798 results about "Gps positioning" patented technology

The invention relates to the technical field of flight control, in particular to a fixed-wing unmanned aerial vehicle attitude correction and precise flight control method fused with visual navigation, which comprises the following steps: extracting ORB feature points and constructing a dynamic feature point cloud; simultaneously acquiring angular velocity and acceleration data of the inertial measurement unit; performing tight coupling fusion processing on the dynamic feature point cloud and the inertial measurement data to generate real-time pose data; inputting the real-time pose data into an attitude correction parameter calculation module, and generating an attitude correction parameter through a dynamic weight distribution algorithm in combination with a preset flight path planning model and GPS positioning data; and a control surface deflection instruction and an accelerator control instruction are generated through the multi-constraint optimization model, and unmanned aerial vehicle flight attitude closed-loop correction is completed. According to the method, aerodynamic parameters such as the air speed, the wind field lateral disturbance and the attack angle are introduced to serve as coupling input, the control surface deflection amount and limiting conditions are dynamically adjusted, and self-adaptive modeling and control over the nonlinear flight characteristics are achieved.

The invention provides a dynamic monitoring method for a soil mass seepage field of a power transmission tower slope. According to the method, related data of the slope are acquired through multi-source data acquisition and technologies of pressure sensing and static pressure measurement, displacement monitoring, multi-sensor fusion, Beidou / GPS positioning, resistivity method exploration, soil moisture content monitoring and the like; fusing the collected multi-source data, analyzing the dynamic change of the seepage field by using a risk assessment model and a machine learning algorithm, and predicting the evolution trend of the seepage field; a lightweight model is deployed at a monitoring terminal to realize local rapid processing of data, alarm information is immediately sent out once an abnormal state is monitored, and scientific and reasonable decision support is provided for protection and treatment of a transmission tower slope according to early warning information in combination with a pre-made decision scheme; according to the invention, the accuracy and timeliness of transmission tower slope soil seepage field monitoring can be effectively improved, and the threat of landslide and other disasters to a transmission line is reduced.

The invention discloses an intelligent field management system based on an Internet of Things platform, and relates to the technical field of Internet of Things, and the intelligent field management system comprises a multi-source data fusion and analysis module, a waste recovery and resource utilization module, a knowledge graph and question and answer module, and a self-adaptive decision and regulation module. The multi-source data fusion and analysis module comprises a data acquisition sub-module and a data analysis and preprocessing sub-module; the data acquisition sub-module comprises a multi-source heterogeneous data acquisition unit and a real-time dynamic data acquisition unit; the multi-source heterogeneous data acquisition unit uses a wireless sensing technology, a GPS positioning technology, a sensor calibration algorithm and a data interpolation algorithm to acquire meteorological, soil, crop physiology and biological field data. By arranging the multi-source data fusion and analysis module, the problems that traditional agricultural data is difficult to obtain and not deep in analysis, precision agricultural development is difficult to support, and agricultural resources are wasted and crop yield and quality are unstable due to data quality problems can be solved.

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 is suitable for the technical field of electrical equipment corrosion detection, and provides an electrical equipment corrosion detection method based on double-spectrum collaborative analysis and space-time prediction, and the method comprises the following steps: carrying out fusion analysis on a visible light image and an infrared image of acquired electrical equipment, and outputting position information and morphological category of a corrosion region; a binocular vision system and an IMU sensor are combined to calculate three-dimensional space coordinates, main direction angles and environmental parameters of the corrosion area, and pose data are generated; inputting the pose data into a preset corrosion extension path prediction model for processing, and outputting physical characteristic parameters; fusing the dual-frequency RTK GPS positioning data, the IMU attitude data and the physical characteristic parameters to construct a space-time association data set, and inputting the space-time association data set into a preset space-time diagram neural network model to analyze a space-time evolution relationship of diagram structure modeling corrosion nodes; and outputting microscopic and macroscopic prediction results. The identification accuracy of corrosion characteristics and the corrosion detection efficiency can be improved, and the labor cost and the operation risk are reduced.

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 discloses a road de-duplication method based on GPS positioning and comprehensive consideration of disease types and disease sizes, and the method comprises the specific steps: S1, data collection and processing: collecting road image data through an edge detection device, carrying out the road disease recognition of the road image data, obtaining disease data, and processing and storing the disease data; and S2, disease duplicate removal: matching the positioning information, the disease type and the size information of the processed disease data, if the positioning information, the disease type and the size information are all within a preset matching threshold, determining that the disease data are duplicated items, removing the duplicated items, and then storing the disease data after duplicate removal. According to the method, duplicate removal is performed by integrating multiple data dimensions, so that the accuracy of road disease data can be remarkably improved, and errors and redundant information are reduced; the intelligent and automatic level of road maintenance is improved, technical support is provided for sustainable development of road maintenance, and the intelligent road maintenance management system becomes an effective road maintenance management tool.

The invention relates to the technical field of hydraulic ring geological survey, in particular to a hydraulic ring geological survey system and method based on GPS positioning, and the method comprises the following steps: obtaining the three-dimensional coordinate data of a survey area through a GPS positioning device, generating an initial geological positioning point set, inputting the initial geological positioning point set into a three-dimensional modeling system, generating an initial three-dimensional geological model, and carrying out the three-dimensional modeling of the initial three-dimensional geological model. Dynamically correcting the initial three-dimensional geologic model through an environment compensation algorithm to generate a corrected geologic model, dividing a geologic abnormal region through a spatial clustering algorithm based on a rock stratum permeability coefficient abnormal gradient value in the corrected geologic model, comparing the geologic abnormal region with an engineering parameter threshold library to generate a visual investigation report, and performing the visual investigation on the geologic abnormal region. An internet of things monitoring node network is established, displacement monitoring is carried out on a geological abnormal area, if a monitoring value exceeds a preset threshold value, an early warning signal is triggered, and the method can be widely applied to the fields of hydraulic ring geological survey, engineering construction and disaster early warning and has remarkable technical advantages.

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 discloses a railway pedestrian warning system and method based on an improved YOLOv7 algorithm, and the method comprises the steps: dynamically dividing a warning region according to the GPS positioning of a train and a train timetable, and obtaining a plurality of regions; performing pedestrian recognition on the monitoring area by using a high-resolution camera deployed with a pedestrian target detection model to obtain pedestrian coordinates; wherein the pedestrian target detection model is constructed based on an improved YOLOv7 algorithm; calculating pedestrian dynamic potential energy fields and potential energy gradients in real time according to the pedestrian coordinates and the plurality of areas, and obtaining risk scores according to the potential energy gradients and pedestrian motion vectors; and performing graded early warning and voice warning on the pedestrians according to the risk scores. The invention relates to the technical field of railway security and protection, and solves the technical problems of insufficient detection precision, high false alarm rate and insufficient dynamic risk quantification capability of an existing security and protection system in a complex inter-city railway scene.

The invention discloses a general cargo transportation background business management system and method and electronic equipment, the system comprises a data acquisition module, an information management module, a position tracking module and a scheduling management module, and automatic acquisition of cargo position, attribute and safety inspection information is realized through an integrated sensor, an RFID tag technology and an image identification device; the GPS positioning and local wireless network complementary correction technology is utilized to break through the positioning precision limitation in the indoor and outdoor complex environment; based on a distributed database and a real-time data processing engine, high-concurrency query and dynamic information updating are supported; in combination with cargo state and position data, an optimization scheduling instruction is generated through an algorithm, empty driving waiting of loading and unloading equipment is reduced, the method covers intelligent scheduling processes of multi-source data acquisition, standardized processing, real-time tracking and cloud driving, full-life-cycle automatic management of cargos is realized, and electronic equipment realizes full-life-cycle automatic management through a software and hardware collaborative architecture. An existing port management system is compatible, and deployment cost is reduced.

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 forest image processing, in particular to a forest information extraction method based on a remote sensing image. The method comprises the following steps: obtaining remote sensing image data of a forest area and a GPS positioning sample place area, and carrying out sample area remote sensing image processing and vegetation index analysis to obtain vegetation index data corresponding to each forest type in the sample area; performing end member component reflectivity analysis on the remote sensing image data of the sample area to obtain spectral reflectivity values corresponding to all end member components of the forest in the sample area; performing mixed pixel decomposition on a corresponding sample area remote sensing image in the sample area remote sensing image data in combination with a linear mixed pixel decomposition model so as to generate an abundance map of each ground feature type of the sample area; and carrying out pixel forest information classification extraction on each ground feature type abundance map of the sample region to obtain each forest type distribution map of the sample region. According to the invention, different forest information in the remote sensing image can be obtained by integrating the surface reflectance and the vegetation index.

The invention discloses a GPS-RFID dual-mode tracking and positioning method and system for a mailbag, and the method comprises the steps: reading an RFID tag according to a preset period, obtaining a continuous dynamic key, calculating an expected movement track of the mailbag according to the change rule of the dynamic key, and carrying out the tracking and positioning of the mailbag. Wherein the difference value between the dynamic keys obtained in two adjacent modes represents the expected moving distance and the expected moving direction of the mailbag in the time period of obtaining the dynamic keys in two adjacent modes; when it is detected that the change rate of the dynamic key exceeds a preset range or the change direction of the mailbag is different from the expected moving direction according to the change rule, a GPS positioning module on the mailbag is triggered; calculating a deviation value between the real-time position information obtained by the GPS positioning module and the expected moving track; and when the deviation value exceeds a preset threshold value, shortening the preset period and increasing the generation frequency of the dynamic key until the deviation value is not greater than the preset threshold value. The method and the device are used for improving timeliness and accuracy of mailbag tracking and positioning, so that safety and reliability of postal logistics are 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 invention discloses a pavement crack automatic detection system based on machine learning, and relates to the technical field of pavement crack automatic detection, the pavement crack automatic detection system comprises a pavement data acquisition module, a comprehensive processing module and an automatic detection result output module, the pavement data acquisition module is used for acquiring pavement image data and pavement positioning data; the pavement crack automatic detection module is used for collecting pavement image data and preprocessing the collected data, the comprehensive processing module is used for combining the pavement image data with pavement positioning data, analyzing pavement crack types and positioning different types of pavement cracks, and the automatic detection result output module is used for generating a pavement crack automatic detection report in combination with an output result of the pavement crack automatic detection model. According to the system, the visual detection technology, the GPS positioning technology, the image feature extraction technology and the model construction technology are closely combined with the modern information technology, and pavement crack type division and accurate positioning are achieved.

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 provides an intelligent control method and system for operation of a rotary-space magnetic ship, and the method comprises the steps: constructing a three-dimensional model of a surrounding virtual environment, fusing the three-dimensional model with imported map data, and generating a real-time updated environment model; based on the real-time updated environment model, an optimal path from the current position to the target position is planned, and obstacles are avoided; the planned path is converted into a specific control instruction to guide the running direction and speed of the air rotation magnet; the optimal path is continuously adjusted through real-time GPS positioning and environment scanning data feedback; in the running process of the air-spinning magnet, the change of the surrounding environment is continuously monitored; and when a potential collision risk is detected, the running direction is adjusted, and obstacles are avoided. The system comprises an environment model updating module, a path optimizing and adjusting module and an operation obstacle avoidance monitoring module. The technical effect of the system is improved, and the important significance of improving the environment sensing capacity, enhancing the safety, achieving accurate control, guaranteeing stable operation and the like is achieved.

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 discloses a ship intelligent cargo tracking and monitoring method and system, and the method comprises the steps: building a ship intelligent cargo tracking and monitoring frame based on a GPS positioning system and an Internet system, building a cargo tracking and monitoring APP, registering and building account information, registering cargoes, receiving and verifying the cargoes, and tracking the cargoes. Real-time monitoring and cargo inquiry are carried out on ship cargo transportation conditions; a consigner inquires cargo transportation information and receives the cargo through a cargo tracking and monitoring APP system; the cargo arrives at a transportation destination and receives cargo information with a consignee; and the consignee confirms cargo receiving and carries out transportation evaluation. According to the intelligent ship cargo tracking and monitoring method, actual transportation of cargoes is known through GPS positioning tracking, temperature tracking detection, humidity tracking detection, video tracking detection, vibration tracking detection, inclination tracking detection, pressure tracking detection and time tracking detection of ship cargoes.

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 deep learning, aircrafts and the like, and provides an intelligent outdoor flight waistcoat based on deep learning, the intelligent outdoor flight waistcoat is provided with a waistcoat body and an intelligent flight control system, the intelligent flight control system comprises a deep learning control module, a main flight power device, an attitude auxiliary adjusting device and an environment sensing system; the environment sensing system comprises a visual sensor, a laser radar sensor, an ultrasonic sensor, a height sensor and a GPS positioning module and is used for collecting outdoor environment data in real time and transmitting the outdoor environment data to the deep learning control module, and the deep learning control module analyzes the environment data in real time and automatically optimizes the flight state through an autonomous learning algorithm. The main flight power device is controlled to provide lift force and propulsion power, and the attitude auxiliary adjusting device is utilized to automatically correct the flight attitude, so that the flight waistcoat is convenient to wear, intelligent in environmental perception, high in self-adaptability, safe and stable, and meets diversified outdoor flight and rescue task requirements in the future.

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 discloses a fresh food cold chain tracing and early warning system based on the Internet of Things technology, and relates to the technical field of fresh food cold chain tracing, which integrates sensing modules such as GPS positioning, temperature and humidity monitoring, electronic fence management, parking identification, cargo compartment state monitoring and the like, and comprehensively judges the actual parking behavior of a vehicle through a data fusion analysis module. The path execution judgment module further combines a scoring result with a distribution task preset path to carry out intelligent comparison, whether each stop task is completed or not is accurately judged, and if the stop task is not completed, an abnormity early warning module reports abnormity information to a dispatching center in time; the whole-process intelligent identification and real-time early warning of the distribution behavior are realized, and the accuracy, integrity and system response capability of cold chain tracing are effectively improved.