378 results about "Adaptiv" patented technology

Disclosed are a high-performance loosely-coupled multi-modal data fusion system for a smart driving environmental perception system and a vehicle-mounted device, comprising: a fusion detection model based on a modality-independent feature interaction strategy, which is configured for converting a LiDAR point cloud, a camera image, and a millimeter-wave radar point cloud into a unified bird's-eye view representation, and performing multi-modal fusion; and a fusion tracking model based on a motion-appearance feature cascaded coupling data association strategy, which is configured for performing subsequent trajectory tracking and matching according to multi-modal fusion feature information. A VoD data set and a K-Radar data set are selected for training, verifying, and testing the comprehensive performance of the models, and a TensorRT accelerated inference model is applied, then quantized, and deployed to a vehicle-mounted computational testing platform. The present invention is compatible with mainstream sensor deployment solutions, and achieves the efficient complementary fusion of multi-source heterogeneous sensor information, significantly improving the reliability, accuracy, and adaptability of vehicle-mounted perception systems, thereby effectively responding to extreme operating conditions such as complex traffic scenarios and inclement weather.

The invention discloses a glass lens surface scratch detection method and system, relates to the technical field of precision optical detection, and aims to solve the problems of scratch false detection, leak detection and poor algorithm adaptability caused by interference fringes, noise coupling and poor form adaptability in a high-reflection / complex coating process scene in the prior art. According to the scheme, an orthogonal polarization state composite light field is generated based on a multi-angle polarization light source array and a near-infrared compensation light source, and candidate regions are extracted through dynamic threshold segmentation and a direction gradient tensor matrix; gaussian pyramid multi-scale feature fusion and refraction angle consistency verification are utilized to eliminate artifact interference; constructing a direction constraint convolution kernel group to decompose scratches and background textures, and dynamically allocating computing resources in combination with a cascade network; feeding back closed-loop calibration light source wavelength and convolution kernel parameters in real time through coating parameters; according to the method, the precision and robustness of high-reflectivity surface scratch detection are remarkably improved, and meanwhile, the requirements for high-resolution image processing and real-time performance in a high-speed production line are balanced.

The invention relates to the technical field of intelligent detection, in particular to a container surface damage detection method and device based on machine vision, and the device consists of a multi-modal data acquisition module, a dynamic compensation processing module, a multi-modal data fusion module and a damage classification and positioning module. The multi-modal data acquisition module generates a high-density three-dimensional point cloud through double-laser line scanning, and acquires a multispectral image, an infrared thermogram and a real-time motion state. The dynamic compensation processing module integrates an optical flow method and acceleration data to realize sub-pixel-level motion compensation, and combines adaptive exposure control to optimize the imaging quality under complex illumination. The multi-modal data fusion module strengthens defect feature expression through space-time alignment and a double-branch collaborative attention mechanism, and model parameters are reduced through lightweight network design. And the damage classification and positioning module adopts an improved deep network to realize defect classification and complete millimeter-level three-dimensional positioning. The automatic detection requirement of a port is met, and the problems of poor container detection precision, poor adaptability and the like are solved.

The invention relates to the technical field of stamping part detection, and discloses a stamping part size and defect synchronous detection method and system. The method comprises the steps that the multi-sensor measurement module is used for collecting line laser scanning morphology data, infrared thermal image strain data and structured light projection contour data of a stamping part; multi-sensor data registration is achieved through a feature point matching algorithm, and a three-dimensional space coordinate mapping relation is generated; calculating the thermal expansion compensation amount of the material in combination with a thermal deformation correction model; filtering the structured light projection contour data, and extracting key contour feature points and defect region boundaries; inputting the related data into a multi-source data fusion model to obtain a dimensional deviation and defect fusion detection result; based on this, a measurement path is updated through a dynamic path planning algorithm, and a synchronous detection scheme is output. The device can synchronously detect the size and defect of the stamping part, improves the detection precision and efficiency, achieves the quality grade classification, and is high in adaptability.

The invention relates to the technical field of infrared image recognition, and discloses a hot target extraction method and system based on infrared image recognition, and the system comprises a multi-sensor collection module, a data dimension reduction module, a data verification module, a segmentation optimization module, and a dynamic feedback module. Multi-modal data dimension reduction processing is achieved by collecting infrared images, visible light images and environment dynamic parameters and combining Laplacian feature mapping, and a target area is extracted through geometric verification and topological analysis. An energy optimization model and a distribution alignment method are adopted to complete accurate segmentation of a target area, and meanwhile, segmentation model parameters are adjusted in real time based on environment dynamic parameters, so that the system stability is ensured. According to the method, high-precision and robust extraction of the thermal target is realized in a complex dynamic environment, the problems of fuzzy target boundary, noise interference and insufficient dynamic environment adaptability in the prior art are effectively solved, and the method is suitable for the fields of military reconnaissance, fireproof monitoring, electric power inspection and the like.

The invention discloses a bird repelling method and related equipment for a power distribution iron tower, and the method employs a multi-dimensional sensor for cooperative operation, and collects infrared, sound, images and radar reflection signals in real time. Through multi-dimensional data fusion, the appearance, sound wave and behavior characteristics of the bird flock are deeply mined, the bird flock type is accurately identified, and the problem of insufficient detection of a single sensor is solved. Based on the sensitivity difference of different bird flocks to vision, hearing and ultrasonic waves, bird repelling modes such as strong light stroboflash, sound or ultrasonic waves are dynamically matched from the preset bird repelling strategy library, the adaptability of birds to a single bird repelling means is avoided, and the bird repelling success rate is remarkably increased. And a closed-loop optimization mechanism is formed by iteratively optimizing a strategy library matching rule by using bird repelling effect feedback data, so that the system can adapt to the environment and bird behavior changes, and the long-term bird repelling effect is enhanced. Compared with the prior art, line fault risks caused by bird activities are effectively reduced, and safe and reliable operation of a power system is powerfully guaranteed.

The invention provides a bionic polarization semantic SLAM (Simultaneous Localization and Mapping) method based on a neural radiation field. A system comprises three parts, namely a bionic polarization pose estimation module based on factor graph optimization, a dense mapping module based on the neural radiation field and a semantic information positioning module based on bionic polarization infrared rays. The system firstly collects multi-sensor data, establishes a state equation and a measurement equation, and optimizes and estimates the pose by using a factor graph. Secondly, dense mapping is carried out by using the estimated pose and laser radar data and by adopting a neural implicit representation (NeRF-LOAM) model constructed by a laser radar odometer and a map through voxel embedding map generation and grid reconstruction. And finally, processing the polarization infrared fusion image through Yolov8 and SegNeXt, and realizing semantic positioning in combination with laser radar features. According to the system, the anti-interference performance and the environmental adaptability are remarkably enhanced, the mapping training time is shortened, and the system can be applied to multiple fields such as automatic driving, intelligent transportation and intelligent logistics.

The invention relates to the technical field of building structure health monitoring, in particular to a building defect detection method based on infrared thermal imaging and visible light bimodal fusion and a matched intelligent imaging device. According to the method, infrared thermal imaging and visible light bimodal image fusion are combined, and an improved YOLOv8m-seg model is combined, so that accurate detection of the defects of the outer wall of the building is realized. Firstly, a bimodal data set containing multi-material temperature difference data is constructed, ORB feature matching is combined with a feature alignment module (FAM) to achieve accurate image alignment, the defect detection robustness is improved through an improved bimodal feature fusion network, and finally automatic recognition and quantitative analysis of cracks, fractures and other defects are achieved. The problems that single-mode detection is interfered by dirt and is poor in multi-material adaptability are solved, the detection accuracy rate reaches 96% or above, the recall rate exceeds 93%, the single image detection time is shorter than or equal to 0.3 s, and the method is suitable for efficient detection of various building outer wall defects.

The invention relates to an intelligent fish blocking and ship passing system based on multi-mode AI fusion and self-adaptive control, and belongs to the field of water conservancy projects and intelligent control. Aiming at the problems that a traditional electric fish blocking device is low in ship communication efficiency, poor in environmental adaptability and high in maintenance cost, a multi-mode sensor (laser radar, sonar, infrared thermal imaging and the like) fusion technology is adopted, and a reinforcement learning optimization proportion-integral-differential (PID) control parameter and a graph neural network (GNN) path planning algorithm are combined. All-weather high-precision sensing and dynamic gate regulation and control are realized; and equipment faults are predicted through a long short-term memory (LSTM) model, so that the shutdown risk is reduced. According to the system, the ship dredging efficiency is improved by 25%, the accident rate is reduced by 70%, the maintenance cost is reduced by 40%, the system adapts to extreme environments, and intelligent water conservancy construction is supported.

The invention discloses a poultry behavior abnormity real-time monitoring system based on multi-modal image fusion, particularly relates to the technical field of intelligent breeding behavior recognition, and is used for solving the problem of poor behavior monitoring accuracy under feather shielding. The method comprises the following steps: firstly, through combined perception of a visible light image and an infrared image, extracting a claw track interruption point and an anus temperature gradient direction, and realizing analysis of a motion state of a sheltered area; then, in combination with the heat conduction delay characteristic and the group movement direction, the flexion and extension angle of the covered leg joint is inverted, and a complete gait sequence is generated; thirdly, multi-source features such as gaits, temperature differences and body postures are fused, and a dynamic deviation model of the individuals relative to the mass center of the group is constructed; and finally, generating a stress behavior threshold curve according to the ground temperature and the ammonia gas concentration, outputting an abnormal behavior type and confidence, and realizing intelligent distinguishing of mechanical obstacles and adaptive behaviors.

The invention relates to a tunnel lining leakage infrared-millimeter wave fusion detection method and system, and belongs to the technical field of civil engineering detection, and the method comprises the steps: obtaining high-density millimeter wave radar data and thermal infrared imager data; performing data preprocessing to realize space alignment and time synchronization; topological features are extracted, manifold learning mapping is carried out on the preprocessed millimeter wave radar data, and a topological signature matrix is constructed; multi-phase spectrum analysis is carried out, nonlinear phase space reconstruction and multi-scale entropy feature analysis are carried out on the preprocessed infrared thermal imager data, and an entropy feature map is obtained; probability fusion and progressive reasoning are executed, and a leakage area probability graph is generated through a conditional random field model and a progressive belief propagation algorithm; the method overcomes the limitation of a single sensor detection method, achieves the high-precision detection and quantitative evaluation of the leakage of the tunnel lining, and has the technical effects of high detection accuracy, high environmental adaptability, high automation degree and the like.

The invention provides a non-contact multi-mode thermal imager temperature measurement method, and relates to the technical field of non-contact temperature measurement, and the method comprises the steps: collecting the three-dimensional structure information of a measured object, obtaining infrared thermal radiation data, and collecting meteorological parameters; identifying the material and the surface state of the object, and adjusting an infrared emissivity parameter by using a deep learning model; performing cross-modal feature alignment on the related data; and inputting the meteorological parameters into the environment interference model to correct the infrared signal, determining whether the temperature measurement data is qualified, and if yes, repeating until the temperature measurement of the measured area is completed. According to the invention, by fusing the multi-modal data and utilizing the deep learning model, the space-time attention mechanism, the environment interference model and the like, the dynamic calibration of the infrared emissivity, the feature alignment of the multi-modal data and the effective compensation of the environment interference are realized; the problems of large environmental interference, difficult emissivity calibration, insufficient complex scene adaptability and the like of a traditional method are solved, and the temperature measurement precision and reliability are improved.

The invention discloses a single-frame infrared thermal imaging building outer wall hollowing detection method and system based on heat flow evaluation. The method comprises the following steps: acquiring a single-frame infrared thermal image and a visible light image which are synchronously acquired; generating an infrared thermal imaging sequence based on the single-frame infrared thermal image and the Green function of the heat conduction control equation; performing time-space frequency domain transformation and column harmonic function inversion on the infrared thermal imaging sequence, and inversely transforming back to a time-space domain to generate a virtual heat flow image sequence; sequentially carrying out binarization processing and discrimination function enhancement processing on the single-frame virtual heat flow image to obtain an outer wall enhanced binarization image; identifying an interferent from the visible light image by adopting a target detection network; and carrying out registration and mapping on an identification result in the visible light image and the infrared thermal image, and removing interferents in the outer wall enhanced binarization image to obtain a pure outer wall binarization image. The method has the advantages of high environmental adaptability, high recognition precision and the like, and is suitable for safety detection and maintenance of building exterior walls.

The invention relates to the technical field of nondestructive testing, and particularly discloses a detection and positioning method and system for foaming defects of a sandwich foaming board, and the detection and positioning method comprises the following steps: S1, equipment preparation and environment control; s2, data acquisition: comprehensively scanning the sandwich panel by using an infrared thermal imager, and recording temperature distribution data; s3, performing normalization processing on the infrared thermal imaging and electromagnetic induction data, extracting key features such as temperature gradient from the infrared thermal imaging data, and extracting key features such as electromagnetic induction intensity change from the electromagnetic induction data; s4, establishing a sandwich foam board defect identification and classification model, and training a neural network model by using the data of the sandwich foam board marked with defects; and S5, inputting the preprocessed data into the trained sandwich foam board defect identification and classification model. The advanced thermal infrared imager is combined with the electromagnetic induction technology, and the deep learning model is used for intelligent identification and classification, so that the detection precision is improved, and good adaptability and real-time performance are achieved.

The invention provides a smart home intrusion accurate identification and early warning method based on multi-sensor fusion, and relates to the field of intrusion early warning, and the method comprises the steps: firstly obtaining the time sequence distribution of a home switch state and the time sequence distribution of an infrared radiation value, and carrying out the time sequence coding and interaction analysis, so as to obtain a switch-moving state time sequence interaction feature; and then a time sequence of a monitoring image frame is acquired to carry out movement behavior global analysis, and movement behavior analysis is carried out in combination with switch-movement state time sequence interaction characteristics, so that the confidence level is evaluated, and a three-level response mechanism of high-confidence intrusion immediate alarm, medium-confidence artificial review and low-confidence log recording is realized. Thus, the misjudgment problem of a traditional method is solved, the accuracy and environmental adaptability of intrusion recognition are remarkably improved, meanwhile, the user experience is optimized through hierarchical response, and security real-time performance and operation friendliness are both considered.

The invention provides a power adapter appearance quality detection method and system based on machine vision, and particularly relates to the technical field of power adapter appearance quality detection.The method comprises the steps that a main control module controls a light source module to output an illumination condition matched with a surface material of a power adapter, and an industrial camera is triggered to collect a surface image; preprocessing the image to generate preprocessed image data; inputting the preprocessed image data into a defect identification model to extract defect features and generate a defect classification result; if the classification result contains the reflective interference mark, obtaining material information through an infrared sensor, adjusting light source parameters, and re-collecting and processing the image; otherwise, generating a quality detection signal according to a classification result, and transmitting the quality detection signal to a classification execution mechanism to separate the defective power adapter. According to the method, self-adaptive high-precision detection on a low-cost embedded hardware platform is realized, and the efficiency and the adaptability are improved.

The invention relates to the technical field of perimeter intrusion monitoring, in particular to a perimeter intrusion monitoring system based on cooperation of infrared thermal imaging and an intelligent algorithm. The perimeter intrusion monitoring system based on infrared thermal imaging and intelligent algorithm cooperation comprises a multi-source sensing layer, an edge computing node, a cloud analysis platform and a response execution layer. According to the perimeter intrusion monitoring system based on cooperation of infrared thermal imaging and an intelligent algorithm, environmental noise interference is effectively suppressed through a dynamic threshold compensation mechanism, and accurate classification of personnel, vehicles, unmanned aerial vehicles and animals is realized by combining multi-modal feature fusion of infrared thermal imaging, visible light vision and millimeter wave radar; according to the method, intention modes of passing, observation, invasion and the like can be identified, threat level dynamic adaptation is realized in combination with a hierarchical response system, the technical bottlenecks of poor environmental adaptability, high target misjudgment rate, behavior analysis deficiency and the like of a traditional system are solved, and the intelligent level and all-weather protection capability of perimeter security and protection are remarkably improved.

The invention relates to a deep learning method for fire risk monitoring and early warning, and belongs to the technical field of fire safety. Through integration of various sensors (such as an infrared imager, a smoke detector and a temperature sensor), the method realizes omnibearing perception of a monitoring area, and significantly improves the accuracy and reliability of fire monitoring. A convolutional neural network (CNN) and a long and short term memory network (LSTM) are adopted, so that the system can effectively reduce false alarm and missing alarm, monitors key parts in real time, collects multiple parameters such as temperature and voltage, and dynamically evaluates the fire risk through intelligent analysis. In addition, an efficient communication link between the early warning system and other safety systems is established, and it is ensured that emergency measures can be taken quickly when the fire risk is detected. The system also has the functions of target tracking and behavior recognition so as to judge the authenticity and severity of the fire. Through modular design and environmental adaptability optimization, the system still operates stably in various complex environments.

The invention discloses an unmanned aerial vehicle and bird target identification method and system based on multiple modes, and belongs to the technical field of computer vision and target identification. The method comprises the steps of collecting an RGB image, an infrared image and a continuous frame image of a monitoring area, extracting visual features, thermal radiation features and motion features after preprocessing, fusing multi-modal features by adopting an adaptive weight fusion strategy, detecting a potential target through a multi-scale feature fusion detection head, and obtaining a target detection result. The multi-mode classification module judges the target category and triggers early warning; the system comprises a data acquisition module, a preprocessing module, a feature extraction and identification module, a decision and early warning module and a database management module. Through multi-modal fusion, a self-adaptive weight strategy, multi-scale detection and multi-branch classification, the method can effectively overcome the limitation of a single modal, improves the recognition accuracy, the small target detection capability and the environmental adaptability in a complex environment, and meets the requirements of real-time recognition and early warning.

The invention relates to an RGB-T saliency target detection method based on a query guide specific learning network, and the method comprises the steps: constructing a network model of an encoder-decoder architecture, and setting a visible light image saliency detection module, an infrared image saliency detection module and a bimodal saliency detection module according to the network model, visual features of the visible light RGB image are obtained through an encoder of the visible light image saliency detection module, and infrared features of the infrared thermal imaging image are obtained through the infrared image saliency detection module; in a bimodal information fusion module in the bimodal saliency detection module, generating an RGB modal feature, an infrared modal feature and a cross-modal fusion feature; and the three-feature cross block outputs the enhanced multi-modal fusion features. Compared with the prior art, the method has the advantages of high accuracy, high modal adaptability, high efficiency and the like.

The invention relates to the technical field of target detection, in particular to a target detection method, device and equipment based on multi-sensor fusion and a storage medium, and the method comprises the steps: tracking a target in real time through a millimeter wave radar, a photosensitive sensor and an infrared sensor, and judging the state of the target. According to the invention, the method can achieve the recognition of environment interference through the fusion of the three types of sensor data, has more intelligent environment adaptability, has excellent adaptability in different weathers and different scenes, can obtain more comprehensive target information through the mutual complementation of the three types of sensor data, and improves the detection accuracy and reliability.

The invention relates to a camouflage design method and system based on an adversarial sample, and aims to solve the problem that a current camouflage pattern is difficult to adapt to a multi-terrain background camouflage demand, and the method comprises the steps: constructing a training data set; carrying out adversarial camouflage texture initialization by adopting a multi-scale adaptive disturbance weighted Voronoi graph; constructing a camouflage texture generation model based on the confrontation camouflage texture initial value; generating a camouflage target image in the physical scene based on the target mask image in combination with the original image and the camouflage texture output by the camouflage texture generation model, calculating an adversarial loss function through the adversarial image, and obtaining the camouflage color with the optimal effect by iteratively training the camouflage texture generation model; the method can effectively interfere with the recognition capability of the AI detection model, is suitable for the stealth technology, privacy protection, security and other scenes, and remarkably improves the efficiency and adaptability of the camouflage technology.

The invention discloses a power transformation equipment infrared image analysis and diagnosis method and related equipment, and the method achieves the precise analysis and intelligent diagnosis of the operation state of power transformation equipment through the steps of multi-modal data collection, data preprocessing, equipment target recognition, temperature anomaly detection, intelligent fault diagnosis and equipment health trend prediction. And by adopting the improved target detection network, the identification precision of the equipment target is improved, the segmentation effect of key parts of the equipment is optimized in combination with multi-modal data, and the false detection rate in a complex environment is reduced. Based on a dynamic hot area enhancement algorithm and a temperature gradient field analysis method, an abnormal temperature area is effectively extracted, and the accuracy of abnormal temperature detection is improved in combination with an environment compensation mechanism. The equipment state is intelligently analyzed through a deep learning model, fault types such as poor contact, insulation aging, overload heating and cooling abnormity are accurately classified, dependence on a fixed temperature threshold value is reduced, and diagnosis adaptability is enhanced.

The invention discloses a multi-modal data fusion-based DC insulator degradation diagnosis method and system. The method comprises the following steps of obtaining infrared image data and current data of a to-be-detected insulator string; constructing an insulator chain model according to the infrared image data, and obtaining the temperature of each insulator according to the insulator chain model and the infrared image data; obtaining the theoretical temperature of each insulator according to the current data; and according to the theoretical temperature of the insulator and the temperature of the insulator, degradation identification of the insulator is realized. Obtaining historical maintenance data of the insulator, constructing a self-adaptive threshold according to the historical maintenance data of the insulator, and performing secondary judgment on a degradation identification result through the self-adaptive threshold; according to the method, the problems of diagnosis one-sidedness, poor environmental adaptability, low degradation positioning precision and the like caused by dependence on a single data source in a traditional method are effectively solved, and the accuracy, robustness and engineering practicability of direct current insulator degradation diagnosis are remarkably improved.

The invention relates to the technical field of radar data processing, in particular to a method which is suitable for a multi-external radiation source and multi-station system, carries out single-frame and two-frame multi-target measurement value matching under the condition of no bistatic distance information, has good adaptability to the multi-target positioning problem of external radiation source systems of different systems, and is high in positioning accuracy. The bistatic joint multi-target positioning method based on the external radiation source system is beneficial to the processes of multi-target positioning, real-time tracking, track generation and the like, and comprises the following steps: matching multi-source and multi-station angle measurement values to obtain candidate association, and estimating the intersection point position and the speed state of the angle measurement values; approximately constructing a linear equation of bistatic speed and angle measurement value errors, establishing measurement value error estimation of candidate associations and a real target, and removing wrong candidate associations by using an association hypothesis decision; and establishing a cost function according to the measured value error estimation of the candidate association between the two frames and the state mahalanobis distance, and removing the error candidate association between the two frames by using an association hypothesis decision.

The invention provides an embedded intelligent switch control system based on dual-mode detection, and relates to the technical field of intelligent control, and the system comprises a driving logic program building module which is responsible for configuring an infrared and sound cooperative dual-mode control channel; the main control sensing establishing module is responsible for writing driving logic into a main control chip and is connected with an infrared sensor and a sound sensor; the signal judgment module is responsible for driving the sensor to detect and judge the signal in a staggered manner and generating a switching instruction; and the switch state switching module is responsible for controlling the relay to switch the switch state according to the instruction. The technical problems that in the prior art, a traditional switch control system is high in false triggering rate, high in energy consumption and poor in scene adaptability due to a single triggering mode in a complex environment are solved, and the purposes that the false triggering rate and the energy consumption in the complex environment are reduced by judging a time zone and a flexible calibration mechanism through combination of infrared and sound dual-mode detection are achieved; and the scene adaptability is improved.

The invention provides a system device for intelligently sorting coal gangue. The system device comprises a feeding unit, a machine vision recognition unit, a main control unit and a sorting execution unit which are connected in sequence, the feeding unit comprises a vibrating feeder and a refining and shaping mechanism; the machine vision identification unit comprises a high-speed industrial camera and a near infrared spectrum camera; a coal gangue recognition model based on deep learning is arranged in the main control unit; the sorting execution unit comprises a plurality of high-pressure electromagnetic air valve nozzles which are evenly distributed in the transverse direction of the conveying belt. The vibration feeding mechanism and the material uniformizing and shaping mechanism are combined, so that single-layer uniform distribution of materials is ensured, and overlapping and shielding of the materials are effectively avoided; machine vision and near infrared spectrum multi-modal information fusion is adopted, and a deep learning algorithm is combined, so that the accuracy and adaptability of coal gangue recognition are remarkably improved; and through the time delay compensation and accurate positioning technology, it is ensured that the sorting execution mechanism accurately hits the target, and the sorting efficiency is improved.

The invention discloses an Internet of Vehicles safety protection method based on self-learning multi-mode perception, which is characterized in that a unified safety perception model is generated by fusing various data sources from a vehicle-mounted sensor, a visual sensor, a radar, a LiDAR and the like, the comprehensive perception capability of a vehicle environment is enhanced, and the safety of the vehicle environment is improved through machine learning and a self-learning mechanism. The threat detection strategy is continuously optimized, and the recognition precision and the response speed of unknown threats are improved. By making real-time safety protection decisions according to threat types and severity and triggering corresponding emergency response measures, the system can effectively prevent different levels of safety threats, the safety and stability of the Internet of Vehicles system are ensured, and the system has high adaptability and real-time performance.

The invention relates to the technical field of intelligent inspection of power equipment, and particularly discloses a power transmission line inspection system based on an autonomous correction long-endurance unmanned airship. The system aims to solve the problems of short endurance, small operation radius, single image angle, intermittent operation, leak detection, low precision, poor environmental adaptability and the like caused by dependence on manpower or GPS navigation in the conventional unmanned aerial vehicle inspection. The system comprises an energy supply module, a data acquisition module, an intelligent control module and a communication and data return module. Wherein the energy supply module is integrated with a solar energy collection, storage and intelligent scheduling unit to realize day-and-night continuous energy supply; the intelligent control module adopts an RT-DETR visual algorithm to identify a tower in real time, performs automatic deviation correction by fusing pose information, and accurately tracks a preset track; the data acquisition module supports multi-angle high-definition imaging, and the communication module guarantees real-time data return. And high-precision unmanned inspection with ultra-long endurance, full-automatic and wide-area continuous coverage is integrally realized.

The invention relates to the technical field of safety performance testing, and discloses an anti-falling waistcoat protection performance testing method and system. According to the method, a bionic human body model is driven through a multi-degree-of-freedom impact simulation device, a standard test dummy wearing an anti-falling waistcoat is impacted in multiple preset postures including the combination of a pitch angle and a roll angle, and a real falling scene is simulated; a flexible piezoresistive sensor array embedded into the inner layer of the waistcoat is used for collecting dynamic pressure distribution data in real time, and an air bag unfolding time sequence image is synchronously recorded; calculating a pressure dispersion rate based on the pressure data, determining protection response delay in combination with image analysis, and generating an attitude adaptability evaluation index through a multi-attitude test result; and finally, executing a continuous inflation and deflation cycle test to evaluate the sealing performance and the sensor stability. According to the method, the dynamic protection performance, the airbag response efficiency and the long-term use reliability of the anti-falling waistcoat in a complex impact posture can be comprehensively detected, and the consistency of a test result and an actual protection effect is improved.