A ground satellite signal distributed cooperative processing method and system

Abstract

The application provides a ground satellite signal distributed cooperative processing method and system, and relates to the technical field of communication.The method comprises the following steps: extracting a user MAC address hash value, an emergency priority code and a spectrum occupation matrix from a synchronization network to generate a triple feature vector; transmitting the triple feature vector through a double-layer Mesh network, selecting two reference target points with fixed geographic coordinates in a disaster area to obtain three-dimensional coordinates and construct a space reference straight line; calculating the perpendicular projection distance of each node to the space reference straight line according to real-time position data of each node to generate a node offset parameter; dynamically generating a refraction correction coefficient of a signal propagation path based on the comparison result of the node offset parameter and a preset threshold value; and when the offset exceeds the threshold value, using a piecewise linear interpolation method to increase the correction weight. The application can realize high-reliability and low-latency emergency communication coverage.

Description

Technical Field

[0001] This invention relates to the field of communication technology, and in particular to a method and system for distributed collaborative processing of ground satellite signals. Background Technology

[0002] In existing technologies, disaster area communication still relies on fixed ground base stations or temporarily deployed single-type communication nodes, but these solutions have the following limitations:

[0003] For example, traditional node deployment typically employs a uniform distribution strategy, which cannot adaptively adjust to changes in the porosity of disaster area ruins and the dynamic changes in obstacles. This results in large signal coverage blind spots and low resource utilization. In particular, in complex terrain, fixed nodes are easily affected by the displacement of obstacles caused by aftershocks, leading to frequent interruptions in communication links.

[0004] For example, some methods use static path loss models for signal compensation, without considering the real-time refraction effect of signal propagation paths in the dynamic environment of disaster areas. For instance, factors such as the scattering of high-frequency signals by metal debris in ruins and the difference in refractive index of obstacles made of different materials can lead to signal strength fluctuations and time delay jitter. Traditional methods lack dynamic correction mechanisms based on geospatial benchmarks. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a distributed collaborative processing method and system for ground satellite signals, which can achieve highly reliable and low-latency emergency communication coverage.

[0006] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows:

[0007] In a first aspect, a distributed collaborative processing method for ground satellite signals, the method comprising:

[0008] Step S1: Extract user MAC address hash values, emergency priority codes, and spectrum occupancy matrices from the synchronization network to generate triplet feature vectors;

[0009] Step S2: Transmit the triplet feature vector through a two-layer Mesh network, select two reference target points with fixed geographic coordinates on the disaster area ground, obtain their three-dimensional coordinates, and construct a spatial reference line; calculate the vertical projection distance of each node to the spatial reference line based on the real-time position data of each node, and generate node offset parameters; dynamically generate refraction correction coefficients for the signal propagation path based on the comparison results of the node offset parameters and a preset threshold; when the offset exceeds the threshold, use piecewise linear interpolation to increase the correction weight; perform convolution operation on the correction weights and the spectrum occupancy matrix in the triplet feature vector, and perform phase compensation on the user MAC address hash value to generate the corrected triplet feature vector;

[0010] Step S3: The regional master node receives the corrected triplet feature vector, and maps the time slot allocation scheme, frequency band switching strategy and corrected weights through spatiotemporal interleaving coding to generate an enhanced three-dimensional virtual base station configuration containing dynamic beam pointing parameters.

[0011] Step S4: Establish a communication link based on the enhanced three-dimensional virtual base station configuration, and adjust the link direction in real time according to the beam pointing parameters to dynamically optimize the signal strength and latency of the communication link and complete the signal coverage of the disaster area.

[0012] Secondly, a distributed collaborative processing system for ground-based satellite signals includes:

[0013] The generation module is used to extract user MAC address hash values, emergency priority codes, and spectrum occupancy matrices from the synchronization network and generate triplet feature vectors.

[0014] The correction module transmits the triplet feature vector through a two-layer mesh network, selects two reference target points with fixed geographic coordinates on the disaster area ground, obtains their three-dimensional coordinates, and constructs a spatial reference line; calculates the vertical projection distance of each node to the spatial reference line based on the real-time position data of each node, and generates node offset parameters; dynamically generates refraction correction coefficients for the signal propagation path based on the comparison results of the node offset parameters and a preset threshold; when the offset exceeds the threshold, a piecewise linear interpolation method is used to increase the correction weight; the correction weights are convolved with the spectrum occupancy matrix in the triplet feature vector, and phase compensation is performed on the user MAC address hash value to generate the corrected triplet feature vector;

[0015] The mapping module is used by the regional master node to receive the corrected triplet feature vector, and to associate and map the time slot allocation scheme, frequency band switching strategy and corrected weights through spatiotemporal interleaving coding to generate an enhanced three-dimensional virtual base station configuration containing dynamic beam pointing parameters.

[0016] The optimization module is used to establish a communication link based on the configuration of the enhanced three-dimensional virtual base station, and adjust the link direction in real time according to the beam pointing parameters to dynamically optimize the signal strength and latency of the communication link and complete the signal coverage of the disaster area.

[0017] Thirdly, a computing device, comprising:

[0018] One or more processors;

[0019] A storage device for storing one or more programs that, when executed by one or more processors, cause the one or more processors to implement the method.

[0020] Fourthly, a computer-readable storage medium storing a program that, when executed by a processor, implements the method.

[0021] The above-described solution of the present invention has at least the following beneficial effects:

[0022] Based on 3D topographical data of the disaster area, the porosity of the ruins is calculated, and differentiated deployment is carried out while real-time monitoring of obstacle displacement changes is conducted. Combined with a distributed Byzantine fault-tolerant consensus algorithm, the clock synchronization period is dynamically adjusted. A synchronous communication network resistant to terrain interference is formed through multi-hop relay, which effectively copes with the complex terrain of the disaster area and improves communication stability. The user MAC address hash value, emergency priority code and spectrum occupancy matrix are accurately extracted from the synchronous network. By correlating the historical data of user equipment signal transmission power and combining it with the disaster emergency level table, users are dynamically classified, which can allocate resources more rationally and prioritize life detection and rescue command communications. By constructing a spatial reference line, the vertical projection distance from each node to the reference line is calculated to generate node offset parameters. Based on these offset parameters, refraction correction coefficients for the signal propagation path are dynamically generated. Convolution operations are performed on the spectrum occupancy matrix, and phase compensation is applied to the user MAC address hash value, effectively correcting the signal propagation path and improving signal transmission quality. The regional master node uses spatiotemporal interleaving coding to map the time slot allocation scheme, frequency band switching strategy, and correction weights, generating an enhanced 3D virtual base station configuration containing dynamic beam pointing parameters. This configuration can rationally allocate time slots and frequency band resources, optimize beam pointing, and improve the utilization efficiency of communication resources. Communication links are established based on this enhanced 3D virtual base station configuration, and the link direction is adjusted in real time. An adaptive beam adjustment mechanism and a Kalman filter dynamically optimize the signal strength and latency of the communication link, activating coordinated transmission across all network nodes. This effectively covers the target area in the disaster zone, reduces signal blind spots, and improves signal coverage and communication quality in the disaster area. Attached Figure Description

[0023] Figure 1 This is a flowchart illustrating a distributed collaborative processing method for ground satellite signals provided by an embodiment of the present invention.

[0024] Figure 2 This is a schematic diagram of a distributed collaborative processing system for ground satellite signals provided by an embodiment of the present invention. Detailed Implementation

[0025] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0026] like Figure 1 As shown, an embodiment of the present invention proposes a distributed collaborative processing method for ground satellite signals, the method comprising the following steps:

[0027] Step S1: Extract user MAC address hash values, emergency priority codes, and spectrum occupancy matrices from the synchronization network to generate triplet feature vectors;

[0028] Step S2: Transmit the triplet feature vector through a two-layer Mesh network, select two reference target points with fixed geographic coordinates on the disaster area ground, obtain their three-dimensional coordinates, and construct a spatial reference line; calculate the vertical projection distance of each node to the spatial reference line based on the real-time position data of each node, and generate node offset parameters; dynamically generate refraction correction coefficients for the signal propagation path based on the comparison results of the node offset parameters and a preset threshold; when the offset exceeds the threshold, use piecewise linear interpolation to increase the correction weight; perform convolution operation on the correction weights and the spectrum occupancy matrix in the triplet feature vector, and perform phase compensation on the user MAC address hash value to generate the corrected triplet feature vector;

[0029] Step S3: The regional master node receives the corrected triplet feature vector, and maps the time slot allocation scheme, frequency band switching strategy and corrected weights through spatiotemporal interleaving coding to generate an enhanced three-dimensional virtual base station configuration containing dynamic beam pointing parameters.

[0030] Step S4: Establish a communication link based on the enhanced three-dimensional virtual base station configuration, and adjust the link direction in real time according to the beam pointing parameters to dynamically optimize the signal strength and latency of the communication link and complete the signal coverage of the disaster area.

[0031] In this embodiment of the invention, spatial reference straight line modeling solves the synchronization problem of dynamic node position changes, maintaining high-precision coordination between nodes even in GPS-free scenarios and reducing signal demodulation error rate. Dynamic adjustment of signal correction weights based on node offset effectively compensates for signal fading caused by terrain such as ruins and metal structures, reducing signal attenuation in obstructed areas and eliminating communication blind spots. Combining emergency priority and spectrum occupancy to dynamically allocate channel resources prioritizes real-time service transmission such as rescue operations, improving the accuracy of multi-source signal separation and reducing network interruption risks. Dynamically adjusting beam pointing expands signal coverage, focusing on high-demand areas to reduce power consumption at edge nodes, extending device endurance and reducing data packet loss, ensuring stable data transmission. Through dual processing of phase compensation and convolution operations, electromagnetic interference and multipath fading are suppressed, maintaining stable communication even in strong interference environments. This method solves core problems in emergency communication such as synchronization, interference, and coverage, improving network reliability, flexibility, and energy efficiency.

[0032] In a preferred embodiment of the present invention, before step S1, which involves extracting the user MAC address hash value, emergency priority code, and spectrum occupancy matrix from the synchronization network to generate the triplet feature vector, the method further includes:

[0033] Based on the three-dimensional topographical scanning data of the disaster area, the porosity of the ruins was calculated, and high-porosity areas and low-porosity areas were divided. Differentiated deployment strategies were implemented according to the porosity differences to form a node network.

[0034] Based on the node network, the displacement of obstacles is monitored in real time. If the displacement of obstacles causes the local porosity to change beyond a preset threshold, the node is re-hovered at the new porosity position according to the porosity distribution heat map fed back by the individual node to obtain the adjusted hybrid topology network node distribution density.

[0035] Based on the adjusted node distribution density of the hybrid topology network, the dynamic voting mechanism in the distributed Byzantine fault-tolerant consensus algorithm is adopted to dynamically adjust the clock synchronization period according to the signal propagation delay between nodes, and broadcast the synchronization signal to nodes blocked by obstacles through multi-hop relay, forming a synchronization communication network resistant to terrain interference.

[0036] In this embodiment of the invention, when applied in a specific application, the above steps can be implemented in the following ways, for example:

[0037] The 3D scanning data of the disaster area is divided into cubic grid cells according to spatial resolution (e.g., 1m×1m×1m). The volume ratio and void space distribution of the ruin materials (concrete, steel bars, bricks, etc.) in each grid cell are extracted. For each grid cell, the pore volume is calculated by "void volume = total volume of cell - volume of solid material", and the porosity is calculated by "porosity = pore volume / total volume of cell × 100%", forming a global porosity matrix.

[0038] Regional division and differentiated deployment:

[0039] Set a critical porosity value (e.g., 30%) and define areas with porosity > 30% as "high porosity areas" (good signal penetration but easily changeable terrain). Deploy drone hovering nodes: Based on the area and signal coverage radius (e.g., 500m), randomly select hovering points at a safe height (15-30m) to ensure that the overlap rate of signal coverage edges of adjacent nodes is ≤10% to avoid interference.

[0040] Areas with porosity ≤30% are designated as "low porosity areas" (severe signal obstruction). Vehicle-mounted mobile nodes are deployed in a denser manner: nodes are set up on roads or open paths at grid intervals (e.g., 200m×200m), prioritizing locations with higher terrain or fewer obstacles. Vehicle-mounted sensors scan the signal transmission loss within a 50m radius in real time, and the node spacing is dynamically adjusted to ensure signal coverage without blind spots.

[0041] The individual soldier node collects three-axis motion data in real time through the inertial navigation module (accelerometer + gyroscope), and calculates the three-dimensional displacement (Δx, Δy, Δz) of the obstacle it is in every 2 seconds, combined with the barometer altitude information. When the cumulative displacement is greater than 20cm for 3 consecutive times, an early warning is triggered.

[0042] Centered on the displacement obstacle, a 50m radius influence area is defined. The porosity data of the three-dimensional mesh within this area is retrieved, and the porosity change before and after displacement is calculated (Δρ = ρnew - ρoriginal). If Δρ > 15% in a local area (i.e., the porosity decreases or increases significantly), a porosity distribution heat map is generated (with different colors marking the porosity change gradient).

[0043] Drone node relocation strategy:

[0044] After receiving the heat map data, the UAV prioritizes selecting a new location within the affected area with a porosity ≥ the original threshold + 5% (i.e., an unblocked high-porosity channel). Based on the Dijkstra algorithm, it plans an obstacle avoidance path to avoid the displaced obstacles (maintaining a safe distance ≥ 10m). After hovering to the target point, it verifies the coverage effect through signal mutual testing between nodes (requiring a received signal strength ≥ -80dBm). If the requirement is not met, it reselects a second-best location within a 50m radius.

[0045] Signal propagation delay measurement and synchronization period adjustment:

[0046] Each node periodically (initially 10s) sends ping packets with timestamps to its neighboring nodes. The receiver records the reception time and calculates the one-way propagation delay using the bidirectional delay formula ((reception time - transmission time) / 2). The average value of five consecutive measurements is taken as the inter-node delay τ.

[0047] When more than 30% of the nodes in the network have a latency τ > 50ms (indicating a complex network topology or obstruction), a dynamic voting mechanism is triggered: nodes participate in voting based on their own latency values. If a majority of nodes (>2 / 3) support shortening the synchronization period, the period is adjusted to 5s; if the latency is generally < 20ms, the 10s period is restored to balance synchronization accuracy and energy consumption.

[0048] Multi-hop relay anti-interference broadcast:

[0049] After synchronization, the beacon node prioritizes neighboring nodes with signal strength > -70dBm and latency < 30ms as relay nodes to build multi-hop paths.

[0050] For nodes obstructed by obstacles (failed to receive synchronization signals 3 times consecutively), the "blind relay" mode is activated: the beacon node broadcasts an enhanced signal (power increased by 3dB) towards the obstructed area, and the relay node uses the RSSI signal strength to infer the obstruction location, bypasses the obstacle (such as bypassing building ruins) to forward the signal, ensuring that the synchronization signal arrival rate is ≥95%.

[0051] In this embodiment of the invention, through three-dimensional porosity quantification analysis, UAVs and vehicle-mounted nodes are deployed differently based on the signal propagation characteristics of high / low porosity regions. UAVs provide flexible coverage in low-obstruction areas, while vehicle-mounted nodes provide dense relaying in strong-obstruction areas, improving the overall signal coverage balance of the network. Real-time sensing of porosity abrupt changes caused by obstacle displacement allows for dynamic adjustment of UAV node positions based on heatmaps, ensuring that nodes remain on the optimal signal transmission path in dynamic scenarios such as aftershocks and collapses, avoiding local communication blind spots caused by environmental changes. The synchronization period is dynamically adjusted based on signal propagation delay, shortening the synchronization interval in complex topologies to reduce clock skew. Combined with multi-hop relay and blind relay strategies, the limitations of obstacles on synchronization signals are overcome, ensuring the time consistency of distributed nodes and improving collaborative processing efficiency and system reliability.

[0052] In a preferred embodiment of the present invention, step S1: extracting the user MAC address hash value, emergency priority code, and spectrum occupancy matrix from the synchronization network to generate a triplet feature vector may include:

[0053] Step S1-1: Capture the broadcast signal of the user equipment through the synchronization network, extract its MAC address, and generate a fixed-length unique hash identifier using the SHA-256 hash algorithm;

[0054] Step S1-2: Based on the hash identifier, associate the historical signal transmission power data of the user equipment, and combine it with the preset disaster emergency level table to dynamically classify the user: if the signal transmission power is consistently higher than the threshold and the identifier belongs to the rescue equipment whitelist, then mark it as life detection; if the signal latency is lower than the threshold and the identifier belongs to the command center database, then mark it as rescue command; the rest are marked as ordinary communication, and assign a corresponding emergency priority code to each category;

[0055] Step S1-3: Based on the emergency priority coding, sample the frequency band where life detection users are located, and sparsely sample the frequency band where ordinary communication users are located, and integrate the signal strength of different frequency bands into a spectrum occupancy matrix according to the time series.

[0056] Step S1-4: Encapsulate the hash identifier, the emergency priority code, and the spectrum occupancy matrix into a triplet feature vector in a preset binary format. In the data structure of the triplet feature vector, the hash identifier is used as the header field, the emergency priority code is used as the intermediate control field, and the spectrum occupancy matrix is ​​used as the tail load field. The three are seamlessly spliced ​​together using a length identifier.

[0057] In this embodiment of the invention, when applied in a specific way, the above steps can be implemented in the following manner, for example:

[0058] The implementation process of step S1-1 above can be as follows:

[0059] Broadcast signal capture: Access nodes in the synchronous network continuously listen for broadcast signals (such as Wi-Fi Beacon frames and Bluetooth broadcast packets) in the 2.4GHz / 5GHz band. They filter valid signals by signal strength detection (RSSI > -90dBm) and extract the source MAC address (48-bit hexadecimal string) from the signal.

[0060] Hash algorithm processing: The MAC address is converted into a byte stream, input into the SHA-256 hash algorithm, and after 64 rounds of iterative operations (including message padding, grouping, and compression function processing), a 256-bit fixed-length hexadecimal hash value (such as a 64-bit string) is generated as the unique identifier of the user device, ensuring that the hash values ​​of different MAC addresses are collision-free and irreversibly restored.

[0061] The implementation process of the above steps S1-2 can be as follows:

[0062] Historical data association: Each node maintains a signal transmission power log for user equipment (records transmission power every 10 seconds, retains data from the most recent 5 minutes), and calculates the average transmission power (e.g., the default transmission power of a mobile phone is 20dBm, while professional equipment can reach 30dBm).

[0063] Classification criteria determination:

[0064] Life detection: If the average transmission power is greater than 25dBm (threshold) for 5 minutes and the hash identifier matches the preset rescue equipment whitelist (such as the MAC prefix of life detectors and drone control terminals), it is marked as the highest priority (code 001).

[0065] For rescue command: If the signal transmission delay (round-trip time RTT) is less than 50ms (threshold), and the hash identifier exists in the command center's dedicated equipment database (such as the MAC list of vehicle-mounted command terminals and satellite phones), it is marked as secondary priority (code 010).

[0066] Ordinary communication category: User equipment that does not meet the above conditions (such as mobile phones of disaster victims) is marked as basic priority (code 100).

[0067] Encoding allocation: Based on the classification results, each user equipment is assigned a 3-bit binary emergency priority code (001 / 010 / 100) for subsequent resource scheduling.

[0068] The implementation process of steps S1-3 above can be as follows:

[0069] Differentiated sampling strategy: For life detection users, high-density sampling is implemented in their communication frequency bands (such as the dedicated rescue frequency band 400-470MHz), collecting signal strength every 10ms (covering 200 frequency points, each frequency point spaced 100kHz apart), continuing for 1 second to form a 200×100 time-intensity matrix. For ordinary communication users, sparse sampling is implemented in public frequency bands (such as 2.4GHz Wi-Fi), collecting signal strength every 100ms (covering 50 frequency points), continuing for 1 second to form a 50×10 time-intensity matrix.

[0070] Matrix integration: The frequency band sampling data of different users are concatenated according to the dimension of "frequency band number × timestamp". The rows represent frequency points (arranged in ascending order), the columns represent time (granularity 10ms), and the cell values ​​are signal strength (unit dBm). Finally, a spectrum occupancy matrix with dimension N×M is formed (N is the total number of frequency points, and M is the time series length).

[0071] The implementation process of steps S1-4 above can be as follows:

[0072] Field definition:

[0073] Header field (hash identifier): Convert the 256-bit hash value into 32 bytes of binary data. Add a field length identifier (0x0020) to the first 2 bytes to ensure that the receiver can recognize the header boundary.

[0074] Intermediate field (priority encoding): 3-bit binary encoding is extended to 1 byte (e.g., 001→0x01, 010→0x02, 100→0x04), and a length identifier (0x0001) is added to the first byte.

[0075] Tail field (spectrum occupancy matrix): The matrix is ​​converted into a binary stream and stored in "row-major" order (first store the intensity of frequency points 1-10ms in the first row, then store the intensity of frequency points 1-10ms in the second row). The matrix dimension information (N×M) is used as a prefix (4 bytes N + 4 bytes M), and the total length is identified as 8 + N×M bytes.

[0076] Concatenation rules: Concatenate the data in the order of "header length identifier + header data + middle length identifier + middle data + tail length identifier + tail data" to form a complete binary feature vector. Each field is parsed unambiguously through the length identifier (e.g., the receiving end first reads the header length 0x0020, and then reads the 32-byte hash value).

[0077] In this embodiment of the invention, device identifiers are generated using SHA-256 hashing to ensure the uniqueness of different devices and data security, avoiding privacy risks caused by directly exposing MAC addresses, and providing a reliable identity benchmark for subsequent user classification. Combining transmission power, signal latency, and device whitelists, the system accurately distinguishes between life detection, rescue command, and ordinary communication users, enabling hierarchical resource scheduling in emergency scenarios and improving the response efficiency of critical services. High-priority users undergo high-density spectrum sampling, with real-time monitoring of their signal occupancy to ensure uninterrupted operation of rescue frequency bands. Ordinary users are sampled sparsly to reduce system computational load and balance spectrum monitoring accuracy with resource consumption. By encapsulating data in triplets with fixed field formats and length identifiers, efficient splicing and parsing of different data types is achieved, providing a unified input interface for subsequent distributed collaborative processing and improving system compatibility and processing efficiency.

[0078] In a preferred embodiment of the present invention, step S2: transmitting the triplet feature vector through a two-layer mesh network, selecting two reference target points with fixed geographic coordinates on the disaster area ground, obtaining their three-dimensional coordinates, and constructing a spatial reference line may include:

[0079] Step S2-1: Based on the topological features of the disaster relief map, select the top of a building with a stable foundation at the entrance of the disaster area as the first target point, and select the base of the directional antenna tower deployed by the rescue center as the second target point. Obtain the geodetic coordinate system latitude, longitude and altitude data of the two target points.

[0080] Step S2-2: Convert the obtained geodetic coordinates to a spatial rectangular coordinate system. Specifically, this includes: calculating the transformation matrix from geodetic coordinates to spatial rectangular coordinates of the target point based on the WGS-84 ellipsoid parameters; and outputting the three-dimensional spatial coordinates of the two target points through the coordinate transformation interface built into the BeiDou positioning module.

[0081] Step S2-3: Based on the three-dimensional spatial coordinates of the two target points, construct a spatial reference line using a two-point linear equation.

[0082] In this embodiment of the invention, when applied in a specific way, the above steps can be implemented in the following manner, for example:

[0083] The implementation process of step S2-1 above can be as follows:

[0084] Target point selection criteria:

[0085] First target point: Based on the road network and building distribution in the disaster relief map, priority is given to the top of buildings with more than 3 floors at the entrance of the disaster area, with no cracks or tilting in the foundation (such as hotels and office buildings). It is required that there are no obstacles higher than 1 / 2 of their height within 100 meters around them to ensure that the Beidou signal reception is unobstructed.

[0086] The second target point is the base of the directional antenna tower that is fixedly deployed at the rescue command center. If there is no open area to choose from (such as the center of a square or a mountaintop), the terrain must be flat and there must be no dynamic displacement of the ground surface (such as soft sandy areas are excluded). The two points must be able to see each other (i.e. the height of obstacles in the direction of the line should not exceed 10% of the difference in altitude between the two points).

[0087] Coordinate acquisition process:

[0088] Using a Beidou terminal equipped with RTK differential positioning (positioning accuracy ±2cm), latitude and longitude (B, L) and altitude (H) data of each target point are continuously collected for 1 minute (1 second interval). After removing outliers with a deviation of more than 5cm, the average value is taken to finally obtain stable geodetic coordinates (B1, L1, H1) and (B2, L2, H2).

[0089] The implementation process of step S2-2 above can be as follows:

[0090] Ellipsoid parameter initialization:

[0091] Configure the basic parameters of the WGS-84 ellipsoid: semi-major axis a = 6378137m, semi-minor axis b = 6356752.3142m, flattening f = (ab) / a = 1 / 298.257223563, for geometric modeling of subsequent coordinate transformation.

[0092] Coordinate transformation implementation:

[0093] For each target point, first calculate the meridian radius of curvature N and the ramidal radius of curvature M (based on latitude B) as geometric parameters for coordinate transformation;

[0094] Using the coordinate transformation engine built into the BeiDou positioning module (such as an API interface that supports the NMEA-0183 protocol), the module automatically transforms the geodetic coordinates (B, L, H) into a spatial rectangular coordinate system (X, Y, Z) by inputting geodetic coordinates (B, L, H) and outputting three-dimensional coordinates (X1, Y1, Z1) and (X2, Y2, Z2). The transformation accuracy is controlled within ±5cm due to the limitations of the module's hardware.

[0095] The implementation process of steps S2-3 above can be as follows:

[0096] The direction vector of the line is determined:

[0097] Starting from the first target point P1(X1, Y1, Z1) and ending at the second target point P2(X2, Y2, Z2), calculate the three-dimensional spatial distance between the two points (by synthesizing the XYZ axis coordinate difference using the Pythagorean theorem). The distance must be ≥500 meters to ensure the spatial representativeness of the baseline line. If it is insufficient, a new target point is selected.

[0098] The coordinate differences are extracted as direction vectors (ΔX, ΔY, ΔZ) to describe the spatial orientation of the reference line.

[0099] Line equation construction and storage:

[0100] The spatial reference line is defined using a parametric representation: the coordinates of any point P on the line can be expressed as P = P1 + t·(ΔX, ΔY, ΔZ), where t is a real parameter;

[0101] The starting coordinates and direction vector of the baseline line are encapsulated into a standard data structure (such as a structure containing (X1, Y1, Z1), ΔX, ΔY, ΔZ), and synchronized to the local coordinate system database of all nodes through a two-layer Mesh network, serving as a reference for subsequent spatial positioning.

[0102] In this embodiment of the invention, by selecting target points with stable foundations and good visibility, and combining them with RTK differential positioning technology, it is ensured that the reference coordinates are not affected by local terrain changes in dynamic environments such as aftershocks and building collapses, providing a long-term reliable spatial reference for the entire disaster area communication network. Using WGS-84 ellipsoid parameters and the built-in conversion algorithm of the BeiDou module, traditional geodetic coordinates are converted into rectangular coordinates suitable for three-dimensional spatial calculations, eliminating coordinate system differences between different positioning systems and enabling position calculation of all network nodes under a unified spatial reference. This facilitates the subsequent construction and collaborative processing of three-dimensional signal propagation models. The constructed spatial reference line can serve as the axis for disaster area grid division, the geometric reference for UAV flight paths, and the constraint condition for node three-dimensional coordinate calibration, improving the positioning accuracy of distributed nodes in complex ruin environments and providing precise spatial coordinate support for tasks such as life detector positioning and precise UAV deployment. By synchronizing the reference line parameters to all nodes through a two-layer mesh network, each node can perform relative position calibration based on the reference line during multi-hop signal transmission, reducing collaborative processing deviations caused by inaccurate local node coordinates and enhancing the network's anti-interference capability in extreme terrain.

[0103] In a preferred embodiment of the present invention, the vertical projection distance from each node to the spatial reference line is calculated based on the real-time position data of each node, and node offset parameters are generated, which may include:

[0104] Step S2-4: Obtain the three-dimensional coordinates of the node's real-time position, project them onto the plane where the spatial reference line is located, and calculate the Euclidean distance between the node coordinates and the nearest point on the line as the vertical projection distance;

[0105] Step S2-5: Based on the influence of the surface material of the disaster area on the signal refractive index, add a terrain correction factor to the vertical projection distance to generate normalized node offset parameters.

[0106] In this embodiment of the invention, when applied in a specific way, the above steps can be implemented in the following manner, for example:

[0107] The implementation process of steps S2-4 above can be as follows:

[0108] Obtaining node coordinates:

[0109] Each node acquires its own three-dimensional spatial coordinates (X, Y, Z) in real time via its built-in BeiDou / GPS module at a frequency of 1 time per second. A sliding window filter (averaging the most recent 5 data points) is used to eliminate multipath effects and transient noise, resulting in stable real-time position coordinates (X, Y, Z). n Y n Z n ).

[0110] Reference line parameter call:

[0111] Read the starting coordinates (X1, Y1, Z1) and direction vector (ΔX, ΔY, ΔZ) of the spatial reference line from the local database of the node, where the direction vector is the difference between the coordinates of the two reference points (X2-X1, Y2-Y1, Z2-Z1).

[0112] Projection point calculation:

[0113] Construct a vector (X) from the starting point of the baseline line to the node. n -X1, Y n -Y1, Z n -Z1), using the principle of vector projection, determine the nearest point of the node on the reference line:

[0114] Calculate the projection ratio of the angle between this vector and the reference line direction vector, and find the coordinates (X) of the point on the line closest to the node. p Y p Z p ).

[0115] Vertical distance calculation:

[0116] For node coordinates (X) n Y n Z n ) and the coordinates of the nearest point (X) p Y p Zp To calculate the Euclidean distance in three-dimensional space, calculate the coordinate difference along the X, Y, and Z axes respectively, and synthesize the final distance using the Pythagorean theorem, which is used as the perpendicular projection distance D from the node to the reference line.

[0117] The implementation process of steps S2-5 above can be as follows:

[0118] Surface material classification:

[0119] Based on the 3D scanning data of the disaster area and the image recognition results transmitted back by individual soldiers at the scene, the surface material at the location of the node is divided into three categories:

[0120] The initial correction factors for high-reflectivity materials (such as metal ruins and glass curtain walls), medium-reflectivity materials (such as concrete and brick), and low-reflectivity materials (such as soil and vegetation) are set to 1.5, 1.0, and 0.8, respectively.

[0121] Application of correction factor:

[0122] Based on the surface material type of the node's real-time location, the vertical projection distance D is weighted and corrected: if the node is located in a high-reflectivity material area, the corrected distance D' = D × 1.5; no correction is made for medium-reflectivity material areas (D' = D); and D' = D × 0.8 for low-reflectivity material areas.

[0123] Normalization process:

[0124] Calculate the corrected distances of all nodes in the network, determine the maximum distance threshold Dmax (e.g., 500 meters), and convert the D' of each node into an offset parameter δ in the range of 0-1:

[0125] δ = D' / Dmax (if D' > Dmax, then take δ = 1) to ensure that the offsets of different regions have a uniform dimension, which is convenient for subsequent algorithm processing.

[0126] In this embodiment of the invention, the spatial offset of nodes relative to a reference line is quantified by calculating the vertical projection distance, providing geometric constraints for multi-hop path planning in a two-layer mesh network, reducing diffraction loss of signals in complex terrain, and improving transmission efficiency. A surface material correction factor is introduced to compensate for the influence of different materials on signal refraction and reflection, making the offset parameters closer to the actual signal transmission path deviation, providing accurate environmental parameters for subsequent spectrum allocation and interference avoidance algorithms. The normalization of the offset parameters eliminates the influence of geographical differences in different disaster areas, enabling the algorithm to uniformly process node distribution data in different scenarios such as plains and mountains, improving the system's cross-scenario adaptability, and updating node positions and surface material information in real time to ensure that the offset parameters are dynamically adjusted according to changes in the ruins environment, providing data support for real-time optimization of the emergency communication network.

[0127] In a preferred embodiment of the present invention, a refraction correction coefficient for the signal propagation path is dynamically generated based on the comparison result between the node offset parameter and a preset threshold. When the offset exceeds the threshold, a piecewise linear interpolation method is used to increase the correction weight, which may include:

[0128] Step S2-6: Based on the statistical distribution of node offset parameters, set a first threshold and a second threshold to divide the nodes into low offset regions (offset ≤ first threshold), medium offset regions (first threshold < offset ≤ second threshold), and high offset regions (offset > second threshold), and initialize a dynamic compensation mechanism according to the region type.

[0129] Step S2-7: Implement differentiated correction strategies for different region types: For nodes in low offset regions, use a fixed correction coefficient and overlay terrain refraction compensation; for nodes in medium offset regions, calculate a linear proportional weight factor based on the offset gradient and smooth the weight by integrating the offset trends of adjacent nodes; for nodes in high offset regions, enable an exponential compensation function and dynamically adjust the compensation intensity in combination with the hovering height of the UAV node.

[0130] Step S2-8 When three or more nodes are detected consecutively in the high offset region, a local reconstruction of the hybrid topology network is triggered: an offset heat map is generated based on the spatial distribution of the high offset nodes, and the hovering position and altitude of the UAV nodes need to be adjusted are calculated; after the reconstruction scheme is confirmed by the distributed consensus algorithm, the UAV nodes are controlled to move to the new position to reduce the overall offset; the corrected weight matrix is ​​recalculated based on the reconstructed node distribution and updated to all associated nodes to obtain the final corrected weight matrix.

[0131] In this embodiment of the invention, when applied in a specific way, the above steps can be implemented in the following manner, for example:

[0132] The implementation process of steps S2-6 above can be as follows:

[0133] Threshold setting:

[0134] Based on the statistical distribution of the network's node offset parameters (e.g., using the 50th percentile as the first threshold T1 and the 80th percentile as the second threshold T2), the nodes are divided into:

[0135] Low offset region (δ≤T1): The signal propagation path is close to the reference straight line, and the terrain has little impact;

[0136] Mid-offset region (T1<δ≤T2): The linear effect of terrain refraction on the signal needs to be considered;

[0137] High offset region (δ>T2): The signal deviates significantly from the reference straight line and requires enhanced compensation.

[0138] Compensation mechanism initialization:

[0139] Initial correction coefficients are assigned to different regions: K1 = 1.0 (base value) for low offset region, K2 = 1.2 (light compensation) for medium offset region, and K3 = 1.5 (heavy compensation) for high offset region. A region-compensation mapping table is established and stored in each node.

[0140] The implementation process of steps S2-7 above can be as follows:

[0141] Low offset region processing:

[0142] A fixed correction coefficient K1 = 1.0 is adopted, and the terrain refraction compensation is superimposed (adjusted according to the surface material correction factor in step S2-5, such as +0.3 for metal areas and -0.1 for soil areas). The final coefficient K = K1 + terrain factor.

[0143] Processing of the middle offset area:

[0144] Calculate the offset gradient (the difference between the offset of the current node and the offset of the adjacent nodes) and generate a linear scaling weight factor (e.g., if the gradient is greater than 0.1, the weight is increased by 0.2).

[0145] The offset trends (rising / falling) of three adjacent nodes are combined, and the weight changes are smoothed by a moving average (if the weight rises continuously, the weight is increased by 0.1). The final coefficient K = K2 × (1 + gradient weight + trend weight).

[0146] High offset region processing:

[0147] When an exponential compensation function is enabled, the compensation intensity increases exponentially with the larger the offset.

[0148] The compensation intensity is dynamically adjusted based on the current hovering height of the drone node (e.g., compensation coefficient × 1.2 when height > 20m, × 0.8 when height < 10m), and the final coefficient K = exponential compensation × height factor.

[0149] The implementation process of steps S2-8 above can be as follows:

[0150] Heatmap generation and analysis:

[0151] When three or more nodes are detected consecutively in a high offset region, the three-dimensional coordinates of these nodes are extracted to generate a spatial heatmap (color depth indicates the magnitude of the offset).

[0152] Calculate the geometric center of the hotspot area and combine it with disaster area topographic data (such as building height and porosity) to predict the optimal signal propagation path.

[0153] Drone node scheduling:

[0154] Based on the heat map analysis results, plan new hovering positions for drone nodes (prioritize areas 50-100m above the heat map with porosity >40%).

[0155] A consensus is reached among relevant nodes through a distributed Byzantine fault-tolerant algorithm (such as PBFT) to confirm the reconstruction plan.

[0156] Network Reconstruction and Coefficient Update:

[0157] Control the drone to move to the new position and recalculate the projected distance and offset of all network nodes to the reference line;

[0158] Based on the reconstructed node distribution, steps S2-6 to S2-7 are re-executed to generate a new corrected weight matrix, which is then synchronized to all nodes via multi-hop broadcast.

[0159] In this embodiment of the invention, by using threshold division and regional differentiation correction, the refraction compensation intensity of the signal propagation path is dynamically adjusted according to the degree of node offset, ensuring communication quality while avoiding resource waste caused by overcompensation. The network reconstruction mechanism triggered in high offset areas effectively reduces the signal offset in local areas through the active scheduling of UAV nodes, improving network connectivity and anti-interference capabilities in complex terrain. The linear interpolation and trend fusion strategy in medium offset areas avoids abrupt changes in compensation coefficients, making signal path adjustment smoother and reducing network fluctuations caused by minor environmental changes. Network reconstruction is triggered only in high offset areas, concentrating limited UAV resources on the areas most in need of optimization, achieving a balance between resource utilization efficiency and communication quality. The reconstruction scheme is confirmed through a distributed consensus algorithm, ensuring the reliability and consistency of the decision-making process.

[0160] In a preferred embodiment of the present invention, the modified weights are convolved with the spectrum occupancy matrix in the triplet feature vector, and phase compensation is performed on the user MAC address hash value to generate the modified triplet feature vector, which may include:

[0161] Step S2-9: Based on the final corrected weight matrix, perform dynamic convolution processing on the spectrum occupancy matrix in the triplet feature vector: use the corrected weight matrix as the convolution kernel, and perform two-dimensional sliding window convolution operation along the time axis and frequency axis of the spectrum occupancy matrix; update the occupancy probability distribution of each frequency band according to the convolution result to generate the corrected spectrum matrix;

[0162] Step S2-10: Based on the frequency band occupancy probability in the corrected spectrum matrix, calculate the phase compensation amount of the user MAC address hash value: extract the time slot number corresponding to the high occupancy probability frequency band in the spectrum matrix, perform a modulo operation on it with the hash value length to obtain the base number of cyclic shifts; perform a cyclic left shift operation on the binary sequence of the user MAC address hash value, and dynamically adjust the number of shifts by the product of the base number of shifts and the correction weight.

[0163] Step S2-11: Reconstruct the data from the corrected spectrum matrix, the phase-compensated hash value, and the emergency priority code: normalize and scale the value of the emergency priority code according to the correction magnitude of the spectrum matrix; repackage it into a corrected triplet feature vector in the order of the head field (hash value), the middle control field (scaled priority code), and the tail field (spectrum matrix).

[0164] In this embodiment of the invention, when applied in a specific way, the above steps can be implemented in the following manner, for example:

[0165] The implementation process of steps S2-9 above can be as follows:

[0166] Convolution kernel configuration:

[0167] The final corrected weight matrix is ​​adjusted to a two-dimensional convolution kernel that matches the spectrum occupancy matrix, ensuring that the kernel size (e.g., 3×3 or 5×5) covers at least 3 consecutive frequency points and 3 time windows.

[0168] Sliding window convolution:

[0169] The convolution kernel slides along the time axis (column direction) and frequency axis (row direction) of the spectrum matrix, covering a local region at a time;

[0170] The signal strength value in each local area is multiplied by the corresponding correction weight and then summed to obtain the convolutional value, which reflects the comprehensive correction result of the signal in that area affected by the terrain.

[0171] Probability distribution update:

[0172] The convolution results are normalized, and the occupancy probability of each frequency band in different time slots is recalculated (e.g., the original probability of 0.8 is adjusted to 0.92 after convolution).

[0173] Generate a revised spectrum matrix, highlighting the frequency bands occupied by high-priority users (such as increasing the probability of frequency band occupancy for life detection users).

[0174] The implementation process of the above step S2-10 can be as follows:

[0175] Base number of bits calculation:

[0176] Extract the time slot numbers (such as time slot 5 and time slot 12) corresponding to the frequency bands with an occupancy probability ≥ 0.7 from the corrected spectrum matrix;

[0177] After summing these time slot numbers, take the modulo of the hash value length (256 bits) to obtain the base number of bits for the cyclic shift (e.g., if the sum is 345, modulo 256 gives 89).

[0178] Dynamic phase adjustment:

[0179] The user's MAC address hash value is converted into a binary sequence, and the actual shift number is determined by the product of the base number of bits and the corrected weight of the corresponding node (e.g., weight 1.5 × base 89 = 133.5, rounded down to 134 bits).

[0180] Perform a circular left shift operation on the binary sequence (e.g., shift the first 134 bits of the original sequence to the end) to generate a phase-compensated hash value.

[0181] The implementation process of step S2-11 above can be as follows:

[0182] Priority encoding scaling:

[0183] Based on the correction magnitude of the spectrum matrix (such as the maximum probability boost value), the urgent priority coding is normalized and scaled:

[0184] If the correction magnitude is greater than 20%, then the priority code value is multiplied by 1.2 (e.g., 010 → 011);

[0185] If the correction is less than 5%, the original value remains unchanged.

[0186] Data reassembly and encapsulation:

[0187] Repackage the corrected triplet feature vectors in the following order:

[0188] Header field: The phase-compensated hash value (256-bit binary sequence);

[0189] Intermediate control field: scaled emergency priority code (expanded to 4 bits, such as 010→0011);

[0190] Tail field: Corrected spectrum matrix (a two-dimensional array stored in row-major order);

[0191] Add length markers to each field (e.g., 32 bytes for the header, 1 byte for the middle, and N×M bytes for the tail) to ensure that the receiving end can parse it correctly.

[0192] In this embodiment of the invention, terrain refraction correction is directly integrated into the spectrum matrix through convolution operations, dynamically adjusting the occupancy probability of each frequency band to improve signal penetration and reliability in complex terrain and reduce the impact of multipath fading and signal blockage. Dynamic phase compensation is applied to the MAC address hash value, causing the hash value to change in real time according to spectrum usage, effectively resisting replay attacks and identity spoofing, and improving the security of the emergency communication network. Priority coding based on spectrum correction amplitude scaling further allocates resources to high-priority users, ensuring uninterrupted critical communication in harsh environments. The basic structure of the triplet feature vector remains unchanged, and enhanced functionality is achieved through dynamic adjustment of field values, reducing upgrade costs. Combining terrain correction and priority scheduling improves the system's anti-interference capability while avoiding excessive spectrum resource consumption, achieving an optimal balance between communication quality and resource consumption.

[0193] In a preferred embodiment of the present invention, step S3: the regional master node receives the corrected triplet feature vector, and maps the time slot allocation scheme, frequency band switching strategy, and corrected weights through spatiotemporal interleaving coding to generate an enhanced three-dimensional virtual base station configuration containing dynamic beam pointing parameters, including:

[0194] Step S3-1: Based on the corrected weights in the modified triplet feature vector, the weight values ​​are mapped to the time domain through spatiotemporal interleaving coding: a time slot allocation priority table is generated according to the magnitude of the corrected weights, in which user equipment with higher weights is given priority in allocating continuous time slots; the update cycle of the time slot allocation weight table is dynamically adjusted in combination with the scaling value of the emergency priority coding.

[0195] Step S3-2: Based on the time slot allocation weight table, perform spectrum hole detection on the corrected spectrum occupancy matrix: identify unoccupied spectrum hole areas within the frequency band range of the allocated time slots; dynamically plan frequency band switching paths according to the time slot requirements of high-priority users;

[0196] Step S3-3: Based on the frequency band switching path and time slot allocation weight table, generate dynamic beam pointing parameters through beamforming algorithm: weighted fusion of correction weights and spatial location coordinates of nodes to calculate the phase gradient of beam formation; adjust the main lobe direction of the beam to cover the area where high-priority users are located according to the time-frequency characteristics of the frequency band switching path, and generate null regions in the direction of interference sources.

[0197] Step S3-4: Perform a three-dimensional correlation mapping of the time slot allocation scheme, frequency band switching strategy, and dynamic beam pointing parameters: construct a three-dimensional configuration matrix with the time slot allocation scheme as the time dimension constraint, the frequency band switching path as the frequency dimension constraint, and the beam pointing parameters as the spatial dimension constraint; associate the three-dimensional configuration matrix with the corrected weights through spatiotemporal interleaving coding to generate an enhanced three-dimensional virtual base station configuration containing dynamic beam pointing parameters.

[0198] In this embodiment of the invention, when applied in a specific way, the above steps can be implemented in the following manner, for example:

[0199] The implementation process of step S3-1 above can be as follows:

[0200] Priority table generation:

[0201] Arrange the corrected weights in the modified triplet feature vectors in descending order to construct a time slot allocation priority table. User equipment with higher weights is given priority in allocating consecutive time slots (e.g., users with weights > 1.5 are given priority in obtaining 3 consecutive time slots).

[0202] Dynamic update cycle adjustment:

[0203] The update frequency of the time slot allocation table is dynamically adjusted based on the scaling value of the emergency priority code.

[0204] The time slot allocation table for high-priority users (such as those coded as 0011 after scaling) is updated every 100ms;

[0205] The time slot allocation table for ordinary users (coded as 1000) is updated every 500ms to balance real-time performance and computational overhead.

[0206] The implementation process of step S3-2 above can be as follows:

[0207] Hollow area identification:

[0208] Within the frequency band of the allocated time slots, the corrected spectrum occupancy matrix is ​​scanned line by line to identify continuous frequency point regions with signal strength <0.3 (i.e., unoccupied) and marked as spectrum holes.

[0209] Dynamic path planning:

[0210] For high-priority users (such as life detection users) with time slot requirements, priority should be given to finding spectrum holes near their current frequency bands.

[0211] If there are no available slots in the local frequency band, a cross-band handover path (such as switching from 2.4GHz to 5GHz) should be planned to ensure that the signal interruption time during the handover process is less than 10ms.

[0212] The implementation process of step S3-3 above can be as follows:

[0213] Phase gradient calculation:

[0214] The corrected weights are weighted and fused with the three-dimensional spatial coordinates of the nodes. The higher the weight of the node, the greater its influence on the beam direction.

[0215] Calculate the phase difference between each node to generate the phase gradient required for beamforming.

[0216] Beam direction adjustment:

[0217] The beam main lobe direction is dynamically adjusted based on the time-frequency characteristics of the frequency band switching path.

[0218] For areas where high-priority users are located (such as rescue command centers), the main lobe gain will be increased by 3dB;

[0219] A zero-depression region with a depth of -15dB is generated in the direction of the interference source (such as a highly reflective metal ruin) to suppress the interference signal.

[0220] The implementation process of steps S3-4 above can be as follows:

[0221] 3D matrix construction:

[0222] A three-dimensional configuration matrix is ​​constructed with the time slot allocation scheme as the time dimension (X-axis), the frequency band switching path as the frequency dimension (Y-axis), and the beam pointing parameters as the spatial dimension (Z-axis).

[0223] Each element of the matrix stores parameters such as beam gain and phase at the corresponding spatiotemporal location.

[0224] Spatiotemporal interleaving coding:

[0225] The three-dimensional configuration matrix is ​​associated with the modified weights, and the matrix elements are spatiotemporally interleaved and encoded.

[0226] In the time domain, the configuration parameters of adjacent time slots are differentially encoded to reduce transmission redundancy;

[0227] In the frequency domain, convolutional coding is applied to the frequency band switching path to enhance anti-interference capabilities;

[0228] Generate an enhanced 3D virtual base station configuration that includes dynamic beam pointing parameters.

[0229] In this embodiment of the invention, spatiotemporal interleaving coding deeply integrates resource scheduling across three dimensions: time slot allocation, frequency band switching, and beam pointing, achieving three-dimensional collaborative optimization across the entire spatial, temporal, and frequency domains to improve spectrum utilization. Based on corrected weights and emergency priority coding, time slot priority allocation, dedicated frequency band protection, and beam pointing enhancement are implemented for high-priority users to ensure the reliability of critical communications in complex environments. Through spectrum hole detection and dynamic null beamforming, obstacle reflections and multipath interference are effectively avoided, improving signal transmission quality and reducing the bit error rate. Configuration parameters are updated in real time with the corrected weights, supporting rapid response to changes in disaster area environments and maintaining the dynamic stability of the communication network. Through three-dimensional virtual base station configuration, the spatial limitations of traditional physical base stations are overcome, achieving comprehensive signal coverage in complex disaster areas and reducing communication blind spots.

[0230] In a preferred embodiment of the present invention, step S4: establishing a communication link based on the enhanced three-dimensional virtual base station configuration, and adjusting the link direction in real time according to the beam pointing parameters, dynamically optimizing the signal strength and latency of the communication link to achieve signal coverage in the disaster area, including:

[0231] Step S4-1: Establish an initial communication link based on the dynamic beam pointing parameters in the enhanced 3D virtual base station configuration, and detect the link quality in real time by receiving signal strength indication;

[0232] Step S4-2: If the detected signal strength is lower than the preset threshold, the adaptive beam adjustment mechanism is triggered: the signal attenuation direction is determined according to the signal strength distribution map, the beam pointing angle is gradually adjusted according to the spiral search mode, and the signal strength is re-detected after each adjustment; when the signal strength reaches the threshold, the current beam pointing angle is locked and the dynamic beam pointing parameters are updated.

[0233] Step S4-3: Based on the adjusted beam pointing parameters, dynamically optimize the link delay and complete signal coverage: predict the delay jitter trend using a Kalman filter; if the predicted value exceeds the tolerance range, mark the current link as unstable; switch to the pre-configured redundant link according to the marking result, and recalculate the transmission path based on the updated spectrum occupancy matrix; match and verify the optimized transmission path with the corrected weights to generate the final communication link configuration; activate the coordinated transmission of all network nodes through the communication link configuration, cover the target area of ​​the disaster area according to the beam pointing parameters, and monitor the signal blind spot filling status in real time until the preset coverage threshold is reached.

[0234] In this embodiment of the invention, when applied in a specific way, the above steps can be implemented in the following manner, for example:

[0235] The implementation process of step S4-1 above can be as follows:

[0236] Link initialization:

[0237] The regional master node sends a link establishment request to the target node based on the dynamic beam pointing parameters (including phase gradient, main lobe direction, and null position) in the enhanced 3D virtual base station configuration, carrying the initial phase and gain parameters required for beamforming.

[0238] After receiving the data, the target node adjusts the phase offset of its own antenna array according to the parameters to form an initial communication link (such as aligning the main lobe of the directional antenna with a high-priority region near the reference line).

[0239] Real-time quality monitoring:

[0240] Link quality is detected by receiving signal strength indication (RSSI) at 50ms intervals, and the real-time signal strength value (unit: dBm) is recorded.

[0241] Set a signal strength threshold (e.g., -85dBm). If the detection value is lower than the threshold for three consecutive times, it is determined that the link quality has deteriorated, and the beam adjustment mechanism in step S4-2 is triggered.

[0242] The implementation process of step S4-2 above can be as follows:

[0243] Attenuation direction positioning:

[0244] Collect signal strength distribution data of all network nodes, generate a three-dimensional signal strength heat map, and locate the direction where the signal attenuation exceeds 10dB (e.g., if the signal strength of a certain area is 15dB lower than the surrounding area, it is marked as the attenuation direction).

[0245] Spiral search adjustment:

[0246] Centered on the current beam pointing angle, adjust the beam pointing in a spiral increment (5° initially, increasing by 2° each time) within the range of "azimuth ±15°, elevation ±10°". After each adjustment, wait 20ms for stabilization before detecting the signal strength.

[0247] If the signal strength improves by ≥5dB after adjustment, continue fine-tuning in that direction; if there is no improvement after 5 consecutive adjustments, then search in the opposite direction.

[0248] Pointer locking and parameter updates:

[0249] When the signal strength reaches or exceeds the threshold (e.g., -80dBm) and remains stable for 5 consecutive tests, lock the current beam pointing angle (azimuth θ, elevation angle). The new parameters are then synchronized to the dynamic beam pointing parameter table of the regional master node.

[0250] The implementation process of step S4-3 above can be as follows:

[0251] Latency jitter prediction:

[0252] The link delay is modeled using a Kalman filter. By inputting historical delay data (such as round-trip time (RTT) within the past 100ms), the delay jitter trend in the next 50ms is predicted.

[0253] Set a latency tolerance range (e.g., mean ±20ms). If the predicted value exceeds the tolerance, mark the link as "unstable".

[0254] Redundant link switching and path calculation:

[0255] Three backup links are pre-configured (based on different projection directions of the reference line), and paths with node offset ≤ the first threshold and signal strength ≥ -75dBm are preferentially selected as redundant links;

[0256] By combining the updated spectrum occupancy matrix (including the corrected frequency band occupancy probability), the transmission path is recalculated using the Dijkstra algorithm to avoid high-interference frequency bands (such as frequency bands with an occupancy probability > 0.9).

[0257] Matching verification and coordinated launch:

[0258] The optimized transmission path is matched with the node offset parameters and correction weights, requiring that the average correction weight of the nodes on the path be ≤1.2 (to avoid overcompensation areas);

[0259] Activate the coordinated transmission of all network nodes. The master node adjusts the transmission direction according to the new beam pointing parameters, and the UAV nodes adjust the hovering height synchronously (e.g., descend to 15m to enhance the signal when covering blind spots). Real-time monitoring of signal blind spots (areas with coverage of <90% are marked as blind spots);

[0260] Repeat beam adjustment and path optimization until the signal coverage in the disaster area is ≥95% (preset threshold).

[0261] In this embodiment of the invention, real-time RSSI monitoring and spiral search adjustment quickly compensate for signal attenuation caused by terrain obstruction or obstacle movement, ensuring that the link signal strength remains stable within a reliable range and improving the communication success rate in complex disaster areas. A Kalman filter predicts latency jitter in real time, identifying unstable links in advance and switching to redundant paths, reducing data transmission latency and packet loss rate, and ensuring the continuity of real-time services such as rescue command. Combined with dynamic beam pointing adjustment based on correction weights and node distribution, UAV nodes actively fill blind spots, ensuring signal coverage in disaster area edges and low-porosity obstructed areas, solving the blind spot problem that is difficult to deploy with traditional base stations. The coordinated optimization of beam pointing, transmission paths, and correction weights autonomously adapts to dynamic environmental changes such as aftershocks and obstacle displacement, reducing manual intervention and improving the self-sustaining capability of the emergency communication network. Priority is given to high-priority user links (such as life detection equipment), meeting communication needs while reducing overall network energy consumption and extending equipment runtime through coordinated transmission and dynamic power adjustment, adapting to power shortage scenarios in disaster areas.

[0262] like Figure 2 As shown, embodiments of the present invention also provide a ground satellite signal distributed collaborative processing system 20, comprising:

[0263] The generation module 21 is used to extract user MAC address hash values, emergency priority codes and spectrum occupancy matrices from the synchronization network and generate triplet feature vectors.

[0264] The correction module 22 is used to transmit the triplet feature vector through a two-layer mesh network, select two reference target points with fixed geographic coordinates on the disaster area ground, obtain their three-dimensional coordinates, and construct a spatial reference line; calculate the vertical projection distance of each node to the spatial reference line based on the real-time position data of each node, and generate node offset parameters; dynamically generate refraction correction coefficients for the signal propagation path based on the comparison results of the node offset parameters and a preset threshold; when the offset exceeds the threshold, a piecewise linear interpolation method is used to increase the correction weight; perform convolution operation on the correction weights and the spectrum occupancy matrix in the triplet feature vector, and perform phase compensation on the user MAC address hash value to generate the corrected triplet feature vector;

[0265] Mapping module 23 is used for the regional master node to receive the corrected triplet feature vector, and to associate and map the time slot allocation scheme, frequency band switching strategy and corrected weights through spatiotemporal interleaving coding to generate an enhanced three-dimensional virtual base station configuration containing dynamic beam pointing parameters.

[0266] The optimization module 24 is used to establish a communication link based on the configuration of the enhanced three-dimensional virtual base station, and adjust the link direction in real time according to the beam pointing parameters to dynamically optimize the signal strength and delay of the communication link and complete the signal coverage of the disaster area.

[0267] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A distributed collaborative processing method for ground satellite signals, characterized in that, The method includes: Step S1: Extract user MAC address hash values, emergency priority codes, and spectrum occupancy matrices from the synchronization network to generate triplet feature vectors; Step S2: Transmit the triplet feature vector through a two-layer Mesh network, select two reference target points with fixed geographic coordinates on the disaster area ground, obtain their three-dimensional coordinates, and construct a spatial reference line; calculate the vertical projection distance of each node to the spatial reference line based on the real-time position data of each node, and generate node offset parameters; dynamically generate refraction correction coefficients for the signal propagation path based on the comparison results of the node offset parameters and a preset threshold; when the offset exceeds the threshold, use piecewise linear interpolation to increase the correction weight; perform convolution operation on the correction weights and the spectrum occupancy matrix in the triplet feature vector, and perform phase compensation on the user MAC address hash value to generate the corrected triplet feature vector; Specifically, the triplet feature vector is transmitted through a two-layer mesh network; two reference target points with fixed geographic coordinates are selected on the disaster area ground; their three-dimensional coordinates are obtained; and a spatial reference line is constructed, including: Based on the topological features of the disaster relief map, the top of a building with a stable foundation at the entrance of the disaster area was selected as the first target point, and the base of the directional antenna tower deployed at the rescue center was selected as the second target point. The geodetic coordinate system latitude, longitude and altitude data of the two target points were obtained. The acquired geodetic coordinates are converted to a spatial rectangular coordinate system, specifically including: calculating the transformation matrix from geodetic coordinates to spatial rectangular coordinates of the target point based on the WGS-84 ellipsoid parameters; and outputting the three-dimensional spatial coordinates of the two target points through the coordinate transformation interface built into the BeiDou positioning module. Based on the three-dimensional spatial coordinates of two target points, a spatial reference straight line is constructed using a two-point straight line equation. Specifically, based on the real-time position data of each node, the vertical projection distance from each node to the spatial reference line is calculated, and node offset parameters are generated, including: Obtain the three-dimensional coordinates of the node's real-time position, project them onto the plane containing the spatial reference line, and calculate the Euclidean distance between the node's coordinates and the nearest point on the line as the vertical projection distance; Based on the influence of the surface material of the disaster area on the signal refractive index, a terrain correction factor is added to the vertical projection distance to generate normalized node offset parameters. Specifically, based on the comparison between the node offset parameter and a preset threshold, a refraction correction coefficient for the signal propagation path is dynamically generated. When the offset exceeds the threshold, a piecewise linear interpolation method is used to increase the correction weight, including: Based on the statistical distribution of node offset parameters, a first threshold and a second threshold are set to divide nodes into low offset regions (offset ≤ first threshold), medium offset regions (offset < first threshold ≤ second threshold), and high offset regions (offset > second threshold). A dynamic compensation mechanism is then initialized according to the region type. Differentiated correction strategies are implemented for different regions: For nodes in low offset regions, a fixed correction coefficient is used and terrain refraction compensation is superimposed; for nodes in medium offset regions, a linear proportional weight factor is calculated based on the offset gradient and the offset trends of adjacent nodes are fused for weight smoothing; for nodes in high offset regions, an exponential compensation function is enabled and the compensation intensity is dynamically adjusted in combination with the hovering height of the UAV node. When three or more nodes are detected consecutively in a high offset region, a local reconstruction of the hybrid topology network is triggered: an offset heat map is generated based on the spatial distribution of the high offset nodes, and the hovering position and altitude of the UAV nodes need to be adjusted are calculated; after the reconstruction scheme is confirmed by a distributed consensus algorithm, the UAV nodes are controlled to move to a new position to reduce the overall offset; the corrected weight matrix is ​​recalculated based on the reconstructed node distribution and updated to all associated nodes to obtain the final corrected weight matrix; Specifically, the modified weights are convolved with the spectral occupancy matrix in the triplet feature vector, and phase compensation is performed on the user MAC address hash value to generate the modified triplet feature vector, including: Based on the final corrected weight matrix, the spectrum occupancy matrix in the triplet feature vector is dynamically convolved: the corrected weight matrix is ​​used as the convolution kernel, and a two-dimensional sliding window convolution operation is performed along the time axis and frequency axis of the spectrum occupancy matrix; the occupancy probability distribution of each frequency band is updated according to the convolution result to generate the corrected spectrum matrix. Based on the frequency band occupancy probability in the corrected spectrum matrix, the phase compensation amount of the user MAC address hash value is calculated: extract the time slot number corresponding to the high occupancy probability frequency band in the spectrum matrix, perform a modulo operation on it with the hash value length to obtain the base number of cyclic shifts; perform a cyclic left shift operation on the binary sequence of the user MAC address hash value, and dynamically adjust the number of shifts by the product of the base number of shifts and the correction weight. The data is reconstructed by modifying the spectrum matrix, the phase-compensated hash value, and the emergency priority code: the value of the emergency priority code is normalized and scaled according to the modification magnitude of the spectrum matrix; and the modified triplet feature vector is repackaged in the order of the head field (hash value), the middle control field (scaled priority code), and the tail field (spectrum matrix). Step S3: The regional master node receives the corrected triplet feature vector, and uses spatiotemporal interleaving coding to associate and map the time slot allocation scheme, frequency band switching strategy, and corrected weights to generate an enhanced 3D virtual base station configuration containing dynamic beam pointing parameters, including: Based on the corrected weights in the modified triplet feature vector, the weight values ​​are mapped to the time domain through spatiotemporal interleaving coding: a time slot allocation priority table is generated according to the magnitude of the corrected weights, in which user equipment with higher weights is given priority in allocating continuous time slots; the update cycle of the time slot allocation weight table is dynamically adjusted by combining the scaling value of the emergency priority coding. Based on the time slot allocation weight table, spectrum hole detection is performed on the corrected spectrum occupancy matrix: within the frequency band range of the allocated time slots, unoccupied spectrum hole regions are identified; frequency band switching paths are dynamically planned according to the time slot requirements of high-priority users. Based on the frequency band switching path and time slot allocation weight table, dynamic beam pointing parameters are generated through beamforming algorithm: the correction weights are weighted and fused with the spatial coordinates of the nodes to calculate the phase gradient of beam formation; according to the time-frequency characteristics of the frequency band switching path, the direction of the main lobe of the beam is adjusted to cover the area where high-priority users are located, and a null region is generated in the direction of the interference source. The time slot allocation scheme, frequency band switching strategy, and dynamic beam pointing parameters are mapped in three dimensions: a three-dimensional configuration matrix is ​​constructed with the time slot allocation scheme as the time dimension constraint, the frequency band switching path as the frequency dimension constraint, and the beam pointing parameters as the spatial dimension constraint; the three-dimensional configuration matrix is ​​associated with the correction weight through spatiotemporal interleaving coding to generate an enhanced three-dimensional virtual base station configuration containing dynamic beam pointing parameters. Step S4: Establish a communication link based on the enhanced three-dimensional virtual base station configuration, and adjust the link direction in real time according to the beam pointing parameters to dynamically optimize the signal strength and latency of the communication link and complete the signal coverage of the disaster area.

2. The distributed collaborative processing method for ground satellite signals according to claim 1, characterized in that, Before step S1, which involves extracting the user MAC address hash, emergency priority code, and spectrum occupancy matrix from the synchronization network to generate the triplet feature vector, the following steps are also included: Based on the three-dimensional topographical scanning data of the disaster area, the porosity of the ruins was calculated, and high-porosity areas and low-porosity areas were divided. Differentiated deployment strategies were implemented according to the porosity differences to form a node network. Based on the node network, the displacement of obstacles is monitored in real time. If the displacement of obstacles causes the local porosity to change beyond a preset threshold, the node is re-hovered at the new porosity position according to the porosity distribution heat map fed back by the individual node to obtain the adjusted hybrid topology network node distribution density. Based on the adjusted node distribution density of the hybrid topology network, the dynamic voting mechanism in the distributed Byzantine fault-tolerant consensus algorithm is adopted to dynamically adjust the clock synchronization period according to the signal propagation delay between nodes, and broadcast the synchronization signal to nodes blocked by obstacles through multi-hop relay, forming a synchronization communication network resistant to terrain interference.

3. The distributed collaborative processing method for ground satellite signals according to claim 2, characterized in that, Step S1: Extract user MAC address hash values, emergency priority codes, and spectrum occupancy matrices from the synchronization network to generate triplet feature vectors, including: By capturing the broadcast signal of user equipment through a synchronous network, extracting its MAC address, and generating a fixed-length unique hash identifier using the SHA-256 hash algorithm; Based on hash identifiers, the system associates historical data of signal transmission power of user equipment with a pre-set disaster emergency level table to dynamically classify users: if the signal transmission power is consistently higher than the threshold and the identifier is in the rescue equipment whitelist, it is marked as a life detection type; if the signal latency is lower than the threshold and the identifier is in the command center database, it is marked as a rescue command type; the rest are marked as ordinary communication type, and a corresponding emergency priority code is assigned to each type. Based on the emergency priority coding, sampling is performed on the frequency band where life detection users are located, and sparse sampling is performed on the frequency band where ordinary communication users are located. The signal strength of different frequency bands is integrated into a spectrum occupancy matrix according to the time series. The hash identifier, the emergency priority code, and the spectrum occupancy matrix are encapsulated and arranged into a triplet feature vector in a preset binary format. In the data structure of the triplet feature vector, the hash identifier is used as the header field, the emergency priority code is used as the intermediate control field, and the spectrum occupancy matrix is ​​used as the tail load field. The three are seamlessly spliced ​​together using a length identifier.

4. The distributed collaborative processing method for ground satellite signals according to claim 3, characterized in that, Step S4: Establish a communication link based on the enhanced 3D virtual base station configuration, and adjust the link direction in real time according to the beam pointing parameters to dynamically optimize the signal strength and latency of the communication link, thereby achieving signal coverage in the disaster area, including: An initial communication link is established based on the dynamic beam pointing parameters in the enhanced 3D virtual base station configuration, and the link quality is detected in real time by the received signal strength indication. If the detected signal strength is lower than the preset threshold, the adaptive beam adjustment mechanism is triggered: the signal attenuation direction is determined according to the signal strength distribution map, the beam pointing angle is gradually adjusted in a spiral search mode, and the signal strength is re-detected after each adjustment; when the signal strength reaches the threshold, the current beam pointing angle is locked and the dynamic beam pointing parameters are updated. Based on the adjusted beam pointing parameters, the link delay is dynamically optimized and signal coverage is achieved: the delay jitter trend is predicted using a Kalman filter, and if the predicted value exceeds the tolerance range, the current link is marked as unstable; the link is switched to a pre-configured redundant link based on the marking result, and the transmission path is recalculated based on the updated spectrum occupancy matrix; the optimized transmission path is matched and verified with the corrected weights to generate the final communication link configuration; the coordinated transmission of all network nodes is activated through the communication link configuration, and the target area of ​​the disaster area is covered according to the beam pointing parameters, while the signal blind spot filling status is monitored in real time until the preset coverage threshold is reached.

5. A distributed collaborative processing system for ground-based satellite signals, wherein the system implements the method as described in any one of claims 1 to 3, characterized in that, include: The generation module is used to extract user MAC address hash values, emergency priority codes, and spectrum occupancy matrices from the synchronization network and generate triplet feature vectors. The correction module is used to transmit the triplet feature vector through a two-layer mesh network, select two reference target points with fixed geographic coordinates on the disaster area ground, obtain their three-dimensional coordinates, and construct a spatial reference line; calculate the vertical projection distance of each node to the spatial reference line based on the real-time position data of each node, and generate node offset parameters; based on the comparison result of the node offset parameters and a preset threshold, dynamically generate the refraction correction coefficient of the signal propagation path; when the offset exceeds the threshold, a piecewise linear interpolation method is used to increase the correction weight. The corrected weights are convolved with the spectrum occupancy matrix in the triplet feature vector, and the user MAC address hash value is phase compensated to generate the corrected triplet feature vector. The mapping module is used by the regional master node to receive the corrected triplet feature vector, and to associate and map the time slot allocation scheme, frequency band switching strategy and corrected weights through spatiotemporal interleaving coding to generate an enhanced three-dimensional virtual base station configuration containing dynamic beam pointing parameters. The optimization module is used to establish a communication link based on the configuration of the enhanced three-dimensional virtual base station, and adjust the link direction in real time according to the beam pointing parameters to dynamically optimize the signal strength and latency of the communication link and complete the signal coverage of the disaster area.

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