933 results about "Thermographic camera" patented technology

The invention relates to the technical field of rail traffic safety detection, in particular to a railway freight train key part online monitoring system based on an unmanned aerial vehicle, which comprises an unmanned aerial vehicle control module, a multi-modal data acquisition module, an edge calculation module, a central processing module and a feedback execution module. A dynamic three-dimensional grid flight path is generated by fusing a train Beidou positioning signal and a millimeter wave radar sensing result, a system is provided with an infrared thermal imager, a laser radar and a high-speed polarization camera, bearing temperature, train body point cloud and a train coupler image sequence are obtained, and temperature rise area identification, structural deformation extraction and coupling state modeling are completed on the edge side. The central processing module outputs a multi-dimensional safety assessment result, and the feedback module generates a compensation control instruction and a graded early warning signal based on the risk fusion index. The method has the advantages of being high in autonomy degree, high in recognition precision and high in response speed, and is suitable for full-time structural intelligent inspection and early warning of the freight train in a high-speed operation environment.

The invention discloses a multi-modal visual fusion complex scene small target detection tracking method and system, and relates to the technical field of unmanned aerial vehicle target tracking, and the method comprises the steps: employing a visible light camera, an infrared thermal imager and a laser radar sensor which are carried on an unmanned aerial vehicle platform, and synchronously collecting RGB images, thermal infrared images and point cloud data; the consistency of the multi-modal data is ensured through data preprocessing and space-time alignment; constructing a lightweight double-branch network to extract multi-scale features, generating a fusion feature map by adopting adaptive weighted fusion, and generating depth information by utilizing point cloud to assist in scale estimation; a small target detection head is designed based on the fusion feature map, and precise detection is realized in combination with a feature pyramid network, adaptive scale prediction and a context awareness suppression mechanism; furthermore, through multi-mode cooperative tracking, including target association, spatio-temporal context modeling, trajectory prediction and a re-detection mechanism, tracking continuity is ensured.

The invention discloses a long downhill section truck brake pad high temperature detection and cooling method and system, and the method comprises the steps: collecting the surface temperature data of a truck brake pad in real time according to high-precision infrared temperature sensor arrays and thermal imagers disposed at the two sides of a long downhill section, and generating a time-space correlation temperature field distribution diagram; based on the temperature field distribution diagram, dynamically adjusting a temperature safety threshold curve through reinforcement learning, and outputting a dynamic early warning trigger signal; according to the dynamic early warning trigger signal, a corresponding spraying instruction is generated so as to drive a piezoelectric ceramic microvalve array of a roadside spraying device to conduct spraying cooling on the brake pad; and according to the temperature attenuation rate and infrared thermal image feedback data of the brake pad after spraying, the temperature safety threshold curve and the spraying instruction are corrected through a Bayesian optimization algorithm, and closed-loop safety regulation and control are formed. According to the embodiment of the invention, the temperature of the brake pad can be monitored in real time, accurate spraying cooling control is realized, and safe and stable braking performance of a truck is ensured.

The invention discloses a three-dimensional panoramic intelligent monitoring method and device for a coal conveying gallery, and relates to the technical field of industrial automation and intelligent monitoring, and the method comprises the steps: obtaining initial point cloud data and an initial multispectral image of the coal conveying gallery through a rotary laser radar, a high-dynamic-range camera and an infrared thermal imager; inertial measurement units are arranged in the devices to collect vibration data, and the vibration data are used for carrying out vibration compensation on point cloud and images to generate target point cloud data and target multispectral images; thirdly, performing three-dimensional modeling and multispectral texture mapping on the coal conveying gallery based on the data to obtain a target three-dimensional model; and finally, mapping equipment, personnel and environment data into the three-dimensional model in real time, displaying each piece of state information, and realizing comprehensive monitoring. According to the application, high-precision three-dimensional panoramic monitoring can be realized in a complex vibration environment of the coal conveying gallery, and the safety management level and the operation and maintenance efficiency of the coal conveying gallery are effectively improved.

The invention relates to the technical field of image detection, in particular to a visible light and infrared image combined photovoltaic defect detection method based on an unmanned aerial vehicle, which comprises the following steps of: determining a photovoltaic power station detection area, carrying out synchronous aerial photography by using a visible light camera carried by the unmanned aerial vehicle and an infrared thermal imager, and carrying out local division to extract texture and temperature characteristics; the screening area performs fitting affine parameter generation on an extraction center point, corrects an infrared image to extract contour lines and texture change features, and screens a defect area to extract a positioning coordinate set; according to the method, through synchronously screening generated visible light and infrared image pairs, the resolution of an abnormal region is enhanced, a complex background and an abnormal target are accurately separated, fine-grained adaptive registration is realized through central point extraction and affine parameter derivation, and defect region characteristics are verified bidirectionally through a temperature contour closing proportion and a texture density variable quantity; thermal features and texture features are fused in the defect screening process, the recognition capability of weak anomalies is enhanced, and the defect positioning accuracy is improved.

The invention provides an automatic intelligent defect analysis method and system based on power transmission and distribution line unmanned aerial vehicle image acquisition. The method comprises the following steps: acquiring infrared thermal imaging time sequence data and multispectral imaging data of the surface of a wire through an unmanned aerial vehicle carrying an infrared thermal imager and a multispectral camera; calculating a temperature gradient direction of a conductor main body area, screening an abnormal hot spot area whose axial included angle with the conductor is smaller than a preset angle, and positioning an oxidation area boundary by using a spectral absorption attenuation curve of an oxidation layer in the multispectral data; performing space matching on the hot spot migration direction and the oxidation boundary to generate a visual map; establishing a dynamic association rule according to an included angle variable quantity of a hot spot path in the map and a spectrum abrupt change point in the oxidation region; according to the critical angle threshold value and the sudden change point number threshold value, the defect grade is judged, and a defect analysis report is generated. According to the technical scheme provided by the invention, the automation degree and accuracy of defect detection are improved.

The invention provides an intelligent inspection method and system for fan blades based on an unmanned aerial vehicle. The method comprises the steps that a visible light camera, an infrared thermal imager and a laser radar are integrated on the unmanned aerial vehicle; generating a flight route covering the whole surface based on the fan blade three-dimensional model; when the unmanned aerial vehicle is controlled to fly along the flight route, visible light images, infrared temperature data and laser point clouds are synchronously collected; the visible light image, the infrared temperature data and the laser point cloud are input into a defect analysis model, the defect type and grade are identified through conjoint analysis, a temperature abnormal area is positioned, and geometric deformation is quantified; a health assessment report is generated, the health assessment report comprises a defect positioning map and a graded maintenance decision matched with defect grades, multi-source data are collected through integration of a visible light camera, an infrared thermal imager and a laser radar, and then fusion analysis and recognition are carried out, so that compared with a manual inspection mode, the intelligent level of fan blade inspection is improved, and the inspection efficiency is improved. And the inspection efficiency of the fan blade is also improved.

The invention relates to the technical field of building construction monitoring, in particular to a construction site real-time monitoring system and method based on an unmanned aerial vehicle technology. The method specifically comprises the steps that an unmanned aerial vehicle carries a high-definition camera, an infrared thermal imager and a laser radar and collects multi-modal data of a construction site in real time; a deep learning algorithm is adopted to carry out multi-modal semantic segmentation, RGB images, infrared thermal imaging and laser point cloud projection data are fused, YOLOv8 target detection and time sequence behavior modeling technologies are combined, and abnormal risks are dynamically identified; by constructing a context-aware comprehensive risk scoring model, the risk level of a construction scene is quantified, and a graded early warning mechanism is triggered; and meanwhile, based on a three-dimensional modeling technology, tank vertex cloud data is analyzed by utilizing an LIO-SLAM algorithm, and the inclination degree of the structure is detected and is linked with emergency response. According to the invention, through multi-sensor fusion, dynamic risk assessment and closed-loop control, intelligentization and precision of construction monitoring are realized.

The invention discloses a multi-modal thermal imager intelligent monitoring method based on image processing. The method comprises the following steps: S1, performing dual-band synchronous imaging; s2, carrying out cross-modal image registration; s3, multispectral image fusion: performing smoothing processing on the infrared and visible light images through anisotropic diffusion to obtain a main structure layer image, calculating to obtain an infrared and visible light detail layer image, and linearly combining the detail layer image through PCA principal component weight, and finally, combining the detail layer image and the main structure layer image according to the self-defined weight to obtain a final fusion image. And S4, gas leakage detection is carried out, so that gas leakage detection and early warning can be realized. According to the invention, a gas enhancement algorithm is adopted to realize real-time detection of leaked gas, interference of most background noise can be eliminated, and the real-time performance and reliability of the system are improved.

The invention discloses a ship plane segmented welding multi-robot path planning system based on visual inspection, and relates to the technical field of ship manufacturing, the ship plane segmented welding multi-robot path planning system comprises a multispectral visual inspection module which is mounted on a portal frame and comprises a laser scanning sensor and an infrared thermal imager, the system is used for collecting three-dimensional point cloud and temperature distribution data of ship plane segments in real time. According to the ship plane segmented welding multi-robot path planning system based on visual inspection, the problem of path planning misalignment caused by assembly errors, thermal deformation and complex working conditions in large ship plane segmented welding is fundamentally solved through a multispectral visual real-time perception and material self-adaption dynamic correction mechanism. A composite correction vector and space-time grid collaborative pre-judgment technology is adopted, the welding seam track precision is effectively improved, it is ensured that the weld leg size strictly reaches the standard in the high-difficulty processes such as wrap angle welding and vertical position welding of the maximum-size workpiece, the defects such as air holes and cracks are eliminated, and the rework rate and the quality risk are greatly reduced.

The invention relates to the technical field of substation equipment fault diagnosis, in particular to a substation equipment thermal fault diagnosis method and system based on an infrared image. The invention discloses a substation equipment thermal fault diagnosis method based on an infrared image. The method comprises the following steps: acquiring an equipment temperature distribution image through an infrared thermal imager; carrying out denoising, contrast enhancement and normalization preprocessing on the image; extracting features such as temperature anomaly, temperature gradient and hot spot areas; a YOLOv13 model is adopted to identify the equipment type; inputting the features into a deep learning model for fault classification; and implementing multi-level alarm according to the classification result confidence. According to the method, temperature gradient analysis and a regional dynamic contrast enhancement technology are creatively fused, so that the fault detection precision and the early warning capability are remarkably improved, and intelligent diagnosis and graded early warning of the thermal fault of the substation equipment are realized.

The invention relates to the technical field of unmanned aerial vehicles, and particularly discloses a high-precision small unmanned aerial vehicle real-time tracking method which comprises the steps that a multi-modal sensor array is constructed through a millimeter wave radar, a binocular vision module and an infrared thermal imager, and the multi-modal sensor array is used for collecting three-dimensional motion data of a target object in real time; constructing a fusion tracking model based on improved Kalman filtering by using the collected three-dimensional motion data, dynamically distributing the weight of a multi-source sensor through a convolution attention mechanism, and compensating a motion trail prediction error by using an adaptive noise covariance matrix; through cooperative work of the millimeter wave radar, the binocular vision module and the infrared thermal imager, challenges such as illumination change and target shielding can be effectively handled, three-dimensional motion data of a target object are collected in real time, and the target positioning and tracking precision is improved. Meanwhile, the improved Kalman filtering algorithm is combined with a convolution attention mechanism, so that the weight of the sensor can be dynamically adjusted, and a motion track prediction error is compensated.

The invention relates to a shipbuilding safety monitoring system and a monitoring method thereof, and belongs to the technical field of shipbuilding monitoring, and the system comprises a multi-modal data collection module, an edge computing node, a digital twin platform, a digital twin platform, an analysis module, a block chain traceability module, and a feedback control module. The multi-modal data acquisition module comprises a visual sensor, an acoustic sensor, a thermal imager, a strain sensor, a laser displacement sensor and an environment sensor. The visual sensor, the acoustic sensor, the thermal imager, the strain sensor and the environment sensor are installed in a shipbuilding workshop. According to the invention, by fusing visual, acoustics, thermodynamics and other multi-sensor data and combining edge calculation real-time processing and digital twinning dynamic simulation, process quality prediction, defect detection and carbon emission tracking are realized, and the problems of insufficient real-time performance, difficulty in cross-system collaboration and lack of green manufacturing monitoring in the prior art are solved.

The invention relates to the technical field of intelligent warehousing monitoring, and discloses an intelligent warehousing monitoring system based on digital twinning, which comprises a data acquisition layer, a digital twinning modeling layer, an intelligent analysis layer and an execution control layer, and is characterized in that three-dimensional coordinates and temperature distribution data of goods are acquired through a laser radar array and an infrared thermal imager; constructing a dynamic point cloud data set in combination with RFID positioning; an improved YOLOv7 model is adopted to be embedded into a CBAM attention mechanism to recognize the cargo form risk, and an LSTM time sequence model is fused to predict a temperature anomaly trajectory; an AGV obstacle avoidance path is optimized based on a genetic-ant colony hybrid algorithm, and a temperature control system and a security device are driven to perform linkage execution through an OPC UA protocol. And finally, millisecond-level synchronous mapping of the physical warehouse and the digital twinborn body, real-time deviation correction regulation and control of an equipment running track and cross-system collaborative response of an emergency strategy are realized, and the safety early warning accuracy and dynamic protection robustness of the storage environment are improved.

The invention relates to the technical field of new energy aluminum alloy precision casting multi-modal quality monitoring, in particular to a new energy aluminum alloy precision casting multi-modal quality monitoring device and method. According to the technical scheme, the new energy aluminum alloy precision casting multi-mode quality monitoring device comprises a quality monitoring device body, an infrared thermal imager probe, a germanium single crystal protection lens, an integrated spectrometer probe debugging port, an acoustic detection microphone array and a honeycomb acoustic shielding cover. An infrared thermal imager probe is arranged on one side of the quality monitoring device main body, and a germanium single crystal protection lens is arranged on the outer side of the infrared thermal imager probe; synchronous acquisition and fusion analysis of multi-physical field data are realized by integrating six modes of spectrum, infrared thermal imaging, vibration, machine vision, acoustics and pressure deformation, and meanwhile, when the shrinkage risk is predicted by the infrared thermal imaging, a die-casting process compensation mechanism is automatically triggered to kill defects in the germination stage, so that the defect rate is greatly reduced.

The invention provides an intelligent detection method and system for the tensile property of a plastic cable tie. Gradient temperature rise control is executed through the carbon fiber heating tube array, microscopic deformation fluctuation characteristics are captured through the double-grating displacement sensor, and temperature field distribution is collected through the infrared thermal imager. Establishing dynamic correlation between deformation and a temperature field to generate a three-dimensional space-time atlas, identifying a migration path of a thermal aggregation area, recording a standard path, and calculating a thermal anomaly coefficient in combination with pressure data; analyzing temperature conduction hysteresis to generate a heat conduction parameter, and dynamically correcting a threshold value by cross-modal correlation with an abnormal coefficient; a structure degradation index is generated based on the migration path, mapping between the structure degradation index and a temperature field is established to generate a thermal damage compensation coefficient, an abnormal coefficient is calibrated accordingly, and grading early warning is triggered in combination with the path deviation degree. According to the technical scheme provided by the invention, multi-scale damage evaluation from thermodynamic associated data acquisition to molecular chain fracture inversion is realized, and the high-temperature tensile failure critical point is accurately pre-judged through a dynamic calibration early warning mechanism.

The invention relates to the technical field of industrial detection, and discloses a multi-modal data fusion industrial defect intelligent detection system, which comprises a multi-modal data acquisition module which comprises a high-resolution visible light camera, an infrared thermal imager and an ultrasonic sensor and is used for acquiring product images and data information from different dimensions, the devices are reasonably arranged on a production line to realize comprehensive acquisition; the preprocessing module is used for denoising and normalizing the collected data; the limitation of single-mode detection is overcome through the multi-mode data acquisition module, and omnibearing detection is realized; an advanced fusion strategy is adopted, and feature interference and insufficient information interaction are avoided; the environment self-adaptive adjustment module enables the system to stably work in a complex environment; the parallel computing module improves the data processing efficiency and meets the real-time detection requirement; the encryption module ensures data security, the modular design facilitates maintenance and upgrading, and the data backup and recovery module ensures production and data integrity.

The invention discloses a virtual fire extinguishing training system and method based on deep learning flame recognition. The virtual fire extinguishing training system comprises a flame image acquisition module, a deep learning flame recognition module, a fire scene generation module, a fire extinguishing equipment simulation module and a scene customization module. And the flame image acquisition module is used for acquiring flame images and video data in different environments in real time through a high-definition camera, an infrared thermal imager, a panoramic camera and a sensor network. According to the invention, feature extraction and model training are carried out on mass flame images by using a deep learning flame recognition algorithm, accurate recognition of multi-dimensional information such as flame form, color, dynamic features and the like is realized, the flame detection capability in a complex environment is remarkably improved, and meanwhile, the system combines a physical engine and a scene generation module, so that the system is more accurate and reliable. The three-dimensional fire scene can be dynamically generated according to the real-time recognition result, the effects of fire spreading, smoke diffusion and the like are achieved, the constraint of a preset scene is eliminated, and training is more flexible and authentic.

The invention discloses a gas pipeline leakage detection method based on an unmanned aerial vehicle, and the method comprises the following steps: S1, controlling the unmanned aerial vehicle to fly along a preset pipeline inspection path, and synchronously collecting the following data: S2, carrying out the real-time fusion processing of the data collected in S1, and generating a leakage probability score; s3, when the leakage probability score exceeds a preset threshold value, controlling the unmanned aerial vehicle to hover and marking a leakage point coordinate; and S4, generating an evaluation report containing the leakage aperture, the risk level and the environmental parameters, and encrypting and transmitting the evaluation report to the emergency terminal through the hybrid network. The unmanned aerial vehicle is adopted to carry the laser methane sensor, the multispectral thermal imager and the directional microphone array to detect whether the gas pipeline leaks or not, so that the labor intensity of workers is reduced, and meanwhile, the large-scale pipe network detection time can be shortened by adopting multi-machine cooperative task allocation, so that the detection efficiency is improved.

The invention relates to the technical field of data processing, and discloses a high-temperature early warning method and system for a welding device. The method comprises the following steps: carrying out multi-point temperature measurement by using a K-type thermocouple and an infrared thermal imager; fourier heat conduction calculation is conducted on the welding heat field; extracting temperature anomaly region features through thermal gradient analysis; carrying out multi-parameter risk assessment by combining material melting point parameters; a multi-channel alarm instruction is output through the industrial control bus; and current-voltage curve real-time adjustment is conducted on the welding power source, and closed-loop self-adaptive control is achieved. According to the method, a complete closed-loop system from multi-sensor temperature collection, thermal field analysis, abnormal feature extraction and risk assessment to multi-stage alarm and self-adaptive control is established, so that early recognition and accurate control of thermal abnormality in the welding process are realized, and the welding quality and the production safety are remarkably improved.

The invention relates to the technical field of massage, in particular to an intelligent abdominal acupoint massage system and method based on multi-modal sensing, and the system comprises a multi-modal sensing module which comprises a 3D depth camera, an infrared thermal imager, a myoelectricity sensor and a millimeter wave radar; the intelligent decision-making module is used for updating a massage strategy in real time by applying a Poisson reconstruction algorithm, a multi-modal fusion positioning algorithm and a dynamic coordinate mapping method; the execution device comprises an end effector and is composed of a bionic massage head for executing massage, a six-dimensional force sensor for feeding back a pressure value in real time and an infrared temperature control module; the three-degree-of-freedom mechanical arm is composed of xyz translation compensation and a rotating joint and is used for controlling the end effector to move; the power module is used for supplying power to the system. The device has the advantages that accurate acupoint tracking and positioning can be provided for a user, safe, controllable, autonomously adjustable massage force is provided, and different massage schemes are set according to individuation of the physique of the user.

The invention provides an unmanned aerial vehicle-based high-rise hyperbolic glass curtain wall automatic inspection method and system, and the system comprises an unmanned aerial vehicle, a data acquisition module and a controller. The data acquisition module is carried on the unmanned aerial vehicle and comprises an ultrasonic sensor array, an infrared thermal imager and an image acquisition module; the controller is used for performing time-frequency domain analysis on the reflected wave to obtain the signal intensity of the reflected wave; the controller performs temperature gradient calculation on the structural adhesive surface temperature distribution image to obtain temperature anomaly region features, obtains optical image textures according to the surface optical image, and outputs an inspection result according to the reflected wave signal intensity, the temperature anomaly region features and the optical image textures; generating an unmanned aerial vehicle flight path according to the three-dimensional model and the curvature parameter of the hyperbolic glass curtain wall, generating obstacle avoidance position information according to environment information set by the hyperbolic glass curtain wall, and updating the unmanned aerial vehicle flight path according to the obstacle avoidance position information; and controlling the unmanned aerial vehicle to automatically inspect the hyperbolic glass curtain wall according to the flight path of the unmanned aerial vehicle.

The invention relates to the technical field of health care, and discloses a bath chair self-adaptive adjusting system based on multi-modal sensing. According to the system, a pressure sensor array, an infrared thermal imager and an inertial measurement unit are used for collecting multi-dimensional real-time data, and the multi-modal sensing fusion module is used for processing the multi-dimensional real-time data to generate a structured physiological feature vector. The user intention recognition module predicts user behavior intention and gives a comfort level score on the basis, and the posture self-adaptive adjustment module optimizes seat supporting curved surface parameters according to the comfort level score to achieve real-time adjustment. In addition, the safety monitoring module evaluates the slip risk, the closed-loop control module guarantees safety, the online learning module optimizes the adjustment strategy, and the abnormal response module deals with the emergency situation. According to the system, the comfort and safety of the bath chair are improved, personalized adjustment is achieved, and the system has high practical value.

The invention discloses a vehicle driving early warning and braking control method and system based on visible light and infrared light, and belongs to the field of image processing and vehicle control. The method comprises the following steps: acquiring bimodal data, namely visible light data and infrared data, by using a visible light camera and an infrared thermal imager, and preprocessing the bimodal data; feature fusion is carried out on the preprocessed bimodal data, and a YOLOV5 model is input for training, so that a Yolo-DMFF-object model is obtained; a Yolo-DMFF-object model is used for detecting a target object and measuring the distance; according to the detected target object and distance, the emergency braking module is controlled to give out voice early warning and emergency braking; according to the method, object identification and vehicle early warning braking functions are fused through fusion of identification and distance measurement technologies, function connection from target detection to vehicle control is realized, an efficient and reliable solution is provided for intelligent driving in a complex traffic environment, and driving safety, response speed and driving convenience are improved.

The invention discloses an isomeric low-alloy high-strength steel structure electric arc additive manufacturing process, and relates to the technical field of electric arc additive manufacturing, the isomeric low-alloy high-strength steel structure electric arc additive manufacturing process comprises the following steps: carrying out heat treatment and tensile test on a low-alloy high-strength steel wire to obtain test data, and establishing a process parameter-performance prediction model according to the test data; obtaining a performance demand of a target structural member, and drawing up a process parameter by using a process parameter-performance prediction model according to the performance demand; an electric arc additive manufacturing system is used for conducting electric arc additive manufacturing on the low-alloy high-strength steel wire, and a low-alloy high-strength steel structural part is obtained; acquiring temperature information of the low-alloy high-strength steel structural member by using an infrared thermal imager, and adjusting the process parameters in real time according to the temperature information to obtain a target structural member; gradient distribution of precipitation strengthening phases is achieved, isomerism of alloy element in-situ strengthening low-alloy high-strength steel materials is achieved, and the diversified requirements of target structural parts for different strengths and plasticity are met.

The invention provides a three-dimensional vision and infrared thermal imaging combined calibration method and system and a medium, and the method comprises the steps: carrying out the processing of a calibration plate based on a special infrared response material or an electric heating insert, and obtaining a visible light angular point pattern and a calibration plate of a thermal response structure region; shooting an image of the calibration plate based on a three-dimensional vision camera, and extracting image coordinates; acquiring an infrared image of the calibration plate to obtain a thermal image, and analyzing distribution information of thermal characteristic elements on the calibration plate; establishing a nonlinear mapping relationship between the image coordinates and the distribution information of the thermal characteristic elements; analyzing a projection error based on the nonlinear mapping relation, and performing parameter calibration on the three-dimensional vision camera and the infrared thermal imager based on the projection error; the thermal imager and the three-dimensional vision camera are responded at a time through the bimodal calibration plate, a nonlinear injection relation is established, parameter calibration is accurately carried out on the three-dimensional vision camera and the infrared thermal imager according to projection errors, the calibration precision is improved, and the method is suitable for complex environments such as industry and medical treatment.

The invention relates to the technical field of infrared thermal imaging, and particularly discloses a fire personnel intelligent search and rescue method and device based on infrared thermal imaging, and the method comprises the steps: synchronously collecting fire scene data through an infrared thermal imager and a laser radar, so as to obtain infrared thermal imaging data and fire scene three-dimensional point cloud data; further introducing a data processing technology based on deep learning to carry out feature extraction on the infrared thermal imaging data and the three-dimensional point cloud data of the fire scene so as to capture infrared thermal radiation distribution features and three-dimensional space structure features of the fire scene, and then carrying out fine-grained feature alignment joint perception on the infrared thermal radiation distribution features and the three-dimensional space structure features; therefore, the deep correlation characteristics of the fire scene heat distribution and the three-dimensional space can be captured, and the intelligent detection of the human body target can be realized on the basis. In this way, the problem that heat radiation information and space structure information are separated in a traditional method can be effectively solved, the information utilization rate is increased, and the accuracy and efficiency of human body target recognition are enhanced.

The invention discloses a mine disaster rescue robot based on multi-modal sensing and intelligent recognition and a mine disaster monitoring method, and relates to the technical field of mine disaster rescue robots. The mine disaster rescue robot based on multi-modal sensing and intelligent recognition mainly comprises an image processing and edge calculation unit, a UWB positioning module, an inertial measurement unit, a laser radar, a binocular depth camera, a thermal imager, a sound sensor, a gas sensor, an RGB camera, a motion control unit and a power supply system. According to the mine disaster rescue robot and the mine disaster monitoring method based on the multi-mode perception and intelligent identification, the positioning precision and the autonomous navigation capability of the rescue robot in a complex mine environment can be improved, and the accuracy and the real-time performance of state judgment of trapped workers are improved.

The invention discloses a power grid equipment defect identification system based on a visual large model, and the system comprises the following parts: an image collection module which employs a high-definition industrial camera and an infrared thermal imager to collect an appearance image and a temperature image of power grid equipment; the image preprocessing module is used for preprocessing the appearance image and the temperature image; the defect recognition module is used for recognizing defects, including cracks, corrosion, deformation and discharge traces, on the power grid equipment through a deep learning model and image processing according to the appearance image and the temperature image; and the defect analysis report generation module is used for scoring the defects and generating a defect analysis report according to a scoring result, and the defect analysis report comprises defect types and defect severity. According to the method, more detailed and accurate power grid equipment image information is acquired, so that the accuracy of power grid equipment defect identification is improved.

The invention relates to the field of virtual reality (VR) and medical image processing, in particular to an acupuncture copper human visual frame selection system combined with 3D digital human body modeling. The main components of the scheme comprise 3D modeling software, a multi-level 3D acupuncture copper human model (including skin, meridians and collaterals, acupuncture points and skeleton layers), a J2000 coordinate system, an interactive interface, an infrared thermal imager, a six-axis force sensor, an AI clustering algorithm, a force steering algorithm, a decision tree algorithm, an ICP algorithm, a v-mode l technology, a BERT model and the like, and the components work cooperatively to construct a high-precision 3D model and calibrate coordinates. A frame selection instruction is received through multi-modal interaction, temperature and force data are collected to generate a dynamic matrix, label arrangement and rule generation are carried out, multi-source data are integrated and exported, real-time binding and intelligent recommendation of acupuncture points and models are achieved, a complete acupuncture point visual frame selection system is formed, and the acupuncture point positioning accuracy and the operation intelligent level are improved.