338 results about "Thermography" patented technology

The invention provides an all-terrain autonomous navigation and task planning method and system for a photovoltaic inspection robot, and relates to the technical field of photovoltaic inspection, and the method comprises the steps: constructing a three-dimensional grid map and generating an initial inspection path, dividing hexagonal sub-regions and calculating a passing cost matrix, and optimizing a global inspection path based on the cost matrix. The motion state is predicted in real time, the motion track is corrected, thermal imaging and visible light data are collected to generate an inspection report, autonomous navigation and efficient inspection under complex terrains can be achieved, the inspection efficiency and accuracy are improved, and the labor cost is reduced.

The invention relates to the technical field of image processing, and discloses a multispectral fusion night low-illumination image enhancement and shielding compensation method, which comprises the following steps of: cooperatively acquiring multi-modal data through a visible light camera, an infrared sensor, a thermal imaging sensor and a millimeter wave radar; comprising a low-illumination basic image, dark light texture details, target temperature distribution and contour and motion information of an object behind the shelter; fusing visible light and infrared textures by adopting a multi-scale transformation algorithm to generate a transition fusion image, and performing illumination compensation based on temperature distribution; predicting texture details of the occlusion area through a u-Het deep learning network in combination with detection data of the millimeter wave radar; and filling the missing texture according to the shielding contour, and generating a final target image through edge optimization and illumination smoothing technologies. According to the invention, the definition of a night low-illumination image can be improved.

The invention relates to the technical field of image recognition, in particular to a lightning arrester fault thermal imaging picture recognition method and system combined with gaze prediction, and the method comprises the steps: carrying out the down-sampling of an original thermal imaging image to a fixed size, sequentially passing through a multi-layer convolution and a Spatial Softmax layer, and outputting a predicted gaze point track sequence, extracting a key area and a non-key area according to the intensity of the fixation point track sequence; reconstructing the key area to obtain a compressed and recombined target area; inputting the non-key region into a variational auto-encoder to obtain a low-dimensional potential feature vector; and inputting the image-structure joint feature representation into an EffiCroprViT model, and finally obtaining a lightning arrester fault classification result. Local and global features are efficiently fused, the calculation complexity is reduced, background noise interference is effectively suppressed, real-time and accurate identification and early warning of the fault state of power equipment are realized, and the fault classification method has the advantages of high efficiency, high reliability and high reliability. Therefore, the safety and stability of power grid operation are ensured.

The invention discloses a rotor punching and stacking welding defect identification system based on edge calculation, and relates to the technical field of rotor welding identification, and the rotor punching and stacking welding defect identification system comprises a data acquisition module which is used for carrying out real-time acquisition on smoke dust data generated in the welding process, and the smoke dust data comprises smoke concentration, suspended particulate matter concentration and smoke infrared thermal imaging temperature; the model matching module is used for comparing the smoke concentration, the suspended particulate matter concentration and the smoke infrared thermal imaging temperature with corresponding preset reference models respectively to obtain abnormal data with an abnormal trend; and the judgment module is used for performing time sequence analysis on the abnormal data, calculating a standard deviation and a mean deviation rate based on a sliding window, and generating a corresponding early warning growth value. According to the method, a dynamic alarm value generation and attenuation mechanism is constructed, so that false alarm caused by continuous influence of historical data is avoided, and the real-time response accuracy of the integrated value is improved. And through a pre-judgment mechanism, it is ensured that alarm value attenuation is executed only on the premise that data are stable or no early warning growth trend exists.

The invention relates to the field of data processing, in particular to a multi-parameter collaborative optimization method and system based on a data center cooling system, and the method comprises the steps: obtaining a cooling subsystem of a computing equipment integrated cabinet, and carrying out the short-time temperature analysis of the computing equipment integrated cabinet based on infrared monitoring data and temperature sensing data, if it is confirmed that the limiting temperature of the component is not higher than the corresponding preset temperature threshold value according to the current temperature distribution, and it is confirmed that the continuous heating temperature is lower than the preset temperature change threshold value based on the temperature change trend, the temperature change trend and the current temperature distribution are obtained; if yes, returning to the step of executing multi-modal data acquisition on the computing equipment integration cabinet and the cooling subsystem, and otherwise, synthesizing the predicted temperature data, obtaining a cooling subsystem configuration strategy and setting the cooling subsystem. According to the invention, the cooling monitoring and adjustment of the computing equipment of the data center are realized by integrating the data collected by the temperature sensor and the thermal imaging probe which are reasonably arranged.

Thermal imaging and navigation systems and related techniques are provided to improve the operation of manned or unmanned mobile platforms, including passenger vehicles. A system includes a thermal imaging device configured to be mounted on a vehicle. The thermal imaging device is configured to, when mounted on the vehicle, capture a first image of a scene encompassing a portion of the vehicle and capture a second image associated with a reflection of the scene from the portion of the vehicle. The system further includes a logic device configured to communicate with the thermal imaging device and determine a disparity map based on the first image and the second image.

The invention provides an intelligent target identification method and system based on an unmanned intelligent turntable. According to the method, an unmanned intelligent rotary table is used for collecting a thermal imaging video at night, a thermal radiation map is generated through compensation, and a background heat conduction coefficient is extracted. After the high-temperature heat source is positioned, calculating a centroid trajectory, and combining a background coefficient and an environment gradient to establish a dynamic reference temperature field; the heat source is divided into a contact sub-region and a non-contact sub-region according to the heat exchange rate difference, and the boundary mutation of the contact sub-region is marked. And analyzing the collaborative response of the temperature gradient change rate and the boundary deformation rule of the contact sub-region to judge an abnormal behavior. If abnormal, tracking parameters are generated based on the mass center acceleration to adjust the posture of the rotary table to keep the target in the middle, and non-contact subarea temperature diffusion is utilized to verify continuity so as to optimize tracking. According to the method, intelligent discrimination and adaptive closed-loop tracking of night abnormal behaviors are realized by analyzing the dynamic thermodynamic characteristics of the heat source contact area and the deformation rule cooperative response.

The invention provides a defect detection method and system for a prepreg laying layer and a computer storage medium, and the method comprises the steps: carrying out the frequency conversion thermal excitation of a to-be-detected prepreg laying layer, collecting an infrared phase-locked diagram of the to-be-detected prepreg laying layer, and converting the infrared phase-locked diagram into an infrared phase-locked gray-scale map; carrying out boundary identification on the infrared phase-locked grey-scale map, and then carrying out image segmentation on the infrared phase-locked grey-scale map by taking a grey-scale threshold value as a constraint condition to obtain an infrared phase-locked grey-scale map with divided grey-scale regions; and performing shape recognition on the gray region greater than the gray threshold to obtain a region containing a singular point and / or an edge curve, and further obtaining the defect type of the to-be-detected prepreg paving layer and the total area of the defect region. According to the method, the linearly increased variable-frequency infrared excitation source is adopted to excite the prepreg paving layer, so that defect information of different depths can be excited, infrared signals radiated by near-surface and deeper defects can be detected by an external detector, and the problem that deep defects are difficult to detect by an infrared phase locking method in the prior art is solved.

The methods described herein provide improvements in composite infrared absorption imaging. The scanning approach described herein reduces erroneous measurements caused by thermal drift and optical interference that occurs between light scattering from the top surface of the sample and light scattering from the underlying substrate. Embodiments described herein relate to imaging and spectroscopy, and more particularly, to improvements in photo-thermal imaging and spectroscopy systems, and techniques for obtaining spectral information characterizing optical properties and / or materials or chemical compositions of a sample, such as information related to infrared (IR) absorption spectroscopy.

The invention relates to the technical field of solar street lamps, and provides a solar street lamp sectional illumination control system with human body induction, which comprises a local module and a cloud service platform, and is characterized in that the local module comprises a sensor group unit, a power supply unit, an illumination unit, a data processing unit and a communication unit; the local module obtains a multi-source data packet through a sensor of the local module, the data processing unit judges the existence of a human body through the multi-source data packet, and the lighting unit is controlled through a judgment result to form a corresponding lighting mode. According to the invention, a graded awakening mechanism of the millimeter wave radar, the PIR sensor and the thermal imaging sensor is adopted, the PIR sensor detects a dynamic heat source in real time, the radar and thermal imaging are awakened to carry out accurate detection, and the radar and the thermal imaging sensor carry out detection alternately and periodically, so that missing detection can be avoided while the power consumption is reduced.

The invention relates to the technical field of high-place operation safety equipment, in particular to an outer wall defect detection method based on infrared thermography, which comprises the following steps: acquiring a construction building drawing of each building, acquiring report records about wall body cracks in an intelligent property platform and forming a plurality of crack type sets and related crack type sets, combining the related crack type sets into a related crack set, determining a crack building drawing of each building in combination with report records, determining a plurality of crack clustering centers of the corresponding building according to the crack building drawing, and determining whether the crack clustering centers are inner side defect points or not according to the crack clustering centers and the target positions; determining an outer wall detection position of the infrared thermal image according to the inner side defect point, and controlling the detection unmanned aerial vehicle to move to the outer wall detection position to obtain a corresponding outer wall infrared thermal image; and determining whether a temperature abnormal area exists or not according to the outer wall infrared thermal image so as to determine wall defects. Through crack clustering and directional detection, the problems of blind detection, low efficiency and insufficient accuracy in outer wall defect detection are solved.

The invention discloses a gas leakage detection method. The method comprises the following steps: S10, carrying out denoising and image enhancement on an acquired visible light image and an acquired thermal infrared image; s20, carrying out image feature point matching; s30, fusing the visible light image and the thermal infrared image after registration; and S40, gas leakage detection based on the improved YOLOv8 framework. According to the invention, thermal imaging, multispectral and visible light imaging multi-source fusion technology is adopted, so that the problems of efficient detection and accurate identification of multiple types of gases in a broadband range are solved.

The invention provides a photovoltaic module fault image recognition system based on infrared thermal imaging analysis, and relates to the field of image recognition, comprising: an asynchronous image acquisition module calculates a response delay value based on component spatial distribution, a shielding state and a thermal capacity coefficient, and generates a dynamic scheduling path to control an infrared thermal imager to capture component images of different thermal response processes; the thermal anomaly trend analysis and recognition module constructs a time difference thermal response matrix, recognizes continuous multi-frame candidate temperature rise areas with the temperature rising continuously, and builds a temperature trend prediction model to judge thermal anomaly; the response behavior intervention and disturbance execution module calculates a disturbance influence factor, decides to apply disturbance to a target or an adjacent component and triggers image acquisition; and the routing inspection scheduling feedback updating module dynamically adjusts the priority and frequency of the acquisition path according to the thermal anomaly judgment result and the intervention data.

The invention discloses a PCB welding spot defect positioning detection method and system based on deep learning, and the method comprises the steps: S1, collecting visible light data and thermal imaging data of a PCB, carrying out the preprocessing, and obtaining the preprocessed visible light data and thermal infrared data of the PCB; s2, according to the pre-processed visible light data and thermal infrared data of the PCB, feature extraction and feature alignment are carried out respectively, and a textural feature map and a temperature feature map after alignment are obtained; s3, carrying out feature calibration, and generating a fusion feature map; s4, calculating a positioning feature map and an optimized enhanced feature map, and finally outputting a welding spot defect detection frame set; s5, calculating a sub-pixel feature map, and outputting a defect distribution probability map; and S6, converting the defect distribution probability graph into a binary defect mask, and outputting a final defect detection result in combination with morphological closed operation. The method can solve the problem that a traditional PCB welding spot defect positioning method is insufficient in small defect recognition accuracy.

The invention discloses a fire early warning cooperative system composed of a thermal imaging double-spectrum intelligent ball, an intelligent analysis platform and a fire extinguishing unmanned aerial vehicle, which is applied to the technical field of fire early warning, and comprises the thermal imaging double-spectrum intelligent ball, an integrated visible light sensor and a thermal imaging sensor, and a double-spectrum fusion monitoring technology is adopted to carry out omnibearing and all-weather monitoring; the intelligent analysis platform simulates a fire spreading path based on a fire spreading prediction model of a fluid dynamics and machine learning algorithm, generates a three-dimensional fire danger thermodynamic diagram, and completes a whole-process response from regional early warning to command center decision making to unmanned aerial vehicle cluster task distribution through a multi-level early warning mechanism; the fire extinguishing unmanned aerial vehicle adopts a multi-model collaborative operation system to meet the fire extinguishing requirements of different fire scenes. According to the invention, in a complex and changeable fire scene, the fire hazard can be quickly and accurately identified, and the efficient disposal of fire extinguishing operation is realized.

The invention discloses a pipeline leakage detection method, device and equipment and a medium. The method comprises the steps of determining a target pipeline image according to a target pipeline image collected by an unmanned aerial vehicle; wherein the unmanned aerial vehicle carries a visible light camera and a thermal imaging camera, and the target pipeline image comprises a target visible light image and a target thermal imaging image at the same moment; performing pipeline leakage detection on the target visible light image by using a pre-trained first detection model to obtain a first detection image; performing pipeline leakage detection on the target thermal imaging image by using a pre-trained second detection model to obtain a second detection image; and determining a pipeline leakage detection result according to the first detection image and the second detection image. According to the scheme, quick, accurate, low-cost and real-time detection of the leakage condition of the pipeline can be realized by utilizing the pipeline images shot by the visible light camera and the thermal imaging camera of the unmanned aerial vehicle, powerful technical support is provided for leakage control of an urban water supply pipe network, water resource waste is reduced, and water supply safety is guaranteed.

The invention provides a perfluorohexanone active fire extinguishing system applied to an energy storage power station, and belongs to the technical field of active fire extinguishing systems, the perfluorohexanone active fire extinguishing system comprises a multi-mode composite detection array composed of a thermal imaging sensor, an air pressure sensor, a visible light camera and an ultraviolet light sensor, the multi-mode composite detection array is configured to collect temperature field data, air pressure data, visible light video streams and ultraviolet spectrum data in the energy storage battery cabin by a preset timestamp synchronization mechanism, and output four-mode original data streams to the thermal runaway micro-hotspot recognition engine and the like; early discovery, accurate positioning, fine release, true suppression and self-evolution full-closed-loop active fire extinguishing of the energy storage power station in the high-altitude extreme environment are achieved, and the safety, economical efficiency and long-term maintainability of the energy storage power station are remarkably improved.

The invention relates to the technical field of distribution boxes, and particularly discloses a distribution box operation state monitoring method and system based on multi-source data fusion. According to the method, a multi-dimensional feature analysis system is constructed by acquiring current signals and thermal imaging video information, time-frequency domain decomposition is performed on the current signals, short-time fluctuation features and high-frequency resonance features are extracted, and transient anomalies caused by hidden faults such as intermittent poor contact are accurately captured; meanwhile, dynamic analysis is conducted on a thermal imaging sequence, instantaneous temperature characteristics are obtained through a thermal diffusion value and a temperature gradient change diagram, real-time monitoring of local overheating is achieved, the three types of characteristics are input into a dynamic fusion model, comprehensive characteristic values are generated, and the operation state of the distribution box can be recognized more accurately through analysis of a characteristic fusion value; therefore, intermittent abnormity can be found and captured in time, and hidden faults can be found.

The invention discloses an insulation hidden danger diagnosis method for a multi-mode alternating current power supply system for a transformer substation, and relates to the technical field of power system insulation state monitoring, and the method comprises the following steps: obtaining acoustic signal data, thermal imaging data and electrical parameter data; performing synchronous feature processing on the acoustic signal data, the thermal imaging data and the electrical parameter data to generate synchronous feature group data; performing multi-modal interaction feature extraction on the synchronous feature group data to form electricity-sound-heat anomaly triple data; and performing cross feature fusion on all the electricity-sound-heat anomaly triple data to obtain a multi-modal fusion feature vector. According to the method, the time synchronism of the multi-modal abnormal events is forcibly correlated by establishing the electricity-sound-heat anomaly triple data, the abnormal events with time correlation in the multi-modal data can be effectively identified, the problem of missing detection caused by single-modal analysis is solved, and therefore the reliability of a diagnosis result is improved.

The invention discloses a nuclear dynamic defect detection method based on a hydrogen-containing composite material for automobile manufacturing, and relates to the technical field of industrial solid waste treatment material management.The nuclear dynamic defect detection method comprises the following steps that an integrated detection system of a thermal imaging sensor, an acceleration sensor, a temperature sensor and a sound sensor is installed on the structure of the hydrogen-containing composite material for an automobile; sensor data is continuously collected, and the sensor data comprises a temperature distribution image, a vibration signal, temperature data and a sound signal; by fusing multi-modal data, such as a temperature distribution image and a vibration signal, the influence of environmental factors such as temperature and light on a detection result is reduced, so that a reliable detection result can still be stably output and the detection precision is improved under the complex working conditions of high-speed running of an automobile and day and night temperature difference, and the detection precision is improved by combining various sensor data. The defects of a single imaging detection method are overcome, and more comprehensive and more accurate material state evaluation is provided.

The invention relates to the technical field of unmanned aerial vehicle detection and security and protection, and discloses a low-altitude unmanned aerial vehicle dual-mode detection method based on micro Doppler radar and infrared thermal imaging. According to the method, a monitoring area is continuously scanned through a micro-Doppler radar, and when a suspected target with the signal intensity lower than a preset threshold value is recognized, an infrared thermal imaging device is triggered to be started. The infrared device directly adjusts the orientation and focuses to a specific area according to the target orientation and distance information provided by the radar, and obtains a high-resolution thermal imaging sequence. And then the radiation temperature distribution, the temperature gradient and the thermal profile morphological characteristics of the target are analyzed, and final judgment is completed. According to the invention, on-demand starting and rapid and accurate guiding of the infrared sensor are realized, the overall power consumption of the system is reduced, and the cooperative detection response speed and the identification precision of the rapid moving target are improved at the same time.

The invention discloses an intelligent switching station monitoring method based on thermal imaging click selection and multi-source data fusion, and belongs to the technical field of intelligent monitoring. Firstly, thermal imaging videos are collected in real time through thermal imaging equipment, and an operation instruction forwarding path is judged based on cascade identification; secondly, mapping from a screen to physical coordinates is achieved through a space coordinate transformation matrix, a pixel-level data index channel is established, a multi-source data fusion compensation model is constructed, and a temperature analysis result is output through noise suppression filtering and time domain drift correction; based on a dynamic calibration mechanism, the temperature measurement precision of the surfaces of different materials is dynamically calibrated; and finally, displaying the mouse click position temperature or regional average temperature according to the operation instruction, and when the temperature exceeds a threshold value, triggering an alarm and positioning a fault point, thereby realizing high-precision click temperature measurement. The method breaks through the limitation of traditional regional temperature measurement, improves the industrial detection efficiency and precision, and is suitable for intelligent operation and maintenance in the fields of electric power, chemical engineering and the like.

The invention discloses a multi-mode safety perception emergency linkage system, method, equipment and medium. The system comprises a multi-mode perception module, and a thermal imaging double-spectrum intelligent ball is used for temperature detection and smoke detection; the intelligent analysis module is in communication connection with the multi-mode sensing module, receives the detection data transmitted by the thermal imaging double-spectrum intelligent ball, analyzes the detection data to judge the type of a fire behavior, calculates an optimal flight path when the fire behavior is judged, and generates a fire extinguishing instruction containing the coordinate position of a fire source and the optimal flight path; the unmanned aerial vehicle linkage module is in communication connection with the intelligent analysis module; and the comprehensive control module is in communication connection with the intelligent analysis module. When temperature abnormity and smoke signals are captured at the same time, the two kinds of detection data are verified mutually, the false alarm problem of a traditional single detection mode is solved, and more importantly, the fire type can be accurately judged.

1. The name of the design product: thermal imaging night vision device. 2. The use of the design product: for night observation patrol, etc. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: perspective view 1.

The invention provides a belt deviation detection method based on multi-modal data, and relates to the technical field of fault detection, and the method comprises the steps: firstly constructing a no-label data set and a label data set based on a historical data set; then, based on the label-free data set and the label data set, training a target detection model by adopting an illumination-guided mask auto-encoder IG-MAE of self-supervised representation learning as an auxiliary agent task; and based on the trained target detection model, analyzing by combining the visible light video stream and the thermal imaging video stream when the to-be-detected belt runs, and obtaining a belt deviation detection result. According to the method, the illumination-guided mask auto-encoder IG-MAE based on self-supervised representation learning is used as an auxiliary agent task for training, and good robustness can be kept under low illumination or complex backgrounds.

The invention relates to a control method, system and equipment of a range hood. The control method comprises the steps that a thermal imaging video of a stove area is collected; performing data processing on the thermal imaging video to obtain temperature data; based on the temperature data, generating a temperature distribution diagram of the kitchen range area, and performing hot spot area division on the temperature distribution diagram; and determining the operation gear of the range hood according to the temperature data of each hot spot area. According to the range hood, all-around temperature monitoring of the kitchen range area is achieved, the accuracy and precision of temperature data are improved, the operation gear of the range hood is determined according to the high-precision temperature distribution diagram, the operation gear of the range hood is adjusted in real time, and the operation efficiency and the energy-saving effect of the range hood are improved.

The invention belongs to the technical field of surveying and collecting equipment, and discloses geographic information surveying and collecting equipment and method for urban and rural planning, and the method comprises the steps: a data collection module collects geological data, building data and vegetation data of an urban and rural planning region; the route planning module plans an optimal flight route of the unmanned aerial vehicle; the unmanned aerial vehicle carries a data acquisition module to carry out data survey and acquisition on urban and rural planning regions; the image analysis module analyzes the collected image; the thermal imaging analysis module analyzes the collected thermal images; the radar data analysis module performs preprocessing identification and modeling on the point cloud data acquired by the laser radar; and the model building module fuses the image data, the thermal imaging data and the radar data to build a two-dimensional or three-dimensional model of the urban and rural planning region. According to the invention, the working efficiency can be improved, data acquisition is carried out on urban and rural planning regions from different angles, image data, thermal imaging data and radar data are fused, and an accurate two-dimensional or three-dimensional model is constructed.

The invention provides a multi-mode deep collaborative fire confidence assessment method in a complex industrial environment, and relates to the technical field of fire confidence assessment. Comprising the following steps: collecting multi-modal data of a to-be-detected area, and determining a corresponding temperature abrupt change rate and diffusion entropy according to thermal imaging image data; inputting the temperature abrupt change rate and the diffusion entropy into a built smoldering probability model to obtain a smoldering probability; determining a smoke confidence coefficient according to the visible light image data; determining a fire confidence coefficient according to the smoldering probability and the smog confidence coefficient; and evaluating the fire in the to-be-detected area by using the fire confidence coefficient. According to the invention, the problems of high false alarm rate and poor real-time performance of a fire early warning technology in the prior art are solved.

The invention provides a crop moisture utilization rate estimation method and system based on unmanned aerial vehicle remote sensing data, and the method comprises the steps: obtaining meteorological data of a crop planting region, and laser radar point cloud data, multispectral data and thermal imaging data of a target crop group in the crop planting region; according to the multispectral data, the thermal imaging data and the meteorological data, obtaining the corresponding target evapotranspiration of the target crop group in each day; inputting the laser radar point cloud data, the multispectral data and the thermal imaging data into the crop population overground part dry matter accumulation amount prediction model to obtain overground part dry matter weight prediction data; according to the overground part dry matter weight prediction data corresponding to different prediction moments, performing fitting to obtain a target dry matter accumulation amount corresponding to the target crop group in each day; and obtaining the crop water utilization rate of the target crop group according to the target evapotranspiration and the target dry matter accumulation amount. The precision and efficiency of crop water utilization rate estimation are improved.

The invention discloses a self-identification and early-warning method for abnormal high temperature of a vehicle in a free flow state, and the method achieves the precise identification of vehicle tire overheating and fire hazards through the fusion of thermal imaging analysis and a dynamic scene modeling technology. The method comprises the following steps: firstly, positioning a tire area in a thermal imaging image in real time by using a Yov11 model, segmenting an axle and a tread through a clustering algorithm in combination with temperature distribution and position characteristics, calculating a local average temperature and quantifying an abnormal index; meanwhile, environmental interference is eliminated by adopting a dynamic background modeling technology based on statistical analysis, efficient classification is performed on smoke areas in combination with a full convolutional neural network, and a smoke probability is output. Furthermore, the system dynamically evaluates the comprehensive risk level by fusing the temperature abnormal data and the smoke detection result, and links a grading early warning mechanism. According to the method, the detection precision and the response speed in a complex free flow scene are remarkably improved, the method can be widely applied to the fields of logistics transportation, dangerous goods vehicle monitoring and the like, and intelligent guarantee is provided for driving safety.