Ground satellite signal distributed cooperative processing method and system

Abstract

The invention provides a ground satellite signal distributed cooperative processing method and system, and relates to the technical field of communication, and the method comprises the steps: extracting a user MAC address hash value, an emergency priority code and a spectrum occupancy matrix from a synchronous network, and generating a triple feature vector; transmitting the triple feature vectors through a double-layer Mesh network, selecting two reference target points with fixed geographic coordinates on the ground of the disaster area, obtaining three-dimensional coordinates of the reference target points, and constructing a space reference straight line; according to the real-time position data of each node, calculating a vertical projection distance from each node to the space reference straight line, and generating a node offset parameter; and dynamically generating a refraction correction coefficient of the signal propagation path based on a comparison result of the node offset parameter and a preset threshold value, and when the offset exceeds the threshold value, increasing a correction weight by adopting a piecewise linear interpolation method. According to the invention, high-reliability and low-delay emergency communication coverage can be realized.

Description

Technical Field

[0001] The present 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 Art

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

[0003] For example, traditional node deployment usually adopts a uniform distribution strategy, which is unable to adaptively adjust according to the porosity of the ruins in the disaster area and the dynamic changes of obstacles, resulting in large signal coverage blind spots and low resource utilization. Especially in complex terrain, fixed nodes are easily affected by the displacement of obstacles caused by aftershocks, causing frequent interruptions of communication links.

[0004] For example, some approaches use static path loss models for signal compensation, failing to consider the real-time refraction effects of signal propagation paths in the dynamic environment of disaster zones. For example, factors such as the scattering of high-frequency signals by metal debris in the rubble and the differences in refractive index between obstacles made of different materials can lead to fluctuations in signal strength and latency jitter. Traditional approaches lack a dynamic correction mechanism based on geospatial references. Summary of the Invention

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

[0006] In order to solve the above technical problems, the technical solutions of the present invention are as follows:

[0007] In a first aspect, a method for distributed collaborative processing of terrestrial satellite signals is provided, the method comprising:

[0008] 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;

[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 ground in the disaster area, 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 a node offset parameter; based on the comparison result of the node offset parameter with a preset threshold, dynamically generate the refraction correction coefficient of the signal propagation path; when the offset exceeds the threshold, use piecewise linear interpolation to increase the correction weight; perform a convolution operation on the correction weight and the spectrum occupancy matrix in the triplet feature vector, and perform phase compensation on the user MAC address hash value to generate a corrected triplet feature vector;

[0010] Step S3: The regional master node receives the modified triplet feature vector, associates and maps the time slot allocation scheme, the frequency band switching strategy and the modified weights through time-space interleaving coding, and generates an enhanced three-dimensional virtual base station configuration including dynamic beam pointing parameters;

[0011] Step S4: establishing a communication link based on the enhanced three-dimensional virtual base station configuration, adjusting the link direction in real time according to the beam pointing parameters, dynamically optimizing the signal strength and delay of the communication link, and completing signal coverage of the disaster area.

[0012] In a second aspect, a distributed collaborative processing system for ground satellite signals includes:

[0013] A generation module is used to extract the user MAC address hash value, emergency priority code and spectrum occupancy matrix from the synchronization network to generate a triplet feature vector;

[0014] A correction module is used to transmit the triplet feature vector through a double-layer mesh network, select two reference target points with fixed geographic coordinates on the ground in the disaster area, 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 a node offset parameter; based on the comparison result of the node offset parameter with a preset threshold, dynamically generate a refraction correction coefficient for the signal propagation path; when the offset exceeds the threshold, use piecewise linear interpolation to increase the correction weight; perform a convolution operation on the correction weight and the spectrum occupancy matrix in the triplet feature vector, and perform phase compensation on the user MAC address hash value to generate a corrected triplet feature vector;

[0015] A mapping module is configured to receive the modified triplet feature vector at the regional master node, associate and map the time slot allocation scheme, the frequency band switching strategy, and the modified weights through time-space interleaving coding, and generate an enhanced three-dimensional virtual base station configuration including dynamic beam pointing parameters;

[0016] The optimization module is used to 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, dynamically optimize the signal strength and delay of the communication link, and complete signal coverage in the disaster area.

[0017] According to a third aspect, a computing device includes:

[0018] one or more processors;

[0019] The storage device is used to store one or more programs, and when the one or more programs are executed by the one or more processors, the one or more processors implement the method.

[0020] In a fourth aspect, a computer-readable storage medium stores a program, which implements the method when executed by a processor.

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

[0022] Based on the three-dimensional scanning data of the disaster area terrain, the porosity of the ruins is calculated, differentiated deployment is carried out, and the displacement changes of obstacles are monitored in real time. The clock synchronization period is dynamically adjusted in combination with the distributed Byzantine fault-tolerant consensus algorithm. A synchronous communication network that is 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 the signal transmission power of user equipment and combining it with the disaster emergency level table, users are dynamically classified, which can more reasonably allocate resources and give priority to life detection and rescue command communications. By constructing a spatial reference line and calculating the vertical projection distance from the node to the reference line to generate node offset parameters, the refraction correction coefficient of the signal propagation path is dynamically generated based on the offset parameters. A convolution operation is performed on the spectrum occupancy matrix and phase compensation is performed on the user MAC address hash value, effectively correcting the signal propagation path and improving signal transmission quality. The regional master node uses time-space interleaving coding to associate and map the time slot allocation plan, frequency band switching strategy, and correction weights to generate an enhanced three-dimensional virtual base station configuration containing dynamic beam pointing parameters. This can rationally allocate time slot and frequency band resources, optimize beam pointing, and improve the utilization efficiency of communication resources. Communication links are established based on the enhanced three-dimensional virtual base station configuration, and the link direction is adjusted in real time. The signal strength and delay of the communication link are dynamically optimized through an adaptive beam adjustment mechanism and Kalman filter, activating the coordinated transmission of all nodes in the network, effectively covering the target area of ​​the disaster area, reducing signal blind spots, and improving signal coverage and communication quality in the disaster area. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a flowchart of 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 DESCRIPTION

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

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

[0027] 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;

[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 ground in the disaster area, 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 a node offset parameter; based on the comparison result of the node offset parameter with a preset threshold, dynamically generate the refraction correction coefficient of the signal propagation path; when the offset exceeds the threshold, use piecewise linear interpolation to increase the correction weight; perform a convolution operation on the correction weight and the spectrum occupancy matrix in the triplet feature vector, and perform phase compensation on the user MAC address hash value to generate a corrected triplet feature vector;

[0029] Step S3: The regional master node receives the modified triplet feature vector, associates and maps the time slot allocation scheme, the frequency band switching strategy and the modified weights through time-space interleaving coding, and generates an enhanced three-dimensional virtual base station configuration including dynamic beam pointing parameters;

[0030] Step S4: establishing a communication link based on the enhanced three-dimensional virtual base station configuration, adjusting the link direction in real time according to the beam pointing parameters, dynamically optimizing the signal strength and delay of the communication link, and completing signal coverage of the disaster area.

[0031] In an embodiment of the present invention, spatial reference straight line modeling is used to solve the synchronization problem of dynamic node position changes, and high-precision coordination between nodes can still be maintained in a GPS-free scenario, reducing the signal demodulation error rate; the signal correction weight is dynamically adjusted based on the node offset to effectively compensate for signal fading caused by terrain such as ruins and metal structures, reduce signal attenuation in blocked areas, and eliminate communication blind spots. Channel resources are dynamically allocated based on emergency priority and spectrum occupancy, giving priority to the transmission of real-time services such as rescue, improving the accuracy of multi-source signal separation, and reducing the risk of network interruption; dynamic adjustment of beam pointing expands signal coverage, focusing on high-demand areas to reduce edge node power consumption, extend device life and reduce packet loss, and ensure stable data transmission; through dual processing of phase compensation and convolution operations, electromagnetic interference and multipath fading are suppressed, and stable communication can still be maintained in a strong interference environment. This method solves the core problems of synchronization, interference, coverage, etc. in emergency communications, and improves network reliability, flexibility, and energy efficiency.

[0032] In a preferred embodiment of the present invention, before 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, the method further includes:

[0033] The porosity of the ruins is calculated based on 3D scanning data of the disaster area terrain, and the areas are divided into high-porosity and low-porosity areas. Differentiated deployment strategies are implemented based on the porosity differences to form a node network.

[0034] Based on the node network, the obstacle displacement changes are monitored in real time. If it is detected that the obstacle displacement causes the local porosity change to exceed the preset threshold, the node will be re-hovered to the new pore position based on the pore distribution heat map fed back by the individual node to obtain the adjusted node distribution density of the hybrid topology network.

[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 synchronous communication network that is resistant to terrain interference.

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

[0037] The 3D scanning data of the disaster area are divided into cubic grid units according to the spatial resolution (such as 1m×1m×1m), and the volume proportion and void space distribution of the ruins materials (concrete, steel bars, bricks and stones, etc.) in each grid are extracted; for each grid unit, the pore volume is calculated through "void volume = total unit volume - solid material volume", and the porosity = pore volume / total unit volume × 100%, forming a global porosity matrix.

[0038] Regional division and differentiated deployment:

[0039] A porosity critical value (e.g., 30%) is set, and areas with porosity greater than 30% are defined as "high-porosity areas" (where signal penetration is good but terrain is changeable). UAV hovering node deployment is adopted: based on the area and signal coverage radius (e.g., 500m), hovering points are randomly selected at a safe height (15-30m), ensuring that the overlap rate of the signal coverage edges of adjacent nodes is ≤10% to avoid interference.

[0040] Areas with a porosity of ≤30% are classified as "low-porosity areas" (severe signal obstruction) and are deployed using vehicle-mounted mobile nodes for encrypted deployment: nodes are set up on roads or open paths at a grid spacing (e.g., 200m×200m), with priority given to locations with higher terrain or fewer obstacles. Vehicle-mounted sensors are used to 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 combines it with the barometer altitude information to calculate the three-dimensional displacement (Δx, Δy, Δz) of the obstacle every 2 seconds. When the cumulative displacement is greater than 20cm for three consecutive times, an early warning is triggered.

[0042] With the displacement obstacle as the center, an impact area with a radius of 50m is delineated. The porosity data of the three-dimensional grid in this area are retrieved, and the porosity change before and after displacement (Δρ = ρnew - ρoriginal) is calculated. If Δρ in a local area is greater than 15% (i.e., the porosity decreases or increases significantly), a pore distribution thermogram is generated (the porosity change gradient is marked with different colors).

[0043] Drone node relocation strategy:

[0044] After receiving the thermal map data, the drone prioritizes new locations within the affected area with a porosity ≥ the original threshold + 5% (i.e., unblocked high-porosity channels). It plans an obstacle avoidance path based on the Dijkstra algorithm to avoid displaced obstacles (maintaining a safe distance ≥ 10m). After hovering to the target point, it verifies the coverage effect through signal mutual measurement between nodes (received signal strength ≥ -80dBm). If the standard is not met, a suboptimal location is reselected within a nearby range of 50m.

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

[0046] Each node periodically (initial period 10s) sends a timestamp ping packet to its neighboring node. The receiver records the reception time and calculates the one-way propagation delay using the two-way delay formula ((receiving time - sending 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 delay τ greater than 50ms (indicating a complex network topology or obstruction), a dynamic voting mechanism is triggered: nodes vote based on their own delay values. If the majority of nodes (>2 / 3) support shortening the synchronization period, the period is adjusted to 5s; if the delay is generally less than 20ms, the 10s period is restored to balance synchronization accuracy and energy consumption.

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

[0049] After synchronization, the beacon node preferentially selects adjacent nodes with signal strength greater than -70dBm and delay less than 30ms as relay nodes to build a multi-hop path.

[0050] For nodes blocked by obstacles (failed to receive synchronization signals for three consecutive times), the "blind relay" mode is activated: the beacon node broadcasts an enhanced signal (power increased by 3dB) in the direction of the blocked area, and the relay node reversely infers the blocking position based on the RSSI signal strength, bypassing the obstacles (such as bypassing the ruins of buildings) to forward the signal, ensuring that the synchronization signal arrival rate is ≥95%.

[0051] In an embodiment of the present invention, through three-dimensional porosity quantitative analysis, drones and vehicle-mounted nodes are deployed differentially according to the signal propagation characteristics of high / low porosity areas. UAVs are used for flexible coverage in low-obstruction areas, and vehicle-mounted nodes are used for dense relay in strong-obstruction areas to improve the balance of signal coverage across the entire network. Porosity mutations caused by obstacle displacement are perceived in real time, and the positions of drone nodes are dynamically adjusted based on heat maps to ensure that in dynamic scenarios such as aftershocks and collapses, the nodes are always on the optimal signal transmission path to avoid local communication blind spots caused by environmental changes. The synchronization period is dynamically adjusted according to the signal propagation delay, and the synchronization interval is shortened in complex topologies to reduce clock deviation. Combined with multi-hop relay and blind relay strategies, the occlusion restrictions of obstacles on synchronization signals are broken through, the time consistency of distributed nodes is guaranteed, and the collaborative processing efficiency and system reliability are improved.

[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 use the SHA-256 hash algorithm to generate a unique hash identification code of fixed length;

[0054] Step S1-2: Based on the hash identification code, the user device's signal transmission power history data is associated with the preset disaster emergency level table to dynamically classify the user: if the signal transmission power is continuously higher than the threshold and the identification code belongs to the rescue equipment whitelist, it is marked as life detection; if the signal delay is lower than the threshold and the identification code belongs to the command center database, it is marked as rescue command; the rest are marked as ordinary communication, and a corresponding emergency priority code is assigned to each category;

[0055] Step S1-3: Based on the emergency priority code, sample the frequency band where life detection users are located, and perform sparse sampling on the frequency band where general communication users are located. The signal strengths of different frequency bands are integrated into a spectrum occupancy matrix in time series.

[0056] Step S1-4: encapsulate the hash identification code, the emergency priority code and the spectrum occupancy matrix, and arrange them into a triple feature vector in a preset binary format, wherein, in the data structure of the triple feature vector, the hash identification code is used as the header field, the emergency priority code is used as the middle control field, and the spectrum occupancy matrix is ​​used as the tail payload field, and the three are seamlessly spliced ​​together through a length identifier.

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

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

[0059] Broadcast signal capture: Access nodes in the synchronization network continuously monitor broadcast signals in the 2.4GHz / 5GHz band (such as Wi-Fi Beacon frames and Bluetooth advertising packets), filter valid signals through 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 and input into the SHA-256 hash algorithm. 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 identification code of the user device, ensuring that the hash values ​​of different MAC addresses have no collisions and are irreversible.

[0061] The implementation process of the above step S1-2 can be:

[0062] Historical data association: Each node maintains a log of the user device's signal transmission power (recording the transmission power every 10 seconds and retaining the most recent 5 minutes of data) 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:

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

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

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

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

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

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

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

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

[0072] Field definition:

[0073] Header field (hash identifier): Convert the 256-bit hash value into 32-byte binary data, and add the field length identifier (0×0020) to the first 2 bytes to ensure that the receiving end can identify the header boundary.

[0074] Middle field (priority code): The 3-bit binary code is expanded to 1 byte (such as 001→0×01, 010→0×02, 100→0×04), and the length identifier (0×0001) is added to the first byte.

[0075] Tail field (spectrum occupancy matrix): The matrix is ​​converted into a binary stream and stored in "row-first" order (the first row of frequency point intensities 1-10ms is stored first, followed by the second row of frequency point intensities 1-10ms). 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] Splicing rules: Splice in the order of "header length identifier + header data + intermediate length identifier + intermediate data + tail length identifier + tail data" to form a complete binary feature vector. Each field is parsed unambiguously through the length identifier (for example, the receiver first reads the header length 0×0020 and then reads the 32-byte hash value).

[0077] In an embodiment of the present invention, a device identification code is generated through SHA-256 hashing to ensure the uniqueness and data security of different devices, avoid privacy risks caused by direct exposure of MAC addresses, and provide a reliable identity benchmark for subsequent user classification; combined with transmission power, signal delay and device whitelist, life detection, rescue command and ordinary communication users are accurately distinguished, resource hierarchical scheduling is achieved in emergency scenarios, and the response efficiency of key services is improved; high-density spectrum sampling is implemented for high-priority users, and their signal occupancy is monitored in real time to ensure the smooth flow of rescue frequency bands; sparse sampling is used for ordinary users to reduce the system computing load and balance spectrum monitoring accuracy and resource consumption. Through triple encapsulation with fixed field format and length identifier, efficient splicing and parsing of different types of data are 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 ground in the disaster area, obtaining their three-dimensional coordinates, and constructing a spatial reference line may include:

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

[0080] Step S2-2: Converting the acquired geodetic coordinate system coordinates into a spatial rectangular coordinate system, specifically including: calculating a conversion matrix from the geodetic coordinates of the target point to spatial rectangular coordinates based on the WGS-84 ellipsoid parameters; and outputting the three-dimensional spatial coordinates of the two target points through the built-in coordinate conversion interface of the Beidou positioning module;

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

[0082] In the embodiment of the present invention, when it is specifically applied, the above steps can be implemented in the following manner, for example:

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

[0084] Target point screening criteria:

[0085] First target point: Based on the road network and building distribution on the disaster area rescue map, prioritize the top of a building (such as a hotel or office building) at the entrance of the disaster area that is three stories or higher and has no cracks or tilt in the foundation. It is required that there are no obstacles higher than half the height of the building within 100 meters to ensure unobstructed Beidou signal reception.

[0086] Second target point: The base of the directional antenna tower fixedly deployed at the rescue command center is the first choice. If there is no open area available (such as the center of a square or a mountain top), the terrain must be flat and there must be no dynamic surface displacement (such as soft sand areas are excluded). There must be line of sight between the two points (that is, the height of obstacles in the connecting direction does not exceed 10% of the difference in altitude between the two points).

[0087] Coordinate collection process:

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

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

[0090] Ellipsoid parameter initialization:

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

[0092] Coordinate transformation implementation:

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

[0094] By inputting the geodetic coordinates (B, L, H) through the built-in coordinate conversion engine of the Beidou positioning module (such as the API interface that supports the NMEA-0183 protocol), the module automatically completes the conversion from the geodetic coordinate system (latitude and longitude + altitude) to the spatial rectangular coordinate system (X, Y, Z), and outputs the three-dimensional coordinates (X1, Y1, Z1) and (X2, Y2, Z2). The conversion accuracy is controlled within ±5cm due to the hardware limitations of the module.

[0095] The implementation process of the above step S2-3 can be:

[0096] Determination of the straight line direction vector:

[0097] Take the first target point P1 (X1, Y1, Z1) as the starting point and the second target point P2 (X2, Y2, Z2) as the end point, and calculate the three-dimensional distance between the two points (using the Pythagorean theorem to synthesize the XYZ axis coordinate difference). The distance must be ≥ 500 meters to ensure the spatial representativeness of the reference line. If it is insufficient, reselect the target point.

[0098] The coordinate difference is extracted as a direction vector (ΔX, ΔY, ΔZ) to describe the spatial direction 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 number parameter;

[0101] The starting coordinates and direction vector of the reference 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 double-layer Mesh network as a reference for subsequent spatial positioning.

[0102] In this embodiment of the present invention, by selecting target points with stable foundations and good visibility and combining them with RTK differential positioning technology, the reference coordinates are protected from 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 BeiDou module's built-in conversion algorithm, traditional geodetic coordinates are converted to rectangular coordinates suitable for three-dimensional spatial calculations, eliminating coordinate system differences between different positioning systems. This allows for position calculation of all network nodes within a unified spatial reference, facilitating the subsequent construction of three-dimensional signal propagation models and collaborative processing. The constructed spatial reference line can serve as the axis for disaster area grid division, a geometric reference for drone flight paths, and a constraint for node three-dimensional coordinate calibration. This improves the positioning accuracy of distributed nodes in complex debris environments and provides precise spatial coordinate support for tasks such as life detector positioning and drone precision delivery. The reference line parameters are synchronized to all nodes via a two-layer mesh network, enabling relative position calibration based on the reference line during multi-hop signal transmission. This reduces collaborative processing errors caused by local node coordinate inaccuracies and enhances the network's anti-interference capability in extreme terrain.

[0103] In a preferred embodiment of the present invention, calculating the vertical projection distance of each node to the spatial reference line based on the real-time position data of each node to generate the node offset parameter may include:

[0104] Step S2-4: Obtain the three-dimensional coordinates of the real-time position of the node, 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 impact of the surface material in the disaster area on the signal refractive index, a terrain correction factor is added to the vertical projection distance to generate a normalized node offset parameter.

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

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

[0108] Node coordinate acquisition:

[0109] Each node collects its own three-dimensional spatial coordinates (X, Y, Z) in real time through the built-in Beidou / GPS module. The collection frequency is 1 time per second. The sliding window filter (retaining the average of the last 5 data) is used to eliminate multipath effects and transient noise to obtain stable real-time position coordinates (X, Y, Z). n , Y n , Z n ).

[0110] Datum line parameter call:

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

[0112] Projection point calculation:

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

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

[0115] Vertical distance calculation:

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

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

[0118] Surface material classification:

[0119] Based on the 3D scanning data of the disaster area and the image recognition results sent back by the on-site individual nodes, the surface materials at the node locations are divided into three categories:

[0120] For highly reflective materials (such as metal ruins and glass curtain walls), medium-refractive materials (such as concrete and masonry), and low-refractive materials (such as soil and vegetation), the initial correction factors are set to 1.5, 1.0, and 0.8, respectively.

[0121] Correction Factor Application:

[0122] The vertical projection distance D is weightedly corrected based on the surface material type of the node's real-time location: if the node is located in a highly reflective material area, the corrected distance D' = D × 1.5; in medium-reflective material areas, no correction is made (D' = D); in low-reflective material areas, D' = D × 0.8.

[0123] Normalization processing:

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

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

[0126] In an embodiment of the present invention, vertical projection distance calculation is used to quantify the spatial offset of nodes relative to a reference line, providing geometric constraints for multi-hop path planning in a two-layer mesh network, reducing signal diffraction loss in complex terrain, and improving transmission efficiency. A surface material correction factor is introduced to compensate for the effects of different materials on signal refraction and reflection, making the offset parameter more closely aligned with the actual signal transmission path deviation and providing accurate environmental parameters for subsequent spectrum allocation and interference avoidance algorithms. Normalization of the offset parameter eliminates the impact of geographical differences in different disaster areas, enabling the algorithm to uniformly process node distribution data in different scenarios, such as plains and mountainous areas. This improves the system's cross-scenario adaptability, updates node locations and surface material information in real time, ensures that the offset parameter dynamically adjusts as the ruins environment changes, and provides data support for real-time optimization of the emergency communication network.

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

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

[0129] Step S2-7: Execute differentiated correction strategies based on regional types: For nodes in the low-offset area, a fixed correction coefficient is used and terrain refraction compensation is superimposed. For nodes in the medium-offset area, a linear proportional weight factor is calculated based on the offset gradient, and the offset trends of adjacent nodes are integrated for weight smoothing. For nodes in the high-offset area, an exponential compensation function is enabled and the compensation strength is dynamically adjusted based on the hovering altitude of the drone node.

[0130] In step S2-8, when three or more nodes are detected in a row in a high-offset area, 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 height that the drone node needs to adjust are calculated; after the reconstruction plan is confirmed by the distributed consensus algorithm, the drone node is 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.

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

[0132] The implementation process of the above step S2-6 can be:

[0133] Threshold setting:

[0134] Based on the statistical distribution of the offset parameters of all nodes in the network (e.g., the 50% quantile is used as the first threshold T1, and the 80% quantile is used as the second threshold T2), the nodes are divided into:

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

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

[0137] High deviation area (δ>T2): The signal deviates seriously from the reference straight line and requires enhanced compensation.

[0138] Compensation mechanism initialization:

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

[0140] The implementation process of the above steps S2-7 may be:

[0141] Low offset area processing:

[0142] A fixed correction coefficient K1 = 1.0 is used, and the terrain refraction compensation amount 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] Mid-offset area processing:

[0144] Calculate the offset gradient (the difference between the current node offset and the adjacent node offset) and generate a linear proportional weight factor (e.g., weight +0.2 when the gradient is > 0.1).

[0145] The offset trends (increase / decrease) of the three adjacent nodes are integrated, and the weight changes are smoothed by sliding average (if the weight increases continuously, the weight is additionally +0.1). The final coefficient K = K2×(1+gradient weight+trend weight).

[0146] High offset area processing:

[0147] Enable exponential compensation function. The compensation strength increases exponentially as the offset increases.

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

[0149] The implementation process of the above step S2-8 can be:

[0150] Heat map generation and analysis:

[0151] When three or more nodes are detected in a high-offset area, the three-dimensional coordinates of these nodes are extracted to generate a spatial heat map (the color depth indicates the offset size);

[0152] Calculate the geometric center of the hotspot area and combine it with the terrain data of the disaster area (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 the new hovering position of the drone node (preferably an area 50-100m above the hotspot and with a porosity >40%).

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

[0156] Network reconstruction and coefficient update:

[0157] Control the drone to move to a new location and recalculate the projection 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 modified weight matrix, which is then synchronized to all nodes via multi-hop broadcast.

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

[0160] In a preferred embodiment of the present invention, performing a convolution operation on the modified weight and the spectrum occupancy matrix in the triplet feature vector and performing phase compensation on the user MAC address hash value to generate a modified triplet feature vector may include:

[0161] Step S2-9: Based on the final modified weight matrix, dynamically convolve the spectrum occupancy matrix in the triplet feature vector: using the modified weight matrix as the convolution kernel, perform a 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 based on the convolution result to generate a modified spectrum matrix;

[0162] Step S2-10: Calculate the phase compensation for the user MAC address hash value based on the corrected frequency band occupancy probabilities in the spectrum matrix: Extract the time slot numbers corresponding to the frequency bands with high occupancy probabilities in the spectrum matrix, perform a modulo operation on the timeslot numbers with the hash value length, and obtain the reference number of cyclic shift bits. Perform a cyclic left shift on the binary sequence of the user MAC address hash value, with the shift number dynamically adjusted by the product of the reference number of bits and the correction weight.

[0163] Step S2-11: Reconstruct the data of the corrected spectrum matrix, the hash value after phase compensation, and the emergency priority code: normalize and scale the value of the emergency priority code according to the correction amplitude of the spectrum matrix; repackage it into a corrected triplet feature vector in the order of the header field, i.e., the hash value, the middle control field, i.e., the scaled priority code, and the tail field, i.e., the spectrum matrix.

[0164] In the embodiment of the present invention, when it is specifically applied, the above steps can be implemented in the following manner, for example:

[0165] The implementation process of the above step S2-9 can be:

[0166] Convolution kernel configuration:

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

[0168] Sliding window convolution:

[0169] Slide the convolution kernel along the time axis (column direction) and frequency axis (row direction) of the spectrum matrix, covering a local area each time;

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

[0171] Probability distribution update:

[0172] Normalize the convolution results and recalculate the occupancy probability of each frequency band in different time slots (for example, the original probability of 0.8 is adjusted to 0.92 after convolution);

[0173] Generate a modified spectrum matrix to highlight the frequency bands of high-priority users (for example, the occupancy probability of life detection users is increased).

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

[0175] Benchmark digit calculation:

[0176] Extract the time slot numbers corresponding to the frequency bands with occupancy probability ≥ 0.7 (e.g., time slot 5, time slot 12) from the corrected spectrum matrix;

[0177] After accumulating these time slot numbers, modulo the hash value length (256 bits) to obtain the reference number of bits for cyclic shift (for example, the accumulated sum is 345, modulo 256 is 89).

[0178] Dynamic Phase Adjustment:

[0179] Convert the user MAC address hash value into a binary sequence, and determine the actual number of shift bits based on the product of the reference number of bits and the modified weight of the corresponding node (e.g., weight 1.5 × reference 89 = 133.5, rounded to 134 bits).

[0180] A cyclic left shift operation is performed on the binary sequence (e.g., the first 134 bits of the original sequence are moved to the end) to generate a phase-compensated hash value.

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

[0182] Priority encoding scaling:

[0183] According to the correction amplitude of the spectrum matrix (such as the maximum probability improvement value), the emergency priority code is normalized and scaled:

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

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

[0186] Data reassembly and encapsulation:

[0187] Repack the corrected triplet feature vector in the following order:

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

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

[0190] Tail field: corrected spectrum matrix (two-dimensional array stored in row priority);

[0191] Add a length identifier to each field (such as 32 bytes for the head, 1 byte for the middle, and N×M bytes for the tail) to ensure that the receiving end can parse it correctly.

[0192] In an embodiment of the present invention, terrain refraction correction is directly integrated into the spectrum matrix through convolution operations, and the occupancy probability of each frequency band is dynamically adjusted to improve the penetration and reliability of signals in complex terrains and reduce the impact of multipath fading and signal obstruction. Dynamic phase compensation is implemented for the MAC address hash value so that the hash value changes in real time with spectrum usage, effectively resisting replay attacks and forged identities, and improving the security of emergency communication networks. Priority coding is scaled according to the spectrum correction amplitude to achieve further resource allocation for high-priority users, ensuring the smooth flow of critical communications in harsh environments. The basic structure of the triplet feature vector is maintained unchanged, and enhanced functions are achieved through dynamic adjustment of the values ​​within the field, reducing upgrade costs. Combining terrain correction with priority scheduling, while improving the system's anti-interference capability, it avoids excessive occupation of spectrum resources and achieves 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 modified triplet feature vector, associates and maps the time slot allocation scheme, the frequency band switching strategy, and the modified weights through time-space interleaving coding, and generates an enhanced three-dimensional virtual base station configuration including dynamic beam pointing parameters, including:

[0194] Step S3-1: Based on the modified weights in the modified triplet feature vector, the weight values ​​are mapped to the time domain through space-time interleaving coding. A time slot allocation priority table is generated according to the magnitude of the modified weights, wherein user equipment corresponding to higher weights are preferentially allocated consecutive time slots. The update period of the time slot allocation weight table is dynamically adjusted in combination with the scaling value of the emergency priority code.

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

[0196] Step S3-3: Based on the frequency switching path and the time slot allocation weight table, a beamforming algorithm is used to generate dynamic beam pointing parameters. The modified weights are weighted and fused with the node's spatial position coordinates to calculate the beamforming phase gradient. Based on the time-frequency characteristics of the frequency switching path, the main lobe direction of the beam is adjusted to cover the area where high-priority users are located, and a null area is generated in the direction of the interference source.

[0197] Step S3-4: Perform three-dimensional correlation mapping on 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 space dimension constraint; associate the three-dimensional configuration matrix with the correction weight through time-space interleaving coding to generate an enhanced three-dimensional virtual base station configuration containing dynamic beam pointing parameters.

[0198] In the embodiment of the present invention, when it is specifically applied, the above steps can be implemented in the following manner, for example:

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

[0200] Priority table generation:

[0201] The modified weights in the modified triplet feature vector are arranged in descending order to construct a time slot allocation priority table. User equipment with higher weights are given priority in allocating consecutive time slots (e.g., users with weights greater than 1.5 are given priority in obtaining 3 consecutive time slots).

[0202] Dynamic update cycle adjustment:

[0203] Dynamically adjust the update frequency of the time slot allocation table according to the scaling value of the emergency priority code:

[0204] The time slot allocation table of high-priority users (e.g., coded as 0011 after scaling) is updated every 100ms;

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

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

[0207] Cavity area identification:

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

[0209] Dynamic path planning:

[0210] For time slot requirements of high-priority users (such as life detection), priority is given to finding spectrum holes near their currently used frequency bands.

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

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

[0213] Phase gradient calculation:

[0214] The correction weight is weightedly fused with the three-dimensional spatial coordinates of the node. The node with higher weight has greater influence on the beam direction.

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

[0216] Beam direction adjustment:

[0217] According to the time-frequency characteristics of the band switching path, the main lobe direction of the beam is dynamically adjusted:

[0218] For areas with high-priority users (such as rescue command centers), the main lobe gain is increased by 3dB;

[0219] A null zone with a depth of -15dB is generated in the direction of the interference source (such as strongly reflective metal debris) to suppress the interference signal.

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

[0221] Three-dimensional 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 the beam gain, phase and other parameters corresponding to the time and space position.

[0224] Space-time interleaved coding:

[0225] Associate the three-dimensional configuration matrix with the correction weights and perform space-time interleaving encoding on the matrix elements:

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

[0227] In the frequency domain, convolution coding is performed on the band switching path to enhance anti-interference capability;

[0228] Generates enhanced 3D virtual base station configurations with dynamic beam pointing parameters.

[0229] In the embodiment of the present invention, through time-space interleaving coding, the resource scheduling of the three dimensions of time slot allocation, frequency band switching and beam pointing is deeply integrated to achieve three-dimensional collaborative optimization of the entire airspace, the entire time domain and the entire frequency domain, thereby improving spectrum utilization. Based on the correction weight and emergency priority coding, time slot priority allocation, frequency band dedicated guarantee and beam directional enhancement are implemented for high-priority users to ensure the reliability of critical communications in complex environments. Through spectrum hole detection and dynamic zero-pitched beamforming, obstacle reflections and multipath interference are effectively avoided, signal transmission quality is improved, and bit error rate is reduced. Configuration parameters are updated in real time with the correction weight, supporting rapid response to environmental changes in disaster areas 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 broken through, and all-round signal coverage of complex disaster areas is achieved, 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, adjusting the link direction in real time according to the beam pointing parameters, dynamically optimizing the signal strength and delay of the communication link, and completing signal coverage in the disaster area, includes:

[0231] Step S4-1: establishing an initial communication link based on the dynamic beam pointing parameters in the enhanced three-dimensional virtual base station configuration, and detecting the link quality in real time through the received signal strength indicator;

[0232] Step S4-2: If the detected signal strength is lower than a preset threshold, the adaptive beam adjustment mechanism is triggered. The signal attenuation direction is determined based on the signal strength distribution diagram, and the beam pointing angle is gradually adjusted in a spiral search mode. The signal strength is rechecked 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, the link delay is dynamically optimized and signal coverage is completed: the delay jitter trend is predicted by the Kalman filter. If the predicted value exceeds the tolerance range, the current link is marked as unstable; according to the marking result, it switches to the pre-configured redundant link, and recalculates the transmission path based on the updated spectrum occupancy matrix; the optimized transmission path is matched and verified with the corrected weight to generate the final communication link configuration; through the communication link configuration, the coordinated transmission of the nodes in the entire network is activated, the target area in the disaster area is covered according to the beam pointing parameters, and the signal blind spot filling status is monitored in real time until the preset coverage threshold is reached.

[0234] In the embodiment of the present invention, when it is specifically applied, the above steps can be implemented in the following manner, for example:

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

[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 three-dimensional virtual base station configuration, carrying the initial phase and gain parameters required for beamforming.

[0238] After receiving the signal, the target node adjusts the phase offset of its own antenna array according to the parameters to form an initial communication link (for example, the main lobe of the directional antenna is aligned with the high-priority area near the reference line).

[0239] Real-time quality monitoring:

[0240] The link quality is detected by the Received Signal Strength Indicator (RSSI) at a period of 50ms, and the real-time signal strength value (in dBm) is recorded.

[0241] A signal strength threshold is set (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 of step S4-2 is triggered.

[0242] The implementation process of the above step S4-2 may be:

[0243] Attenuation direction positioning:

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

[0245] Spiral search adjustments:

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

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

[0248] Pointing lock and parameter update:

[0249] When the signal strength reaches or exceeds the threshold (such as -80dBm) and is stable for 5 consecutive detections, the current beam pointing angle (azimuth angle θ, pitch angle ) and synchronize the new parameters to the dynamic beam pointing parameter table of the regional master node.

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

[0251] Delay jitter prediction:

[0252] Use the Kalman filter to model link delay, input historical delay data (such as the round-trip time (RTT) within the past 100ms), and predict the delay jitter trend for the next 50ms.

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

[0254] Redundant link switching and path calculation:

[0255] Preconfigure three backup links (based on different projection directions of the reference line), and prioritize paths with node offset ≤ the first threshold and signal strength ≥ -75dBm as redundant links;

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

[0257] Matching verification and coordinated launch:

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

[0259] Activate coordinated transmission of all network nodes. The master node adjusts the transmission direction according to the new beam pointing parameters. The drone nodes synchronously adjust the hovering height (for example, lower it to 15m to enhance the signal in coverage blind spots). Real-time monitoring of signal blind spots (areas with coverage < 90% are marked as blind spots).

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

[0261] In an embodiment of the present invention, real-time RSSI monitoring and spiral search adjustment are used to quickly compensate for signal attenuation caused by terrain obstruction or obstacle movement, ensuring that the link signal strength is stable within a reliable range and improving the communication success rate in the complex environment of the disaster area. The Kalman filter predicts delay jitter in real time, identifies unstable links in advance and switches to redundant paths, reducing data transmission delays and packet loss rates, and ensuring the continuity of real-time services such as rescue command. The beam pointing is dynamically adjusted based on the correction weight and node distribution, and the drone node actively fills in the blind spots to ensure signal coverage in the edge areas of the disaster area and low-porosity obstruction areas, solving the blind spot problem that traditional base stations are difficult to deploy. The coordinated optimization of beam pointing, transmission path and correction weights allows autonomous adaptation 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). While meeting communication needs, the energy consumption of the entire network is reduced through coordinated transmission and dynamic power adjustment, extending the battery life of equipment and adapting to power shortage scenarios in disaster areas.

[0262] like Figure 2 As shown, an embodiment of the present invention further provides a terrestrial satellite signal distributed collaborative processing system 20, comprising:

[0263] A generating module 21 is configured to extract a user MAC address hash value, an emergency priority code, and a spectrum occupancy matrix from the synchronization network to generate a triplet feature vector;

[0264] Correction module 22 is configured to transmit the triplet feature vector through a two-layer mesh network, select two reference target points with fixed geographic coordinates on the ground in the disaster area, 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 a node offset parameter; dynamically generate a refraction correction coefficient for the signal propagation path based on a comparison result of the node offset parameter with a preset threshold; when the offset exceeds the threshold, increase the correction weight using piecewise linear interpolation; perform a convolution operation on the correction weight with the spectrum occupancy matrix in the triplet feature vector, and perform phase compensation on the user MAC address hash value to generate a corrected triplet feature vector;

[0265] A mapping module 23 is configured for the regional master node to receive the modified triplet feature vector, associate and map the time slot allocation scheme, the frequency band switching strategy and the modified weights through time-space interleaving coding, and generate an enhanced three-dimensional virtual base station configuration including dynamic beam pointing parameters;

[0266] The optimization module 24 is used to 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, dynamically optimize the signal strength and delay of the communication link, and complete signal coverage in the disaster area.

[0267] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as 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 comprises: 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; Step S2: Transmit the triplet feature vector through a two-layer Mesh network, select two reference target points with fixed geographic coordinates on the ground in the disaster area, 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 a node offset parameter; based on the comparison result of the node offset parameter with a preset threshold, dynamically generate the refraction correction coefficient of the signal propagation path; when the offset exceeds the threshold, use piecewise linear interpolation to increase the correction weight; perform a convolution operation on the correction weight and the spectrum occupancy matrix in the triplet feature vector, and perform phase compensation on the user MAC address hash value to generate a corrected triplet feature vector; Step S3: The regional master node receives the modified triplet feature vector, associates and maps the time slot allocation scheme, the frequency band switching strategy and the modified weights through time-space interleaving coding, and generates an enhanced three-dimensional virtual base station configuration including dynamic beam pointing parameters; Step S4: establishing a communication link based on the enhanced three-dimensional virtual base station configuration, adjusting the link direction in real time according to the beam pointing parameters, dynamically optimizing the signal strength and delay of the communication link, and completing signal coverage of the disaster area.

2. The distributed collaborative processing method for ground satellite signals according to claim 1, characterized in that: In step S1, before extracting the user MAC address hash value, the emergency priority code, and the spectrum occupancy matrix from the synchronization network to generate a triplet feature vector, the method further includes: The porosity of the ruins is calculated based on 3D scanning data of the disaster area terrain, and the areas are divided into high-porosity and low-porosity areas. Differentiated deployment strategies are implemented based on the porosity differences to form a node network. Based on the node network, the obstacle displacement changes are monitored in real time. If it is detected that the obstacle displacement causes the local porosity change to exceed the preset threshold, the node will be re-hovered to the new pore position based on the pore distribution heat map fed back by the individual node to obtain the adjusted node distribution density of the hybrid topology network. 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 synchronous communication network that is resistant to terrain interference.

3. The distributed collaborative processing method for ground satellite signals according to claim 2, characterized in that: Step S1: Extract the user MAC address hash value, emergency priority code, and spectrum occupancy matrix from the synchronization network to generate a triplet feature vector, including: Capture the broadcast signal of the user device through the synchronization network, extract its MAC address and use the SHA-256 hash algorithm to generate a unique hash identification code of fixed length; Based on the hash identification code, the user device's signal transmission power history data is associated with the preset disaster emergency level table to dynamically classify the user: if the signal transmission power is continuously above the threshold and the identification code belongs to the rescue equipment whitelist, the user is marked as life detection; if the signal delay is below the threshold and the identification code belongs to the command center database, the user is marked as rescue command; the rest are marked as ordinary communication, and each category is assigned a corresponding emergency priority code. Based on the emergency priority code, the frequency bands where life detection users are located are sampled, and the frequency bands where general communication users are located are sparsely sampled. The signal strengths of different frequency bands are integrated into a spectrum occupancy matrix in time series. The hash identification code, the emergency priority code and the spectrum occupancy matrix are encapsulated and arranged into a triple feature vector in a preset binary format. In the data structure of the triple feature vector, the hash identification code is used as the header field, the emergency priority code is used as the middle control field, and the spectrum occupancy matrix is ​​used as the tail payload field. The three are seamlessly spliced ​​together through a length identifier.

4. The distributed collaborative processing method for ground satellite signals according to claim 3, characterized in that: Step S2: Transmit the triplet feature vector through a two-layer Mesh network, select two reference target points with fixed geographic coordinates on the ground in the disaster area, obtain their three-dimensional coordinates, and construct a spatial reference line, including: Based on the topological characteristics of the disaster area rescue map, the top of a building with a stable foundation at the entrance to the disaster area was selected as the first target point. The base of the directional antenna tower deployed by 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. Convert the acquired geodetic coordinate system coordinates into a spatial rectangular coordinate system, specifically including: calculating the conversion matrix from the geodetic coordinates of the target point to the spatial rectangular coordinates based on the WGS-84 ellipsoid parameters; outputting the three-dimensional spatial coordinates of the two target points through the built-in coordinate conversion interface of the Beidou positioning module; Based on the three-dimensional spatial coordinates of two target points, a two-point straight line equation is used to construct a spatial reference line.

5. The distributed collaborative processing method for ground satellite signals according to claim 4, characterized in that: Based on the real-time position data of each node, the vertical projection distance of each node to the spatial reference line is calculated to generate the node offset parameters, including: 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; According to the influence of the surface material in the disaster area on the signal refractive index, a terrain correction factor is added to the vertical projection distance to generate the normalized node offset parameter.

6. The distributed collaborative processing method for ground satellite signals according to claim 5, characterized in that: Based on the comparison between the node offset parameter and the preset threshold, the refraction correction coefficient of the signal propagation path is dynamically generated. When the offset exceeds the threshold, the correction weight is increased using piecewise linear interpolation, including: Based on the statistical distribution of node offset parameters, a first threshold and a second threshold are set to divide the nodes into a low offset zone (i.e., offset ≤ first threshold), a medium offset zone (i.e., first threshold < offset ≤ second threshold), and a high offset zone (i.e., offset > second threshold), and initialize a dynamic compensation mechanism according to the zone type; Differentiated correction strategies are implemented for different regional types: for nodes in low-offset areas, a fixed correction coefficient is used and terrain refraction compensation is superimposed; for nodes in medium-offset areas, a linear proportional weight factor is calculated based on the offset gradient, and the offset trends of adjacent nodes are integrated for weight smoothing; for nodes in high-offset areas, an exponential compensation function is enabled and the compensation intensity is dynamically adjusted based on the hovering height of the drone node. When three or more consecutive nodes are detected in the high-offset area, the local reconstruction of the hybrid topology network is triggered: an offset hotspot map is generated according to the spatial distribution of the high-offset nodes, and the hovering position and height that need to be adjusted for the drone node are calculated; after the reconstruction plan is confirmed by the distributed consensus algorithm, the drone node is 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.

7. The distributed collaborative processing method for ground satellite signals according to claim 6, characterized in that: The modified weight is convolved with the spectrum occupancy matrix in the triplet feature vector, and the user MAC address hash value is phase compensated to generate a modified triplet feature vector, including: Based on the final modified weight matrix, the spectrum occupancy matrix in the triplet feature vector is dynamically convolved. The modified 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 a modified spectrum matrix. Based on the corrected frequency band occupancy probabilities in the spectrum matrix, the phase compensation for the user's MAC address hash value is calculated. The time slot numbers corresponding to the frequency bands with high occupancy probabilities in the spectrum matrix are extracted and modulo-calculated with the hash value length to obtain the reference number of bits for cyclic shift. A cyclic left shift is then performed on the binary sequence of the user's MAC address hash value, with the shift number dynamically adjusted by the product of the reference number of bits and the correction weight. The corrected spectrum matrix, phase-compensated hash value, and emergency priority code are reconstructed: the value of the emergency priority code is normalized and scaled according to the correction amplitude of the spectrum matrix; and the header field (i.e., hash value), the middle control field (i.e., scaled priority code), and the tail field (i.e., spectrum matrix) are repackaged into a corrected triplet feature vector in this order.

8. The distributed collaborative processing method for ground satellite signals according to claim 7, characterized in that: Step S3: The regional master node receives the modified triplet feature vector, associates and maps the time slot allocation scheme, the frequency band switching strategy, and the modified weights through space-time interleaving coding, and generates an enhanced three-dimensional virtual base station configuration including dynamic beam pointing parameters, including: Based on the modified weights in the modified triplet feature vector, the weight values ​​are mapped to the time domain through space-time interleaving coding. A time slot allocation priority table is generated based on the size of the modified weights, in which user equipment corresponding to higher weights are preferentially allocated consecutive time slots. The update period of the time slot allocation weight table is dynamically adjusted in combination with the scaling value of the emergency priority code. Based on the time slot allocation weight table, spectrum hole detection is performed on the modified spectrum occupancy matrix. Unoccupied spectrum holes are identified within the frequency band of the allocated time slots. Frequency switching paths are dynamically planned based on the time slot requirements of high-priority users. Based on the stepped frequency switching path and the time slot allocation weight table, a beamforming algorithm generates dynamic beam pointing parameters. The modified weights are weighted and fused with the node's spatial position coordinates to calculate the beamforming phase gradient. Based on the time-frequency characteristics of the frequency switching path, the beam main lobe direction is adjusted to cover the area where high-priority users are located, and a null zone 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 a three-dimensional manner: 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 space dimension constraint. The three-dimensional configuration matrix is ​​associated with the correction weights through time-space interleaving coding to generate an enhanced three-dimensional virtual base station configuration containing dynamic beam pointing parameters.

9. The distributed collaborative processing method for ground satellite signals according to claim 8, characterized in that: Step S4: establishing a communication link based on the enhanced three-dimensional virtual base station configuration, adjusting the link direction in real time according to the beam pointing parameters, dynamically optimizing the signal strength and delay of the communication link, and completing signal coverage in the disaster area, including: Establishing an initial communication link based on dynamic beam pointing parameters in an enhanced 3D virtual base station configuration and monitoring link quality in real time using received signal strength indicators; If the detected signal strength falls below a preset threshold, the adaptive beam adjustment mechanism is triggered. The system determines the direction of signal attenuation based on the signal strength distribution graph and gradually adjusts the beam pointing angle in a spiral search pattern. After each adjustment, the signal strength is re-checked. When the signal strength reaches the threshold, the system locks the current beam pointing angle and updates the dynamic beam pointing parameters. Based on the adjusted beam pointing parameters, the link delay is dynamically optimized and signal coverage is achieved: the delay jitter trend is predicted through the Kalman filter. If the predicted value exceeds the tolerance range, the current link is marked as unstable; according to the marking result, the pre-configured redundant link is switched to, 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 nodes in the entire network is activated through the communication link configuration, the target area in the disaster area is covered according to the beam pointing parameters, and the signal blind spot filling status is monitored in real time until the preset coverage threshold is reached.

10. A distributed collaborative processing system for ground satellite signals, the system implementing the method according to any one of claims 1 to 9, characterized in that: include: A generation module is used to extract the user MAC address hash value, emergency priority code and spectrum occupancy matrix from the synchronization network to generate a triplet feature vector; 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 ground in the disaster area, 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 a node offset parameter; based on the comparison result of the node offset parameter with a preset threshold, dynamically generate the refraction correction coefficient of the signal propagation path; when the offset exceeds the threshold, use piecewise linear interpolation to increase the correction weight; Performing a convolution operation on the modified weight and the spectrum occupancy matrix in the triplet feature vector, and performing phase compensation on the user MAC address hash value to generate a modified triplet feature vector; A mapping module is configured to receive the modified triplet feature vector at the regional master node, associate and map the time slot allocation scheme, the frequency band switching strategy, and the modified weights through time-space interleaving coding, and generate an enhanced three-dimensional virtual base station configuration including dynamic beam pointing parameters; The optimization module is used to 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, dynamically optimize the signal strength and delay of the communication link, and complete signal coverage in the disaster area.

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