364 results about "GPS signals" patented technology

The invention relates to the technical field of electric heavy trucks, and discloses an electric heavy truck self-adaptive positioning method and system based on multi-sensor fusion, and the method is characterized in that a sensor data preprocessing module and a space-time synchronization module are constructed to carry out the preprocessing of data obtained by sensors; performing basic positioning on the electric heavy truck based on an Apriltag label positioning algorithm and a laser slam positioning algorithm; carrying out real-time positioning and correction on the electric heavy truck in a moving state by an Apriltag-assisted laser slam correction algorithm based on ESKF; the stability of a GPS signal in a current environment is automatically detected and analyzed through an adaptive controller module, a fuzzy control selector is adopted to adaptively switch a positioning mode according to a real-time condition, and an Apriltag auxiliary laser slam positioning algorithm based on an ESKF is designed according to the difference of different sensors carried by the electric heavy truck. And then considering the operation requirements of the electric heavy truck in complex environments such as unstable GPS signals in a mining area, designing an adaptive positioning system of the electric heavy truck so as to improve the positioning precision and robustness.

A highly reliable and high-precision navigation and positioning method and system for UAV under GPS-DENIED conditions includes: obtaining measurements of inertial sensor and image of binocular camera, extracting and tracking point features of the image, and obtaining altitude value; calculating position and attitude of camera and depth of landmark point based on the parallax and baseline of the binocular camera; fusing the measurement data of the inertial sensor and the binocular camera, and optimizing the data to obtain high-precision position and attitude data; obtaining the altitude value of the ranging radar and performing four-degree-of-freedom position and attitude graph optimization after adding the detected keyframes when repeated occurrence of the UAV at the same location is detected; encapsulating optimized position and attitude data to form a pseudo-GPS signal, inputting the pseudo-GPS signal to the UAV for positioning.

The invention provides an unmanned aerial vehicle obstacle avoidance method and system based on inertial navigation, and belongs to the technical field of unmanned aerial vehicle obstacle avoidance systems. The problem that an existing unmanned aerial vehicle is insufficient in obstacle avoidance capacity under the environment that GPS signals are weak, vision is limited or a sensor detection blind area exists is solved. Acquiring broadband high-frequency dynamic response data output by the inertial navigation system of the unmanned aerial vehicle in real time; establishing or acquiring a reference high-frequency dynamic response model when the unmanned aerial vehicle is in a specific flight state and a power system working mode and is not influenced by an external obstacle; the signal is dynamically responded according to high frequency; identifying whether a specific air medium disturbance echo feature formed by disturbance generated by motion of the unmanned aerial vehicle through an external obstacle exists or not; and finally, generating and executing an obstacle avoidance flight instruction according to the estimation information. The method does not depend on a traditional external environment sensor for initial detection, physical interaction is sensed through the INS, and the autonomous obstacle avoidance capacity and safety of the unmanned aerial vehicle in the complex and challenging environment are improved.

The invention relates to the technical field of unmanned aerial vehicle positioning, discloses an unmanned aerial vehicle accurate positioning method and device fusing multi-source positioning data, electronic equipment, a computer readable storage medium and a computer program product, and is used for solving the technical problem that accurate and stable positioning of an unmanned aerial vehicle cannot be realized in an indoor weak texture scene in the prior art. The method comprises the following steps: acquiring initial positioning data of an unmanned aerial vehicle in real time, the initial positioning data being at least from two information sources; constructing a positioning fusion algorithm based on an unscented Kalman filtering algorithm, evaluating the credibility of initial positioning data from each information source in real time, and calculating a positioning covariance matrix; and adjusting the fusion weight of each initial positioning data based on the positioning covariance matrix, and calling a positioning fusion algorithm to perform weighted fusion on the initial positioning data of each information source to obtain fusion positioning data of the unmanned aerial vehicle. According to the method, the positioning accuracy and stability of the unmanned aerial vehicle in a scene in which GPS signals are lacked and environment textures are weak can be improved.

The present invention relates to a positioning and inspection control method for a multi-sensor-fusion pipeline unmanned aerial vehicle. The method comprises: acquiring a video stream image frame of real-time pipeline inspection by means of a binocular camera; determining the darkness degree of the image frame, and if the image frame does not meet a brightness requirement, performing real-time low-light enhancement on the image frame; determining the blur degree of the image frame, and if the image is blurry, performing real-time deblurring processing on the image frame; performing pose calculation on the image frame; and outputting positioning data and pose data of an unmanned aerial vehicle at a real-time location point, and a pipeline inspection image. The present invention realizes safe and effective positioning of an unmanned aerial vehicle in a dark environment where a GPS signal is lost, and realizes safe and normal inspection of the unmanned aerial vehicle in environments such as drainage channels and pipelines on the basis of the positioning of the unmanned aerial vehicle.

A drone station according to an embodiment of the present invention may comprise: a station main body (10) having, mounted thereon, a charging device (20) allowing a drone to land thereon so as to be charged with drone power; a cover (30) provided at the station main body (10) so as to open / close the charging device (20) while rotating around an axle (12); and a window panel (34) provided in the cover (30), and made of a nonconductor material to transmit a GPS signal therethrough.

The invention discloses a logistics transportation platform monitoring system and method based on big data, and relates to the technical field of logistics monitoring. Based on historical road conditions, traffic flow, accident distribution and high-risk areas, combined with physicochemical properties of dangerous goods and real-time weather conditions, comprehensive risk values and adaptive values of paths are calculated; the route safety is guaranteed, and meanwhile, the transportation environment is adapted; meanwhile, based on the GPS signal intensity, the interruption area is marked, and a standby path is planned, so that the path continuity is improved; in the transportation process, a vehicle sensing module collects position information, environmental parameters and cargo state data in real time, integrates a GPS, inertial navigation and a historical track prediction model, dynamically corrects the position of a vehicle, verifies the accuracy of the position in combination with road signs, and enhances the monitoring precision; in addition, based on multi-source data, abnormal conditions are judged in real time, grading processing is carried out according to the severity of anomalies, and transportation safety is effectively guaranteed.

The invention provides an unmanned aerial vehicle positioning method and system, an unmanned aerial vehicle and a medium, and the method comprises the steps: firstly, obtaining inertial motion data and visual perception data in real time, and continuously providing the inertial motion data through employing the characteristic that an inertial sensor is not limited by a signal environment, so as to avoid a positioning interruption problem when a positioning signal is weak; meanwhile, the spatial pose data is generated by performing pose analysis on the visual perception data, and the first position information is generated by combining the preset satellite map database in a matching manner, so that the defect of inertial navigation accumulative errors is made up. The inertial motion data, the spatial pose data and the first position information are fused and calculated to generate the second position information, and the second position information is converted into the GPS signal and accessed to the GPS system of the unmanned aerial vehicle, so that the error defect of a single sensor can be effectively inhibited, continuous and accurate positioning in a complex environment is realized, and the positioning accuracy is improved. The problem of application limitation of a traditional GPS positioning system in special environments with weak positioning signals and the like is fundamentally solved from the technical level.

The invention provides an intelligent positioning method and system for a vehicle rescue state, and relates to the technical field of vehicle rescue positioning. The method comprises the steps that after historical position information of a vehicle-mounted end is received, if a curve corresponding to the historical position information is analyzed to be a zigzag curve of which the amplitude and the vibration frequency exceed threshold values within a set time length, it is determined that the vehicle enters a GPS signal failure area. After a rescue request signal is received, a last effective positioning point, vehicle speed data and historical track information before failure are obtained, a predicted driving distance is determined by combining a vehicle speed and effective time, candidate paths are determined by combining historical tracks, the positioning point and a terrain path map, and a candidate position set matched with the predicted distance is determined on the candidate paths; and determining the nearest final to-be-rescued position in combination with the position information of the ground base station. According to the multi-dimensional information fusion positioning method, intelligent prediction positioning of the vehicle position can be realized under the condition of GPS signal failure, and a reliable position basis is provided for carrying out rescue work in time.

The invention relates to the technical field of aircrafts and navigation, in particular to a return flight control method and device, a chip and an unmanned aerial vehicle. The method comprises the following steps: after the autonomous aircraft takes off, acquiring motion state information of the autonomous aircraft through satellite positioning equipment and / or one or more sensors; when the autonomous aircraft flies until the satellite positioning equipment can provide a positioning signal with specified precision, the current position information of the autonomous aircraft at the current moment is obtained through the satellite positioning equipment, and the initial position information of the autonomous aircraft is reversely deduced through the current position information and the historical motion state information; the initial position information is used for performing return flight control on the autonomous aircraft during return flight of the autonomous aircraft. Therefore, based on the multi-source navigation data, the positioning blind area in the take-off stage is avoided through the mode of calculating the initial position in a delayed mode, the problem that the homeward voyage precision of the lightweight aircraft is insufficient in the environment that GPS signals are interfered is solved, and therefore the homeward voyage precision is improved, and the safety and reliability of the system are improved.

The invention discloses an intelligent surveying and mapping system for catenary equipment in a tunnel, which relates to the technical field of catenary engineering detection and comprises a multi-source data acquisition module, a multi-source data fusion module, an identification and parameter calculation module, a modeling and interaction module and a data management and application output module. According to the invention, by deploying the multi-dimensional sensor, the limitation of traditional manual inspection or dependence of a single sensor is broken through aiming at the environments of insufficient illumination, high dust concentration and the like in the tunnel, the laser scanner can penetrate through the dust to obtain the three-dimensional geometric framework of the equipment, and the Beidou positioning assembly is bound with the collection action in real time; the problem of positioning deviation caused by weak GPS signals in the tunnel is solved, the environment sensor group monitors parameters such as temperature, humidity and illumination in real time, and through a dynamic adaptation fusion coefficient formula, sensor data weight is calculated, original coordinate deviation is corrected, a four-dimensional fusion data set is constructed, and errors caused by environment interference or data isolation in a traditional method are solved.

The invention relates to the technical field of GPS interference source monitoring and troubleshooting, in particular to a civil aviation GPS interference source monitoring and troubleshooting method and system. The method comprises the following steps: acquiring track position data, attitude parameter data, GPS signal receiving data and interference detection performance parameters of a plurality of aircrafts, and establishing aircraft nodes and interference nodes; generating a common edge between any two aircraft nodes, generating a corresponding special edge between each interference node and the aircraft node within a preset influence radius, and constructing a space-time interference propagation topological graph at each moment; calculating an interference behavior characteristic parameter to obtain an interference behavior characteristic parameter vector, and calculating and outputting an interference behavior type identifier and an interference evolution trend vector corresponding to each interference node by using an interference behavior evolution trend inversion algorithm; and predicting an early warning interference source and generating an interference early warning result. According to the invention, real-time positioning, behavior identification and future influence range prediction of multiple types of interference sources in the civil aviation airspace are realized.

The invention provides a low-altitude multi-source equipment data base fusion method and system based on deep learning, and belongs to the technical field of low-altitude data processing. A multi-modal dynamic strategy generation engine is adopted to analyze multi-dimensional context information such as equipment modality, space environment and task intention and generate an optimal fusion strategy instruction; according to the present invention, adaptive adjustment fusion is carried out according to the current situation, when the GPS signal is interfered, the dependence on the GPS signal is automatically reduced, the weight of other reliable signal sources is enhanced, and when the optical sensor fails in the heavy fog weather, the data processing assembly line is dynamically reconstructed, the image processing micro service is bypassed, and the radar data is preferentially processed. The dynamic self-adaption and toughness anti-interference capability of the low-altitude multi-source data fusion process is improved, various interferences can be effectively resisted, high-precision fusion output is always kept in a complex and changeable low-altitude environment, data source selection and weight are flexibly adjusted according to a real-time dynamic environment and task requirements, and the real-time dynamic environment and task requirements are met. And the accuracy and the response speed of low-altitude multi-source data fusion are improved.

The invention discloses an indoor dynamic environment three-dimensional reconstruction method and system based on deep learning, and the method comprises the steps: employing SuperPoint and LightGlue to carry out the extraction and matching of feature points, carrying out the precise recognition of a dynamic target in combination with YOLOv11 and SAM technologies, and carrying out the implementation of an image pyramid feature selection strategy, and can uniformly reserve key feature points layer by layer, thereby achieving the precise recognition of a dynamic target. The feature points of the dynamic object are effectively eliminated, so that the efficiency is further improved, and finally, a high-precision, high-robustness and high-real-time visual synchronous localization and mapping (VSLAM) system for high-precision localization and mapping is realized in an indoor dynamic environment. The method is suitable for the fields of robot navigation, unmanned vehicles, indoor positioning and navigation and the like in indoor environments with weak GPS signals and more dynamic objects, such as large underground parking lots, shopping malls, office buildings and the like.

The invention discloses an autonomous positioning and obstacle avoidance method for ground-approaching flight of a fixed-wing unmanned aerial vehicle, and the method achieves the high-precision positioning through constructing a stereogram and carrying out the feature point matching with a pre-stored base map, and achieves the safe and efficient obstacle avoidance through employing a dynamic constraint hierarchical progressive trajectory planning method. The problems of navigation and obstacle avoidance under the conditions of limited GPS signals and dynamic change of obstacles in ground-approaching flight are solved, and the survivability and task execution efficiency of the unmanned aerial vehicle in a complex environment are improved.

The invention discloses a bridge maintenance operation supervision method, and belongs to construction site management, and the method comprises the steps: building a sensor model, a personnel model and a bridge BIM model; registering the sensor model and the bridge BIM model by using a geographic coordinate system; integrating a personnel model, the registered sensor model and the bridge BIM model through a GIS platform to obtain a digital twin scene based on BIM + GIS; the method comprises the following steps: acquiring GPS coordinate data of an operator through an intelligent safety helmet, and converting the GPS coordinate data into coordinate data under a Cartesian coordinate system; establishing a multi-source data correction mechanism, and correcting the coordinate data under the Cartesian coordinate system; and according to the corrected coordinate data, the position of the operator is displayed in real time in the digital twinborn scene through the operator model. In order to solve the problem that the positioning precision of operators is low due to GPS signal drifting in bridge maintenance, the monitoring and positioning precision is improved.

The invention discloses a power transmission tower attitude monitoring and vibration intrusion identification method based on multi-source data fusion, and the method comprises the steps: carrying out the deep fusion of GPS displacement data and accelerometer inclination angle data, employing a dynamic weight fusion mechanism: adjusting a fusion coefficient alpha in real time according to the GPS signal quality, guaranteeing the accuracy and stability of data fusion, and at the same time, carrying out the real-time adjustment of the fusion coefficient alpha, and carrying out the recognition of the vibration intrusion of a power transmission tower. And continuously optimizing the error propagation model by taking the final inclination angle theta final of the power transmission tower as a state variable, updating the attitude estimation of the power transmission tower, and realizing + / -0.01-degree high-precision attitude monitoring. According to the invention, the system can output a high-precision three-dimensional attitude angle in real time by combining the rapid dynamic response of the accelerometer and the long-term stability of the GPS. Besides, an LCD-MPE (feature extraction technology) is introduced, lightweight neural network classification is combined, vibration signal sampling, noise reduction and abnormal signal segment identification, analysis and extraction are carried out on the transmission tower through an MEMS accelerometer, and second-level detection and behavior type discrimination of invasion behaviors around the transmission tower are realized. The core technical barrier from'inability perception 'to'accurate identification' is overcome, and subversive upgrading of power facility safety protection from'passive disposal 'to'active defense' is promoted.

The invention belongs to the technical field of satellite navigation, and particularly relates to a Beidou terminal performance closed-loop test system and method, and the system comprises a multi-system GNSS navigation signal simulation source which is used for simulating a test signal and evaluating the core performance of a navigation terminal in real time; the Beidou RDSS and short message test system is used for simulating RDSS outbound signals and completing capturing, tracking, analysis and parameter measurement of RDSS inbound signals; the satellite navigation signal forwarding system is used for receiving satellite signals and completing work such as machine testing, system debugging, time service and positioning; the portable data acquisition and playback system is used for acquiring four signals, namely, a BDS signal, a GPS signal, a GLONASS signal and a Galileo signal; and the microwave anechoic chamber is used for providing a wireless shielding test environment, supporting the rotary table to spin and simulating the running state of the missile-borne receiver. According to the invention, the comprehensive testing of the basic function performance of various navigation products, such as passive positioning, active positioning, position report and short message, can be realized, and the method has a good market application prospect.

The invention discloses an indoor and outdoor navigation method, device and equipment, and relates to the technical field of computers. The method is applied to an edge device, the edge device is carried on a robot, and the method comprises the steps that after it is detected that the robot is started outdoors, a first planned path is generated based on the initial position of the robot, an outdoor offline map and the indoor entrance position; when it is detected that the intensity of the entrance marker and the GPS signal at the indoor entrance position is smaller than an intensity threshold value, an indoor navigation mode is triggered; constructing an indoor coordinate system by taking the indoor entrance position as an original point; indoor motion data of the robot and image data of the environment where the robot is located are obtained through an inertial measurement unit and image acquisition equipment; determining a current indoor coordinate of the robot based on the indoor map, the indoor motion data and the image data; and generating a second planning path based on the indoor coordinate system, the current indoor coordinate and the target task point. According to the scheme, the navigation efficiency can be improved.

The invention discloses a position detection and cheating device and method for an unmanned aerial vehicle, and the device integrates a position information obtaining module, a GNSS signal receiving module, and a GNSS cheating signal generating and transmitting module, obtains the position of the unmanned aerial vehicle through a Wi-Fi network card or a Bluetooth receiver, and a USRP B210 device and related software collects and processes a GNSS signal and generates a cheating signal. The method comprises three processes of unmanned aerial vehicle position information detection, real GPS signal receiving and position deception signal generation, and the safety of the unmanned aerial vehicle is evaluated through multi-link analysis, signal receiving and simulation signal generation and emission. The unmanned aerial vehicle position and the GPS signal can be acquired in real time, the authenticity of the deception signal is improved, and the test effectiveness is enhanced; and the device has the advantages of high simulation degree, high precision, simplicity and convenience in operation, low cost and the like, and an efficient scheme is provided for unmanned aerial vehicle position safety testing and anti-deception capability evaluation.

The invention relates to the technical field of vehicle-mounted terminals, and discloses a vehicle-mounted terminal control method and system and a vehicle, and the method comprises the following steps: S1, multi-dimensional environment perception and scene judgment: collecting GPS signal intensity, Beidou signal intensity, mobile network signal intensity, vehicle altitude and environment illumination intensity through a perception module; through Beidou + inertial navigation integrated positioning + null shift calibration, the basement positioning precision is higher, the parking position monitoring and vehicle searching requirements are met, the three-level redundant link (mobile network + LoRa + Bluetooth) and distributed gateway design is adopted, the basement information transmission success rate is higher, emergency information is not interrupted, data security is guaranteed through full-link encryption, and the service life of the system is prolonged. Misjudgment of temporary weak signals on the ground is avoided through multi-dimensional sensing (signal + altitude + illumination + duration), the mode switching accuracy is higher, bandwidth waste is reduced through a priority dynamic adjustment mechanism, the emergency information response speed is increased, and link congestion is avoided through a common information caching strategy.

The present invention discloses a GPS signal management method and system based on artificial intelligence, which belongs to the field of artificial intelligence technology. The method and system include receiving original signals of multiple GPS satellites, performing preliminary screening and noise filtering preprocessing. The method builds a geographic environment model based on signal characteristics in combination with multiple sensors, and adjusts GPS parameters in real time according to environmental changes, so as to adapt to different environmental conditions and improve the adaptability of the signal. The method also performs dynamic signal optimization and enhancement through an intelligent antenna array, and can dynamically adjust the antenna according to the signal strength and the predicted multi-path signal state, so as to further enhance the stability of the signal. The optimized and enhanced GPS signal and the data of multiple sensors are fused for high-precision positioning. By fusing the GPS signal and the geographic environment model information data, the error correction of the initial position estimation can be performed, and the fused position estimation is matched and verified with the geographic environment model, so as to significantly improve the positioning accuracy.

The invention relates to the technical field of satellite navigation and wireless communication, and discloses an indoor positioning method and system based on feeder multiplexing Beidou double-frequency, and the method comprises the steps: receiving outdoor B1I and B2a signals through a high-gain Beidou double-frequency receiving antenna, carrying out the frequency separation and signal conditioning through a double-frequency separation filter, and carrying out the positioning of the B1I and B2a signals; and transmitting the B1I signal to an indoor distributed antenna for broadcasting through the existing communication feeder network, and transmitting the B2a signal to a central directional antenna to establish a time synchronization reference. A mobile terminal receives a dual-frequency signal, capturing and tracking are carried out through digital signal processing, and pseudo-range and carrier phase observation values are extracted. And performing pairing processing on observation data of the same antenna, calculating a dual-frequency pseudo-range difference value, eliminating system errors, and performing position calculation by adopting a weighted least square method and Kalman filtering. The method solves the problem that the traditional GPS signal cannot cover the indoor environment, and has a good application prospect in the scenes of underground commercial complexes, tunnels, parking lots and the like.

A vehicle system includes a RTK hub, a vehicle, and one or more processing circuits. The RTK hub is configured to be positioned at a known location, and includes a first communications interface configured to facilitate acquiring a hub GPS position of the RTK hub based on a hub GPS signal acquired from a GNSS satellite. The vehicle includes a sensor configured to acquire a vehicle GPS position of the vehicle based on a vehicle GPS signal from the GNSS satellite and a second communications interface configured to facilitate communications between the vehicle and the RTK hub. The processing circuits are configured to determine corrective position data based on the hub GPS position and the known location, determine a corrective position of the vehicle based on the vehicle GPS position and the corrective position data, and control an operation of the vehicle based on the corrective position of the vehicle.

The invention discloses an unmanned aerial vehicle cluster autonomous behavior decision-making method in a high-risk police affair actual combat scene, and belongs to the technical field of police affair actual combat unmanned aerial vehicles. Starting an environment sensing system, collecting environment information through an optical camera in the multi-mode sensor unit, and carrying out noise reduction and correction on original data through a data preprocessing module; s2, the environment information obtained in the S1 is transmitted to a data fusion module, the data fusion module uses an extended Kalman filter to carry out fusion processing on data from different sensors, and a comprehensive environment model is generated; and S3, initializing a positioning and navigation system, compensating the GPS signal by a positioning correction unit based on a visual SLAM technology in combination with a geomagnetic navigation mode, and automatically switching to the geomagnetic navigation mode when the GPS signal is unavailable. According to the invention, more comprehensive environment information can be provided.

The embodiment of the invention provides a new energy inspection robot node positioning method and system, and the method comprises the steps: firstly carrying out the dual confidence degree dynamic evaluation of GPS and SLAM data, and quantifying the respective reliability in real time; and then, inputting the confidence into a fusion state machine decision engine, so that the fusion state machine decision engine can intelligently decide that the system should be in an optimal fusion state such as GPS leading, pure SLAM or GPS recovery transition. Finally, the state decision directly guides the adaptive adjustment of the measurement covariance matrix in the extended Kalman filtering, thereby dynamically adjusting the weights of different sensors at the algorithm level. According to the closed-loop design from state perception to strategy decision and then to parameter self-adaption, positioning impact caused by GPS signal sudden change can be effectively restrained, smooth transition during signal recovery is achieved, and therefore it is guaranteed that a positioning result has high precision, high continuity and high accuracy under various working conditions.

The invention discloses an intelligent construction and optimization method for an electronic map based on artificial intelligence, and the method comprises the steps: collecting user navigation path planning data through a navigation terminal, and recognizing a blind region in a navigation path according to a regional GPS error distribution diagram; if the blind area exists in the navigation path, the feature marker corresponding to the blind area is imported into the user electronic map from the feature marker information base, and secondary construction of the electronic map is completed; navigation is carried out based on the secondarily constructed electronic map, when the user enters the blind area, road scene images are collected in real time through a camera device of the navigation terminal, markers of the blind area are recognized, and the recognized markers are matched with feature markers in the electronic map of the user to determine the estimated position of the user; judging whether the user deviates from the navigation path or not according to the estimated position of the user, and correspondingly updating the path or recording error data; according to the invention, the navigation precision problem in a weak GPS signal area is effectively solved, and the reliability of the electronic map in a complex environment is improved.

The invention belongs to the related technical field of construction safety patrol, and particularly relates to an unmanned aerial vehicle safety patrol method for a dianthus chinensis landform mountain construction area, which comprises the following steps: S1, preparation work before patrol: inspecting patrol equipment and confirming patrol environment conditions on the same day; wherein inspection of the patrol equipment comprises inspection of an unmanned aerial vehicle body and all parts, inspection of battery conditions, inspection of high-definition camera equipment and sensing equipment and inspection of communication and positioning conditions. According to the invention, the unmanned aerial vehicle equipment and the high-definition monitoring camera equipment are checked before the unmanned aerial vehicle carries out safety patrol, so that the situation that the safety patrol is difficult to complete smoothly due to equipment problems in the safety patrol process is avoided to a great extent; and meanwhile, the real scene information and the position information acquired in the safety patrol process can be ensured to be smoothly received by the monitored personnel by checking the transmission communication signal and the GPS signal before safety patrol.

The invention discloses a Beidou space-time signal frequency conversion compatible time service method, and belongs to the technical field of satellite navigation and time service, and the method comprises the steps: obtaining a Beidou space-time signal, monitoring the comprehensive electromagnetic characteristics of the Beidou space-time signal in a current environment, and generating a real-time environment fingerprint; matching the real-time environment fingerprint with an environment strategy knowledge base to obtain a current time service strategy; according to the current time service strategy, dynamically adjusting the receiving frequency and processing parameters of the Beidou space-time signal, and reconstructing a time service signal chain to obtain a reconstructed Beidou time service signal; and reconstructing the Beidou time service signal to establish a unified space-time grid, projecting the pre-received GPS signals of the plurality of GPS systems to the unified space-time grid for alignment correction, and generating a fusion time service result. Signal chain dynamic reconstruction combining environment perception and strategy matching is adopted, and multi-system fusion based on a unified space-time grid is adopted, so that the precision and robustness of time service in a complex electromagnetic environment can be improved.

The invention discloses a driving control method and device for a photovoltaic module installation robot. And generating a global point cloud map of the mounting bracket of the photovoltaic module through a laser mileage calculation method. The method does not need to depend on pavement marks or GPS signals, realizes high-precision map construction of a complex environment, and has relatively high environmental adaptability and anti-interference capability. Acquiring a target area where the mounting bracket is located based on the global point cloud map, extracting a linear contour of the mounting bracket in the target area based on the point cloud map of the target area, and determining a main linear contour; the efficient and accurate identification of the mounting bracket is realized, and the obstacle interference is effectively eliminated. And a driving path is generated in combination with the main linear contour and the position of the photovoltaic module installation robot, and the photovoltaic module installation robot is controlled to drive according to the driving path. A single and fixed preset path is not adopted, so that the photovoltaic module mounting robot accurately generates a path in a complex and changeable environment and stably runs along the edge of the bracket.