2082results about "Radiation thermography" patented technology

The invention provides a power chip packaging detection method and device, and the method comprises the steps: obtaining a structure image, an infrared thermal imaging frame sequence and a working electrical parameter data set of a target power chip after packaging, and carrying out the layering and regional peak analysis, so as to construct a morphology feature parameter set and a thermal anomaly feature set; inputting the morphology feature parameter set, the thermal anomaly feature set and the working electrical parameter data set into a preset defect identification model to obtain a chip packaging anomaly identification map; and performing hierarchical division and node aggregation on the chip packaging abnormity identification map to obtain a packaging defect type result. By constructing a multi-modal packaging detection system taking a structural image, an infrared thermal imaging frame sequence and a working electrical parameter data set as input, accurate identification of the power chip is realized, the accuracy of packaging defect detection is effectively improved, and the problems of low identification precision and information isolation of an existing detection method are improved.

The invention relates to the technical field of power equipment state monitoring, in particular to a lightning arrester state monitoring method, system and equipment based on multi-physics field coupling and a storage medium. Acquiring current-voltage characteristic parameters, temperature distribution data and mechanical stress data of the lightning arrester, performing electro-thermal-mechanical coupling analysis based on multi-physical field monitoring data, identifying overlapping positions of an electric field distortion area, a temperature abnormal area and a stress concentration area, and determining the overlapping positions as a degradation key area; establishing a correlation response relationship between the leakage current and the temperature and a transfer response relationship between the temperature and the mechanical stress for the deteriorated key area; on the basis of the leakage current change trend of the degradation key area and the coupling influence of the superimposed temperature field and stress field, a degradation feature fusion factor is constructed, the evolution law of the degradation feature fusion factor in the time sequence is analyzed, and the degradation threshold value under the multi-field synergistic effect is determined in combination with the electric-thermal coupling acceleration effect and the thermal-mechanical coupling weakening effect.

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 vehicle navigation, and discloses an unmanned aerial vehicle navigation method and system based on computer vision, and the unmanned aerial vehicle navigation method based on computer vision comprises the following steps: collecting a current visual field image of an airborne camera of an unmanned aerial vehicle, and carrying out adaptive image enhancement; performing scene semantic understanding; semantic perception feature extraction is carried out; feature matching and outer point elimination are carried out; floor identification is carried out, and multi-sensor fusion positioning is carried out; plane segmentation, structural constraint extraction and map normalization are carried out; performing visual navigation evaluation, judging a navigation mode according to an evaluation result, and generating an exploration control instruction quantity based on the navigation mode; generating a cross-floor path sequence, optimizing a track in real time, and performing control fusion in combination with a navigation mode identifier and an exploration control vector; according to the method, the navigation problem of the unmanned aerial vehicle in complex scenes such as smoke interference, multi-story buildings and illumination dramatic change in a GPS denial environment is solved, and high-precision autonomous navigation is realized.

The invention relates to the technical field of power equipment state monitoring, and particularly discloses a transformer fault diagnosis method and system based on image recognition, and the method comprises the steps: collecting a transformer multi-mode image sequence in real time, and carrying out the definition and part integrity evaluation and screening to form an initial image set; performing multi-scale space registration on the initial image set and a transformer normal state standard template to generate a reference image, and reversely deriving a displacement vector field based on pixel-level difference; carrying out smooth optimization and geometric reconstruction on the displacement vector field under the geometric constraint of the transformer structure, and generating a correction image with a real structure; fault feature enhancement is carried out in a gradient domain of the corrected image, a fault area is identified through matching of multichannel feature extraction and a transformer typical fault feature library, and a diagnosis report integrating fault types, confidence coefficients and geometric parameters is generated; according to the method, the problem of image geometric deformation caused by shooting condition differences is effectively solved, and the accuracy and reliability of fault identification are improved.

The invention relates to the technical field of substation abnormity identification, and discloses a substation switch cabinet abnormity identification method and system, and the method comprises the steps: obtaining the real-time temperature data of a plurality of monitoring points in a switch cabinet, and uploading the real-time temperature data to a big data analysis platform through a wired or wireless communication mode; and continuous monitoring of the operation state of the equipment is realized. The highest temperature of the monitoring area, the highest temperature difference value of the adjacent monitoring periods and the temperature sudden change value are calculated based on the real-time temperature data, and when the parameters exceed preset threshold values, a corresponding alarm strategy can be triggered in time. And through the measures of generating an infrared thermogram, extracting a temperature distribution difference area, marking high-risk monitoring points and the like, the accuracy and timeliness of anomaly recognition are further improved. Meanwhile, the temperature trend prediction model is used for predicting the future temperature development trend, an early warning signal is generated in advance, a preventive alarm mechanism is started, and a powerful guarantee is provided for safe and stable operation of a power system.

The invention discloses a battery change cabinet battery abnormity detection method and system based on a thermal image, and relates to the related field of battery safety monitoring, and the method comprises the steps: collecting a surface thermal image sequence in a battery compartment through an infrared thermal imager arranged in a battery change cabinet, and enabling the surface thermal image sequence to have a time sequence and a charging stage label; extracting temperature field features of a space-time dimension from the thermal image sequence, and identifying an abnormal state and an abnormal region of the battery based on the temperature field features; and acquiring real-time battery management system (BMS) data of the battery, performing correlation verification on the abnormal state or the abnormal area and the BMS data, and determining an abnormal level and positioning a fault battery unit according to a correlation verification result. According to the invention, the technical problems of inaccurate and untimely detection and inaccurate fault positioning in the existing battery abnormality detection of the battery changing cabinet are solved, and the technical effects of improving the accuracy, timeliness and positioning accuracy of battery abnormality detection are achieved.

The invention discloses an electronic component early fault detection method based on infrared thermal imaging, and the method comprises the following steps: collecting an infrared thermal imaging image, synchronously collecting current and voltage data, and obtaining an infrared thermal imaging sequence; distortion correction, noise suppression and radiance correction are carried out on the sequence, and metal reflection false high-temperature points are removed; executing time sequence difference to extract a continuous heating area, and generating a time sequence temperature feature vector; inputting the feature vector into an improved Times Net model, and outputting time sequence feature representation; calculating a reconstruction error, a prediction residual error and a frequency spectrum deviation, and fusing into an abnormal score; comparing the abnormal score with a dynamic threshold value, and outputting an electronic component identifier, a potential fault type and an abnormal type; and an early fault trend report is generated by combining historical data comparison. The method realizes early detection and trend prediction of electronic component faults, and is suitable for state monitoring and reliability guarantee of a complex circuit system.

The invention relates to the technical field of surface defect detection, in particular to a hardware surface defect online detection method based on multi-modal sensing fusion, which comprises the following steps: step 1, arranging multi-modal sensor groups above and on the side of a conveyor belt of a hardware production line; 2, preprocessing the multi-modal sensor data acquired in the step 1; 3, extracting features from the multi-modal sensor data preprocessed in the step 2; 4, performing reliability evaluation on the multi-modal features extracted in the step 3; 5, generating a fusion feature vector, wherein adaptive weighted fusion adopts a weighted feature splicing method; and step 6, outputting defect types and position information to a production line control system. The combined use of the multi-modal sensor can comprehensively detect the surface of the hardware from multiple dimensions, and the accuracy and stability of the system are further improved through reliability evaluation and weighted fusion.

The invention discloses an unmanned aerial vehicle infrared thermal imaging method and device for cold leakage detection of a low-temperature storage tank, and relates to the field of automatic inspection and detection of storage tanks, and the method comprises the steps: determining the inspection range and detection distance of an unmanned aerial vehicle based on geometric parameters, a cold leakage frequency region and environment parameters of a site; calculating the distance between the shooting points and the vertical coverage height of single-circle flight, further dividing flight elevation layering, and calculating the number of the shooting points of each circle of flight; establishing a three-dimensional model; collecting a visible light image and an infrared thermal imaging image of each shooting point; the method comprises the following steps: performing multi-dimensional correction and temperature image conversion on an acquired infrared thermal imaging image, performing image registration on a temperature image with a temperature scale and a visible light image, performing cold leakage area identification to obtain a cold leakage area, performing quantitative calculation to obtain three-dimensional positioning of a cold leakage position and a cold leakage area, and performing cold leakage area identification on the cold leakage area. And generating a visual detection result, a detection report and a maintenance suggestion. According to the invention, automatic detection and accurate analysis of the cold leakage area of the low-temperature storage tank can be realized.

The invention relates to an industrial gas tank storage place early warning method and system. The method comprises the following steps: acquiring image time sequence data at least representing temperature field distribution of a target monitoring area, generating a temperature image sequence, determining area temperature change characteristics and tank body surface reflection characteristics in the area, and generating reflection area probability distribution of the target monitoring area to determine initial gas leakage probability data of the target monitoring area; generating baseline feature data at least representing background thermal field distribution of the target monitoring area; and performing association processing on the initial gas leakage probability data and the baseline feature data to generate fusion time sequence features and determine a gas leakage diffusion weight so as to correct the initial gas leakage probability data to obtain corrected gas leakage probability data. By adopting the method, the gas leakage probability can be accurately judged by comprehensively utilizing temperature field time sequence information, tank body surface reflection characteristics and background thermal field distribution characteristics under the condition of a complex background thermal field, so that early warning is realized.

The invention relates to a multi-spectral diagnosis result credibility evaluation method. The method comprises the following steps: synchronously acquiring three-spectral images and environmental parameters of target equipment; the collected three-spectrum image is preprocessed; inputting the preprocessed image into the improved model to obtain a prediction probability distribution vector of fusion features and three-spectral-band branches; obtaining a prediction category and uncertainty entropy; carrying out confidence score calculation; judging whether to trigger a feedback condition; according to the method, the complementary characteristics of three spectrums of visible light, infrared light and ultraviolet light are utilized, and the CBAM attention module is introduced, so that key characteristics under different spectrums are enhanced, and the sensing ability of the model to an abnormal region is improved; the method achieves the prediction result confidence evaluation based on a Monte Carlo Dropout reasoning mechanism, effectively recognizes a low-confidence result, gives a diagnosis conclusion, can also evaluate the confidence, is suitable for deployment in key scenes such as a high-pressure valve hall, and facilitates the improvement of the overall safety and stability of a system.

The invention discloses an automatic driving environment sensing method and system based on multispectral fusion, and relates to the field of auxiliary driving, and the method comprises the steps: collecting environment data in a complex environment through a sensor; performing time-space synchronization and standardized preprocessing on the environment data to obtain preprocessed data; constructing a sensor confidence estimation network, and inputting the preprocessed data into the confidence estimation network; features are extracted and fused through a structure combining a shared encoder and a branch encoder, and a real-time confidence score vector of each sensor is output; a preset mapping function of confidence and weight is obtained, the confidence score vector is converted into a dynamic weight vector, and the sum of all components of the weight vector is 1; and integrating the sensing results of the sensors by adopting a weighted fusion strategy, verifying the sensing results of the high-weight sensors through a cross validation mechanism, and outputting final environment sensing data.

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

The invention provides a substation safety intelligent monitoring method based on infrared binocular vision, which comprises the following steps: acquiring an infrared radiation image, a visible light image and environmental parameters of equipment, and accurately inverting the real temperature of the equipment after eliminating environmental interference in combination with a depth distance calculation and physical compensation model; a historical time sequence of a real temperature is used to construct a multi-dimensional state vector fusing a thermal feature, a spatial feature and an environment feature, and intelligent classification evaluation of the security risk of the transformer substation is realized through an improved decision tree model. Meanwhile, a time sequence prediction model is introduced to predict the temperature rise trend of the equipment, the remaining time for reaching an early warning threshold value is calculated, and a dynamic grading early warning report is generated. According to the method, the temperature measurement precision and the risk identification accuracy are effectively improved, early warning and active prevention and control of the operation state of the equipment are realized, and the operation safety and the intelligent operation and maintenance level of the transformer substation are remarkably improved.

The invention relates to the technical field of night monitoring, and discloses a night dynamic target tracking and image restoration method, which comprises the following steps of: establishing a heat-motion correlation model of a target through a space alignment algorithm in combination with heat source contour and intensity information acquired by a thermal imaging sensor and motion parameters detected by a millimeter wave radar; outputting an initial position and a motion state; inputting a target initial state and radar point cloud data based on an LSTM network, predicting a future N-frame trajectory, and dynamically correcting a prediction result by using real-time radar data; inputting the position information of the occlusion area and the trajectory prediction value into a GAN network to generate a complemented image, fusing the complemented image with the original heat source image, and recovering the complete contour of the target; and superposing and rendering the complemented image and the trajectory prediction into an AR picture, and when the target distance triggers a preset threshold value, realizing multi-channel safety early warning through vibration feedback, AR highlight warning and voice prompt. According to the invention, high-precision tracking and safety early warning of the night dynamic target can be improved.

The invention discloses an all-weather sound source positioning system and method based on multi-sensor fusion, and the method comprises the steps: firstly, obtaining acoustic sensing data, millimeter wave radar data and infrared thermal imaging data, and enabling each data to have a collection timestamp; then, cross-modal time alignment processing is carried out on the multi-source data to map the multi-source data to a unified time reference, and multi-modal fusion features under a unified time axis are obtained; for each time point in the time-aligned multi-modal fusion features, according to the confidence coefficient of the data of each sensor, adaptive weighted fusion is carried out on the data of different modals, and fused common feature representation is generated; and finally, time sequence modeling and joint reasoning are carried out based on the common feature representations of a plurality of continuous time points, and continuous position information of the sound source in the three-dimensional space is regressed. According to the method, high-precision positioning of three-dimensional positions of a plurality of sound sources is realized, so that the accuracy and the stability of sound source positioning are improved in a complex environment and an all-weather condition.

The invention relates to the technical field of laser welding, and discloses a laser welding method for accurately controlling weld penetration. According to the invention, through fusion of coaxial multispectral sensing and a back monitoring unit, multi-dimensional information synchronous acquisition of weld front molten pool morphology, a temperature field and a back thermal radiation signal is realized, and limitation of single sensing is overcome; furthermore, a dynamic instruction is generated based on the extracted multi-characteristic parameters and an adaptive fuzzy PID algorithm, thereby realizing precise closed-loop control of laser energy and heat input, and improving real-time performance and robustness of control. Particularly, a digital twin model is introduced to carry out forward prediction, cross validation is carried out with back monitoring signals, a triggered lead compensation mechanism effectively overcomes hysteresis of traditional feedback control, finally, precision control and stable forming of weld penetration are achieved, the defects of incomplete penetration, burnthrough and the like are effectively avoided, and the welding quality is improved. And the device is particularly suitable for occasions with extremely high welding quality requirements.

According to the infrared fast patrol method based on task sequence control provided by the invention, the robot is guided to pre-adjust the pan-tilt angle and the infrared focal length during advancing through task sequence planning, and the target is intelligently positioned by synchronously utilizing the equipment template library, so that continuous temperature measurement during movement is realized; performing multi-view infrared image comparative analysis on the temperature exceeding equipment through a multi-angle abnormity rechecking strategy; abnormal equipment outside a preset point location is positioned in real time in combination with a laser point cloud map and space coordinate conversion, the parking time of the robot is eliminated, the temperature measurement efficiency is improved, environmental interference is effectively eliminated, the misjudgment rate is reduced, and a high-timeliness and high-reliability global temperature monitoring defense line is constructed for safe operation of industrial equipment.

The invention relates to optical plane correction, and discloses an infrared focal plane array non-uniformity correction method based on integral time adjustment, which comprises the following steps: for each pixel of an infrared focal plane array, respectively establishing a nonlinear response model representing an incidence relation among integral time, radiation flux and detector output response; according to the radiation distribution characteristics of the current scene and the independent response characteristics of each pixel, the optimal integral time is determined for each pixel through an adaptive optimization algorithm; dynamically updating the parameters of the nonlinear response model of each pixel based on the deviation between the actual output of the detector and the output of the nonlinear response model; and compensating the response nonlinear difference between different pixels based on the optimal integral time, and performing further linear compensation on the compensated signal based on a two-point correction formula. With the adoption of the method, the defects of a traditional correction method in the aspects of nonlinear correction, scene adaptation, drift adaptation and the like are overcome, and the imaging quality of the infrared focal plane array is effectively improved.

The invention discloses a temperature measurement calibration method and system for an infrared temperature measurement thermal imager, a medium and a product, and the method comprises the steps: uniformly selecting a plurality of temperature points in a temperature measurement range, and building a first mapping relation between a response value and a temperature measurement value of the infrared temperature measurement thermal imager through a least square method; based on the acquired environment temperature, establishing a first correlation function of the shutter response value and the environment temperature; based on the environment temperature, establishing a second correlation function of the blackbody response value of the middle reference temperature and the environment temperature; on the basis of a preset three-item calibration model, the first correlation function and the second correlation function are combined, and the response value of the temperature measurement thermal imager is calibrated; based on the calibrated response value of the temperature measurement thermal imager and the first mapping relation, temperature data after pixel difference calibration, temperature drift calibration and additional radiation calibration are calculated; according to the invention, the temperature measurement precision of the infrared temperature measurement thermal imager is improved, the calibration process is simplified, the implementation difficulty is reduced, and the calibration cost is reduced.

The invention relates to the field of tunnel engineering, in particular to a tunnel surrounding rock stability intelligent evaluation system based on multi-source image fusion, which comprises an image acquisition module, an image processing module, an image recognition module, a time sequence analysis module, a risk monitoring module and a surrounding rock evaluation module, the image processing module adopts a pyramid feature matching method and a feature similarity algorithm to realize registration and multi-scale superposition fusion of different image source features; the image recognition module recognizes surrounding rock abnormal features by using a deep learning network model; the time sequence analysis module maps the multi-time-sequence surrounding rock state characteristics to a high-dimensional manifold based on a Riemannian manifold model, and calculates a deformation rate and an acceleration through a geodesic theory and curvature analysis; the risk monitoring module calculates a real-time monitoring index and sends out an early warning signal; the surrounding rock evaluation module evaluates the surrounding rock grade through the surrounding rock stability index and analyzes the support safety risk.

The invention discloses a fire point detection device applied to a satellite, and belongs to the technical field of space remote sensing. The method comprises the steps that first detection equipment and second detection equipment are installed on a satellite; the first detection device is in communication connection with a control computer of a satellite, and the second detection device is in communication connection with a data management computer of the satellite; the first detection equipment has a first field angle and a first resolution ratio and is used for determining the position of a fire point and sending the position of the fire point to the control computer, so that the control computer determines a maneuvering target based on the position of the fire point and sends the maneuvering target to the data management computer; the data management computer is used for controlling the working state of the second detection equipment based on the maneuvering target; the second detection equipment has a second field angle and a second resolution ratio and is used for imaging the fire point under the control of the data management computer; wherein the first field angle is larger than the second field angle, and the first resolution ratio is lower than the second resolution ratio. According to the invention, through a cooperation mechanism of the first detection device and the second detection device, rapid discovery and accurate detailed investigation of global fire points by a satellite are realized, and the observation efficiency and the resource utilization rate are significantly improved.

The invention relates to the technical field of unmanned aerial vehicle radio detection, in particular to an unmanned aerial vehicle detection and judgment method based on cooperation of an air-ground system and radio detection, and the method comprises the steps: finding a suspected target through scanning of a multi-frequency-band radar, triggering an infrared thermal imager carried by an aerial unmanned aerial vehicle to approach an ultra-wide-frequency-band spectrum sensor for verification; accurate positioning is realized by combining ground photoelectric equipment multi-view triangulation positioning and a signal arrival time difference algorithm, a command and control center calculates threat levels through a multi-source data fusion engine, and navigation decoy of dynamically adjusting intensity is adopted for targets smaller than or equal to two levels. Hard killing of ground laser locking rotor center and air electromagnetic pulse synchronous coverage is executed on a target of more than or equal to three levels, the equipment state is synchronized in real time through an optical fiber link, high-energy-consumption equipment is intelligently closed after threat is relieved, a platform starting strategy is optimized, a detection closed loop of'three-dimensional perception-graded treatment-intelligent energy saving 'is constructed, and the intelligent energy saving effect is achieved. And an efficient and accurate low-altitude safety protection scheme is provided for a sensitive area.

The invention relates to a ring main unit intelligent detection and fault early warning method and system. The method comprises the following steps: deploying an ultrahigh frequency sensor, an infrared thermal imager, an ultraviolet sensor and a high-frequency current transformer, and synchronously acquiring multi-modal data such as partial discharge, temperature distribution, corona discharge and grounding current of the ring main unit; carrying out feature extraction on the collected data, wherein the feature extraction comprises a time-frequency statistical feature and a temperature-load correlation feature of the partial discharge signal; the Dempster-Shafer evidence theory is utilized to carry out fusion calculation on the multi-source feature evidence, and confidence coefficients for insulation degradation, poor contact, arc faults and other fault types are obtained; arc fault prediction is carried out in combination with a long-short-term memory network model; and finally executing a grading early warning strategy according to the fault confidence. The problems of low detection reliability and fault early warning lag of a single sensor are solved, early accurate diagnosis and predictive maintenance of the insulation fault of the ring main unit are realized, and the operation safety and reliability of power equipment are remarkably improved.

The invention relates to an insulator image segmentation method based on an infrared enhancement image of an unmanned aerial vehicle, and relates to the technical field of image processing, and the method comprises the steps: collecting an infrared image sequence through an infrared enhancement camera, obtaining a standard infrared image sequence, and carrying out the state recognition through an image perception prior engine, outputting a predicted insulator attention heat map and a predicted boundary confidence map; optimizing and adjusting the initial threshold segmentation algorithm and the initial region growing algorithm, and obtaining an adaptive threshold segmentation algorithm and an adaptive region growing algorithm; and carrying out image segmentation on the standard infrared image sequence to output an insulator image sequence, and carrying out early warning judgment on the insulator image sequence based on an adaptive early warning mechanism. The method solves the problems that a traditional insulator image segmentation method cannot effectively deal with the problems of low infrared image temperature contrast ratio, large noise interference and complex background, so that the insulator segmentation is easy to cause mistaken segmentation and missing segmentation, and the high-precision requirement of fault detection is difficult to meet.

The invention relates to the technical field of electric power facility safety monitoring, in particular to a substation equipment foundation settlement observation system and method, and the system comprises an infrared monitoring array and a data processing terminal; the method comprises the following steps: acquiring and correcting real-time three-dimensional point cloud data of the basic surface of the equipment; obtaining an effective settling volume; environment parameters or equipment vibration signals are monitored in real time, the collection frequency of the infrared monitoring array is automatically adjusted according to the monitoring result, and an early warning signal is sent out when the effective settling volume exceeds a preset threshold value; through software and hardware cooperation, the problems that in the prior art, a monitoring blind area is large, the anti-interference capacity is weak, and a linkage mechanism is lacked are solved, an intelligent settlement observation solution is provided for a substation equipment foundation, and safe and stable operation of a power grid is effectively guaranteed.

The invention relates to the technical field of infrared measurement, in particular to an intelligent calibration method and system for temperature measurement of an infrared sensor, and the method comprises the steps: enabling an infrared camera to align with a blackbody radiation source to form a calibration scene, collecting the output of a plurality of groups of pixels under different radiation temperatures and different internal part temperature states, fitting a temperature signal response model containing an exponential term and a bias term according to least square to obtain amplitude, exponential, temperature offset and output bias parameters; during measurement, a thermal image containing a target object and two reference blackbody sources is obtained, and parameters such as emissivity, environment equivalent radiation temperature, transmission medium and optical transmission, camera temperature and the like are synchronously obtained; performing non-uniformity and offset correction and abnormal pixel replacement on the thermal image; the initial temperature is inverted based on radiation transfer decomposition and compensation of internal radiation of the camera; and carrying out statistical averaging on the temperatures of the two blackbody reference regions to obtain a double-point correction coefficient, and outputting correction temperature or temperature distribution. According to the method, the problems that temperature inversion model parameters are difficult to adaptively update along with working conditions and on-site temperature measurement errors are difficult to effectively suppress when an existing infrared sensor array is in a long-distance non-contact temperature measurement scene can be solved.

The invention relates to the technical field of carbon detection, in particular to an online real-time dynamic detection method for the carbon content of converter steelmaking molten steel, which comprises the following steps: acquiring multi-source sensing data, and acquiring a molten steel surface temperature image acquired by a thermal infrared imager; acquiring multiband spectral data, namely controlling a visible band sensor to acquire radiation intensity in a range of 500-600nm, and controlling a near-infrared band sensor to acquire reflectivity characteristics in a range of 850-1100nm; analyzing and fusing the data, analyzing the temperature image, and generating a molten pool two-dimensional temperature field; analyzing the multiband spectral data, and extracting a spectral feature vector related to the carbon content; fusing the two-dimensional temperature field and the spectral feature vector, and generating a molten steel surface carbon content distribution diagram based on a pre-trained carbon content machine learning model; and outputting an optimized detection result, processing multi-node data through a distributed computing framework, and outputting a dynamic carbon content detection report. According to the invention, the effects of breaking through the limitation of traditional static detection, real-time component monitoring, full molten pool covering and dynamic feedback are achieved.

The invention relates to the technical field of infrared temperature measurement of power equipment, and provides an infrared temperature measurement compensation method combining physical transmission and depth residual error, which comprises the following steps: acquiring an infrared image and physical parameters related to radiation transmission; establishing a differentiable forward physical model, and calculating radiation brightness; performing first-order physical inversion according to the differentiable forward physical model to obtain a baseline temperature of the target area; constructing a deep residual learning network, taking the infrared image, the baseline temperature and the physical parameters as input, outputting a pixel-level temperature residual estimation value, and correcting the baseline temperature to obtain a corrected temperature; closed-loop joint optimization is constructed and executed, and the closed-loop joint optimization is based on the consistency constraint of the simulated radiance and the actually measured radiance, and parameters of the target distance and the deep residual learning network are synchronously updated; and based on the corrected temperature after closed-loop joint optimization, calculating the temperature difference between the hot spot area and the reference area, carrying out distance normalization processing on the temperature difference, and outputting a standardized temperature difference result.