7228 results about "Satellite" patented technology

The invention relates to a power transmission line forest fire risk assessment early warning system and method, and belongs to the technical field of power transmission line risk assessment. The system comprises an environment DNA power transmission line forest fire risk assessment and early warning system, a quantum sensing power transmission line fire remote monitoring system, a power transmission line forest fire early warning emergency training system, a digital twin power transmission line fire risk dynamic management system, and a multi-node power transmission line fire early warning system of an edge AI and a low orbit satellite. The system comprises a thermal imaging and deep learning integrated power transmission line fire risk distribution prediction system, an AI-driven power load dynamic adjustment and fire prevention system, an intelligent fire-fighting equipment linked power transmission line fire spreading path prediction system and a distributed quantum computing power transmission line fire emergency simulation and optimization response system. According to the method, the inflammable vegetation density and biocenosis change can be dynamically analyzed through a plant and microorganism gene sequencing technology.

The disclosed technology is generally directed to a method for controlling a satellite system comprising at least one satellite. The method may include receiving and processing a set of control parameters associated with an orientation of the at least one satellite via a classic control model to generate a set of actions to control the orientation of the at least one satellite and storing the set of actions as data in a buffer. The processing and storing are iteratively repeated until the data stored in the buffer exceeds a threshold. When the data stored in the buffer exceeds the threshold, the method may further include iteratively implementing, based on each of the set of control parameters and the data stored in the buffer, the machine learning model to control the orientation of the at least one satellite to stabilize the at least one satellite.

The invention relates to the technical field of unmanned aerial vehicle flight path planning, in particular to an unmanned aerial vehicle real-time path planning system and method based on dynamic weight distribution and multi-source data fusion. The system comprises a multi-source data fusion module, an integrated laser radar, a millimeter wave radar, a visual sensor and a Beidou positioning unit. The dynamic weight distribution module dynamically adjusts the weight coefficient of each sensor according to the environmental complexity, the threat level and the state of the unmanned aerial vehicle by adopting a mixed decision-making mechanism combining fuzzy logic and reinforcement learning; an improved RRT * algorithm and a Markov decision process are built in the real-time path planning module, and a global optimal path and a local obstacle avoidance track are generated by adopting a layered planning architecture; the unmanned aerial vehicle cooperative control module comprises a dual-redundancy flight control system and a dynamic obstacle avoidance unit; and the communication relay module supports 5G and low-orbit satellite dual-mode communication, updates an environment cognitive model of each unmanned aerial vehicle through federated learning, and realizes multi-source fusion real-time path planning based on dynamic weight distribution and the unmanned aerial vehicles.

A method and device for inverting ocean-atmosphere optical parameters and storage medium. The method includes: obtaining a vector apparent reflectance ρl, ρQ, ρU of water-leaving radiance at top of atmosphere and observation geometry of a multi-angle polarized satellite, and inputting the vector apparent reflectance ρl, ρQ, ρU and the observation geometry into a trained ocean-atmosphere optical parameter model, to output a group of ocean-atmosphere optical parameters, the output ocean-atmosphere optical parameters comprise: PBOAw, FAOD / CAOD, FRm / CRm, W, CHL and SPM, the ocean-atmosphere optical parameter model is an XGBoost machine training model. In this way, the ocean-atmosphere optical parameters can be quickly and stably inverted.

The invention relates to the technical field of communication, in particular to a low-delay data transmission system and method for marine observation data, and the system comprises a data collection module which collects the multi-modal data of a marine environment, and carries out the priority classification of the data according to the real-time performance and importance; the route optimization module dynamically adjusts a multi-path fragmentation strategy and single-path task distribution in combination with a reinforcement learning algorithm and path optimization data; the communication module supports multi-mode communication modes such as laser communication and low-orbit satellite communication, and monitors the link state in real time; and the data fusion and analysis module performs dimension reduction, fusion and real-time analysis on the data by using manifold learning and a graph neural network, and meanwhile, predicts a long-term trend through time sequence analysis to generate observation report data. The method effectively reduces the transmission delay of high-priority data, improves the response efficiency of the system to abnormal events, and is suitable for the scenes of marine disaster early warning, environment monitoring and the like.

A method for retrieving tropospheric wet delay and atmospheric water vapor content over polar sea ice with TDS-1 satellite grazing angle spaceborne GNSS-R is provided, including: Si, obtaining TDS-1 GNSS-R raw intermediate frequency signal data, VMF3 grid data, GPT3 grid data and ERA5 data; S2, correcting an error of tropospheric wet delay estimation of grazing angle spaceborne GNSS-R; S3, constructing a grazing angle spaceborne GNSS-R tropospheric wet delay estimation model; S4, calculating grazing angle spaceborne GNSS-R ZWD; S5, calculating a Tm value of a target point based on GPT3 model, substituting the Tm value into a conversion factor II, and combining calculated GNSS-R ZWD to obtain a GNSS-R IWV estimated value; and S6, verifying inversion performance of GNSS-R ZWD and integrated water vapor (IWV) by using reference data.

The invention relates to the technical field of satellite communication, in particular to a high-orbit and low-orbit satellite communication resource scheduling method and system, which collects real-time state information of high-orbit and low-orbit satellites, performs digital simulation of a satellite network based on the real-time information, constructs a satellite network management model and predicts satellite trajectory change. The method comprises the following steps: performing multi-dimensional evaluation on a satellite network management model by using a pre-constructed network evaluation framework, generating cross-orbit link evaluation information, analyzing parameters such as link quality and bandwidth, obtaining current service demand information, analyzing a communication resource allocation strategy in combination with a cross-orbit link evaluation result, and formulating a resource scheduling strategy according to strategy analysis. And a high-orbit and low-orbit satellite communication network is constructed through execution of a hierarchical network management mechanism and a cross-orbit data transmission protocol.

The invention discloses a low-orbit satellite beam resource allocation method based on deep reinforcement learning, and the method comprises the steps: firstly building a satellite system model according to a low-orbit satellite system hopping beam scene, obtaining the communication traffic demand and geographic position of a ground user through a satellite traffic monitoring facility, and carrying out the preprocessing; secondly, carrying out user clustering on information of communication traffic demands of ground users, and dividing a ground cell into a hot spot area and a non-hot spot area; and then based on a satellite system model, establishing an optimization problem of multi-beam resource allocation and control, adopting a deep reinforcement learning method, dynamically adjusting a beam resource allocation strategy, allocating narrow beam resources to a hot spot area, and allocating wide beam resources to a non-hot spot area to realize system throughput maximization and user fairness. According to the method, the resource allocation problem in the low-orbit satellite hopping beam scene is effectively solved, and the throughput of a satellite system is improved while the user fairness is ensured.

The invention discloses a natural resource dynamic monitoring system and method based on a multi-source remote sensing image. The system comprises a multi-source data acquisition module, a preprocessing module, a feature extraction module, a dynamic monitoring module, a visual output module and a distributed storage module. According to the method, the precision and timeliness of natural resource change detection are remarkably improved by constructing a multi-source remote sensing image intelligent fusion framework. The system adopts a parallelization preprocessing assembly line to effectively eliminate radiation and geometric differences among multi-source data; a feature fusion module based on deep learning automatically extracts multi-scale change features, and overcomes the environmental adaptability defect of a traditional threshold method; and the dynamic monitoring module realizes automatic extraction and classification of sub-meter change pattern spots, and the monitoring efficiency is improved. Meanwhile, the system supports collaborative analysis of multi-platform data of satellites, aviation, unmanned aerial vehicles and the like, the cloud shielding influence is greatly reduced, and total-factor and high-frequency decision support is provided for natural resource supervision.

The invention provides a low-orbit satellite resource allocation method and device based on beam and frequency domain collaborative optimization, and the method comprises the steps: dividing the overall bandwidth of a user region into a central sub-band and an edge sub-band through employing a dynamic soft frequency reuse scheme; all ground users are subjected to spatial clustering according to geographic positions and traffic demands, coverage cells are dynamically divided according to clustering results, and balanced service is achieved; on the basis of dynamic cell division, user clusters with high service volume are selected according to priorities, a beam activation set is generated, and a hopping beam matrix is updated; establishing a channel model between a user and a beam and a switching matrix between satellites; constructing a system utility target, and constructing a multi-target optimization problem model by taking the system utility target as a main part and integrating interference, switching overhead and delay penalty; and the multi-dimensional resource collaborative optimization framework based on deep reinforcement learning reconstructs a multi-objective optimization problem model, and outputs a solution of the multi-objective optimization problem model, including an optimal bandwidth and a power allocation result. According to the invention, resource allocation can be carried out on low-orbit satellites.

The invention provides an inertial navigation attitude resolving method and device based on sensor fusion. The method comprises the following steps: carrying out multi-clock domain synchronous calibration processing on a double-antenna GNSS system, an IMU inertial measurement unit and an antenna servo system in a vehicle-mounted communication-in-motion system; according to the time synchronization reference, carrying out quality evaluation and weight distribution processing on the GNSS satellite signal under the satellite communication in motion antenna pointing constraint; and carrying out multi-sensor fusion resolving processing on the inertial attitude of the vehicle body according to the time synchronization reference and the GNSS observation data weight distribution result. According to the method, millisecond-level time synchronization is realized by establishing a robust extended Kalman filtering model of a multi-dimensional extended state vector, GNSS signal quality is optimized by adopting an adaptive weight distribution strategy of antenna pointing constraint, and attitude fusion precision is enhanced by utilizing high-precision angle feedback of an antenna servo system. The problem that a traditional attitude resolving method in a vehicle-mounted communication-in-motion system is not high in precision is solved.

The present disclosure discloses a circularly polarized satellite antenna based on a magnetoelectric dipole. A floor is slotted, and an equivalent magnetoelectric dipole is excited using a grounded right-angle folded dipole, while two square parasitic patches are loaded to enhance a radiation performance, thus exciting a broadband high-gain circularly polarized radiation beam. The present disclosure utilizes the L-shaped right-angle folded dipole to simultaneously realize an ingenious combination of an electric dipole and a magnetic dipole, realizing a good circularly polarized radiation performance. At the same time, the present disclosure realizes circularly polarized radiation using a form of the magnetic dipole, which results in a stable pattern and a high gain.

Systems, methods, and computer-readable media for aerial localization in the absence of satellite (e.g., global navigation satellite system (GNSS)) signals while flying over heterogeneous environments. The present embodiments provide a training strategy for robust cross-seasonal image matching in complex, real-world environments. The challenge put forward is to successfully localize a UAV flying over both densely and sparsely populated areas by means of image registration. To this end, the focus of the present embodiments can be on devising loss components to a visual recognition network that adequately embeds the diverse feature types on the ground. A normalized error metric can be provided as well in order to render a conceptually different method comparable to the present approach. The present technical contributions can provide robust visual recognition algorithms in challenging natural environments.

The invention discloses a multi-satellite network converged communication link optimization and anti-interference method, which belongs to the technical field of wireless communication networks, and comprises the following steps: acquiring Doppler frequency shift parameters of satellite nodes, adjacent satellite radiation quantity parameters and preset geographical shielding coefficients, and generating a link dynamic quality coefficient; constructing a dynamic topological graph according to the link dynamic quality coefficient; performing radio frequency fingerprint extraction and frequency domain waveform phase reversal processing on the detected interference signal to generate an anti-interference compensation signal; and when the link dynamic quality coefficient is greater than or equal to a preset quality threshold value, selecting a target transmission path according to the dynamic topological graph, and injecting the anti-interference compensation signal into the target transmission path. According to the invention, a mechanism of cooperation of dynamic link quality evaluation, interference fingerprint feature matching and multi-path fragmentation transmission is adopted, and real-time optimization selection of communication paths, accurate suppression of interference signals and efficient distribution of network resources can be realized in a complex air and space environment.

The invention provides a vehicle cluster tunnel access positioning method based on intelligent cooperation and a vehicle-mounted terminal, relates to the technical field of intelligent driving control, and solves the problems of positioning interruption, inertial navigation error accumulation and insufficient cooperative positioning reliability caused by satellite signal failure of a vehicle cluster in a tunnel scene. The method comprises the following steps: judging whether each vehicle enters or exits from a tunnel area, when part of vehicles enter a tunnel, dynamically selecting a reference node from vehicles with effective satellite signals outside the tunnel, and broadcasting original observation values of the satellite signals; establishing a relative position constraint model of a vehicle cluster, acquiring relative position data of adjacent vehicles in real time among the vehicles, and performing dynamic weighted fusion on observation values, IMU data and relative position data of the vehicles with effective satellite signals by adopting a combined filtering algorithm; the satellite signal failure vehicle performs positioning calculation by combining the broadcast observation value with the IMU data and the relative position constraint model; and pre-capturing satellite signal loop parameters when driving out of the tunnel.

The invention discloses an aerospace network hierarchical cooperation routing optimization method and system, and relates to the field of aerospace information network communication, and the method comprises the steps: constructing a system model comprising an unmanned plane, satellite nodes and a communication link, defining link attributes, building a routing optimization objective function, introducing a reputation value-based multi-agent cooperation mechanism, and carrying out the routing optimization of the unmanned plane, the satellite nodes and the communication link. A hierarchical reinforcement learning framework is designed, inter-cluster decision and intra-cluster decision are divided, an upper layer selects an inter-cluster path according to global information, a lower layer plans an unmanned aerial vehicle path and defines a state, an action space and a reward function according to local information, an intelligent agent interacts with an environment to collect empirical data, and optimal routing communication is selected in combination with a real-time reputation value after training. Adjustment can be carried out according to network changes; according to the invention, the cooperative reliability is improved through a reputation value mechanism, the decision complexity is reduced through hierarchical learning, the routing performance is optimized, the network robustness, expandability and adaptability are enhanced, and the cooperative communication efficiency of the unmanned aerial vehicle and the satellite and the system stability are improved.

The invention belongs to the technical field of air-space-ground integrated networks, and discloses an unmanned aerial vehicle track optimization and task offloading method for an air-space-ground integrated network. According to the method, the task unloading efficiency in the air-space-ground integrated network is optimized, optimization of energy consumption and time delay is focused on, various conditions of the air-space-ground integrated network are comprehensively considered, a decoupling communication mode is considered, and the influence of service interruption is reduced. And constructing an optimization model to minimize the weighted sum of time delay and energy consumption. In order to improve the collaboration of heterogeneous agents in a space-air-ground integrated network, based on a hierarchical reinforcement learning framework, a hierarchical learning-based two-stage Qmix algorithm is proposed, and the collaboration of a low-orbit satellite and an unmanned aerial vehicle is reinforced. According to Pycharm platform simulation and multi-scene verification, the method has remarkable advantages in average response time delay, energy consumption and QoS indexes, the network performance is effectively improved, and user requirements are met.

In embodiments, a device of a first satellite for providing non-terrestrial network (NTN) access is provided. The device may comprise memory including instructions, at least one processor, and at least one transceiver. The instructions may, when executed by the at least one processor, cause the device to perform a communication between a first terminal and a second terminal via the first satellite, by transmitting data received from the first terminal to the second terminal or transmitting data received from the second terminal to the first terminal; identify, within a satellite group associated with the first satellite, a second satellite different from the first satellite; and transmit a configuration message including configuration information related to the communication to the second satellite through the at least one transceiver.

The invention discloses a satellite formation autonomous control method based on relative motion fitting, belongs to the technical field of spacecraft control, and aims to solve the problem that a traditional formation control method cannot meet formation control requirements in an environment with navigation signal deficiency. The method comprises the following steps: step 1, when a main satellite passes through an ascending node, performing relative motion fitting by utilizing inter-satellite spacing and direction of a plurality of discretely distributed sub-satellites relative to the main satellite observed in a previous orbital period so as to obtain coefficients of relative motion functions of the sub-satellites; step 2, obtaining orbit parameter deviation of the child satellites relative to the primary satellite according to the coefficient of the relative motion function of the child satellites; step 3, acquiring a control vector and a control duration required by the formation adjustment of the child satellites according to the orbit parameter deviation; and step 4, each sub-satellite performs autonomous control according to the control vector and the control duration obtained in the step 3. The method is used for satellite formation autonomous control.

The invention relates to the technical field of satellites, and discloses an artificial intelligence aerospace infrastructure agent system, which comprises a satellite agent, and the satellite agent comprises an energy management module, a calculation module, a task execution module and a communication module. The artificial intelligence aerospace infrastructure agent method comprises the following steps: acquiring real-time states of a plurality of satellite agents, performing task scheduling and resource allocation by using an optimal control theory, optimizing a task allocation strategy in combination with a game theory model, and optimizing a communication path according to a channel capacity theorem, so as to obtain an artificial intelligence space infrastructure agent; and the task sequence and the resource allocation strategy are adjusted in real time in the task execution process, so that efficient collaboration and task completion are ensured. According to the method, task scheduling is optimized by combining the optimal control theory and the game theory, a communication path is optimized by adopting the channel capacity theorem, the task execution sequence is adjusted in real time, adaptive resource allocation is performed, efficient cooperation between satellite agents is ensured, and the task execution efficiency, the resource utilization rate and the system stability are effectively improved.

The invention provides a multi-satellite cooperative distributed routing method for multi-agent reinforcement learning, and the method comprises the steps: building a satellite network static topology model through a time slicing technology, and building a target function for minimizing the end-to-end time delay of a data packet; constructing a satellite agent network based on the satellite network static topology model, and obtaining satellite interaction experience data; wherein the satellite agent network comprises a multi-satellite cooperative hybrid network and a satellite decision network corresponding to each agent; training a satellite agent network according to the satellite interaction experience data to obtain a trained satellite agent network; and respectively deploying the satellite decision networks in the trained satellite agent network to corresponding agents, so that the agents perform routing decision based on the deployed satellite decision networks. Through cooperative work of the satellite decision network and the multi-satellite cooperative hybrid network, efficient distributed routing decision of a low-orbit satellite constellation can be realized, time delay is reduced, and load is balanced.

The invention provides a direct communication method and device based on a satellite, electronic equipment and a medium, and relates to the technical field of satellite communication. The satellite-based direct communication method comprises the following steps: acquiring initial communication data of a target terminal in a unit time period; based on the initial communication data, communication data quality and data receiving information corresponding to the target terminal are determined, and the data receiving information represents whether the initial communication data received by the current satellite is continuous or not; under the condition that the communication data quality and the data receiving information meet preset requirements, determining target vacant areas, communication resource occupancy amounts and communication frequency bands corresponding to the plurality of satellites to be selected respectively; and determining a target satellite from the plurality of satellites to be selected based on the target vacant area, the communication resource occupation amount and the communication frequency band, and controlling the target satellite to provide a communication service for the target terminal. The communication stability of the target terminal can be improved.

The invention relates to the technical field of positioning, in particular to an unmanned aerial vehicle cluster positioning method and device based on Kalman filtering in a satellite denial environment, which can be applied to unmanned aerial vehicle cluster positioning in the satellite denial environment, and can utilize a plurality of ground base stations to position a plurality of aircrafts first. Therefore, the distance change between the adjacent aircrafts is verified by using the observation data of the inertial sensors of the adjacent aircrafts during clustering, and when the verification is abnormal, the abnormal unmanned aerial vehicle is rapidly determined by using the calculated abnormal distance. And then calling Kalman filtering to carry out positioning correction on the section of the abnormal positioning data of the abnormal unmanned aerial vehicle. According to the method, the Kalman filtering is executed by screening the abnormal points, so that the aircraft cluster can be accurately positioned by using the base station with relatively low computing power, and the method has relatively high applicability.

The invention relates to a satellite-borne laser communication terminal in-orbit optical axis rapid calibration method, and aims to improve the pointing precision and stability of a satellite-borne laser communication system. According to the method, the optical axis of the laser communication terminal is quickly and accurately calibrated by comprehensively applying technical means such as coordinate system conversion, an error correction matrix, least square method optimization and a golden section method. According to the method, complex coordinate system conversion and an error correction matrix are comprehensively applied, and high-precision correction of a theoretical pointing vector is achieved; the golden section method is combined with the optimization strategy of the least square method, so that the convergence speed and precision of the calibration process are improved; higher real-time performance and automation degree are achieved, related data can be acquired and processed in real time, and real-time correction of the optical axis pointing error is achieved; the method has wide applicability and expandability, and can be applied to other spacecraft systems needing high-precision pointing control.

The invention relates to a simulation-based satellite scheduling task dynamic planning method, device and equipment, and the method comprises the steps: carrying out the modeling according to various time-varying environment dynamic parameters in an environment where a satellite is located, and obtaining a dynamic environment model which comprises an environment factor model and an emergency triggering mechanism; the method comprises the following steps: constructing a satellite resource dynamic model according to various resources of satellites, resource consumption during task execution and resource supply, constructing a multi-satellite collaborative model according to an interaction relationship among a plurality of satellites and related information of each satellite, and constructing a multi-satellite collaborative model under a dynamic environment model, the satellite resource dynamic model and the multi-satellite collaborative model. According to the method, tasks in a to-be-executed task data set containing single-satellite and cooperative tasks are dynamically scheduled by adopting a task planning algorithm, a task execution planning table of satellites is obtained, satellite task planning can be carried out aiming at emergencies and multi-satellite cooperative tasks, and the planning is closer to the actual situation.

The invention discloses a method for judging an RTK abnormal value in an automatic driving integrated navigation system, and relates to the technical field of automatic driving high-precision integrated navigation, and the method comprises the steps: collecting RTK observation data, inertial measurement unit data, a wheel speed pulse signal and LiDAR point cloud data, carrying out timestamp alignment and coordinate system unification, and generating a fusion data value; calculating a carrier-to-noise ratio weight signal quality index of the satellite through the fused data value, and generating a signal quality report; and combining the signal quality report, the current satellite geometric accuracy factor and the vehicle motion acceleration, calculating a residual threshold, constructing a coriolis force compensated double-integral prediction model by using inertial measurement unit data, and predicting the position of the vehicle at the current moment. According to the method, multi-dimensional features such as satellite signal quality, vehicle motion state and residual analysis are fused through multi-source decision, and the anomaly detection capability of complex scenes such as urban canyons is effectively improved.

Novel tools and techniques are provided for implementing detection and estimation of direct and reflected navigation satellite (e.g., global navigation satellite system (“GNSS”), etc.) signal parameters in a multipath environment. In various embodiments, logic of semiconductor package that is disposed on a user device concurrently receives a plurality of signals from a satellite(s), each signal travelling along a different path between each satellite(s) and the user device within a multipath environment. The logic identifies two or more signal peaks that fall within a tracking aperture based on analysis of the received signals, and determines peak parameter estimates for each signal peak based on measurements of signal parameters from at least one signal peak. The logic provides the determined peak parameter estimates for each signal peak to a position engine (“PE”) of the user device to calculate a navigation solution (e.g., position, velocity, and / or time, etc.) for the user device.

The invention provides an intelligent remote controller control method, device and equipment based on gesture recognition, and the method comprises the steps: obtaining multi-modal sensor data through an integrated time of flight (ToF) camera, a millimeter wave radar and an inertial measurement unit (IMU), carrying out the feature extraction to generate gesture spatial-temporal features, and carrying out the dimension exchange of the spatial-temporal features to generate exchange dimension features; constructing a candidate gesture probability superposition state based on the exchange dimension features, establishing a semantic negative hypothesis and generating a reverse verification parameter; performing weight correction on the probability superposition state by using the inversion verification parameter to generate a correction probability state, and determining a target gesture through probability aggregation; re-extracting verification feature data based on the target gesture, selecting a grading classification model according to a preset grading condition to perform secondary verification, and outputting a gesture recognition result; and detecting a communication environment state, constructing a satellite flash and UWB dual-mode communication race state, aggregating into an optimal communication mode, and completing intelligent control of the remote controller.

The invention relates to the field of satellite communication, and discloses a parabolic antenna pointing tracking method in an inter-satellite communication scene, and the method comprises the following steps: carrying out the attitude initialization, dynamic prediction, error compensation and closed-loop control, and achieving the high-precision satellite tracking of an antenna. The method adapts to large dynamic changes of target satellites and ensures stable and reliable communication; the invention discloses a parabolic antenna pointing tracking system in an inter-satellite communication scene. The system comprises an antenna angle sensor module, a signal receiving module, a prediction module, an error compensation module and a closed-loop control module. According to the method, orbit prediction, an extrapolation algorithm, adaptive control and a closed-loop feedback mechanism are fused, and high-precision and high-robustness antenna satellite tracking is realized. The method can adapt to large dynamic change of a target satellite, reduces dependence on real-time position information, still keeps stable communication under the condition of orbit sudden change or data missing, and improves system reliability and engineering practicability.

The invention provides an unmanned aerial vehicle intelligent power line inspection system, and relates to the technical field of power line inspection. The unmanned aerial vehicle intelligent power line inspection system comprises an unmanned aerial vehicle platform which comprises an unmanned aerial vehicle body, a flight control system, a power system and a navigation system; the sensor module comprises a high-definition camera, an infrared camera, a laser radar and an ultrasonic sensor; the data processing module comprises an embedded processor, a data storage unit and a data transmission module and is used for processing and analyzing the data acquired by the sensor; the intelligent algorithm module is used for intelligently analyzing and judging the state of the power line; and the communication module comprises a wireless communication module and a satellite communication module and is used for realizing data transmission and remote control between the unmanned aerial vehicle and a ground control center. The unmanned aerial vehicle is used for replacing manual inspection, so that the work of personnel in dangerous environments such as high altitude and complex terrains is reduced, and the personnel risk is reduced.