1183 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 provides a smoke and fire detection method and system based on deep reinforcement learning and electronic equipment, and relates to the technical field of fire fighting. The method comprises the following steps: acquiring thermal imaging data of a forest area through a thermal imager, obtaining temperature distribution conditions of different positions, and determining position coordinates, the number and specific temperature values of temperature abnormal points for an area with abnormal temperature rise; if the temperature value of the temperature abnormal point exceeds a preset temperature threshold value, judging that the temperature abnormal point is a suspected fire point, and collecting a visible light video image of the area through a visible light camera according to the position of the suspected fire point; smog detection processing is carried out on the collected visible light video image, whether smog exists in the image is judged by analyzing the color, texture, motion and other characteristics of the image, and the concentration, morphological characteristics and diffusion speed of the smog are obtained; the meteorological parameters such as the wind speed and the wind direction of a fire occurrence area are obtained through the meteorological sensor, the meteorological parameters are input into the fire spreading model, and the spreading direction and the spreading range of the fire under the current wind field condition are simulated.

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 relates to the technical field of unmanned aerial vehicle assistance, and discloses an unmanned aerial vehicle assisted fire scene real-time monitoring and commanding method, which comprises the following steps: acquiring fire disaster situation data of a fire scene in real time through equipment such as a high-definition camera and a red thermal imager of an unmanned aerial vehicle, and outputting the fire disaster situation data to a commanding center; analyzing the fire behavior development direction, the fire source position and the smoke concentration of the fire disaster situation data; evaluating a fire scene scale and a dangerous area according to an analysis result, forming a rescue scheme according to an evaluation result, and commanding firefighters and a fire extinguishing unmanned aerial vehicle based on the rescue scheme; dynamically adjusting a rescue scheme according to the fire disaster situation data of the fire scene collected by the unmanned aerial vehicle in real time; when the fire is extinguished, the blind fire area is continuously monitored, and if abnormity occurs, an alarm is given out immediately; the unmanned aerial vehicle serves as an air base station for communication. According to the invention, instant and comprehensive fire scene information is provided for a command center, and rapid decision making is facilitated.

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 a multi-scene self-adaptive intelligent bottom argon blowing control method and system. The method comprises the steps that after a steel ladle reaches a working position, the air permeability is determined, a ladle setting signal is received, argon blowing is started, an infrared thermal imager is started to monitor a picture to judge the liquid steel level, and the liquid level area of the whole steel ladle is completely filled; pLC (Programmable Logic Controller) of all equipment in the whole refining process is intelligently linked, the bottom blowing stirring power is determined, the bottom blowing gas pressure and the bottom blowing gas flow are calculated, and relevant parameters such as the exposed area and the exposed edge slag layer height of molten steel in a continuous video stream are tracked and calculated in real time according to the metallurgical purpose of different stages, so that the bottom blowing control is further optimized; and a flow data instruction is issued to a bottom blowing PLC to control the pressure and flow of bottom blowing gas. According to the requirements of field production, video images of molten steel in the argon blowing state are automatically and continuously received in a zero-delay mode, the argon flow and pressure are automatically distributed through intelligent bottom blowing control, it is guaranteed that each furnace achieves the good bottom blowing stirring effect, field production is guided, and the production labor intensity is reduced.

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 relates to the technical field of airport safety protection, and discloses an airport bird detecting and repelling integrated system, which comprises a multi-band phased array radar detection module for scanning the peripheral area of an airport in real time to obtain the initial positioning information of a bird target; the photoelectric tracking and monitoring module is used for providing the preliminary positioning information according to the radar detection module, and the invention further discloses an airport bird detecting and repelling method which comprises the following steps: detecting the peripheral area of an airport through the multi-band phased array radar to obtain the preliminary positioning information of birds. According to the invention, by adopting a cooperative detection technology of the multi-band phased array radar, the photoelectric tracking equipment and the infrared thermal imager, real-time monitoring and dynamic tracking of bird targets in an airport area are realized, and the defects of limited detection range and multiple monitoring blind areas in the prior art are overcome; meanwhile, a pulse type laser radar is linked with an FOD detection radar, and a closed-loop mechanism for quickly repelling birds and cleaning foreign matters is established.

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 discloses a wall flatness correction method and system for building construction, and relates to the technical field of building construction quality detection.The method comprises the following specific steps that S100, joint detection preparation is conducted, specifically, a wall is cleaned, a high-precision thermal imager and a flatness measuring tool are prepared, and preparation is made for joint detection; s200, thermal imaging and flatness synchronous detection, wherein a thermal imager is used for conducting comprehensive scanning on the wall face, and meanwhile a flatness measuring tool is used for conducting surface flatness detection; the thermal imaging technology and the flatness detection technology are fused, the problems of internal temperature abnormity and external flatness of the wall surface can be detected at the same time, comprehensive and deep evaluation of the wall surface quality is achieved, the defect that a traditional flatness detection means is insufficient in internal defect detection is overcome, and through data fusion analysis, the detection accuracy of the wall surface is improved. And areas with temperature anomaly and flatness problems can be accurately identified, so that the comprehensiveness and the accuracy of detection can be obviously improved.

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 relates to the technical field of flow batteries, in particular to a flow battery flow field uniformity detection device and a detection method, and the flow battery flow field uniformity detection device comprises a visual flow field assembly, a constant temperature water tank, a circulating pump, a pressure gauge, a thermal imager and a connecting pipeline. The visual flow field assembly, the constant-temperature water tank, the circulating pump and the pressure gauge are connected through connecting pipelines, the shooting range of the thermal imager covers the whole visual flow field area, and the temperature change of the flow field area along with time is recorded. According to the invention, the temperature change rate of the fluid at the outlet is controlled by adjusting the heating power of the water tank, and when the fluid passes through the flow channel of the electrode frame, a relatively large temperature gradient is ensured in the flow channel range of the whole visual flow field assembly. And calculating a temperature deviation value to evaluate the fluid uniformity according to the linear relationship between the temperature difference and the speed difference.

The invention discloses an unmanned aerial vehicle inspection defect image recognition system and method, and relates to the technical field of image recognition, and the system comprises an unmanned aerial vehicle platform, an image collection module, an image processing module, a defect detection and recognition module, and a report generation module. The unmanned aerial vehicle platform comprises an unmanned aerial vehicle body, a high-definition camera, an infrared thermal imager, a cloud processing center, a GPS positioning system and a data transmission module. According to the method, the image collected by the unmanned aerial vehicle is subjected to preprocessing such as denoising, then feature extraction is performed, the influence of factors such as environment and weather on the shot image is effectively eliminated, the defect type of the inspection target is accurately identified through the defect identification model, the identification result is more accurate, the influence on defect identification judgment of the inspection target is avoided, and the inspection efficiency is improved. The efficient, accurate and real-time inspection and defect identification of the target area are realized, the model is optimized and updated regularly, and the accuracy and efficiency of identification can be improved.

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 discloses a method, a system and a medium for detecting unsafe behaviors of personnel on a driving working face, and relates to the field of image data processing.The method comprises the steps that thermal imaging images and binocular images on the two sides of a heading machine are collected by installing an infrared thermal imager and a visible light camera in a preset area of the driving working face, an image analysis algorithm is used for delimiting a belt monitoring area on a thermal imaging image and setting temperature and area threshold values, and a virtual forbidden area and a three-dimensional virtual trip line containing height information are delimiting on a binocular image. In the production process, whether a living body heat source exists in a belt monitoring area or not is judged by analyzing a thermal imaging image, whether a person has violation and dangerous behaviors or not is judged by analyzing a binocular image, and finally whether the person has unsafe behaviors or not is comprehensively judged according to analysis results of thermal imaging and the binocular image. In view of the problem of low coal mine driving face supervision efficiency in the prior art, the application improves the supervision efficiency.

The invention provides an unmanned aerial vehicle assisted sewage pipeline leakage inspection system and device, and relates to the field of sewage pipeline leakage inspection, an unmanned aerial vehicle inspection module is equipped with a flight control system and an autonomous obstacle avoidance system, and flexibly shuttles back and forth in a complex sewage pipeline environment; the multi-modal sensor array module is integrated with a visible light camera, an infrared thermal imager, a laser range finder, a gas sensor and an acoustic sensor and is used for multi-dimensional data acquisition. Through the unmanned aerial vehicle inspection module, the multi-mode sensor array module, the AI intelligent analysis engine module and the remote intelligent monitoring center module, the intelligent level and efficiency of sewage pipeline leakage inspection are remarkably improved, leakage is accurately recognized and positioned, inspection data are recorded in an omnibearing mode, and the inspection efficiency is improved. The inspection strategy can be automatically adjusted according to real-time data and environment information, and a more efficient and intelligent solution is provided for management and maintenance of urban sewage pipelines.