544 results about "Gps positioning" patented technology

The invention provides a road marking quality intelligent evaluation system based on image analysis, and relates to the technical field of road facility monitoring, and the system comprises a detection unit, a marking quality evaluation unit, a marking wear prediction unit, a GPS positioning unit, a vehicle driving information unit, a control unit and a remote central control unit. The marking quality evaluation unit is constructed based on a differential geometry theory and comprises a curvature flow edge detection module, a marking geometric characteristic manifold representation module and a multi-scale differential invariant evaluation module, the system regards a marking as a two-dimensional manifold, and the marking quality is evaluated by calculating differential geometric quantities such as a Gaussian curvature, an average curvature and a shape index. And constructing geodesic distance measurement on the feature manifold, and evaluating the completeness, visibility and reflective performance of the marked line. And the marking wear prediction unit predicts the service life of the marking based on the traffic flow information and the environment characteristic mapping relation model, and generates a maintenance suggestion.

The invention provides a high-precision positioning method and system based on multi-source information fusion. The high-precision positioning method comprises the following steps: acquiring the omm positioning information of the pose of an unmanned aerial vehicle in real time based on a visual inertial navigation unit; a global feature extraction algorithm is combined with a similarity algorithm, and a target satellite image matched with the airborne image of the unmanned aerial vehicle is selected from a satellite image library; using a local feature matching algorithm to combine with the geographic coordinates of the target satellite image to obtain GPS predicted positioning coordinates; fusing the odom positioning information and the GPS predicted positioning coordinates by using a global fusion technology to obtain preliminarily fused odom positioning information; according to the method, secondary fusion is carried out on the basis of GPS positioning data obtained by a real GPS sensor in combination with the preliminarily fused odom positioning information to obtain final odom positioning information, and through a double fusion strategy, the advantages of a multi-source positioning technology are fully integrated, and the positioning accuracy is improved.

The invention relates to the technical field of photovoltaic inspection, in particular to an unmanned aerial vehicle photovoltaic inspection positioning method and system based on extended Kalman filtering, and the method comprises the steps: obtaining the GPS positioning data, attitude data and photovoltaic panel image data of an unmanned aerial vehicle in real time based on a sensor carried by the unmanned aerial vehicle; according to the image data of the photovoltaic panel, fault features of the fault photovoltaic panel are identified, and pixel coordinates of the fault photovoltaic panel are extracted; converting the pixel coordinates of the fault photovoltaic panel into initial position information under a geographic coordinate system; constructing an extended Kalman filtering model, inputting the positioning data, attitude data and initial position information of the unmanned aerial vehicle into the extended Kalman filtering model, and establishing a state vector and an observation vector; and iteratively calculating an optimal estimation value through an extended Kalman filtering algorithm according to the state vector, and outputting geographic coordinates of the fault photovoltaic panel. According to the invention, the method achieves the quick and precise positioning of the fault photovoltaic panel, improves the operation and maintenance efficiency of a photovoltaic power station, and reduces the operation and maintenance cost.

The invention provides an expressway disease detection method capable of accurately positioning. The expressway disease detection method comprises the following steps: S1, image acquisition and data annotation; s2, constructing an instance segmentation model; s3, extracting a multi-scale feature map and fusing multi-scale features; s4, outputting a category label, a bounding box coordinate and a confidence coefficient score of the detection box; s5, performing multi-target trajectory tracking; s6, eliminating global motion influence; s7, establishing an association relationship between the prediction trajectory and the current detection frame; s8, dynamically maintaining a track list; and S9, outputting bounding box coordinates and ID tags in each target continuous frame, and storing the bounding box coordinates and the ID tags as structured data. According to the method, a high-performance target detection framework, an advanced instance segmentation tracking technology and a GPS positioning technology are combined, pixel-level identification and continuous tracking of diseases can be realized while high detection precision is ensured, and the positions of the diseases can be accurately positioned.

The invention provides a multi-source data fusion self-positioning method and system. The method comprises the following steps: acquiring GPS positioning data, visual image data and laser radar point cloud data of an unmanned aerial vehicle; carrying out noise reduction preprocessing on the GPS positioning data by adopting an unscented Kalman filtering algorithm; performing sensor joint calibration based on a visual image and a laser radar point cloud, and establishing a geometric mapping relation between a camera coordinate system and a world coordinate system by retrieving a preset high-precision tower ledger library and solving a PnP problem; inputting the image target detection data, the GPS state estimation value and the geometric mapping data into a pre-trained auto-encoder regression network; anti-interference potential features are extracted through an encoder of the network, and a target position estimation value of the target space position of the unmanned aerial vehicle is output through a regression head. According to the invention, the problem of positioning drift caused by strong electromagnetic interference and complex landform in electric power inspection is effectively solved.

The invention relates to the field of power transaction security, in particular to a data security protection method for power transaction. Comprising the following steps: a registration authentication stage: completing identity registration through triple authentication of a physical token, biological feature recognition and a dynamic password and GPS positioning white list verification; in the key initialization stage, an SM2 asymmetric key pair and an SM4 symmetric communication key are dynamically generated according to a transaction time window and main body attributes, and are issued through a point-to-point encryption channel and updated in time; in the data encryption and binding stage, a transaction instruction is encrypted, signed and subjected to double-layer integrity verification; a matching and on-chain registration stage: executing matching after verifying the signature, encrypting a result, writing the result into the block chain, and generating a zero-knowledge proof; and in the settlement and supervision stage, settlement or alarm responsibility investigation is started after decryption verification. According to the invention, the problems of single identity authentication, static key, easy data tampering and the like in the prior art are solved, full-flow security protection of power transaction is realized, and data confidentiality, integrity and traceability are improved.

The invention relates to the technical field of digital twinning, and discloses an intelligent park inspection method and system based on digital twinning. The method comprises the following steps: acquiring image data, point cloud data, inertial navigation data and GPS positioning data, and performing format standardization and timestamp synchronization processing to obtain a multi-dimensional environment data set; performing point cloud space alignment according to image data and the point cloud data to obtain a three-dimensional space frame; performing spatial position calibration according to inertial navigation data and the GPS positioning data to obtain positioning reference data; constructing a digital twinborn model of the park according to a three-dimensional space frame and the positioning reference data; performing path calculation and rehearsal correction according to the digital twinborn model to obtain an inspection track; and according to the inspection track and the digital twinborn model, generating an inspection instruction, so that inspection equipment performs a park inspection task. According to the method, intelligent inspection rehearsal and path optimization can be realized, and the park inspection efficiency is improved.

The invention relates to the technical field of intelligent traffic systems, in particular to a linear intelligent monitoring system for a bridge fabrication machine, which comprises a multi-source sensing module, a data processing module, a data processing module, a data processing module, a data processing module, a data processing module and a data processing module, and is characterized in that the multi-source sensing module comprises strain sensors, inclinometers, GPS positioning units and machine vision units; the edge computing node is internally provided with a self-adaptive data fusion algorithm and is used for denoising sensor data in real time; the cloud digital twin platform is used for synchronizing the BIM design model and generating a millimeter-level linear deviation early warning signal; the PLC control interface is used for receiving the early warning signal and automatically adjusting parameters of the template system and the walking mechanism; according to the method, environmental noise interference is effectively eliminated through multi-source sensor fusion and a self-adaptive data filtering technology, the monitoring precision is improved to a millimeter level, real-time comparison of construction data and a design model is realized based on a BIM and digital twinning dynamic interaction system, and the timeliness and accuracy of linear control are remarkably improved.

The invention discloses an intelligent transport capacity dispatching system and method based on a GPS. The system comprises a transport capacity state monitoring subsystem and an intelligent dispatching management subsystem. And by deploying a GPS positioning and multi-parameter sensor, real-time acquisition and analysis of information such as the position, the speed and the energy consumption of the transport capacity unit are realized. According to the system, CNN and Transform structure extraction features are fused, LSTM is combined to predict a transport capacity demand, and a PPO reinforcement learning algorithm is adopted to dynamically plan a path. Abnormality detection (SVM), PID feedback adjustment and cloud edge cooperative calculation are integrated in the scheduling process, and the scheduling precision and the response speed are improved. The system has the capabilities of task filing, online optimization and data security guarantee, and is suitable for complex traffic and logistics scheduling scenes.

The utility model discloses an efficient drainage ditch excavation device based on an adjustable shaping excavator bucket, and relates to the technical field of municipal engineering and hydraulic engineering construction. Different design requirements are met by hydraulically adjusting the size of the bucket, the bucket body is formed at a time, manual finishing and template use are reduced, the construction cost is reduced by more than 20%, the excavation precision is controlled by combining GPS positioning and real-time measurement, the groove longitudinal slope and elevation precision is ensured, and the construction efficiency is improved. One-time forming of the drainage ditch and synchronous outward transportation of spoil are achieved, and site accumulation is avoided. According to the scheme, the construction efficiency and quality are remarkably improved, and the method is suitable for municipal drainage engineering under soil texture and strong weathered rock geological conditions.

The invention relates to the field of aviation safety and unmanned aerial vehicle management, and discloses a low-altitude flight real-time situation awareness method, system and device and a storage medium, and the method comprises the steps: deploying a radar group composed of a millimeter wave radar and an optical camera in an airport area, the deployment density of the radar group meeting the detection precision requirement of a small unmanned aerial vehicle, each device communicates with the real-time data processing platform through a distributed network; target detection data of the radar group, image data of the AI image recognition module, GPS positioning data and ADS-B flight data are received in real time; and processing the multi-source data through a preset data fusion algorithm, including time-space calibration of the unified timestamp and the space coordinate reference. A radar group composed of millimeter wave radars and optical cameras is deployed in an airport area, so that the problems that most traditional low-altitude monitoring systems only depend on a single radar data source, so that the omission ratio of small unmanned aerial vehicles is high, and the misjudgment rate of the small unmanned aerial vehicles and targets such as birds is high are solved.

The invention relates to the field of power transmission line risk hidden danger monitoring, and provides a power transmission line channel visual alarm high-incidence area identification method, and the method comprises the steps: collecting visible light video streams, infrared thermal imaging, environment data, GPS positioning information and other multi-source data, carrying out the preprocessing and multi-modal feature extraction, and constructing a multi-modal feature matrix; based on correlation analysis between features, generating a high-dimensional alarm event feature vector, mapping the high-dimensional alarm event feature vector into a graph node, and constructing a space-time diagram containing spatial distance and time interval information; spatial and temporal distribution characteristics are extracted, clustering analysis is carried out, alarm event clusters are identified, risk scoring is carried out in combination with density and persistence indexes, and finally an alarm high-incidence area with risk rating is calibrated, so that risk assessment based on data driving is realized instead of fixed threshold value alarm, the influence of environmental change on alarm frequency can be adapted, and the risk assessment efficiency is improved. Complete process optimization from data acquisition to risk rating is realized, and the accuracy and comprehensiveness of alarm high-incidence area identification are improved.

The invention discloses a visual drawing method and device for an inland ship navigation trajectory and a medium, and the method comprises the steps: collecting GPS positioning data, AIS dynamic parameters and static attribute data of a ship through a ship terminal, carrying out the preprocessing of the GPS positioning data and the AIS dynamic parameters, and storing the processed data in a database according to a timestamp index; calling a geocoding service interface, mapping the processed longitude and latitude coordinates of the ship to an electronic navigation channel chart coordinate system, and periodically updating a ship position mark in an electronic map interface; in response to a time period query instruction input by a user, extracting ship track point data corresponding to the timestamp sequence from the database, and superposing the ship track point data to the electronic channel chart to generate a dynamic track line; a user operation instruction is received through the time axis control, the ship mark position and the course display state on the trajectory line are dynamically adjusted, and visualized backtracking of the historical trajectory of the ship is achieved.

The invention relates to the field of geographic information mutual analysis, and discloses a multi-source dynamic collaborative geographic information mutual analysis method and device, equipment and a medium. The method comprises the following steps: collecting time-synchronized static geographic information, dynamic GPS positioning data and a plurality of pairs of starting and ending point unstructured text data sets, and constructing a dynamic spatio-temporal data pool; high-quality spatio-temporal data is obtained through dual anomaly verification and Kalman filtering cleaning correction; optimizing an address longitude and latitude mapping table in combination with a three-level administrative range and a dynamic high-frequency word; a non-standard address is analyzed into a structured text through a BERT-CRF model, and a precise coordinate and a standardized text are obtained through a hierarchical dynamic matching algorithm. By adopting the method, meter-scale positioning is realized, the non-standard address resolution accuracy is greatly improved, the duplicate name address mismatching rate is reduced, a dynamic scene is adapted, the resolution efficiency and reliability are considered, and an efficient technical support is provided for bidirectional accurate mutual resolution of geographic coordinates and text addresses.

The invention relates to the technical field of intelligent monitoring of drop-out fuses, in particular to an integrated intelligent monitoring device and method for a drop-out fuse, and the device comprises a measurement module which is used for measuring the voltage and current analog signals of the incoming and outgoing line ends of the drop-out fuse; the peripheral controller is connected with the measuring module and is integrated with an AD acquisition unit, a data processing unit, a GPS positioning unit and a wireless communication unit; and the power supply module is connected with the peripheral controller and is used for supplying power to the device. The system has the beneficial effects that a plurality of key functions such as voltage and current acquisition, data processing, GPS positioning and wireless communication are highly integrated in a compact device, so that the size and installation complexity of equipment can be greatly reduced, the overall reliability and stability of the system can be effectively improved, and meanwhile, the failure rate of the equipment is reduced.

The invention relates to an unmanned aerial vehicle navigation decoy defense method based on visual map matching, and solves the technical problems that a traditional relative positioning mode is easy to cause positioning drift and an unmanned aerial vehicle is easy to decoy and interfere under long-term and large-scene conditions. The absolute positioning method based on the scene coordinate regression network is adopted, positioning drift is avoided in mechanism, and the scene coordinate regression network based on deep learning is used for performing positioning information mapping on the visual image and the scene coordinates. And inputting a visual image shot by the unmanned aerial vehicle into the optimized scene coordinate regression network to obtain a scene coordinate predicted value, and calculating the scene coordinate predicted value by using an RANSAC algorithm to obtain a pose estimation result. And comparing the pose estimation result with global navigation satellite system (GPS) positioning information received by the unmanned aerial vehicle to complete positioning signal decoy recognition, so that the unmanned aerial vehicle can effectively defend against GPS decoy signals.

The invention discloses an air microorganism aerosol alarm sampling system and method and an unmanned aerial vehicle system. A main controller receives a current position positioned by a GPS positioning module, and when the current position is in a to-be-detected area, an air pump and an alarm sampling optical module are started to pump air into the alarm sampling optical module; the alarm sampling optical module samples microbial particles in the air to form a fluorescence signal, the main controller analyzes the aerosol concentration according to the fluorescence signal and identifies whether the aerosol is a biological aerosol, when the aerosol concentration exceeds a set threshold value or the aerosol is identified as the biological aerosol, the alarm is controlled, and meanwhile, the sampling module is controlled to be started; the main controller controls the liquid supplementing peristaltic pump to start, the sampling liquid in the sampling liquid bottle is pumped into the sampler, then the motor driver is controlled to drive the fan to start to enable air to enter the sampler for sampling, only the sampling peristaltic pump is controlled to start after sampling is completed, and the sample liquid in the sampler is conveyed into the sample bottle through the three-way pipe. The collection of the microbial aerosol particles is completed.

The invention belongs to the technical field of digital twinning and semi-physical simulation, and discloses an unmanned aerial vehicle semi-physical simulation test platform and method based on digital twinning, and the simulation test platform comprises a physical unmanned aerial vehicle which is a real unmanned aerial vehicle and flies in a real target environment; wherein a first flight controller, a camera, a laser radar, an airspeed meter, a GPS positioning module, an IMU module and an airborne computer are carried on the physical unmanned aerial vehicle; the GPS positioning module and the IMU module are respectively connected with the first flight controller, and the first flight controller, the camera and the laser radar are respectively connected with the airborne computer; the physical host is deployed with a twin domain and comprises a twin environment and a twin unmanned aerial vehicle; the twin environment is a simulation environment the same as a real target environment, and the twin unmanned aerial vehicle is a digital simulation model of the physical unmanned aerial vehicle; the second flight controller is connected to the physical host and used for controlling the twin unmanned aerial vehicle; and the airborne computer and the physical host carry out information interaction through a wireless network.

According to the intelligent operation monitoring system based on multi-source sensing, through cooperative work of the sensing terminal, the positioning base station, the intelligent video monitoring device and the back-end control system, combined monitoring of the operation environment, the personnel state and behavior violation is achieved. The system realizes indoor and outdoor seamless positioning through cooperation of UWB positioning and GPS positioning, and constructs a multi-dimensional security monitoring network in combination with environment sensor data and a video behavior recognition result. Through automatic processing of the alarm module and the video linkage module, rapid response of alarm triggering and video linkage is realized. The system supports historical data backtracking analysis to generate an operation risk thermodynamic diagram, and provides data support for safety management. The system improves the real-time performance and accuracy of operation safety monitoring, reduces the manual inspection cost, improves the emergency response efficiency, achieves the precise early warning of safety risks through multi-source data fusion, and improves the scientificity and refinement level of operation safety management.

The invention relates to the technical field of ground penetrating radar data processing, in particular to a hidden disease-oriented ground penetrating radar space-time registration and detection method. Comprising the following steps: S1, establishing a continuous mapping relation between a radar signal and a geographic space; s2, constructing a local trajectory indexing mechanism based on anisotropy measurement; s3, constructing a space-time joint correction space based on error prior distribution; s4, performing parameter inversion based on maximum posterior probability estimation constraint; s5, establishing a registration quality evaluation and signal feature decoupling mechanism; and S6, constructing a dielectric priori driven dual physical attribute index and adaptive discrimination model. According to the method, the influence of GPS positioning errors and track swing is overcome, centimeter-level physical space alignment is realized, and false alarms caused by registration deviation are remarkably reduced; by introducing the physical constraint of the frequency dimension, the signal-to-clutter ratio under the complex city background is improved, and the false alarm rate is remarkably reduced.

The invention relates to the technical field of computers, in particular to a municipal environmental sanitation cleaning task scheduling method and system based on big data analysis, and the method comprises the steps: obtaining the historical garbage amount data of an urban traffic station, and carrying out the garbage amount partitioning through a K-means + + clustering algorithm according to the historical garbage amount data; acquiring urban meteorological data, and predicting an urban weather condition through a wavelet transform and LSTM neural network hybrid model according to the urban meteorological data; the state of the sanitation vehicle is monitored in real time through a vehicle-mounted sensor, and the position and the driving track of the sanitation vehicle are tracked in real time through the GPS positioning technology; according to the garbage quantity partition, the predicted urban weather condition, the environmental sanitation vehicle state and the position and the driving track of the environmental sanitation vehicle, a cleaning task is distributed to the environmental sanitation vehicle; and real-time traffic conditions are obtained, and the sanitation vehicles are scheduled to go to the garbage quantity subareas to execute cleaning tasks in combination with the real-time traffic conditions. The environmental sanitation management efficiency can be improved.

The invention discloses a power optimization control method of an automatic driving tractor based on an unmanned aerial vehicle, and belongs to the technical field of intelligent unmanned driving control. The method aims at solving the problems that an existing intelligent tractor depends on a GPS positioning system, so that the use area is limited, and the energy efficiency is not fully considered. Field data are collected through aerial photography of the unmanned aerial vehicle, a GI map is constructed, obstacles are recognized, and the data are sent to a tractor control system to plan an optimal path and motion control parameters. According to the deviation between the current position and the target position, the target values of the driving system, the auxiliary power system, the braking system and the steering system are optimized, optimal application of energy is achieved, and the system operation efficiency is improved. According to the method, the cost of a GPS positioning module and a related data center can be saved, the difference of communication signals in different areas is ignored, and the energy utilization is optimized while the working efficiency is improved.

The invention relates to the technical field of vehicle safety monitoring, and discloses a safety detection early warning method and system based on vehicle OBD data and a medium, and the safety detection early warning method based on the vehicle OBD data comprises the steps: collecting vehicle OBD real-time data, GPS positioning track data and environment data, and carrying out the preprocessing to form a time-space aligned multi-dimensional data set; oBD time position three-dimensional correlation features are extracted through a tensor decomposition technology; learning a mapping relation between OBD parameter change modes and safety risks in different driving scenes; constructing a space-time semantic association graph, and performing high-risk region identification through a graph convolutional network; according to the current driving situation, dynamic weighted fusion of the OBD data and the environment information is achieved, and graded early warning information is generated; a risk assessment method with context adaptability is provided, three-dimensional correlation features are extracted through a tensor decomposition technology, and complex interaction among parameters is effectively captured.

The invention relates to an agricultural inspection robot navigation decision planning system and a planning method thereof, and belongs to the technical field of agricultural robots, and the agricultural inspection robot navigation decision planning system comprises a multi-sensor fusion sensing module, a navigation decision planning module, a dynamic obstacle processing module, a transverse-longitudinal combined controller and a task management platform. The multi-sensor fusion sensing module comprises a laser radar, a visual sensor, a Beidou / GPS positioning module and an IMU and is used for collecting environment images, point cloud data and robot poses and recognizing crop row navigation lines through an improved YOLOX-Tiny model. According to the method, high-precision navigation can be achieved, high-precision tracking with the transverse error being smaller than or equal to 0.04 m and the longitudinal error being smaller than or equal to 0.06 m is achieved through transverse-longitudinal combined control and multi-sensor fusion positioning, the self-adaptive capacity is high, the reinforcement learning and path decomposition technology and a dynamic risk evaluation mechanism are combined, and the reliability of the system is improved. The robot can effectively deal with sudden obstacles and complex terrains in a farmland environment.

The invention relates to the technical field of GPS positioning systems, and discloses a GPS positioning correction method and system, and the method comprises the following steps: S1, carrying out the fusion collection of multi-source heterogeneous data, and obtaining the motion state data of a carrier in a variable bandwidth sampling mode, S2, carrying out the deep feature extraction and preprocessing, and carrying out the extraction of a spatial-temporal feature extraction network based on deep learning, the method comprises the following steps: S1, mining potential features of data in time series and spatial distribution, S3, dynamic environment perception and error modeling, and S4, intelligent collaborative error correction. According to the invention, through the established deep Q network, multi-source data characteristics including GPS positioning data, inertial measurement unit data, air pressure and meteorological data and the like are used as input, and environment information can be comprehensively captured. Through deep mining and analysis of the data, the model can more accurately identify the type of the environment where the carrier is located, and meanwhile, by utilizing a reinforcement learning mechanism and setting a reward function, the model is encouraged to accurately identify the type of the environment and evaluate the interference degree, and the adaptability of an error model can be remarkably enhanced.

The invention discloses an electric power inspection unmanned aerial vehicle risk distance measuring and calculating method, system and device for a dynamic complex scene and a storage medium, and the method comprises the following steps: summarizing multivariate data of an electric power inspection unmanned aerial vehicle, carrying out the denoising processing according to the summarized data, and carrying out the single-dimensional influence factor analysis of the denoised data; analyzing and calculating a single-factor safety distance according to the single-dimensional influence factors; the calculated single-factor safety distance is superposed, and meanwhile, each factor of the unmanned aerial vehicle is subjected to standardization processing and then is fused by using a machine learning algorithm; calculating the safety distance of the power inspection unmanned aerial vehicle based on the superposed data and the data fusion result, and establishing a'no-fly-risk-safety-out-of-domain 'power transmission line unmanned aerial vehicle airspace division system according to the safety distance of the power inspection unmanned aerial vehicle; the single-dimensional influence factors comprise a magnetic field, an electric field, a wind power deviation, a Beidou / GPS positioning error, a communication delay, an unmanned aerial vehicle hovering error and an unmanned aerial vehicle performance parameter; the method improves the unmanned aerial vehicle airspace management level of the power transmission line, and has important practical significance for improving the power grid inspection operation level.

An intelligent life buoy unmanned aerial vehicle comprises an unmanned aerial vehicle body and a life buoy which are connected through a rope and a claw buckle, the unmanned aerial vehicle body comprises an intelligent chip, a waterproof rotor wing, an infrared camera, a microphone, a GPS positioning module, a wireless communication module and a remote controller, and the intelligent life buoy unmanned aerial vehicle is characterized by adopting the following working steps that 1, the intelligent life buoy unmanned aerial vehicle body recognizes the position of a drowning person; dragging the life buoy to be close to the drowning person in a vertical posture by using a rope; 2, the intelligent life buoy unmanned aerial vehicle recognizes the face position of the drowning person, a rope is dragged in the opposite direction of the face position of the drowning person, and the life buoy is controlled to turn downwards around the head of the drowning person to sleeve the drowning person; 3, the intelligent life buoy unmanned aerial vehicle drags the rope and the life buoy to drag the sleeved drowning person back to the shoreside in the upward posture. The life buoy can be sleeved on a drowning person, and by means of buoyancy of the life buoy and lift force of the unmanned aerial vehicle, the drowning person can be provided with a stronger escape ability by means of the buoyancy of the life buoy and the lift force of the unmanned aerial vehicle.

The invention discloses a single-board ski shoe fixer with a power supply system, and belongs to the field of skiing sports equipment. Most existing single-board ski shoe fixers are of mechanical structures, lack electronic functions, cannot supply power and are insufficient in protection performance. The ski shoe fixer comprises a ski shoe fixer body, a control circuit board, a battery, a brass elastic sheet contact, a capacitance touch switch, a charging interface module, a silica gel sealing plug and the like. A GPS positioning module, a Bluetooth communication module and a sound-light alarm device are integrated on the control circuit board, speed and track data can be obtained in real time and transmitted to a terminal through power supply of a battery, and positioning and alarming are achieved when a user is in danger or the snowboard is lost; the battery supplies power to the snowboard electronic device through the brass elastic sheet contact, so that equipment dispersion is reduced, and charging is simplified; the capacitive touch switch, the resin pouring sealant and other protective structures effectively prevent water and mistaken touch, it is ensured that an electronic system reliably operates in the low-temperature humid environment, and the safety, convenience and stability of skiing are remarkably improved.

The invention discloses a wearable intelligent obstacle avoidance system, and belongs to the technical field of blind person auxiliary travel. The invention aims to solve the problem that the existing blind person travel auxiliary equipment is inconvenient to use or high in cost. The method includes the steps that an image collection module collects blind sidewalk obstacle images; the GPS positioning module obtains position coordinates of a user; the lightweight target detection model performs obstacle recognition on the blind sidewalk obstacle image based on the position coordinates of the user to obtain obstacle information; the monocular distance measurement module obtains a distance signal between the current position of the user and the obstacle according to the obstacle information; the control module sends the current distance signal to the prompt module and sends a danger prompt signal when the distance between the current position of the user and the obstacle is smaller than a set threshold value; the prompting module sends a distance signal to a user; an alarm is sent to a user according to the danger prompt signal; all the modules are integrated on the wearable article to form the wearable device. The method is used for assisting the blind in going out.

The invention belongs to the technical field of underwater measurement systems, and particularly relates to a novel unmanned aerial vehicle mounting type water depth measurement system which comprises an unmanned aerial vehicle platform, a GPS-RTK module, a photographing lens, a detection host, a data transmission line, a depth measurement probe, a data recording trigger device and a ground control station. The GPS-RTK module, the photographing lens and the detection host are all mounted on the unmanned aerial vehicle platform; the detection host is connected with the sounding probe through a data transmission line. Through real-time differential positioning of the GPS-RTK module, centimeter-level positioning precision can be provided, the position accuracy of sounding data is ensured, compared with traditional shipborne GPS positioning, the precision is improved by one order of magnitude, the unmanned aerial vehicle platform is matched to reach water areas where traditional ships cannot reach, such as shoals, narrow riverways and upstream of reservoirs, and the unmanned aerial vehicle platform is matched with the unmanned aerial vehicle platform to reach the water areas where the traditional ships cannot reach. The application range of sounding is greatly expanded, and meanwhile, the hovering capability and the accurate controllability of the unmanned aerial vehicle enable the sounding operation to be more flexible and efficient.