Multi-machine electric power emergency phased array cooperative route planning processing method and system

Abstract

The invention provides a multi-aircraft electric power emergency phased array cooperative route planning processing method and system, and relates to the technical field of electric power communication, and the method comprises the steps: 1, controlling an unmanned aerial vehicle group equipped with phased array communication equipment, carrying out the cooperative scanning of a fault target region of a power transmission line, and obtaining an unmanned aerial vehicle group; preliminary positioning information of a fault target is obtained through phased array beam forming and target locking; and 2, dynamically extracting spatial configuration parameters based on the preliminary positioning information, analyzing the local change rate of the spatial configuration parameters, setting a change threshold, identifying key turning points in the evolution trend of the spatial configuration parameters, and calculating dynamic compensation parameters by combining the incidence relation among the spatial configuration parameters and the characteristics at the turning points. According to the invention, dynamic collaborative route planning of the unmanned aerial vehicle group in complex terrain power emergency is realized, and route adaptability and task execution safety are improved.

Description

Technical Field

[0001] This invention relates to the field of power communication technology, and in particular to a method and system for collaborative flight path planning and processing of multi-machine power emergency phased array systems. Background Technology

[0002] In the field of power emergency repair, especially for power transmission line faults in complex terrain environments, drones have become an important tool for performing tasks such as fault inspection and material delivery due to their mobility, flexibility and lack of ground traffic restrictions. However, in scenarios where multiple drones work together, there are some limitations in achieving efficient and safe flight path planning.

[0003] Currently, most common multi-UAV flight path planning methods are based on preset paths or static environment models, and the planning process largely relies on pre-collected terrain data and obstacle information. For example, in emergency scenarios involving power transmission line faults in mountainous areas, existing technologies mostly use offline-generated waypoint sequences to control UAV swarms to perform scanning and delivery tasks. These methods can work when the environment is relatively stable and the distribution of obstacles is known. However, in actual emergency rescue sites, factors such as complex terrain, variable weather conditions, line vibration, and the appearance of temporary obstacles may cause the preset path to deviate from the actual airspace conditions. In addition, most existing methods rely on centralized control or simplified communication feedback mechanisms, which make it difficult to perceive local airspace changes in a timely manner and quickly reconstruct flight paths in dynamic environments. This can lead to problems such as flight path conflicts and delayed obstacle avoidance responses when UAV swarms are working together, affecting the efficiency and safety of mission execution. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a method and system for multi-aircraft power emergency phased array collaborative flight path planning, so as to realize dynamic collaborative flight path planning of UAV swarms in power emergency in complex terrain, and improve flight path adaptability and mission execution safety.

[0005] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows: Firstly, a method for collaborative flight path planning and processing of multi-aircraft electric emergency phased array systems, the method comprising: 1. A method for collaborative flight path planning and processing of multi-aircraft power emergency phased array systems, characterized in that the method includes: Step 1: Control a swarm of drones equipped with phased array communication devices to perform a collaborative scan of the target area of ​​the power transmission line fault, and obtain preliminary location information of the fault target through phased array beamforming and target locking; Step 2: Based on the preliminary positioning information, dynamically extract spatial configuration parameters, analyze the local change rate of spatial configuration parameters and set change thresholds, identify key turning points in the evolution trend of spatial configuration parameters, and calculate dynamic compensation parameters by combining the correlation between spatial configuration parameters and the characteristics at the turning points. Step 3: Based on the dynamic compensation parameters, combined with the real-time load status of the UAV, the vibration monitoring data of the line, and the surrounding terrain obstacle information, calculate the set of convex region vertices corresponding to multiple obstacle areas and available airspace, construct a safe flight airspace, and generate a preliminary path for the material delivery route. Step 4: The preliminary path is sent to the ground command center via the phased array communication link, and the path is updated in real time according to the path correction instructions fed back from the field environment, generating an updated delivery path. Step 5: Based on the updated delivery route, control the drone swarm to execute the material delivery task according to the collaborative task allocation; during the task execution, monitor the status of each drone and the changes in the surrounding environment of the route in real time through phased array communication equipment, dynamically adjust the delivery route, and realize multi-drone collaborative route planning.

[0006] Secondly, the multi-aircraft power emergency phased array collaborative flight path planning and processing system includes: The positioning module is used to control a swarm of drones equipped with phased array communication equipment to perform cooperative scanning of the target area of ​​the power transmission line fault, and to obtain the preliminary positioning information of the fault target through phased array beamforming and target locking. The compensation module is used to dynamically extract spatial configuration parameters based on preliminary positioning information, analyze the local change rate of spatial configuration parameters and set change thresholds, identify key turning points in the evolution trend of spatial configuration parameters, and calculate dynamic compensation parameters by combining the correlation between spatial configuration parameters and the characteristics at the turning points. The generation module is used to calculate the set of convex region vertices corresponding to multiple obstacle areas and available airspace based on dynamic compensation parameters, combined with the real-time load status of the UAV, line vibration monitoring data and surrounding terrain obstacle information, to construct a safe flight airspace and generate a preliminary path for the material delivery route. The update module is used to send the initial path to the ground command center through the phased array communication link, and update the path in real time according to the path correction instructions fed back from the field environment, generating an updated delivery path. The adjustment module is used to control the drone swarm to perform material delivery tasks according to the updated delivery route and the collaborative task allocation. During the task execution, the status of each drone and the changes in the surrounding environment of the route are monitored in real time through phased array communication equipment, and the delivery route is dynamically adjusted to realize multi-drone collaborative route planning.

[0007] Thirdly, a computing device, comprising: One or more processors; A storage device for storing one or more programs that, when executed by one or more processors, cause the one or more processors to implement the method.

[0008] Fourthly, a computer-readable storage medium storing a program that, when executed by a processor, implements the method.

[0009] The above-described solution of the present invention has at least the following beneficial effects: By employing swarm scanning and phased array beamforming, the system enables rapid locking and preliminary localization of fault targets, enhancing the comprehensiveness of power fault target identification. Dynamic compensation parameters are calculated based on the evolutionary characteristics of spatial configuration parameters. Combined with real-time UAV status, line vibration, and terrain obstacle information, flight path planning can dynamically adapt to environmental changes and equipment status. A safe flight airspace is formed by constructing a set of convex region vertices, avoiding risks from terrain obstacles and line vibration. Simultaneously, the system considers UAV payload status, ensuring flight stability and reducing the probability of equipment failure during emergency delivery. A multi-UAV collaborative task allocation and real-time flight path update mechanism allows for rapid response to on-site environmental feedback, supporting rapid power fault repair. Attached Figure Description

[0010] Figure 1 This is a flowchart illustrating the multi-machine power emergency phased array cooperative route planning processing method provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of a multi-machine power emergency phased array collaborative route planning and processing system provided in an embodiment of the present invention. Detailed Implementation

[0011] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0012] like Figure 1 As shown, embodiments of the present invention propose a multi-aircraft electric emergency phased array cooperative flight path planning and processing method, the method comprising the following steps: Step 1: Control a swarm of drones equipped with phased array communication devices to perform a collaborative scan of the target area of ​​the power transmission line fault, and obtain preliminary location information of the fault target through phased array beamforming and target locking; Step 2: Based on the preliminary positioning information, dynamically extract spatial configuration parameters, analyze the local change rate of spatial configuration parameters and set change thresholds, identify key turning points in the evolution trend of spatial configuration parameters, and calculate dynamic compensation parameters by combining the correlation between spatial configuration parameters and the characteristics at the turning points. Step 3: Based on the dynamic compensation parameters, combined with the real-time load status of the UAV, the vibration monitoring data of the line, and the surrounding terrain obstacle information, calculate the set of convex region vertices corresponding to multiple obstacle areas and available airspace, construct a safe flight airspace, and generate a preliminary path for the material delivery route. Step 4: The preliminary path is sent to the ground command center via the phased array communication link, and the path is updated in real time according to the path correction instructions fed back from the field environment, generating an updated delivery path. Step 5: Based on the updated delivery route, control the drone swarm to execute the material delivery task according to the collaborative task allocation; during the task execution, monitor the status of each drone and the changes in the surrounding environment of the route in real time through phased array communication equipment, dynamically adjust the delivery route, and realize multi-drone collaborative route planning.

[0013] In this embodiment of the invention, rapid locking and preliminary positioning of fault targets are achieved through UAV swarm collaborative scanning and phased array beamforming, improving the comprehensiveness of power fault target identification. Dynamic compensation parameters are calculated based on the evolution characteristics of spatial configuration parameters. Combined with real-time UAV status, line vibration, and terrain obstacle information, flight path planning can dynamically adapt to environmental changes and equipment status. A safe flight airspace is formed by constructing a set of convex region vertices to avoid risks caused by terrain obstacles and line vibration. At the same time, the UAV's load status is taken into account to ensure flight stability and reduce the probability of equipment failure during emergency delivery. The multi-aircraft collaborative task allocation and real-time flight path update mechanism can quickly respond to on-site environmental feedback and support rapid power fault repair.

[0014] In a preferred embodiment of the present invention, step 1 above, controlling a swarm of drones equipped with phased array communication devices to perform cooperative scanning of the transmission line fault target area and obtaining preliminary location information of the fault target through phased array beamforming and target locking, may include: In this embodiment of the invention, step 110 involves constructing a dynamic self-organizing network communication link between the phased array communication devices carried by each UAV, forming a multi-UAV collaborative communication network that shares data and commands in real time. Specifically, this includes: after each UAV carrying phased array communication devices arrives at an altitude of 500 meters above the power transmission line fault area, the devices immediately and automatically activate the dynamic networking function. Following the power emergency communication specifications, a fixed frequency of 4950MHz within the 4910-5970MHz band is selected, and a detection signal containing the device ID, current approximate location, and communication capability parameters is continuously broadcast twice per second. The initial transmission power of the detection signal is set to 2 watts, and the coverage radius is preset to 5 kilometers to ensure effective detection of surrounding UAV nodes in mountainous and canyon environments. Upon receiving detection signals from other nodes, the phased array communication device of each UAV first decodes and verifies the signals to confirm that the other party is the same as the UAV. After the drone node initiates the mission, it actively requests a communication connection and then enters a two-way parameter negotiation phase. The first step is to synchronize the timestamps, using the standard time issued by the ground command center as the benchmark, and calibrating the time error of all nodes to within 1 millisecond. The second step is to exchange device IDs and communication capability information, clarifying the data transmission rate and encryption algorithm type supported by each node. The third step is to negotiate unified communication parameters, determining that the data transmission baud rate is 115200bps, the data verification method is CRC32 cyclic redundancy check, and the encryption method is AES-256 symmetric encryption algorithm. The key is pre-distributed to each drone by the ground command center through an encrypted channel before the mission starts, and independent communication time slots are allocated to avoid signal conflicts caused by multiple nodes transmitting at the same time. After reaching an agreement, a stable two-way communication link is established between the nodes, forming a multi-drone collaborative communication network architecture without a central node and with multiple link backups.

[0015] Throughout the scanning mission, each drone's phased array communication device monitors its link status with all connected nodes five times per second. The monitoring focuses on three key indicators: signal strength, transmission delay, and packet loss rate. Signal strength is read from the device's built-in signal strength indicator; transmission delay is calculated by sending test data packets and recording the round-trip time; and packet loss rate is calculated as the ratio of the total number of sent data packets to the total number of received acknowledgment data packets. If the signal strength of a link is below -85dBm, the transmission delay exceeds 50 milliseconds, or the packet loss rate exceeds 1%, the corresponding drone will immediately initiate a link optimization program. First, using the phased array antenna's beam agility function, the beam pointing angle is gradually adjusted in 0.2-degree increments within a range of ±10 degrees horizontally and ±5 degrees vertically. Link indicators are monitored after each adjustment. If the indicators improve after beam pointing adjustment... If the situation remains unresolved, the transmission power will be gradually increased in increments of 0.3 watts, up to a maximum of 5 watts, until the link specifications return to acceptable levels: signal strength ≥ -80dBm, transmission delay ≤ 30 milliseconds, and packet loss rate ≤ 0.5%. If the link specifications still fail to meet the standards after 20 beam adjustments and 10 power adjustments, or if the signal is completely interrupted, the device will restart the node discovery process within 2 seconds. It will broadcast a detection signal 3 times per second, quickly scan for available nodes in the vicinity, and prioritize establishing a new communication link with the nearest node with the strongest signal. Simultaneously, it will report the link interruption and reconstruction status to the ground command center in real time through other available links. Throughout the process, the network will automatically adjust the link topology to ensure that the entire communication network remains connected even if some nodes move or are blocked, enabling real-time data and command sharing among the UAV swarm and adapting to the communication environment in complex terrain where UAV movement and obstruction changes frequently.

[0016] Step 111: Through the collaborative communication network, control the UAV swarm to perform layered or sector-based spatial coverage scanning of the fault target area according to the preset collaborative scanning mode, and simultaneously acquire the raw scan echo data collected by each UAV. Specifically, ground command personnel first combine satellite remote sensing maps, terrain photos transmitted by on-site rescue personnel, and power line design drawings to clarify the complex terrain details of the fault target area, such as the altitude difference in mountainous areas, the direction of valleys, and the distribution density of forests. Then, based on the fault repair information, determine the approximate impact range of the fault and formulate a targeted collaborative scanning mode accordingly. If a layered scanning mode is adopted, the fault area is divided into multiple continuous horizontal scanning layers according to altitude. In the gentle area at an altitude of 800-1000 meters, the height interval of each layer is set to 30 meters, with an overlap of 5 meters between adjacent layers; in the steep area at an altitude of 1000-1500 meters, the height interval of each layer is set to 30 meters. The scanning distance is set at 20 meters, with an 8-meter overlap between adjacent layers to ensure that height differences caused by terrain undulations do not result in missed scans. Simultaneously, a serpentine scanning path is planned for each scanning layer, with a path spacing of 50 meters. The scanning direction is perpendicular to the power line direction to avoid repeated scanning or omissions in localized areas caused by a scanning path parallel to the line. If a sector-based scanning mode is adopted, the suspected fault center point is used as the coordinate origin, and the fault area is divided into 16 sector scanning areas according to azimuth angle. The angle range of each sector is 22.5 degrees, with a 3-degree overlap between adjacent sectors. For canyon areas, the sector angle along the canyon direction is expanded to 30 degrees, while the sector angle perpendicular to the canyon direction is reduced to 15 degrees, ensuring that the canyon interior and both sides of the mountain slopes are fully covered. The scanning path for each sector is planned as a spiral path radiating outward from the origin, with the path spacing gradually increasing from the inside out, balancing scanning efficiency and coverage accuracy.

[0017] Through the multi-drone collaborative communication network established in step 110, the ground command center synchronously sends the preset scanning mode parameters and the corresponding scanning area allocation instructions for each UAV to each UAV in the form of data packets. The data packets are verified with checksums to ensure that the instructions are transmitted correctly. After receiving the instructions, each UAV immediately activates its onboard detection equipment and flies at a constant speed according to the assigned scanning area and preset path. The flight speed is set to 12-18 km / h according to the scanning accuracy requirements. At the same time, the data acquisition function is activated. The millimeter-wave radar transmits detection signals at a frequency of 30 frames per second to capture the raw echo data after the signals are reflected by faulty targets, terrain obstacles, trees, etc. The infrared thermal imager collects environmental thermal imaging data at a frequency of 15 frames per second and records the temperature distribution information of the target area simultaneously. During the scanning process, each UAV... The drone not only needs to collect its own raw echo data and thermal imaging data, but also needs to record its flight attitude and speed in real time through its onboard IMU inertial measurement unit, record its real-time spatial position through RTK positioning equipment, and detect the working status of the equipment. All of this data is encapsulated into data frames in a fixed format of device ID-timestamp-location information-attitude information-device status-echo data-thermal imaging data, and sent in real time to all other drones participating in the scan and the ground command center through a cooperative communication network. The transmission interval is 100 milliseconds. If signal interference causes data transmission to be blocked, the drone will automatically cache the data and retransmit it in batches after the link is restored, ensuring that all nodes can integrate the complete scan data in chronological order and avoid scanning coordination imbalance caused by data transmission delays or asynchrony.

[0018] Step 112: Utilizing the beamforming function of each UAV's phased array, the original scan echo data is subjected to spatial filtering and directivity enhancement processing to generate corresponding enhanced echo signals focused on one or more suspected fault points within the fault target area. Specifically, after each UAV acquires its own collected original echo data and auxiliary data synchronized with other UAVs through a cooperative communication network, its onboard phased array communication equipment immediately activates the beamforming function to perform signal optimization processing for the currently assigned scanning area. This phased array antenna consists of 64 elements arranged in a uniform linear array with an element spacing of half a wavelength. Precise beam pointing and shaping can be achieved by independently adjusting the phase and amplitude of each element. First, according to step 111... Based on the determined scanning area and preset beam pointing strategy, each element of the phased array antenna is individually controlled. Using the center azimuth and elevation angles of the scanning area as a reference, the transmit and receive phases of each element are calibrated in 0.05-degree increments using the built-in beam control algorithm to ensure that the signals of all elements are in phase and superimposed at the target azimuth. At the same time, according to the signal reception requirements, the signal amplitude of each element is adjusted in 0.05-dB increments, with the amplitude of the edge elements being 2-3dB higher than that of the center elements. This concentrates the beam energy of the entire phased array antenna towards the azimuth of the most likely fault point in the current scanning area, ultimately compressing the beamwidth to within 8 degrees, improving the signal reception sensitivity to suspected fault points, and reducing energy waste.

[0019] Subsequently, a spatial filtering program was initiated to perform a three-level hierarchical screening of the raw scan echo data. The first level was frequency filtering, extracting the frequency characteristics of all echo signals and converting them to the frequency domain using a Fast Fourier Transform (FFT). Signals with frequencies concentrated between 24-24.2 GHz and frequency fluctuations less than 0.1 GHz were selected, while terrain and tree reflection clutter below 23 GHz or above 25 GHz, as well as meteorological interference signals with drastic frequency fluctuations, were removed. The second level was amplitude filtering, statistically analyzing the peak amplitude of the filtered signals and setting an amplitude threshold of 1.5 times the average amplitude. Weak clutter signals with amplitudes below the threshold were removed, while suspected target signals with higher amplitudes were retained. The third level was time-domain filtering, analyzing the pulse duration and rise / fall slope of the signals. Signals with pulse durations between 3-10 milliseconds and rise slopes greater than 1.5 V / millisecond were retained, while interference signals with excessively long or short pulse durations or flat slopes were removed. Based on spatial filtering, further... The phased array beam directivity is enhanced to improve signal strength. For the azimuth and elevation angles corresponding to the selected suspected target signals, the phase and amplitude of each array element are fine-tuned to increase the beam gain in that azimuth by 12-15 dB compared to other azimuths. This amplifies the effective signal amplitude corresponding to the suspected fault point by 4-6 times, making the signal characteristics of the fault point more prominent. Simultaneously, it suppresses remaining weak interference signals. Throughout the process, the phased array communication equipment monitors signal quality in real time at a frequency of 10 times per second. If sudden events such as strong winds, cloud cover, or electromagnetic interference cause signal fluctuations, the amplitude screening threshold is automatically increased, and the beam direction is fine-tuned in 0.1-degree increments to expand the signal reception range and ensure signal processing stability. If interference persists for more than 5 seconds, the equipment temporarily switches to anti-interference mode, reducing the data acquisition frequency and increasing beam focus. Once the interference weakens, it returns to normal mode, ultimately generating enhanced echo signals focused on one or more suspected fault points within the current scanning area.

[0020] Step 113: Based on the enhanced echo signal, analyze and perform pattern recognition on the multi-source signal features, extract feature information of suspected fault points from each UAV observation perspective, and convert the feature information into corresponding distance observation vectors by combining the known spatial coordinates of the UAV. Specifically, this includes: First, analyzing the enhanced echo signal frame by frame to extract 7 key feature parameters. The extraction process for each parameter is as follows: Amplitude variation pattern: with a 1-second time window, count the number of times the signal amplitude peak occurs within each window, calculate the average time interval between peaks, and record the fluctuation range of the amplitude peaks; Frequency distribution range: perform frequency domain analysis on each frame of the signal to determine the center frequency of the signal, calculate the frequency bandwidth, and record the frequency at any time. The text describes various aspects of signal analysis, including: the trend of signal change; pulse duration (starting from 10% of the peak value at the rising edge and stopping at 10% below the peak value at the falling edge), calculating the pulse duration of each frame and averaging the duration across all frames; rising and falling edge slopes (calculating the voltage change rate from 10% to 90% of the peak value at the rising edge and from 90% to 10% of the peak value at the falling edge); signal correlation (calculating the correlation coefficient between the current frame and the previous and next frames to determine signal continuity); spectral entropy (measuring the uniformity of the spectrum distribution by calculating the entropy value of the signal spectrum; fault signals typically have lower spectral entropy values, while interference signals have higher spectral entropy values); and thermal imaging matching features. By combining synchronously acquired infrared thermal image data, the temperature values ​​and temperature gradients at the locations corresponding to suspected signals are extracted to determine whether abnormal temperature areas exist. Subsequently, these seven extracted feature parameters are compared one by one with the signal feature library built into the drone. This feature library contains two types of data: one is signal feature data under normal power transmission line conditions; the other is standard signal feature data corresponding to 10 common faults such as line breakage, tower tilting, foreign object entanglement, insulator damage, and conductor icing. Each fault category contains over 500 sets of measured data, forming feature parameter ranges. The comparison process uses a weighted scoring system, with the weight of each feature parameter set according to its importance for fault identification, and the weight for amplitude variation patterns being 0.2 and frequency distribution... The weights are calculated as follows: range weight 0.2, pulse duration weight 0.15, rising and falling edge slope weight 0.15, signal correlation weight 0.1, spectral entropy weight 0.1, and thermal image matching feature weight 0.1, with a total weight of 1. The current feature parameter is compared with the standard feature data of a certain type of fault. If the parameter is within the standard range, the corresponding weight is given full marks; if it is outside the range, points are deducted according to the degree of deviation. The final total score is calculated. If the total score exceeds 85 points, the location corresponding to the signal is identified as a suspected fault point. If the score is between 70 and 85 points, it is marked as a point to be verified and further confirmed by combining observation data from other UAVs. If the score is below 70 points, it is identified as a normal signal or interference signal and is discarded.

[0021] Meanwhile, the drone acquires its real-time spatial coordinates using its onboard centimeter-level RTK positioning device, with latitude and longitude accurate to 0.0001 degrees and altitude accurate to 0.1 meters. This coordinate data serves as the benchmark for subsequent calculations. Combined with the propagation time of the enhanced echo signal from the suspected fault point, the straight-line distance between the drone and the suspected fault point is calculated. First, the signal propagation time is multiplied by the electromagnetic wave propagation speed to obtain a preliminary distance; then, the signal processing delay within the phased array communication equipment is subtracted, preset to a fixed value of 0.3 meters, while correcting for atmospheric refraction effects, ultimately yielding the accurate straight-line distance. Simultaneously, based on the current wave of the phased array antenna... The beam pointing angle is used to determine the azimuth information of the suspected fault point relative to the UAV. The horizontal azimuth angle is calculated by rotating clockwise with the UAV's flight direction as 0 degrees. The vertical elevation angle is calculated with the UAV's horizontal flight plane as 0 degrees, with upward as positive and downward as negative. The azimuth angle calculation needs to be corrected in conjunction with the UAV's real-time yaw angle and roll angle to ensure the accuracy of the azimuth information. For example, if the UAV's yaw angle is 10 degrees, then the horizontal azimuth angle needs to be subtracted by 10 degrees. Finally, starting from the UAV's real-time spatial position coordinates, the three key pieces of information, namely the calculated horizontal azimuth angle, vertical elevation angle, and straight-line distance, are integrated to form a complete distance observation vector.

[0022] Step 114: Calculate the unique three-dimensional coordinates of the faulty target using spatial geometric intersection of distance observation vectors, serving as preliminary positioning information. Specifically, this includes: The ground command center continuously collects distance observation vectors generated by all participating UAVs targeting the same suspected fault point via a collaborative communication network. To ensure the uniqueness and accuracy of the positioning results, at least four valid vectors are required for the final calculation. First, all collected distance observation vectors undergo consistency verification, and abnormal vectors are eliminated. The first step is to calculate the average straight-line distance and azimuth of all vectors. The second step is to calculate the difference between the straight-line distance and the average value for each vector; if the difference exceeds 5 meters, it is marked as a distance anomaly. The third step is to calculate the difference between the horizontal azimuth and the average value for each vector; if the difference exceeds 5 degrees, it is marked as a horizontal azimuth anomaly. The fourth step is to recalculate the remaining vectors. The average value is then used, and the above verification process is repeated until the distance difference of all vectors does not exceed 3 meters and the azimuth difference does not exceed 3 degrees, ensuring the consistency of the vectors involved in the calculation. Subsequently, using the real-time spatial position coordinates of each UAV as a reference point, a spatial ray corresponding to each vector is constructed in the WGS-84 geodetic coordinate system based on its corresponding distance observation vector. With the reference point as the origin, the extension direction of the ray is determined according to the horizontal azimuth and vertical elevation angles in the vector, and the length range of the ray is determined according to the straight-line distance in the vector. The ray extends from the origin to the endpoint corresponding to the straight-line distance. Point coordinates are calculated by adding the azimuth rate of change to the reference point coordinates and multiplying by the straight-line distance. For example, if the reference point coordinates of a UAV are (104.0600 degrees, 30.6700 degrees, 1000.0 meters), the horizontal azimuth is 90 degrees, the vertical elevation is 0 degrees, and the straight-line distance is 500 meters, then the ray extends due east, and the endpoint coordinates are (longitude increment corresponding to 104.0600 + 500 meters, latitude increment corresponding to 30.6700 + 500 meters, 1000.0 meters), where the longitude and latitude increments are calculated based on the Earth's radius.

[0023] Next, spatial geometric intersection calculations are performed on all valid spatial rays. The specific steps are as follows: First, perform pairwise intersection calculations for every two rays, calculating the shortest perpendicular distance between the two rays. If the shortest perpendicular distance is less than 0.8 meters, it is considered an approximate intersection, and the coordinates of the approximate intersection point of the two rays are recorded, taking the coordinates of the midpoint of the shortest perpendicular distance. Second, collect all pairwise approximately intersecting intersection points, calculate the average latitude, longitude, and altitude of these intersection points, and obtain a preliminary intersection point. Third, calculate the perpendicular distance from this preliminary intersection point to each valid ray. If all perpendicular distances are less than 1.5 meters, then this preliminary intersection point is the common intersection point of all rays. If there are rays with a perpendicular distance exceeding 1.5 meters, these rays are marked as suspicious rays, temporarily removed, and the intersection point is recalculated to verify the perpendicular distance again. Fourth, if the number of remaining rays is still considerable after removing suspicious rays... If there are at least three intersecting rays, and the vertical distance from the new intersecting point to all remaining rays is less than 1.5 meters, then the intersecting point is determined to be a valid intersecting point. If there are fewer than three remaining rays, the vertical distance threshold is lowered to 2 meters, and the suspected rays are re-included for calculation until a valid intersecting point is obtained. Finally, the coordinates of the valid intersecting points are calibrated and optimized. Combining the terrain data of the satellite map and referring to the known coordinates of the transmission line towers around the fault area, the coordinate deviations caused by terrain obstruction and signal refraction are corrected. If the intersecting point is located inside terrain obstacles, or its relative position with the tower coordinates does not conform to the line direction, the coordinates are finely adjusted according to the terrain trend and line direction, with the adjustment range not exceeding 3 meters. Finally, the precise three-dimensional coordinates of the intersecting point are obtained, with latitude and longitude accurate to 0.0001 degrees and altitude accurate to 0.1 meters. These are determined as the unique three-dimensional coordinates of the fault target and used as preliminary positioning information for subsequent material delivery route planning.

[0024] Dynamic self-organizing networks ensure that drone swarms maintain communication connectivity and real-time data and command sharing in mountainous and canyon environments with varying obstructions by monitoring link status in real time, actively optimizing beam parameters, and quickly rebuilding interrupted links, thus providing stable communication support for multi-drone collaborative operations.

[0025] In a preferred embodiment of the present invention, step 2 above, which involves dynamically extracting spatial configuration parameters based on preliminary positioning information, analyzing the local rate of change of the spatial configuration parameters and setting a change threshold, identifying key turning points in the evolution trend of the spatial configuration parameters, and calculating dynamic compensation parameters by combining the correlation between spatial configuration parameters and the characteristics at the turning points, may include: In this embodiment of the invention, step 220 involves dynamically calculating the relative distance, azimuth, and altitude difference between each UAV and the faulty target based on the preliminary positioning information and combined with the real-time positioning data of the UAV swarm, thus forming spatial configuration parameters. Specifically, the preliminary positioning information is the unique three-dimensional coordinates of the faulty target obtained through phased array beamforming processing and spatial geometric intersection calculations, specifically including the precise east longitude, north latitude, and altitude of the fault point. The real-time positioning data of the UAV swarm is collected in real-time by the phased array communication equipment carried by each UAV and synchronously shared with all UAVs in the swarm through a multi-UAV collaborative self-organizing network. The real-time positioning data of each UAV includes its current precise east longitude, north latitude, and actual flight altitude. During the calculation, for each UAV in the swarm, the following three sets of parameters are calculated individually: relative distance... The calculation involves first comparing the real-time east longitude of the UAV with that of the faulty target. Subtracting the target's east longitude from the UAV's gives the east longitude difference, which is then converted to a horizontal distance difference according to Earth's latitude-longitude conversion rules. Similarly, subtracting the target's north latitude from the UAV's real-time north latitude gives the north latitude difference, which is also converted to a horizontal distance difference using the same rules. Next, subtracting the target's altitude from the UAV's real-time flight altitude gives the vertical altitude difference. Then, the converted east longitude horizontal distance difference, the converted north latitude horizontal distance difference, and the vertical altitude difference are squared. These three squared results are added together to obtain a total value. Finally, the square root of this total value is taken to determine the straight-line relative distance between the UAV and the faulty target.

[0026] Azimuth calculation is performed by constructing a two-dimensional coordinate system on a horizontal plane with the current location of the UAV as the origin. North is defined as the positive direction of the vertical axis, and east as the positive direction of the horizontal axis. The previously calculated difference in east longitude is used as the coordinate difference on the horizontal axis, representing the east-west distance of the faulty target relative to the UAV. The calculated difference in north latitude is used as the coordinate difference on the vertical axis, representing the north-south distance of the faulty target relative to the UAV. By observing the signs of these two coordinate differences, the east, west, south, and north directions of the faulty target relative to the UAV are determined. Finally, based on the proportional relationship between the two coordinate differences, the relative position of the faulty target to the UAV is determined. The horizontal deflection angle of the UAV in the due north direction is the azimuth angle. The altitude difference is calculated by subtracting the altitude of the faulty target from the current real-time flight altitude of the UAV. The result is the vertical altitude difference between the two. If the result is positive, it means that the UAV's flight altitude is higher than the altitude of the faulty target. If the result is negative, it means that the UAV's flight altitude is lower than the altitude of the faulty target. After the relative distance, azimuth angle and altitude difference of all UAVs are calculated, these three sets of data for each UAV are used as an independent set of spatial configuration parameters. The parameters of all UAVs are combined to form a complete spatial configuration parameter system that reflects the spatial positional relationship between the UAV swarm and the faulty target.

[0027] Step 221: Based on the spatial configuration parameters, calculate the local rate of change for each parameter to obtain a numerical sequence describing the speed and direction of parameter change. Specifically, this includes: firstly, considering the real-time requirements of emergency power outages in complex terrain, and referencing the real-time data transmission frequency of phased array communication equipment, setting a fixed time interval as the unified calculation cycle for the local rate of change to ensure that parameter changes can be captured in a timely manner; secondly, for each set of spatial configuration parameters formed in step 220—namely, the relative distance, azimuth, and altitude difference of each UAV—extracting the parameter values ​​within two consecutive adjacent calculation cycles in chronological order, for example, first... Extract the parameter values ​​for the first second, then the parameter values ​​for the second second, and so on. Perform local change rate calculation on each of the three parameters. The local change rate of relative distance is obtained by subtracting the relative distance value of the previous cycle from the relative distance value of the next cycle. If the change is positive, it means that the relative distance between the UAV and the faulty target is increasing; if it is negative, it means that the distance is decreasing. Divide the change by the set time interval to get the local change rate of relative distance within that time period. Its sign is consistent with the change value, reflecting the direction of distance change.

[0028] The local rate of change of azimuth angle is calculated by subtracting the azimuth angle value of the previous period from the azimuth angle value of the next period. A positive value indicates that the azimuth angle of the UAV relative to the faulty target is deflected clockwise, while a negative value indicates counterclockwise deflection. Dividing this change by the time interval yields the local rate of change of azimuth angle, with the sign indicating the direction of deflection. The local rate of change of altitude difference is calculated by subtracting the altitude difference value of the previous period from the altitude difference value of the next period. A positive value indicates that the altitude difference between the UAV and the faulty target is increasing, while a negative value indicates that the altitude difference is decreasing. Dividing this change by the time interval yields the local rate of change of altitude difference, with the sign indicating the direction of change. The local rates of change of relative distance, azimuth angle, and altitude difference obtained in each calculation period are arranged sequentially in chronological order, forming three independent numerical sequences. Each value in each sequence fully reflects the rate and direction of change of the corresponding parameter within a specific time period.

[0029] Step 222: Compare the local rate of change of each parameter with the preset change threshold, identify the moment when the local rate of change exceeds the corresponding threshold, and determine the moment when the spatial configuration evolution trend changes. Specifically, this includes: the preset change threshold needs to be set in combination with the actual scenario characteristics of power emergency rescue in complex terrain, the flight performance limit of the UAV, and the range of environmental fluctuations that may occur in the fault area, to ensure that the threshold can accurately identify real environmental changes without triggering misjudgments due to small fluctuations; the relative distance change threshold, combined with factors such as mountainous terrain undulations and airflow disturbances, sets the maximum reasonable range of relative distance change per second between the UAV and the faulty target, for example, 50 meters / second, and exceeding this range is considered an abnormal distance change; the azimuth change threshold, referring to the upper limit of the influence of wind direction and line vibration on the flight attitude of the UAV, sets the maximum reasonable deflection angle of the azimuth angle per second, for example, 10 degrees / second, and exceeding this angle is considered an abnormal change in flight direction. The altitude difference change threshold is set by considering terrain slope and UAV take-off and landing performance, setting a maximum reasonable range for altitude difference change per second, such as 30 meters per second. Exceeding this range is considered an abnormal altitude change. During the comparison process, the local change rate value obtained in step 221 for each time period is compared with the corresponding preset change threshold one by one. The local change rate value of relative distance for that period is compared with the relative distance change threshold to determine whether it exceeds the threshold. The local change rate value of azimuth angle for that period is compared with the azimuth angle change threshold to determine whether it exceeds the threshold. The local change rate value of altitude difference for that period is compared with the altitude difference change threshold to determine whether it exceeds the threshold. As long as the local change rate value of any parameter in a certain time period is greater than its corresponding preset change threshold, it indicates that the spatial configuration parameters of the UAV swarm and the faulty target have changed significantly and abnormally in that time period. The specific time point corresponding to this time period is determined as the critical turning point.

[0030] Step 223 involves analyzing the changes in multiple spatial configuration parameters at key inflection points to obtain the coupled changes in the mutual influence and constraints between parameters, and extracting the instantaneous characteristic values ​​of each parameter at the inflection point. Specifically, this includes: firstly, collecting complete data at the corresponding moment of the key inflection point, including the specific values ​​of the relative distance, azimuth, and altitude difference between each UAV and the faulty target at that moment, as well as the values ​​of each parameter in the calculation cycle before and after the inflection point, thus clarifying the magnitude of change of each parameter before and after the inflection point. When analyzing the coupled changes between parameters, in conjunction with the actual scenario of complex terrain, the focus is on observing the mutual influence and constraint patterns between the three parameters. If the local rate of change of the relative distance at the key inflection point exceeds the standard, and the change in altitude difference also increases significantly, combined with the characteristics of mountainous terrain, it can be determined that the UAV may have flown into an area of ​​terrain elevation, causing the relative distance and altitude difference to change simultaneously, forming a positively correlated coupling relationship. If the rate of change of the azimuth exceeds the standard, but the change in the relative distance remains stable, combined with the airflow characteristics of the emergency scenario, it can be determined that the UAV may be able to fly into an area of ​​terrain elevation, causing the relative distance and altitude difference to change simultaneously, forming a positively correlated coupling relationship. If the rate of change of the azimuth exceeds the standard, but the change in the relative distance remains stable, combined with the airflow characteristics of the emergency scenario, it can be determined that the UAV may be able to fly into an area of ​​terrain elevation, causing the relative distance and altitude difference to change simultaneously, forming a positively correlated coupling relationship. When encountering lateral airflow interference, only the flight direction changes, while the straight-line distance to the faulty target remains unchanged. In this case, the azimuth and relative distance form an unrelated coupling relationship. If the rate of change of altitude difference exceeds the limit, the azimuth also fluctuates slightly, while the relative distance changes gradually. This may be because the UAV is flying in a turbulent area of ​​a canyon, where both altitude and direction are interfered with, forming a mutually constraining coupling relationship. Through repeated analysis of multiple sets of key turning point data, the mutual influence patterns between parameters under different complex terrain scenarios are identified, forming a clear coupling change relationship. At the same time, the instantaneous characteristic values ​​of each parameter at key turning points are extracted, specifically including the instantaneous value of the parameter, the specific quantified value of the parameter at that moment; the direction of change, whether the parameter increases or decreases at the turning point, such as an increase in relative distance, a counterclockwise deflection of the azimuth, or a decrease in altitude difference; and the percentage of change, the ratio of the change in parameter at the turning point (the value of the next cycle minus the value of the previous cycle) to the instantaneous value of the parameter at the turning point. For example, if the relative distance change is 200 meters and the instantaneous value is 2500 meters, the percentage of change is 8%, which intuitively reflects the severity of the parameter change.

[0031] Step 224: Based on the coupling change relationship and instantaneous characteristic values, calculate the dynamic compensation parameters used for real-time correction of the UAV swarm's flight path planning and attitude. Specifically, this includes: First, based on the coupling change relationship obtained in Step 223, and considering the priority requirements of power emergency repair in complex terrain, determine the influence weight of each spatial configuration parameter on flight path planning. Relative distance directly determines flight path length and flight time, and time efficiency is crucial in emergency repair, therefore its weight is set to the highest, such as 50%. Azimuth directly affects flight direction; if the direction deviates, the UAV will deviate from the target area, so its weight is set to the next highest, 30%. Altitude difference is related to flight safety. Margin: In complex terrain, height deviations may cause risks such as collisions with mountains or scraping the track. Therefore, a weight of 20% is applied. Next, for each spatial configuration parameter of each UAV, the corresponding compensation component is calculated based on its instantaneous characteristic value. The relative distance compensation component is calculated by multiplying the instantaneous value of this parameter at the turning point by its percentage change to obtain the base compensation value. This base compensation value is then multiplied by the relative distance's influence weight. The final result is the compensation component corresponding to the relative distance. For example, an instantaneous value of 2500 meters × percentage change 8% × weight 50% = 100 meters. This component reflects the path length that needs correction. Azimuth compensation component: [The text abruptly ends here, likely due to an incomplete sentence or missing information.] The base compensation value is obtained by multiplying the instantaneous value of the parameter at the turning point by its percentage change. This base compensation value is then multiplied by the azimuth angle's influence weight. The final result is the compensation component corresponding to the azimuth angle. For example, an instantaneous value of 120 degrees × a percentage change of 10% × a weight of 30% = 3.6 degrees. This component reflects the flight direction angle that needs correction. The altitude difference compensation component is obtained by multiplying the instantaneous value of the parameter at the turning point by its percentage change. This base compensation value is then multiplied by the altitude difference's influence weight. The final result is the compensation component corresponding to the altitude difference. For example, an instantaneous value of 300 meters × a percentage change of 5% × a weight of 20%. =3 meters, this component reflects the flight altitude that needs to be corrected; then, taking into account the collaborative operation requirements of the UAV swarm and avoiding multi-aircraft flight path conflicts, the relative distance compensation component, azimuth compensation component, and altitude difference compensation component of each UAV are superimposed and integrated to form a comprehensive dynamic compensation parameter for the UAV. This compensation parameter specifically includes three core components: first, the flight path direction correction angle, used to adjust the UAV's flight direction to adapt to changes in azimuth angle; second, the flight altitude adjustment value, used to adjust the UAV's flight altitude to adapt to changes in altitude difference; and third, the path length correction amount, used to adjust the UAV's flight path length to adapt to changes in relative distance.

[0032] By dynamically calculating the spatial configuration parameters of each UAV and the faulty target, the subtle dynamic changes in their positional relationship in complex terrain can be captured in real time. By analyzing the local rate of change and key turning points of each parameter, sudden environmental disturbances in complex terrain can be detected in a timely manner, avoiding the problem of flight path planning lagging due to failure to respond to changes in a timely manner, and improving the adaptability and safety of multi-aircraft collaborative flight path planning.

[0033] In a preferred embodiment of the present invention, step 3 above, which involves calculating a set of convex region vertices corresponding to multiple obstacle zones and available airspace based on dynamic compensation parameters, combined with the real-time load status of the UAV, line vibration monitoring data, and surrounding terrain obstacle information, to construct a safe flight airspace and generate a preliminary path for the material delivery route, may include: In this embodiment of the invention, step 330 involves using dynamic compensation parameters to correct the preliminary positioning information, obtaining the corrected target point coordinates and flight reference data. Specifically, this includes: firstly, clarifying that the preliminary positioning information is the three-dimensional coordinates of the fault target obtained by the UAV swarm through phased array dynamic self-organizing network collaborative scanning of the fault area, and calculation via beamforming and multi-source signal geometric intersection. The dynamic compensation parameters are calculated based on the local rate of change of spatial configuration parameters, key inflection point features, and coupling relationships between parameters, including three core data: distance compensation, azimuth compensation, and altitude difference compensation. In complex terrain emergency scenarios, factors such as signal obstruction in mountainous areas and airflow disturbances can easily lead to deviations in the preliminary positioning. Therefore, correction is required using dynamic compensation parameters. During correction, first, the relative distance of 10 kilometers between the fault target and UAV A in the preliminary positioning information is taken, and the distance compensation of +0.2 kilometers in the dynamic compensation parameters is added to obtain the corrected relative distance of 10.2 kilometers. Then, the azimuth angle of the fault target relative to UAV A of 30° in the preliminary positioning information is taken, and the dynamic compensation parameters are added... The azimuth compensation is increased by 2° to obtain a corrected azimuth of 32°. Next, the altitude difference of 500 meters between the faulty target and UAV A is extracted from the initial positioning information and added to the altitude difference compensation of -10 meters in the dynamic compensation parameters, resulting in a corrected altitude difference of 490 meters. Then, the real-time three-dimensional coordinates (X: 10000 meters, Y: 5000 meters, Z: 1500 meters) of UAV A, collected by GPS positioning, are obtained. Combined with the corrected relative distance, azimuth, and altitude difference, the coordinates of UAV A are used as a reference. The horizontal direction is determined according to the corrected azimuth, and the corrected relative distance is extended along this direction. The vertical position is then adjusted according to the corrected altitude difference, ultimately yielding the corrected three-dimensional coordinates of the faulty target (X: 19050 meters, Y: 9840 meters, Z: 1010 meters). Finally, using these precise coordinates as the core, flight reference data is determined, including the target reference point for the flight path planning, i.e., the corrected faulty target coordinates, the minimum safe flight altitude reference, and the minimum safe distance reference from the power transmission line.

[0034] Step 331: Based on the corrected target point coordinates and flight reference data, collect and fuse real-time load status data of the UAV, transmission line vibration amplitude data, and terrain elevation and static obstacle data of the work area to form a comprehensive environmental situation. Specifically, this includes: using the corrected target point coordinates and flight reference data obtained in step 330 as the core reference, initiating multi-dimensional data collection and fusion work to fully adapt to the dynamic changes of complex terrain emergency scenarios. In the data collection phase, the load sensor on the UAV body collects real-time load status data such as the current load weight and load center of gravity position to avoid flight attitude imbalance due to uneven load. Vibration sensors pre-installed on key nodes of transmission line towers collect line vibration data such as vibration amplitude and vibration frequency under wind and environmental disturbances to clarify the dynamic risk range of the line. The lidar elevation measurement equipment on the UAV collects the altitude of each point in the work area in real time, and simultaneously retrieves the pre-stored data of the area in the power emergency system. The system supplements the basic topographic data to ensure the integrity of the terrain elevation data. Through phased array collaborative scanning by a swarm of drones, it collects static obstacle data, including the location and dimensions of mountains, buildings, trees, and other power transmission line towers within the work area. In the data processing stage, all collected data is first converted into the WGS84 three-dimensional coordinate system commonly used in power emergency repair operations to ensure coordinate uniformity and comparability. Then, abnormal data is eliminated through data correlation analysis. For example, if a vibration sensor collects a vibration amplitude of 5 meters, far exceeding the reasonable vibration range for this type of line, it is determined to be sensor fault data and eliminated. For missing data in certain areas, the average of three surrounding valid elevation data is used to supplement the missing data. Finally, all valid data is integrated to form a comprehensive environmental situational awareness, including the precise location of the fault target, the drone's own load status, the real-time vibration of the power transmission line, the terrain undulations of the work area, and the distribution of static obstacles, thus completely and accurately reflecting the actual on-site environment.

[0035] Step 332: Based on the comprehensive environmental situation, perform three-dimensional spatial analysis and geometric description of various static obstacles and dynamic risk areas, calculate the projection range on the horizontal plane and vertical space, and determine the convex polygon regions corresponding to each independent available airspace based on the projection range, obtaining the vertex coordinate set of all convex polygon regions; specifically, based on the comprehensive environmental situation formed in step 331, perform detailed three-dimensional spatial analysis of static obstacles and dynamic risk areas in the work area, and define the available airspace range. First, perform three-dimensional spatial analysis of static obstacles. For each static obstacle such as mountains, buildings, and trees, based on its three-dimensional coordinates and outline dimensions, reconstruct its complete three-dimensional shape by stitching together point cloud data scanned by the UAV, and describe its boundary using geometric language. For example, the three-dimensional boundary of a small mountain is X: 17000-18000 meters, Y: 8000-9000 meters, Z: 900-1300 meters; for the dynamic risk areas formed by transmission line vibration, based on... The vibration amplitude and frequency of the collected data are used to calculate the maximum sway range of the line during vibration, i.e., the three-dimensional boundary of the dynamic risk area. This boundary is defined by extending 30 cm horizontally to both sides and 20 cm vertically upwards and downwards from the line's coordinates as the center, thus clarifying the area's occupancy in three-dimensional space. Next, the projection range is calculated, specifically the projection ranges of the static obstacle and the dynamic risk area in the horizontal plane (XY plane) and vertical space (XZ plane, YZ plane). For horizontal plane projection, the three-dimensional boundaries of the obstacle and risk area are projected vertically onto the XY plane to obtain the corresponding planar boundary contours. For example, the projection range of the small mountain range in the XY plane is X: 17000-18000 meters, Y: 8000-9000 meters. For vertical space projection, the small mountain range is projected vertically onto the XZ plane and YZ plane to obtain the vertical boundary contours. The projection range of the small mountain range in the XZ plane is X: 17000-18000 meters, Z: 900-1300 meters.Next, independent available airspace is delineated. Based on these projection ranges, airspace occupied by obstacles and dynamic risk areas is eliminated, leaving the remaining unobstructed airspace divided into multiple non-overlapping independent available airspaces. For example, on the east side of the small mountain, independent airspaces are delineated at X: 18000-19500 meters, Y: 9000-10000 meters, and Z: 1100-1500 meters. Each independent available airspace is a convex polygon on the horizontal plane. A characteristic of convex polygons is that they have no internal depressions, ensuring that the UAV will not have its path obstructed by depressions when flying within the airspace. Finally, the set of vertex coordinates is determined, and each vertex is measured one by one using the UAV's positioning equipment. The specific coordinates of each vertex of a convex polygon in the WGS84 3D coordinate system are recorded. For example, the coordinates of the four vertices of a convex polygon are (X1: 18000m, Y1: 9000m, Z1: 1100m), (X2: 19500m, Y2: 9000m, Z2: 1100m), (X3: 19500m, Y3: 10000m, Z3: 1100m), and (X4: 18000m, Y4: 10000m, Z4: 1100m). This records the vertex coordinates of all regions of the convex polygon, forming a complete set of vertex coordinates for the convex region, clearly defining the specific spatial range of each usable airspace.

[0036] Step 333: Based on the vertex coordinate set, construct a continuous and closed safe flight airspace within the three-dimensional spatial framework. Within this safe flight airspace, using the mission start point and mission target point as the endpoints of the flight paths, and considering the UAV's flight performance constraints, use a path planning algorithm to generate multiple candidate material delivery routes. Specifically, this includes: based on the vertex coordinate set of the convex region obtained in Step 332, and considering the UAV's flight performance constraints, constructing a safe flight airspace and generating multiple candidate routes to ensure that the routes are adaptable to complex terrain and the UAV's flight capabilities; first, constructing the safe flight airspace by importing the vertex coordinate sets of all convex polygon regions into the three-dimensional spatial planning framework, connecting and integrating these independent convex polygon regions, and connecting adjacent convex polygon airspaces with a width not less than three times the UAV's wingspan to ensure that the integrated airspace is continuous and closed, meaning that the UAV can reach the mission target point unimpeded within this airspace from the mission start point; simultaneously, adjusting the boundaries of the integrated airspace to maintain a preset safe distance from surrounding obstacles and dynamic risk areas, based on the UAV's fuselage size, flight speed, and safety requirements of the emergency scenario. A distance of 5 meters is set to avoid collisions caused by airflow fluctuations or attitude adjustments during drone flight. Through the above operations, a complete safe flight airspace is ultimately formed, covering all feasible paths from the mission start point to the target point, and completely avoiding the risk of collision in space. Next, the start and end points of the flight path are defined. The mission start point is set as the takeoff point of the drone swarm, and the mission target point is the material delivery point in the fault area corrected in step 330 (X: 19050 meters, Y: 9840 meters, Z: 1010 meters). Then, combined with flight performance constraints, the maximum flight speed of the drones is determined. Performance parameters such as altitude, maximum climb rate, maximum descent rate, and endurance limits are used to avoid planning flight paths that exceed the drone's flight capabilities. Finally, candidate flight paths are generated, employing path planning logic adapted to complex terrain. Starting from the mission's starting point, feasible waypoints within the safe flight airspace are searched point by point, prioritizing paths with shorter distances and gentler altitude changes, while avoiding the boundaries of various convex polygon airspaces. Ultimately, multiple different candidate supply delivery routes are generated. Each candidate route includes a complete sequence of waypoint coordinates, suggested flight speeds for each segment, and detailed information such as flight altitude change plans.

[0037] Step 334 evaluates the path length, safety margin, and energy consumption of each candidate route, and determines the preliminary route for final material delivery through multi-objective optimization decision-making. Specifically, this includes: for the multiple candidate routes generated in step 333, selecting the optimal preliminary route suitable for complex terrain emergency scenarios through multi-index evaluation and multi-objective optimization decision-making. First, the three core evaluation indicators are clarified: path length, safety margin, and energy consumption. All three indicators meet the efficiency and safety requirements of power emergency rescue. In the indicator calculation stage, the path length is calculated by extracting the waypoint coordinate sequence for each candidate route and calculating the straight-line distance between adjacent waypoints. For example, the straight-line distance from waypoint 1 to waypoint 2 is 800 meters, and the straight-line distance from waypoint 2 to waypoint 3 is... The first step is to calculate the total path length of each candidate route by summing the straight-line distances of all adjacent waypoints, with a line distance of 600 meters. For example, the total length of route 1 is 5000 meters, and the total length of route 2 is 5500 meters. The second step is to calculate the safety margin. For each waypoint on each candidate route, the straight-line distance between that waypoint and the nearest surrounding obstacle and dynamic risk area is measured. For example, the distance between waypoint 5 and the nearest tree on route 1 is 8 meters, and the distance between waypoint 6 and the power line is 6 meters. The minimum distance between all waypoints is taken as the safety margin for that route. For example, the safety margin for route 1 is 6 meters, and the safety margin for route 2 is 7 meters. The third step is to calculate the energy consumption index. Based on the energy consumption characteristics of the UAV, combined with the total path length, the total altitude change during flight, and the climb altitude, the calculation is performed. The cumulative values ​​of altitude changes, such as the total climb altitude of 200 meters and the total descent altitude of 190 meters for route 1, resulting in a total altitude change of 390 meters, are used. The real-time payload of the drone is also considered. Energy consumption is calculated using intuitive logic: 1 unit of energy is consumed for every kilometer flown carrying 1 kg of cargo, 1 unit of energy is consumed for every 10 meters climb, and 0.5 units of energy are consumed for every 10 meters descent. Therefore, the energy consumption of route 1 = (5 kg × 5 km) + (200 m ÷ 10 m × 1 unit) + (190 m ÷ 10 m × 0.5 unit) = 25 + 20 + 9.5 = 54.5 units of energy. The energy consumption of other routes is calculated using the same logic. Then, multi-objective optimization decisions are implemented, assigning reasonable weights to the three indicators, and combining the principles of prioritizing emergency rescue safety while considering efficiency and energy consumption. The safety margin weight is set at 50%, the route length weight at 30%, and the energy consumption weight at 20%. Each route is scored separately for each of the three indicators, with a maximum score of 100 points. Higher scores for better indicators result in higher scores. For example, a safety margin of 8 meters earns 100 points, while 6 meters earns 80 points; a route length of 5000 meters earns 100 points, while 5500 meters earns 90 points; and energy consumption of 54.5 units earns 95 points, while 58 units earns 90 points. The total score for each route is then calculated according to the weights. For example, the total score for route 1 is (80 points × 50%) + (100 points × 30%) + (95 points × 20%) = 40 + 30 + 19 = 89 points, and the total score for route 2 is (85 points × 50%) + (90 points × 30%) + (90 points × 20%) = 42.5 + 27 + 18 = 87 points.5 points; Finally, the route with the highest total score was selected. This route ensured sufficient safety margin, had a short path length, and low energy consumption, making it a comprehensive final route that balanced safety, efficiency, and energy consumption. This route was then chosen as the initial route for material delivery.

[0038] By using dynamic compensation parameters to make targeted corrections to the initial positioning information, positioning deviations caused by signal blockage and airflow disturbances in complex terrain are reduced. Multiple types of real-time data, such as UAV payload, line vibration, terrain elevation, and static obstacles, are collected and integrated to enable flight path planning to fully adapt to changes on site.

[0039] In a preferred embodiment of the present invention, step 4 above, which involves sending the preliminary path to the ground command center via a phased array communication link and updating the flight path in real time based on flight path correction instructions from the field environment to generate an updated delivery flight path, may include: In this embodiment of the invention, step 440 involves sending the coordinate sequence of the preliminary path, the expected flight status, and associated environmental data packets to the ground command center via a phased array communication link. Specifically, this includes: firstly, comprehensively reviewing the core data to be transmitted to ensure the ground command center has complete on-site planning and environmental information, adapting to the dynamic assessment needs of emergency rescue in complex terrain. The first category is the coordinate sequence of the preliminary path, which needs to be listed in detail according to the order of the UAV's flight, including the complete three-dimensional coordinates and key annotations of each waypoint. For example, the mission starting point is an emergency supplies assembly point (WGS84 coordinate system: X15000 meters, Y6000 meters). The coordinates of each waypoint are accurate to the meter, and the waypoint number, estimated arrival time, and terrain features corresponding to that waypoint are clearly marked. The waypoints are X15800 meters, Y6500 meters, Z1020 meters, estimated arrival time 14:08, X16500 meters, Y7000 meters, Z1050 meters, estimated arrival time 14:15, and so on, up to waypoint 12 (X19050 meters, Y9840 meters, Z1010 meters, marked as the fault target delivery point, estimated arrival time 14:50).

[0040] The second category is expected flight status data, which needs to be determined one by one based on the terrain conditions, airspace restrictions, and UAV performance parameters of the initial path. This includes the expected flight speed for each segment, such as: 25 km / h for waypoints 0 to 1 in plains airspace; 22 km / h for waypoints 5 to 6 in canyon airspace with low wind speeds; and 18 km / h for waypoints 8 to 9 requiring a slight climb. It also includes the expected flight attitude for each waypoint, such as: level flight for waypoints 2 to 3; a 3° elevation gain for waypoints 6 to 7; and a 2° descent for waypoints 10 to 11. Finally, it includes the expected flight duration for each segment. The distance from waypoint 0 to waypoint 1 is 930 meters. Calculated at a speed of 25 km / h, the estimated flight time = (930 meters ÷ 1000) ÷ 25 km / h × 60 minutes = 2.23 minutes, approximately 2 minutes and 14 seconds. The estimated remaining flight time of the UAV at each waypoint is as follows: if the UAV takes off from waypoint 0 with a full charge, it will have a flight time of 2 hours; at waypoint 3, the estimated remaining flight time is 1 hour and 45 minutes; and at waypoint 7, the estimated remaining flight time is 1 hour and 10 minutes. All flight time calculations are based on a comprehensive logic of the current payload of 5 kg + the flight distance of this segment + the energy consumption of flight attitude. For example, the energy consumption of the climb segment is 1.5 times that of the level flight segment, and the energy consumption of the descent segment is 0.8 times that of the level flight segment.

[0041] The third category is the associated environmental data package, which must contain all the details of the comprehensive environmental situation formed in step 331. The terrain elevation data must be labeled with the altitude of each grid in the operating area at a grid density of 10 meters × 10 meters. For example, the elevation of the grid around waypoint 3 is 950 meters to 980 meters, and the elevation of the grid around waypoint 7 is 1050 meters to 1100 meters. The static obstacle data must record in detail the type, specific location, size parameters and distance from the preliminary path of each obstacle. For example, a large tree is located at X17200 meters, Y8200 meters, Z980 meters, with a trunk diameter of 0.6 meters, a crown height of 12 meters and a horizontal distance of 30 meters from waypoint 6. The power line vibration data must include the vibration amplitude of key towers, such as tower A with a vibration amplitude of 25 centimeters, tower B with a vibration amplitude of 30 centimeters, vibration frequency and vibration direction. The real-time payload data of the UAV must clearly indicate the current payload weight, cargo type and payload center of gravity position.

[0042] After data processing is complete, the phased array communication link is initiated for transmission. First, the phased array communication equipment of each UAV in the swarm automatically scans for surrounding companion equipment and quickly establishes a dynamic self-organizing network using beam agility technology. The lead UAV acts as the network core, and the phased array equipment of other UAVs transmit beams in a directional manner to interface with UAV-1, forming a core-node communication network to ensure efficient and complete data aggregation. Next, all UAVs transmit their respective flight segment data to UAV-1. UAV-1's phased array equipment integrates the scattered data into a unified data packet. The data packet is sorted according to the structure of coordinate sequence-flight status-environmental data, and the total size is divided according to the actual data volume. Before transmission, the phased array equipment encrypts each data block using a symmetric encryption method with a unique device identifier and timestamp. For example, UAV-1's device identifier is SC-Emergency-001. The timestamp is 20240520140000, and the encryption key is the hash value of the combination of the two, to prevent data from being stolen or tampered with during transmission in complex terrain. During transmission, the UAV-1's phased array equipment uses beamforming technology to focus the signal beam onto the phased array receiving equipment at the ground command center (coordinates X14000 meters, Y5000 meters, Z900 meters). The horizontal scanning range of the beam is 120°, and the elevation scanning range is 120°, ensuring that the signal penetrates the mountainous area and reduces transmission interference. A segmented transmission + two-way verification mechanism is adopted. After each data block is transmitted, the receiving equipment at the ground command center immediately sends back a signal indicating successful or failed reception. If no feedback is received or the feedback fails, the UAV-1 retransmits the data block until all data blocks are confirmed to have been successfully received, ensuring that the coordinate sequence, expected flight status, and environmental data packets are delivered to the ground command center completely and accurately.

[0043] Step 441: The ground command center conducts a comprehensive analysis based on real-time video and meteorological data to generate route correction instructions, including adjustments to the position of specific waypoints, changes in altitude, or replanning of routes. Specifically, this includes: First, constructing a multi-dimensional real-time data receiving system to compensate for the limitations of preliminary path dependence environmental data and accurately capture the dynamic changes of complex terrain emergency sites. The first category is real-time video data, which is divided into aerial and ground perspectives. The aerial perspective is captured in real-time by 4K high-definition cameras carried by the UAV swarm. UAV-1 captures the surrounding scenery of waypoints 0 to 4, UAV-2 captures the scenery of waypoints 5 to 8, and so on. The video images are synchronously labeled according to the waypoint numbers. Each frame contains the current UAV position, shooting timestamp, and image zoom ratio, clearly presenting the terrain changes, obstacle dynamics, and real-time status of power transmission lines around the path. The ground-based perspective was captured by three rescue workers near the fault area using portable 4G / 5G video terminals. The footage focused on the area surrounding the fault, below the path, and key obstacles. The video data was transmitted in real-time to the display terminal at the ground command center via the emergency communication network, providing 360° visualization of the situation. The second category was real-time meteorological data, acquired through a combination of drone data collection and weather station monitoring. Each drone carried a miniature meteorological sensor that collected real-time data on wind speed, wind direction, air humidity, temperature, and precipitation levels around its flight path. This data was uploaded to the ground command center every minute. Simultaneously, the ground command center connected with the three nearest weather stations around the work area to obtain refined weather forecast data, including wind speed trends for the next hour, precipitation probability, and whether there were severe convective weather warnings, forming a real-time monitoring and short-term forecasting meteorological data system.

[0044] Subsequently, a three-person analysis team from the ground control center formed an analysis group to conduct a collaborative analysis, combining real-time data with the preliminary path association data received in step 440. The analysis process consisted of three steps. The first step was video comparison analysis, comparing the real-time aerial video footage with the obstacle distribution and terrain features in the preliminary path environmental data package one by one. For example, when reviewing the footage around waypoint 6 taken by UAV-2, a large tree that had fallen due to strong winds was found in an area where there were no tall obstacles in the original environmental data package, and the trunk was tilted towards the preliminary path. At the same time, ground video confirmed that the fallen tree did not touch the path. The first step involved assessing the impact of the power transmission line, which had already encroached on the drone's original flight airspace. The second step involved meteorological impact assessment. Real-time wind speeds around waypoint 7, collected by the drone, were 8.5 m / s, significantly higher than the initial wind speed forecast. Furthermore, a weather station warning indicated that wind speeds in the area could reach 10 m / s within the next 30 minutes. Strong winds could cause the drone to lose control of its flight attitude or exacerbate vibrations of the power transmission line. The third step involved comprehensive risk assessment. Based on the location and size of the fallen trees and changes in wind speed, the assessment concluded that the section from waypoint 6 to waypoint 7 in the initial path posed a risk of collision between the drone and fallen trees, and that strong winds would reduce flight performance. Stability requires flight path correction. Based on the assessment, targeted flight path correction instructions are generated. Addressing the impact of fallen trees, a local position adjustment instruction is generated, specifying that the X-coordinate of waypoint 6 be adjusted from 17800 meters to 17850 meters and the Y-coordinate from 8800 meters to 8830 meters, with the adjustment direction being northeast, ensuring that the horizontal distance between waypoint 6 and the fallen trees is no less than 10 meters after the adjustment. For strong wind conditions, an altitude modification instruction is generated, specifying that the flight altitude from waypoint 6 to waypoint 7 be increased from 1100 meters to 1150 meters to reduce the impact of strong winds on flight attitude. Increase the safe distance from power transmission lines; if a larger risk is subsequently discovered, generate a global replanning instruction, specifying waypoint 4 (X16800m, Y7500m, Z1080m) as the starting point and waypoint 9 (X18500m, Y9200m, Z1120m) as the ending point, avoiding the landslide-affected area (X17500m-18000m, Y8000m-8500m), and replan the route segment. The safety margin of the new route segment must not be lower than the original route standard. All correction instructions must specify in detail the adjustment object, specific adjustment requirements, adjustment basis, and execution priority.

[0045] Step 442 involves analyzing the flight path correction command to obtain the corresponding correction constraints. Based on these constraints, the initial path is locally adjusted or globally replanned to generate an updated delivery flight path. Specifically, this includes: First, after receiving the flight path correction command via the phased array communication link, the lead UAV in the UAV swarm immediately initiates the command parsing-constraint extraction-cooperative confirmation process. The first step is command parsing: the UAV-1's phased array equipment converts the command into recognizable structured information, clarifying the correction type as local position adjustment and altitude modification, the correction target as waypoint 6 and the flight segment from waypoint 6 to waypoint 7, the adjustment requirements as X+50 meters, Y+30 meters, and Z+50 meters, and the adjustment basis as avoiding the risks of fallen trees and strong winds. The second step is constraint extraction: combining the safety requirements for emergency flight in complex terrain with the UAV's performance parameters, the core constraints are extracted. The constraints are revised as follows: Position adjustment constraints: the adjusted coordinates of waypoint 6 must not exceed 100 meters of the original waypoint's airspace; the horizontal distance to fallen trees must be no less than 10 meters; and the vertical distance to power lines must be no less than 5 meters. Altitude modification constraints: the adjusted flight altitude must not be lower than 1100 meters or higher than 1200 meters; the climb rate from waypoint 6 to waypoint 7 must not exceed 5 meters per second. Segment connection constraints: the segments between the adjusted waypoint 6 and the preceding and following waypoints (waypoint 5 and waypoint 7) must transition smoothly, with flight attitude changes not exceeding 5°. The third step is collaborative confirmation: UAV-1 synchronizes the parsed instructions and constraints to other UAVs (UAV-2 to UAV-5) via a phased array self-organizing network. Path adjustment can only begin after each UAV confirms receipt of the received signal, ensuring all UAVs agree on the adjustment requirements.

[0046] Next, based on the constraints, specific adjustment operations were carried out. First, the adjusted coordinates of waypoint 6 were calculated. The original coordinates of waypoint 6 were X 17800 meters, Y 8800 meters, and Z 1100 meters. As required by the instructions, X was increased by 50 meters (17800 meters + 50 meters = 17850 meters), Y by 30 meters (8800 meters + 30 meters = 8830 meters), and Z by 50 meters (1100 meters + 50 meters = 1150 meters), resulting in the adjusted coordinates of waypoint 6 (X 17850 meters, Y 8830 meters, Z 1150 meters). Then, the safety of the adjustment was verified by measuring the distance carried by the UAV. The measuring equipment measures the horizontal distance between waypoint 6 and the fallen tree in real time after adjustment. The coordinates of the fallen tree are X17835 meters, Y8810 meters, and Z980 meters. The horizontal distance is calculated as the square root of the difference between the X coordinates of the two points + the square root of the difference between the Y coordinates of the two points, i.e., (17850-17835)² + (8830-8810)² = 225 + 400 = 625, which is 25 meters, satisfying the constraint of not less than 10 meters. The vertical distance to the power transmission line is measured to be 15 meters, satisfying the constraint of not less than 5 meters. Then, the flight segment parameters are adjusted, and the distance to waypoint 5 (X1720) is calculated. The distance from Y8200m, Z1100m to the adjusted waypoint 6 is (17850-17200)² + (8830-8200)² + (1150-1100)² = 650² + 630² + 50² = 422500 + 396900 + 2500 = 821900, which is approximately 906.6 meters after taking the square root. Considering the climb rate in the constraints, the altitude of this segment increases from 1100m to 1150m, with a climb height of 50m. The estimated climb time is 50m ÷ 5m / s = 10 seconds. The flight speed for this segment is 20km / h. Based on the calculation, the estimated flight time is approximately 2.72 minutes (906.6 meters ÷ 1000) ÷ 20 km / h × 60 minutes. The flight attitude is set to a gentle climb with a 2° pitch angle to ensure cargo stability. Similarly, the distance and parameters between waypoint 6 and waypoint 7 (original coordinates X18300 meters, Y9300 meters, Z1100 meters) are calculated, and the altitude of waypoint 7 is simultaneously increased to 1150 meters (X18300 meters, Y9300 meters, Z1150 meters). The flight segment is set to level flight at a speed of 20 km / h, with an estimated flight time of approximately 2.9 minutes.

[0047] After the adjustment is completed, UAV-1 reorganizes the updated flight path data, listing all adjusted waypoint coordinates, estimated arrival times, flight speeds, and attitudes in waypoint order, forming a complete updated delivery flight path data table. This data table is synchronized to UAV-2 through UAV-5 via a phased array self-organizing network. Each UAV receives the data and sends back a data confirmation signal, ensuring that all UAVs obtain a unified and accurate updated flight path, laying the foundation for subsequent multi-aircraft coordinated material delivery. If the correction command is a global replanning, the following procedure must be followed: First, based on the origin and destination waypoints and risk areas in the command, eliminate the airspace corresponding to the risk area (X17500m-18000m, Y8000m). (8500m) The terrain and obstacle data of the operation area are rescanned, and a new set of convex polygon vertices of available airspace is parsed out. For example, the vertex coordinates of the newly added convex polygon region are: X17400m, Y7900m, Z1100m; X18100m, Y7900m, Z1100m, etc., to construct a new continuous closed safe flight airspace. Based on the new safe airspace, combined with constraints such as the maximum flight speed and endurance of the UAV, feasible paths are searched to generate 3 candidate replanning routes. Then, the safety margin, path length and energy consumption of each candidate route are evaluated, and the final route is selected. Finally, the new route data is synchronized to all UAVs to complete the global replanning.

[0048] The phased array communication link's directional transmission and encryption verification mechanism ensures the complete and secure transmission of path data, flight status, and environmental information. Based on clear safety constraints and UAV performance constraints, path adjustments are carried out. Whether it is local coordinate and altitude optimization or global replanning, the updated flight path is ensured to both avoid dynamic risks and adapt to the UAV's flight capabilities.

[0049] In a preferred embodiment of the present invention, step 5 above, which involves controlling the UAV swarm to execute material delivery tasks according to the updated delivery route and the collaborative task allocation, and during task execution, dynamically adjusting the delivery route by monitoring the status of each UAV and changes in the surrounding environment of the route in real time through phased array communication equipment to achieve multi-UAV collaborative route planning, may include: In this embodiment of the invention, step 550 involves performing a collaborative task allocation calculation based on the updated spatial and temporal attributes of the delivery route, combined with the remaining endurance, cargo capacity, and current position parameters of each UAV, to generate a task allocation scheme for each UAV, including specific flight segments, designated delivery target points, and task sequence. Specifically, this includes: firstly, comprehensively analyzing the core attributes of the updated delivery route to provide a basis for task allocation; in terms of spatial attributes, clarifying the total segment division of the route, such as the updated route being divided into 8 continuous segments from the starting point to the fault delivery point, segment 1, x15000... Segment 2: X16200m-Y6800m-Z1050m to X17500m-Y7600m-Z1100m… Segment 8: X18800m-Y9600m-Z1030m to X19050m-Y9840m-Z1010m. The distance, terrain features, and designated delivery target points for each segment are specified. Regarding time attributes, the total estimated execution time of the route, the preset flight time for each segment, and the delivery time window are clearly defined. Next, data is collected… Key parameters for each drone: Remaining flight time is calculated based on the remaining battery power. For example, Drone 1 has 75% battery remaining and a full charge provides 2 hours of flight time, so the remaining flight time is 2 hours × 75% = 1.5 hours; Drone 2 has 60% battery remaining and a remaining flight time of 2 hours × 60% = 1.2 hours; Drone 3 has 80% battery remaining and a remaining flight time of 2 hours × 80% = 1.6 hours; Drone 4 has 70% battery remaining and a remaining flight time of 2 hours × 70% = 1.4 hours. Cargo capacity: Each drone has a maximum cargo weight of 5kg. Current cargo weight: Drone 1 carries 3kg of cargo. The payload capacity is calculated as follows: Drone 2 carries 2kg of cargo, Drone 3 carries 4kg of cargo, and Drone 4 carries 1kg of cargo. The remaining payload capacity is calculated as: Maximum payload weight - Carried payload weight. For example, the remaining payload capacity of Drone 1 is 5kg - 3kg = 2kg. The current location parameters are: Drone 1: X14800m - Y5800m - Z990m; Drone 2: X14900m - Y5900m - Z995m; Drone 3: X15100m - Y6100m - Z1005m; Drone 4: X15200m - Y6200m - Z1010m.

[0050] Then, the collaborative task allocation calculation is performed. The core logic is to match the remaining range with the flight segment distance, match the cargo capacity with the delivery weight, and allocate based on the nearest location. The first step is to calculate the maximum flight segment distance that each drone can cover = remaining range × drone's level flight speed. For example, drone 1's maximum coverage distance = 1.5 hours × 20 km / h = 30 km, covering flight segments 1-5. The second step is to match the drone's cargo capacity based on the weight requirements of each delivery point. For example, drone 3 has a cargo capacity of 1 kg + 4 kg already loaded = 5 kg, which can carry 4 kg of cargo to delivery point B. The third step is to calculate the distance from each drone's current location to the starting point of each flight segment and prioritize allocating to the nearest flight segment. For example, the distance from drone 1's current location to the starting point of flight segment 1 = ... ≈283 meters, the closest distance, assign flight segment 1-segment 2 and drop point A; UAV 2's current position is closest to the starting point of flight segment 3, assign flight segment 3-segment 4; UAV 3 is assigned flight segment 5-segment 6 and drop point B; UAV 4 is assigned flight segment 7-segment 8 and drop point C; finally, generate a complete task allocation plan, specifying the specific flight segment, designated drop target point and task sequence for each UAV, ensuring that the tasks of each UAV are not overlapping and the drop time meets the window requirements.

[0051] Step 551 involves distributing the task allocation plan and corresponding flight segment control commands to each UAV via the phased array communication network. Specifically, this includes: First, the lead UAV categorizes the generated task allocation plan by UAV number and matches each UAV with corresponding flight segment control commands. These commands must be tailored to the requirements of flying in complex terrain and include detailed information such as the complete coordinate sequence of the corresponding flight segment, flight speed, flight attitude requirements, delivery operation commands, and emergency response thresholds. Next, the phased array communication network is activated to distribute the commands. The lead UAV's phased array equipment uses beamforming technology to generate an independent communication beam for each UAV. To avoid signal interference in complex terrain, the command data is encrypted before transmission to prevent data leakage. A block transmission and two-way confirmation mechanism is adopted, splitting the command of each UAV into 3 data blocks and transmitting them sequentially. After each data block is transmitted, the receiving UAV sends a signal indicating successful reception. If no feedback is received, the transmission is re-transmitted until all data blocks are transmitted. Finally, to confirm that all UAVs have successfully received the command, the lead UAV queries each UAV through the phased array communication network to inquire whether the command has been completely received. UAVs 2-4 respectively report that the command is complete. After confirmation of execution, the command issuance process is completed, ensuring that each UAV understands its own mission and operational requirements.

[0052] Step 552: Based on the received flight segment control commands and task sequence, each UAV autonomously controls its flight control unit to execute the material transport and delivery mission to the fault target area according to the specified flight route and time requirements. Specifically, after receiving the task allocation plan and flight segment control commands, each UAV autonomously activates its flight control unit and executes the mission according to the process of flight segment flight - attitude adjustment - delivery preparation - precise delivery. During flight, it strictly adheres to the specified flight route and time requirements. For example, if UAV 1 flies according to the coordinate sequence of flight segment 1, it compares its current position with the command coordinates in real time using its own positioning device. If a deviation occurs, it autonomously adjusts its flight direction to correct the deviation until it meets the requirements. The flight speed is executed according to the command settings. Flight segment 1 is in plain airspace, maintaining a speed of 20 km / h to ensure delivery at 14:10. Before arriving at the starting point of flight segment 2, the UAV prepares for delivery in advance. For example, when UAV 3 is flying to the middle of flight segment 6, it starts the cargo hold securing device to check and confirm that the materials are securely fixed. At the same time, it adjusts its flight attitude to the hovering preparation state. After arriving at the delivery point, it hovers as required by the instructions and confirms that there are no personnel or obstacles at the delivery point through the visual sensor. Then, it starts the cargo hold delivery switch and smoothly delivers the materials to the designated location. After the delivery is completed, the UAV autonomously records the delivery time and the status of the materials and feeds back the delivery results to the lead UAV through the phased array communication network. Throughout the entire process, the UAV autonomously makes fine adjustments according to the real-time changes in complex terrain. For example, when flying to the mountainous canyon segment, if it encounters slight airflow disturbances, it automatically reduces its flight speed and increases the frequency of attitude adjustment to ensure flight stability and material safety.

[0053] Step 553: Through the phased array communication network, real-time data on the flight status, position, and cargo status of each UAV are collected, and dynamic environmental information around the flight path is simultaneously perceived to construct a comprehensive flight environment situation. Specifically, this includes: First, establishing a real-time data acquisition link between the lead UAV and each executing UAV through the phased array communication network, with the acquisition frequency set to once per second to ensure dynamic capture of on-site changes. The collected UAV data includes three categories: flight status data, position data, and cargo status data. Simultaneously, dynamic environmental information around the flight path is perceived. The miniature meteorological sensors on each UAV collect wind speed, wind direction, and temperature in real time; the phased array communication equipment perceives surrounding dynamic obstacles through beam scanning and records the position, direction of movement, and speed of the obstacles; and the power line vibration sensor collects real-time vibration data of the power line. Then, the lead UAV receives all collected data, integrates and filters the data, removes abnormal data, and supplements missing data. All valid data is categorized and organized according to UAV status, environmental status, and line status to form a comprehensive flight environment situation containing multi-dimensional real-time information.

[0054] Step 554 involves comparing the overall flight environment situation with pre-stored safe flight conditions in real time to assess the deviation between the current flight status and the predetermined conditions. Specifically, this includes: first, retrieving pre-stored safe flight conditions, which are tailored to complex terrain power emergency scenarios and cover three core standards: UAV flight safety standards; environmental safety standards; and material delivery safety standards. Then, the overall flight environment situation is compared with the pre-stored safety conditions one by one in real time, and the deviation is calculated. Regarding flight status deviations, for example, if the real-time wind speed of UAV 2 is 7 m / s and the pre-stored maximum allowable wind speed is 8 m / s, the deviation is 7 m / s - 8 m / s. If UAV 3 has 1.0 hour of remaining flight time, and the current flight segment requires 0.8 hours of flight time, then 1.2 times the required flight time = 0.8 hours × 1.2 = 0.96 hours, the deviation is 1.0 hour - 0.96 hours = 0.04 hours. Regarding environmental deviations, for example, the real-time distance between dynamic obstacles and UAV 3... ≈141 meters, with a pre-stored minimum safe distance of 5 meters, the deviation = 141 meters - 5 meters = 136 meters; Regarding the deviation of material status, the offset of 4 materials on the drone is 1 centimeter, with a pre-stored standard of 3 centimeters, the deviation = 1 centimeter - 3 centimeters. Finally, all deviations are classified and evaluated, and the deviations are divided into safe deviations and risk deviations. For example, if the real-time wind speed of a certain drone is 9 m / s, the pre-stored maximum allowable wind speed is 8 m / s, the deviation is 1 m / s, which exceeds the threshold and is judged as a risk deviation; All deviation evaluation results are synchronized to the lead drone in real time.

[0055] Step 555: When the deviation exceeds the preset safety threshold, a multi-drone collaborative flight path adjustment instruction set is calculated and generated based on the preset dynamic obstacle avoidance and multi-drone collaboration rules, combined with the real-time pose and task sequence of each UAV. Specifically, this includes: when a risk deviation is detected during the assessment, the multi-drone collaborative adjustment process is immediately initiated. First, the preset dynamic obstacle avoidance and multi-drone collaboration rules are retrieved. The dynamic obstacle avoidance rules prioritize adjustments towards open terrain with no obstacles, with an adjustment distance not less than twice the obstacle size. During adjustment, the flight attitude angle change rate does not exceed 1° / second. The multi-drone collaboration rules require that when adjusting the flight path of a certain UAV, adjacent UAVs in adjacent flight segments must be notified simultaneously to avoid flight path conflicts. Adjacent UAVs can cooperate through delayed flight, fine-tuning of heading, etc. Next, the adjustment amount is calculated based on the real-time pose and task sequence of each UAV. The first step is to calculate the obstacle avoidance adjustment direction and distance for UAV 2. The fallen tree trunk has a diameter of 0.8 meters and a height of 5 meters. The adjustment direction is chosen to be northwest. The adjustment distance = trunk diameter × 2 + safety margin = 0.8 meters × 2 + 3 The first step is to shift the coordinates of UAV 2's flight segment 3 by 4.6 meters to the northwest. The new flight segment 3 starting point coordinates are: X17500 meters - 3 meters = 17497 meters, Y7600 meters + 3.5 meters = 7603.5 meters, and Z1100 meters remain unchanged. The second step is to calculate the flight speed adjustment. Due to increased wind speed, the flight speed of UAV 2 is reduced from 18 km / h to 15 km / h to ensure flight stability. The third step is to coordinate with adjacent UAVs for adjustment. The adjacent UAVs of UAV 2 are... The drone 3 ahead is instructed to delay the start of flight segment 5 by 2 minutes to avoid overlapping with the adjusted flight path of drone 2. Then, a multi-drone collaborative flight path adjustment instruction set is generated. In response to the instruction from drone 2, the coordinates of flight segment 3 are adjusted, the flight speed is reduced to 15km / h, and the pitch angle is maintained at 0°. In response to the instruction from drone 3, flight segment 5 is delayed by 2 minutes, and the start time is changed from 14:40 to 14:42. The instruction set clearly specifies the adjustment execution time, the adjusted waypoint sequence, and the verification requirements.

[0056] Step 556 involves distributing the collaborative flight path adjustment instruction set to the corresponding UAVs via a phased array communication network, and collecting updated status data from the UAVs after instruction execution to achieve multi-UAV collaborative flight path planning during the mission. Specifically, this includes: First, the lead UAV distributes the multi-UAV collaborative flight path adjustment instruction set to the corresponding UAVs via the phased array communication network. During transmission, a priority transmission mechanism is used, with adjustment instructions having higher priority than ordinary status data to ensure rapid delivery. Simultaneously, beam encryption technology is used to prevent interference or tampering with the instructions. Each UAV must provide confirmation of instruction reception within 1 second after receiving the instruction; otherwise, the instruction is immediately retransmitted. Next, the corresponding UAV executes the adjustment instruction. UAV 2 shifts 4.6 meters northwest as instructed, adjusts its flight speed to 15 km / h, and measures the distance to the fallen tree trunk in real time. The adjusted distance is 12.6 meters, meeting the safety threshold. UAV 3 then delays for 2 seconds as instructed. The flight begins in segment 5, hovering during the waiting period to monitor the surrounding environment in real time. Then, the lead drone collects updated status data from each drone after executing commands. Drone 2 reports that adjustments are complete, with current coordinates X17497m-Y7603.5m-Z1100m, obstacle distance 12.6m, flight speed 15km / h, and stable status. Drone 3 reports that flight has been delayed, hovering is normal, and there are no environmental risks. The lead drone integrates the updated status data into the overall flight environment situation report and synchronizes it to all drones, ensuring each drone understands the overall adjustment status. Finally, the execution of the adjusted flight path is continuously monitored. If no new risks or deviations occur, each drone continues the mission according to the adjusted path. If new deviations occur, steps 553-556 are repeated to achieve dynamic flight path planning for multi-drone collaboration during the mission, ensuring the safe and continuous progress of material delivery missions in complex terrain.

[0057] Based on the collaborative task allocation of flight path attributes and the actual capabilities of UAVs, the system avoids overloading or wasting resources on a single UAV. The directional transmission and encrypted confirmation mechanism of the phased array communication network ensures the accurate and secure issuance of task and control commands, and prevents command loss or leakage due to signal interference in complex terrain.

[0058] like Figure 2 As shown, embodiments of the present invention also provide a multi-aircraft power emergency phased array cooperative flight path planning and processing system, including: The positioning module is used to control a swarm of drones equipped with phased array communication equipment to perform cooperative scanning of the target area of ​​the power transmission line fault, and to obtain the preliminary positioning information of the fault target through phased array beamforming and target locking. The compensation module is used to dynamically extract spatial configuration parameters based on preliminary positioning information, analyze the local change rate of spatial configuration parameters and set change thresholds, identify key turning points in the evolution trend of spatial configuration parameters, and calculate dynamic compensation parameters by combining the correlation between spatial configuration parameters and the characteristics at the turning points. The generation module is used to calculate the set of convex region vertices corresponding to multiple obstacle areas and available airspace based on dynamic compensation parameters, combined with the real-time load status of the UAV, line vibration monitoring data and surrounding terrain obstacle information, to construct a safe flight airspace and generate a preliminary path for the material delivery route. The update module is used to send the initial path to the ground command center through the phased array communication link, and update the path in real time according to the path correction instructions fed back from the field environment, generating an updated delivery path. The adjustment module is used to control the drone swarm to perform material delivery tasks according to the updated delivery route and the collaborative task allocation. During the task execution, the status of each drone and the changes in the surrounding environment of the route are monitored in real time through phased array communication equipment, and the delivery route is dynamically adjusted to realize multi-drone collaborative route planning.

[0059] It should be noted that this system is a system corresponding to the above method. All implementation methods in the above method embodiments are applicable to this embodiment and can achieve the same technical effect.

[0060] Embodiments of the present invention also provide a computing device, including: a processor and a memory storing a computer program, wherein the computer program, when executed by the processor, performs the method described above. All implementations in the above method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0061] Embodiments of the present invention also provide a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the method described above. All implementations in the above method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0062] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A multi-aircraft power emergency phased array coordinated flight path planning and processing method, characterized in that, The method includes: Step 1: Control a swarm of drones equipped with phased array communication devices to perform a collaborative scan of the target area of ​​the power transmission line fault, and obtain preliminary location information of the fault target through phased array beamforming and target locking; Step 2: Based on the preliminary positioning information, dynamically extract spatial configuration parameters, analyze the local change rate of spatial configuration parameters and set change thresholds, identify key turning points in the evolution trend of spatial configuration parameters, and calculate dynamic compensation parameters by combining the correlation between spatial configuration parameters and the characteristics at the turning points. Step 3: Based on the dynamic compensation parameters, combined with the real-time load status of the UAV, the vibration monitoring data of the line, and the surrounding terrain obstacle information, calculate the set of convex region vertices corresponding to multiple obstacle areas and available airspace, construct a safe flight airspace, and generate a preliminary path for the material delivery route. Step 4: The preliminary path is sent to the ground command center via the phased array communication link, and the path is updated in real time according to the path correction instructions fed back from the field environment, generating an updated delivery path. Step 5: Based on the updated delivery route, control the drone swarm to execute the material delivery task according to the collaborative task allocation; during the task execution, monitor the status of each drone and the changes in the surrounding environment of the route in real time through phased array communication equipment, dynamically adjust the delivery route, and realize multi-drone collaborative route planning.

2. The multi-aircraft power emergency phased array cooperative route planning and processing method according to claim 1, characterized in that, Controlling a swarm of drones equipped with phased array communication devices to perform cooperative scanning of the fault target area of ​​the power transmission line, and obtaining preliminary location information of the fault target through phased array beamforming and target locking, including: A dynamic self-organizing network communication link is constructed between the phased array communication devices carried by each UAV, forming a multi-UAV collaborative communication network that shares data and commands in real time; Through a collaborative communication network, the drone swarm is controlled to perform a layered or sector-based spatial coverage scan of the faulty target area according to a preset collaborative scanning mode, and the raw scan echo data collected by each drone is acquired simultaneously. By utilizing the beamforming function of each UAV phased array, the original scan echo data is subjected to spatial filtering and directivity enhancement processing to generate corresponding enhanced echo signals focused on one or more suspected fault points within the fault target area. Based on the enhanced echo signal, the characteristics of multi-source signals are analyzed and pattern recognition is performed to extract the feature information of suspected fault points under the observation perspective of each UAV. Combined with the known spatial coordinates of the UAV, the feature information is converted into the corresponding distance observation vector. By calculating the spatial geometric intersection of distance observation vectors, the unique three-dimensional coordinates of the faulty target are determined as preliminary location information.

3. The multi-aircraft electric emergency phased array cooperative route planning and processing method according to claim 2, characterized in that, Based on preliminary positioning information, spatial configuration parameters are dynamically extracted. The local rate of change of these parameters is analyzed, and a change threshold is set. Key inflection points in the evolution trend of the spatial configuration parameters are identified. Combining the correlation between spatial configuration parameters and the characteristics at these inflection points, dynamic compensation parameters are calculated, including: Based on the preliminary positioning information, combined with the real-time positioning data of the UAV swarm, the relative distance, azimuth angle and altitude difference between each UAV and the faulty target are dynamically calculated to form spatial configuration parameters. Based on the spatial configuration parameters, the local rate of change of each parameter is calculated to obtain a numerical sequence describing the rate and direction of parameter change; The local rate of change of each parameter is compared with the preset change threshold, the moment point when the local rate of change is greater than the corresponding threshold is identified, and the moment point is determined as the key turning point when the evolution trend of spatial configuration changes. The changes of multiple spatial configuration parameters at key inflection points are analyzed to obtain the coupling relationship between the mutual influence and constraints of the parameters, and the instantaneous characteristic values ​​of each parameter at the inflection point are extracted. Based on the coupling change relationship and instantaneous characteristic values, dynamic compensation parameters are calculated for real-time correction of the flight path planning and attitude of the UAV swarm.

4. The multi-aircraft electric emergency phased array cooperative route planning and processing method according to claim 3, characterized in that, Based on dynamic compensation parameters, combined with real-time UAV load status, line vibration monitoring data, and surrounding terrain obstacle information, the set of convex region vertices corresponding to multiple obstacle zones and available airspace is calculated to construct a safe flight airspace and generate a preliminary path for material delivery routes, including: The initial positioning information is corrected using dynamic compensation parameters to obtain the corrected target point coordinates and flight reference data; Based on the corrected target point coordinates and flight reference data, real-time payload status data of the UAV, vibration amplitude data of power transmission lines, and terrain elevation and static obstacle data of the operating area are collected and integrated to form a comprehensive environmental situation. Based on the overall environmental situation, three-dimensional spatial analysis and geometric description of various static obstacles and dynamic risk areas are performed, the projection range on the horizontal plane and vertical space is calculated, and the convex polygon region corresponding to each independent available space is determined based on the projection range, thus obtaining the vertex coordinate set of all convex polygon regions. Based on the vertex coordinate set, a continuous and closed safe flight airspace is constructed within a three-dimensional spatial framework. Within the safe flight airspace, multiple candidate routes for material delivery are generated using the mission start point and mission target point as the route endpoints, combined with the flight performance constraints of the UAV. The path length, safety margin, and energy consumption indicators of each candidate route are evaluated, and the preliminary path for final material delivery is determined through multi-objective optimization decision-making.

5. The multi-aircraft electric emergency phased array cooperative route planning and processing method according to claim 4, characterized in that, The initial path is transmitted to the ground command center via the phased array communication link. Based on flight path correction instructions from the field environment, the flight path is updated in real time, generating an updated delivery path, including: The coordinate sequence of the preliminary path, the expected flight status, and the associated environmental data packets are sent to the ground command center via the phased array communication link. The ground control center conducts a comprehensive analysis based on real-time video and meteorological data, and generates route correction instructions, including requirements for adjusting the position of specific waypoints, modifying altitudes, or replanning routes. The route correction instructions are analyzed to obtain the corresponding correction constraints. Based on the correction constraints, the preliminary route is locally adjusted or globally replanned to generate the updated delivery route.

6. The multi-aircraft electric emergency phased array cooperative route planning and processing method according to claim 5, characterized in that, Based on the updated delivery route, control the drone swarm to execute material delivery tasks according to the collaborative task allocation, including: Based on the updated spatial and temporal attributes of the delivery route, and combined with the remaining endurance, cargo capacity, and current position parameters of each UAV, a collaborative task allocation calculation is performed to generate a task allocation scheme for each UAV, including specific flight segments, designated delivery target points, and task sequence. The task allocation plan and the corresponding flight line control commands for each UAV are sent to the corresponding UAVs through the phased array communication network. Based on the received flight path control commands and mission timing, each UAV autonomously controls its flight control unit to carry out material transport and delivery missions to the faulty target area according to the specified flight path and time requirements.

7. The multi-aircraft electric emergency phased array cooperative route planning and processing method according to claim 6, characterized in that, During mission execution, the status of each UAV and changes in the surrounding environment of the flight path are monitored in real time through phased array communication equipment, and the delivery route is dynamically adjusted to achieve multi-UAV collaborative route planning, including: Through the phased array communication network, the flight status, location and cargo status data of each UAV are collected in real time, and the dynamic environmental information around the flight path is perceived simultaneously to construct a comprehensive flight environment situation. The overall flight environment situation is compared with the pre-stored safe flight conditions in real time to assess the deviation between the current flight status and the predetermined conditions. When the deviation exceeds the preset safety threshold, a multi-drone collaborative flight path adjustment instruction set is calculated and generated based on the preset dynamic obstacle avoidance and multi-drone collaboration rules, combined with the real-time pose and task sequence of each drone. The coordinated flight path adjustment instruction set is distributed to the corresponding UAVs for execution through a phased array communication network, and the updated status data of the UAVs is collected after the instructions are executed, so as to realize multi-UAV coordinated flight path planning during the mission.

8. A multi-machine power emergency phased array collaborative flight path planning and processing system, wherein the system implements the method as described in any one of claims 1 to 7, characterized in that, include: The positioning module is used to control a swarm of drones equipped with phased array communication equipment to perform cooperative scanning of the target area of ​​the power transmission line fault, and to obtain the preliminary positioning information of the fault target through phased array beamforming and target locking. The compensation module is used to dynamically extract spatial configuration parameters based on preliminary positioning information, analyze the local change rate of spatial configuration parameters and set change thresholds, identify key turning points in the evolution trend of spatial configuration parameters, and calculate dynamic compensation parameters by combining the correlation between spatial configuration parameters and the characteristics at the turning points. The generation module is used to calculate the set of convex region vertices corresponding to multiple obstacle areas and available airspace based on dynamic compensation parameters, combined with the real-time load status of the UAV, line vibration monitoring data and surrounding terrain obstacle information, to construct a safe flight airspace and generate a preliminary path for the material delivery route. The update module is used to send the initial path to the ground command center through the phased array communication link, and update the path in real time according to the path correction instructions fed back from the field environment, generating an updated delivery path. The adjustment module is used to control the drone swarm to perform material delivery tasks according to the updated delivery route and the collaborative task allocation. During the task execution, the status of each drone and the changes in the surrounding environment of the route are monitored in real time through phased array communication equipment, and the delivery route is dynamically adjusted to realize multi-drone collaborative route planning.

9. A computing device, characterized in that, include: One or more processors; A storage device for storing one or more programs, which, when executed by one or more processors, cause the one or more processors to implement the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a program that, when executed by a processor, implements the method as described in any one of claims 1 to 7.

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