A to node B now needs to be forwarded to node C. Each node maintains a next-hop address table and updates the entries immediately upon receiving the switching command. To avoid data loss during the switching process, the system adopts a "build-then-disconnect" strategy, that is, first establish the connection of the new path, confirm its normal operation, and then stop using the old path. Confirmation of communication channel stability requires continuous observation and verification. After switching to the new path, the system continues to collect data on loss rate and latency every minute. The channel is considered stable only when the loss rate remains below 3% and the latency fluctuation does not exceed 0.2 microseconds for 10 consecutive monitoring periods. This strict stability judgment standard ensures the reliability of communication services in complex mountainous environments, providing users with high-quality data transmission guarantees.

[0090] S107. Based on a stable communication transmission channel, analyze the trend of signal time difference changes during path switching, and determine the switching time interval, signal strength threshold, and node load balancing coefficient for path switching.

[0091] Based on the stable communication transmission channel operation records, the time of each path switching event, the signal propagation time before and after the switching, and the signal strength values ​​are extracted. The difference between the signal propagation time before and after the switching is calculated, and the relationship between this difference and the elapsed time after the switching is recorded as a time series, obtaining the signal time difference change data and the corresponding signal strength record during the path switching process. By performing sliding window statistics on the signal time difference change data, the standard deviation of the time difference within a fixed duration window is calculated. When the standard deviation is lower than a preset value for multiple consecutive windows, it is determined to be a stable state. The duration from the start of the switching to reaching the stable state is counted as the switching recovery duration. The average recovery duration of multiple switching is determined as the switching time interval of the path switching. At the same time, the strength value at the time of triggering the switching is extracted from the signal strength record, and the median is taken as the signal strength threshold. Based on the switching time interval and the signal strength threshold, the number of data packets forwarded by each node in each switching time interval is counted, and the ratio of the forwarding volume of a single node to the average forwarding volume of all nodes is calculated. This ratio is defined as the node load balancing coefficient, determining the three parameters: switching time interval, signal strength threshold, and node load balancing coefficient.

[0092] Specifically, the changes in time difference during path switching reflect the dynamic process of the network adapting to new paths.

[0093] In one possible implementation, when the system performs a path switch at 10:00 AM, the signal propagation time before the switch is 1.8 microseconds. At the moment of the switch, because nodes need to update their routing tables and establish new connections, the propagation time suddenly increases to 2.5 microseconds. Over the next 30 seconds, this time difference gradually decreases, from 0.7 microseconds to 0.3 microseconds, and finally stabilizes below 0.1 microseconds after 45 seconds. This time difference curve exhibits a typical exponential decay characteristic, and recording this data provides a basis for subsequent parameter determination.

[0094] It should be noted that the sliding window statistical method can effectively identify the steady state of the system.

[0095] Specifically, a window length of 5 seconds is set, and the system slides once per second, calculating the standard deviation of the time difference within the window. In the initial stage of path switching, the standard deviation may reach 0.15 microseconds, indicating that the system is in a period of rapid adjustment. When the standard deviation of three consecutive windows is below 0.05 microseconds, the system is considered to have entered a stable state. Analysis of multiple switching events revealed that the recovery time ranges from 40 to 60 seconds. Taking an average of 50 seconds as the switching interval, this means the system needs to wait at least 50 seconds before executing the next switching. Determining the signal strength threshold requires comprehensive consideration of the rationality of the triggering conditions.

[0096] For example, trigger data for 20 path switching events were collected, revealing signal strengths at the trigger times of -88dBm, -85dBm, and -91dBm, among others. These values ​​were sorted, and the median of -87dBm was taken as the signal strength threshold. The median was chosen over the average because it is less sensitive to extreme values, better reflects typical triggering conditions, and avoids setting the threshold too high or too low due to individual anomalies. The calculation of the node load balancing coefficient reveals the distribution characteristics of network traffic.

[0097] In one embodiment, during a 50-second handover interval, node A forwarded 1200 data packets, node B forwarded 800, and node C forwarded 1000, with an average forwarding volume of 1000 packets per node. The calculated load balancing coefficient for node A is 1.2, indicating that it is carrying 20% ​​more traffic than the average level; node B's coefficient is 0.8, indicating a light load. These coefficients directly reflect the load pressure on each node, providing a quantitative basis for subsequent traffic scheduling.

[0098] Preferably, the determination of these three parameters forms a complete environmental adaptation mechanism. The handover time interval ensures that the system has sufficient time to complete the stabilization process of the previous handover, avoiding oscillations caused by frequent handovers; the signal strength threshold provides a clear handover trigger condition, preventing premature handovers that waste resources and delays that affect communication quality; and the node load balancing coefficient supports more refined traffic allocation decisions. This combination of parameters, derived from historical data statistical analysis, enables mountain communication networks to better adapt to complex and changing environmental conditions, significantly improving network stability and reliability.

[0099] Data on the fluctuation amplitude and frequency distribution of signal time difference during path switching are collected. The time nodes and attenuation rates of signal strength abrupt changes are identified. The uneven distribution of node load during switching is statistically analyzed. The lower limit of the switching trigger strength is set according to the signal strength attenuation rate. The target time interval of path switching is determined based on the fluctuation frequency of time difference. The load distribution weight coefficient of each node is determined by the uneven load distribution. A table of correspondence between switching parameters and environmental changes is established.

[0100] Signal strength data sequences from each switching node during path switching are collected. The time difference between adjacent sampling points is calculated, and the frequency domain distribution characteristics of the time difference are obtained through Fourier transform. The main fluctuating frequency components are identified based on the spectral density. The timestamp when the signal strength decrease rate exceeds a preset threshold is recorded as the abrupt change time node, and the average attenuation rate before and after the abrupt change is calculated. Real-time load data of each node during switching is acquired, and the ratio of the load standard deviation to the average load is calculated as an imbalance index. If the imbalance exceeds a preset imbalance threshold, a load redistribution process is initiated. The initial allocation weight is determined based on the difference between the current load and the average load of each node. The weight is corrected by multiplying the attenuation rate by the initial weight to obtain the load allocation weight coefficient for each node. Based on the statistical distribution of the average attenuation rate, the attenuation rate corresponding to a preset quantile is selected as a reference value. The time required for the signal strength to decrease to the receiving sensitivity is calculated by dividing the signal strength by the attenuation rate. A preset time margin is added to this time as the lower limit of the strength for switching trigger. The target time interval for path switching is determined based on the periodic characteristics of the main fluctuating frequency components. Establish a mapping relationship between environmental parameters and switching parameters, record the distribution of abrupt change time nodes, load allocation weight coefficients, and lower limits of switching trigger intensity under different temperatures, humidity, and electromagnetic interference intensities, divide the environmental parameters into segments according to their numerical ranges, and determine the corresponding combination of lower limits of switching intensity, target time intervals, and load allocation weight coefficients for each environmental parameter interval, forming a table of correspondence between environmental changes and switching parameters.

[0101] Specifically, during the path switching process in a wireless communication network, the acquisition of signal strength data sequences forms the basis of the entire switching decision.

[0102] Specifically, the system acquires signal strength values ​​at a fixed sampling frequency using signal receivers deployed at each switching node. The time difference between these sampling points reflects the dynamic characteristics of the network environment. By converting the time difference values ​​from the time domain to the frequency domain using Fourier transform, the periodic fluctuation patterns hidden in the time series can be revealed.

[0103] It should be noted that spectral density analysis can identify the dominant fluctuation frequency components. When the energy of a certain frequency component is significantly higher than that of other frequencies, it indicates that there are regular signal changes at that frequency. The identification of this dominant fluctuation frequency component provides an important basis for subsequently determining the time interval for path switching. At the same time, by setting a threshold value for the rate of signal strength decline, the system can accurately capture the time points of signal abrupt changes, which often indicate a sharp deterioration in the communication environment.

[0104] In one possible implementation, load imbalance is quantified by the ratio of the standard deviation to the mean. This metric intuitively reflects the degree of difference in the amount of traffic carried by each node. When the imbalance exceeds a preset threshold, it indicates that some nodes may be overloaded, while other nodes are idle. Initiating a load redistribution process at this time can effectively improve overall network performance. The weight adjustment process comprehensively considers the impact of signal attenuation rate. Nodes with a higher attenuation rate receive a smaller weight coefficient, thereby reducing the amount of traffic they carry and avoiding service interruptions due to signal quality degradation.

[0105] For example, a statistical method was used to determine the lower limit of the handover trigger strength. By analyzing the distribution of attenuation rates in historical data, the attenuation rate corresponding to a specific quantile was selected as a typical value. Based on this typical attenuation rate, the time required for the signal to decrease from the current strength to the receiving sensitivity was calculated, and an appropriate time margin was added. This method ensures both the timeliness of handover and avoids the system overhead caused by excessively frequent handovers. During the establishment of the mapping relationship between environmental parameters and handover parameters, the system recorded the network performance under different environmental conditions. Temperature changes affect the operating status of equipment and signal propagation characteristics, humidity causes changes in signal attenuation, and electromagnetic interference intensity directly affects signal quality. By segmenting the environmental parameters, each interval corresponds to a set of optimized handover parameter configurations. The establishment of this mapping table enables the system to adaptively adjust the handover strategy according to the current environmental conditions, significantly improving the stability and reliability of the network in complex environments.

[0106] This invention provides a multi-directional communication data forwarding system, mainly comprising: a terrain data acquisition module, used to acquire terrain elevation data and mountain surface reflection characteristics, construct a three-dimensional terrain simulation environment, calculate the initial visible range between each communication node, and obtain a preliminary signal propagation interference distribution map; an interference area identification module, used to identify areas with discontinuous visible ranges based on the preliminary signal propagation interference distribution map, acquire reflected and scattered signals from these discontinuous areas, and determine the location interval of the interfered communication nodes based on signal time differences; an interference source analysis module, used to acquire the terrain occlusion degree and signal strength attenuation of the location interval of the interfered communication nodes, identify the main signal propagation interference source based on the terrain occlusion degree, and determine a dynamic interference weight factor based on the distance between nodes and terrain complexity; and a path candidate set construction module, used to construct a data forwarding path based on the dynamic interference weight factor. The system generates a candidate path set, performs feasibility assessment and interference intensity ranking, extracts alternative paths that meet communication quality requirements, and selects the path combination with the least signal propagation interference by calculating the path length change rate and node connectivity change rate of the alternative paths, thus determining the preliminary forwarding path scheme. A path optimization module is used to acquire signal time difference data for each path in the preliminary forwarding path scheme and generate an optimized forwarding path set. A communication monitoring module is used to monitor the changes in the distribution of communication nodes in the forwarding path set in real time, continuously monitor the changing trend of signal propagation interference, determine the signal loss rate data during path execution, and generate a stable communication transmission channel. A channel stability analysis module is used to analyze the changing trend of signal time difference during path switching based on a stable communication transmission channel, and determine the switching time interval, signal strength threshold, and node load balancing coefficient for path switching. Obviously, those skilled in the art can make various modifications and variations to the embodiments of this application without departing from the spirit and scope of the embodiments of this application. Therefore, if these modifications and variations of the embodiments of this application fall within the scope of the claims of this application and their equivalents, this application also intends to include these modifications and variations.

Claims

1. A multi-directional communication data forwarding method, characterized in that, The method includes: The process involves acquiring terrain elevation data and mountain surface reflection characteristics to construct a 3D terrain simulation environment. This allows for the calculation of the initial visible range between communication nodes, resulting in a preliminary signal propagation interference distribution map. Based on this map, regions with discontinuous visible ranges are identified, and reflected and scattered signals from these regions are collected. The location intervals of interfered communication nodes are determined based on signal time differences. The degree of terrain obstruction and signal attenuation within these intervals are then assessed. The main signal propagation interference source is identified based on the degree of terrain obstruction, and a dynamic interference weighting factor is determined based on the distance between nodes and terrain complexity. Finally, a candidate set of data forwarding paths is constructed based on the dynamic interference weighting factor, and feasibility and interference intensity are evaluated. The system sorts and extracts candidate paths that meet communication quality requirements. By calculating the path length change rate and node connectivity change rate of the candidate paths, it selects the path combination with the least signal propagation interference to determine the initial forwarding path scheme. It obtains the signal time difference data of each path in the initial forwarding path scheme to generate an optimized forwarding path set. It monitors the changes in the distribution of communication nodes in the forwarding path set in real time, continuously monitors the changing trend of signal propagation interference, determines the signal loss rate data during path execution, and generates a stable communication transmission channel. Based on the stable communication transmission channel, it analyzes the changing trend of signal time difference during path switching to determine the switching time interval, signal strength threshold, and node load balancing coefficient for path switching.

2. The multi-directional communication data forwarding method according to claim 1, characterized in that, The process of acquiring terrain elevation data and mountain surface reflection characteristics, constructing a three-dimensional terrain simulation environment, calculating the initial visibility range between each communication node, and obtaining a preliminary signal propagation interference distribution map includes: By scanning and acquiring point cloud data of mountain terrain, a digital elevation matrix is ​​generated, the elevation value of each grid cell is recorded, surface material information of the mountain is collected, and the electromagnetic wave reflection coefficient of each grid cell is determined. A three-dimensional terrain database containing elevation information and electromagnetic wave reflection coefficient is constructed. Based on the latitude and longitude coordinates and elevation information of the communication node distribution locations, the spatial position of each node is located in the three-dimensional terrain database, and the terrain elevation profile data on the node connection path is extracted to generate an initial set of visible nodes for each node. Based on the distance between nodes and the transmission power in the initial set of visible nodes, the free space propagation loss is calculated. The electromagnetic wave propagation path is traced by combining the electromagnetic wave reflection coefficient, the reflected wave intensity is calculated, and the direct signal intensity and reflected signal intensity are superimposed to generate the composite signal intensity value of each node. Based on the composite signal intensity value, a preliminary signal propagation interference distribution map covering the entire mountain area is generated through spatial interpolation.

3. The multi-directional communication data forwarding method according to claim 1, characterized in that, The process of identifying discontinuous areas within the visible range based on a preliminary signal propagation interference distribution map, collecting reflected and scattered signals from these discontinuous areas, and determining the location range of the interfered communication nodes based on signal time differences includes: Based on the preliminary signal propagation interference distribution map, the signal strength values ​​of adjacent grid cells are scanned, discontinuities where the signal strength difference exceeds a threshold are marked, and continuous discontinuities are connected to form discontinuity boundary lines, generating a set of geographic coordinates for the visible discontinuous area. Sampling points are selected from this set of geographic coordinates to receive signals from communication nodes, and the amplitude variation sequence of signals from communication nodes over time is recorded. The arrival times of direct, reflected, and scattered signals are detected, and the time difference between the reflected and direct signals, as well as the time difference between the scattered and direct signals, is calculated to generate a multipath delay difference table. Based on this multipath delay difference table, combined with electromagnetic wave propagation speed and terrain elevation data, the coordinates of the reflecting mountain surface are determined using positioning principles, and the signal transmission path is traced in reverse to determine the location range of the interfered communication node.

4. The multi-directional communication data forwarding method according to claim 1, characterized in that, The process of acquiring the degree of terrain obstruction and signal strength attenuation within the location range of the interfered communication nodes, and identifying the main signal propagation interference source based on the degree of terrain obstruction, includes: Topographic elevation sampling points within the location range of the interfered communication node are extracted. The difference between the node's elevation and the sampling point elevation is calculated, and the proportion of sampling points with positive elevation differences is used as the terrain occlusion coefficient. The actual received signal strength within the location range of the interfered communication node is measured and compared with the theoretical signal strength to generate a signal strength attenuation. Based on the terrain occlusion coefficient and the signal strength attenuation, the space around the node is divided into multiple sectors. The product of the occlusion coefficient and the signal strength attenuation in each sector is calculated. The sector with the largest product value is selected, and the mountain in the direction of that sector is identified as the main source of signal propagation interference, generating interference source azimuth distribution data.

5. The multi-directional communication data forwarding method according to claim 1, characterized in that, The process of constructing a candidate set of data forwarding paths based on dynamic interference weighting factors, performing feasibility assessments and interference intensity rankings, and extracting alternative paths that meet communication quality requirements includes: Based on the dynamic interference weight factor, adjacent node pairs in the communication network are traversed, and node pairs with weight factors below the threshold are marked as available links. All reachable paths from the starting node to the receiving node are searched, and the path node sequence and link weight factor sequence are recorded to generate a data forwarding path candidate set. Based on the data forwarding path candidate set, the link weight factor of each path is accumulated to generate a total path interference value. Paths with total interference values ​​below the threshold are retained as feasible paths. The paths with the smallest interference values ​​are selected as the candidate path set according to the total interference values.

6. A multi-directional communication data forwarding method according to claim 5, characterized in that, After selecting the path with the smallest interference value as the candidate path set, the following steps are included: For each path in the candidate path set, calculate the ratio of the number of link segments contained in the path to the minimum number of link segments to generate the path length change rate; count the number of neighboring nodes that each relay node in the candidate path set can reach after the path is removed, calculate the ratio of this number to the number before removal, and generate the node connectivity change rate; based on the sum of the path length change rate and the node connectivity change rate, select the path with the smallest sum value to generate the path combination with the least signal propagation interference.

7. A multi-directional communication data forwarding method according to claim 1, characterized in that, The generated optimized forwarding path set includes: For each path in the forwarding path scheme, a test signal is sent, and the arrival times of the signal at each relay node and the endpoint are recorded. The difference between the propagation time between adjacent nodes and the theoretical time is calculated to generate signal time difference data. Based on the signal time difference data, the time difference values ​​of all link segments on the path are accumulated to generate the total path delay deviation. A location with high altitude and no obstruction is searched around the midpoint of the link segment with the largest time difference value to generate a replacement relay node location. Based on the replacement relay node location, the path configuration is updated, the signal propagation loss is measured, the power of the transmitting node is adjusted, and an optimized forwarding path set is generated.

8. A multi-directional communication data forwarding method according to claim 1, characterized in that, The real-time monitoring of the communication node distribution changes in the forwarding path set, continuous monitoring of the changing trend of signal propagation interference, determination of signal loss rate data during path execution, and generation of a stable communication transmission channel include: The system monitors the online status and location coordinates of each node in the optimized forwarding path set in real time, records changes in node distribution, measures the deviation of signal strength and delay, and generates signal propagation interference change trend data. Based on the signal propagation interference change trend data, the system calculates the signal loss rate of the path and selects the candidate path with the highest signal strength as the alternative path. Based on the alternative path, the system updates the transmission configuration, sends a new next-hop node address, and generates a stable communication transmission channel.

9. A multi-directional communication data forwarding method according to claim 1, characterized in that, The step of analyzing the signal time difference variation trend during path switching based on a stable communication transmission channel, and determining the switching time interval, signal strength threshold, and node load balancing coefficient for path switching, includes: Extract the time of the path switching event and the signal propagation time in the stable communication transmission channel, calculate the time difference before and after the switching, and generate signal time difference change data; calculate the time difference standard deviation based on the signal time difference change data, determine the switching recovery time, and generate the path switching time interval; extract the signal strength value when the switching is triggered, and generate the signal strength threshold; count the number of data packets forwarded by each node, calculate the ratio of the node forwarding volume to the average forwarding volume, and generate the node load balancing coefficient.

10. A multi-directional communication data forwarding system, characterized in that, The system includes: a terrain data acquisition module for acquiring terrain elevation data and mountain surface reflection characteristics, constructing a three-dimensional terrain simulation environment, calculating the initial visible range between each communication node, and obtaining a preliminary signal propagation interference distribution map; an interference area identification module for identifying areas with discontinuous visible ranges based on the preliminary signal propagation interference distribution map, acquiring reflected and scattered signals from these discontinuous areas, and determining the location intervals of the interfered communication nodes based on signal time differences; an interference source analysis module for acquiring the degree of terrain occlusion and signal strength attenuation within the location intervals of the interfered communication nodes, identifying the main signal propagation interference source based on the degree of terrain occlusion, and determining a dynamic interference weight factor based on the distance between nodes and terrain complexity; and a path candidate set construction module for constructing a data forwarding path candidate set based on the dynamic interference weight factor. The system performs feasibility assessments and interference intensity rankings to extract candidate paths that meet communication quality requirements. By calculating the path length change rate and node connectivity change rate of the candidate paths, it selects the path combination with the least signal propagation interference to determine the initial forwarding path scheme. The path optimization module is used to acquire signal time difference data for each path in the initial forwarding path scheme and generate an optimized forwarding path set. The communication monitoring module is used to monitor the changes in the distribution of communication nodes in the forwarding path set in real time, continuously monitor the changing trend of signal propagation interference, determine the signal loss rate data during path execution, and generate a stable communication transmission channel. The channel stability analysis module is used to analyze the changing trend of signal time difference during path switching based on a stable communication transmission channel, and determine the switching time interval, signal strength threshold, and node load balancing coefficient for path switching.

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