633 results about "Ft ir spectra" patented technology

The invention relates to the technical field of remote monitoring, in particular to a remote monitoring system and method based on a cloud platform. The method comprises the following steps: acquiring multispectral video image data of an industrial site; analyzing the difference characteristics of visible light and infrared spectrum based on the original data of the multispectral video image, identifying the characteristic mode of electromagnetic interference, and generating spectrum difference interference fingerprint data; identifying an uninterfered area according to the spectrum difference interference fingerprint data to obtain spectrum reliability distribution data; extracting non-interfered spectrum section information based on the spectrum reliability distribution data; performing complementary fusion on the non-interfered spectrum section information and the interfered area to obtain texture enhanced video data; and analyzing the time-varying characteristic of electromagnetic interference by using the texture enhanced video data to obtain interference time sequence characteristic data. According to the invention, dynamic calibration of interference is realized by analyzing the deformation characteristic of the calibration frame, and the adaptive capacity of the system to a changing electromagnetic environment is remarkably improved.

The invention relates to an infrared spectrum baseline drift dynamic compensation method and system based on multi-parameter fusion, and the method comprises the steps: continuously obtaining spectrum data, current environment parameters and light source state data of a target oil full wave band, carrying out the feature extraction, and obtaining a baseline drift sensitive feature set and a coupling feature set; constructing a prediction model taking the baseline drift amount as output, and taking the baseline drift sensitive feature set and the coupling feature set as input; the reverse baseline drift amount based on the baseline offset of each wave band is superposed in the spectral data as compensation; according to the invention, through multi-source data acquisition and analysis, monitoring of spectrum baseline drift influence factors is realized; accurate correction of different spectral characteristics is realized through a dynamic compensation strategy special for a wave band; and through a continuous self-adaptive updating mechanism, stable tracking of the long-term drift trend is realized, and the effect of prolonging the equipment maintenance period is achieved.

The invention discloses a soil heavy metal inversion method and system integrating satellite remote sensing and near-end sensing, and the method comprises the steps: collecting a soil sample, and measuring the soil heavy metal content and a soil visible light-near infrared spectrum; obtaining a time sequence multispectral image of a research area, calculating a spectral index, and selecting and screening bare soil pixels through a threshold value to obtain a bare soil image; performing spectrum correction on the bare soil image; obtaining a joint dictionary and a sparse coefficient through sparse representation and dictionary learning, and reconstructing a hyperspectral image of the bare soil image; converting the one-dimensional spectral data into a two-dimensional spectrogram by using continuous wavelet transform, extracting spectral features in combination with a 2D-CNN algorithm, and constructing a soil heavy metal inversion model; and using the trained inversion model to predict the soil heavy metal content of the research area based on the reconstructed hyperspectral image. According to the method, satellite remote sensing and near-end sensing are integrated to obtain a large-scale accurate soil heavy metal content distribution map, deep features are extracted in combination with a 2D-CNN algorithm, and the inversion model precision and model efficiency are improved.

A rock mass mechanical parameter prediction method based on an underground powerhouse of a pumped storage power station relates to the technical field of mechanical parameter prediction, and comprises the following steps: identifying an embedding position and distribution characteristics of a sulfur-containing shale interlayer by constructing a three-dimensional geologic structure model and lithologic information of a construction area, and establishing a structural domain partition parameter prediction area system; constructing a nonlinear regression function model taking infrared spectrum characteristic factors and environment control parameters as input and taking elasticity modulus and the like as output by combining infrared spectrum characteristic data and a historical evolution track; the model is embedded into a regional system for dynamic assignment, and a parameter space-time change atlas is constructed; the laser point cloud data and the micro-seismic monitoring data are fused to invert the actual response of the surrounding rock; when the monitoring value deviates from the prediction map, triggering parameter degradation function re-calibration to complete parameter dynamic updating and prediction closed loop; according to the method, the accuracy of rock mass mechanical parameter prediction of the pumped storage power station underground powerhouse can be improved.

The present invention belongs to the technical field of coal gangue recognition and sorting, and in particular, relates to a coal gangue recognition method based on visible-near-infrared spectrum and image multi-modal information fusion. The method includes: S1 collecting spectral information and image information about a sample to be recognized; S2 preprocessing the spectral information and the image information respectively; S3 extracting spectral features from a spectral data set by using a spectral feature extraction neural network model; and extracting image features from an image data set by using an image feature extraction neural network model; S4 inputting the spectral features and the image features obtained from feature extraction into a two-stream fusion network; S5 inputting extracted spectral features, extracted image features and a comprehensive feature into a spectral branch classifier, an image branch classifier and a fusion branch classifier, respectively; S6 calculating importance weights of a spectral branch, an image branch and an image-spectrum fusion branch; and S7 subjecting the importance weights and corresponding confidence to multiply-accumulate operation to obtain a score matrix of coal gangue, and using the score matrix to achieve coal gangue recognition.

The invention provides a near-infrared spectrum rapid detection method and system for effective components of a herbicide, which are applied to the technical field of agricultural detection.The near-infrared spectrum rapid detection method comprises the steps of obtaining near-infrared spectrum data of a to-be-detected soil area and moisture content and compactness numerical values reflecting soil texture differences, and calculating the effective components of the herbicide on the basis of a preset interval to which the moisture content numerical value belongs; according to the method, the corresponding preset scattering correction parameter is selected, the actual scattering correction parameter is obtained through compactness numerical calculation, fine adjustment of the scattering correction parameter is achieved, and the quality and reliability of spectral data are improved through spectral correction; and the corrected spectrum is analyzed, so that the real condition of the herbicide in the soil can be reflected more accurately. Therefore, the interference of complex soil texture in the field on near infrared spectrum herbicide detection is effectively overcome, and the beneficial effect of rapidly and accurately detecting the effective components of the herbicide under the complex soil condition is achieved.

The invention provides a near infrared spectrum qualitative discrimination method and system, and the method comprises the steps: collecting a near infrared spectrum data set, and converting a one-dimensional spectrum sequence into a polar coordinate image; constructing a lightweight convolutional network architecture, and processing the polar coordinate image; k samples of each type are dynamically extracted from the training set, a support set is constructed based on the K samples and a few-sample strategy, and a query support similarity matrix is constructed through a cosine similarity algorithm; and judging the test set based on the query support similarity matrix, the support set and a preset threshold to obtain the highest similarity between the test set and the support set, and if the highest similarity exceeds the preset threshold, judging that the test set is a corresponding category. According to the method, the cost and time of sample collection and preparation can be reduced, the feature information of the spectral data is effectively extracted, and the method has relatively high flexibility during discrimination.

The invention relates to the field of gas concentration detection, and particularly discloses a real-time feedback and adaptive learning method for natural gas infrared spectrum measurement, which comprises the following steps: S1, acquiring infrared spectrum information of a natural gas body by using an infrared spectrometer, and constructing a historical sample set; s2, preprocessing the spectral data of the historical sample set; s3, selecting an optimal algorithm and a hyper-parameter by adopting XGBoost and Bayesian optimization; s4, constructing a qualitative model to identify gas types and match data; s5, calculating the similarity between a field sample and a historical sample through a Siamese network, and setting a threshold value to screen local data; s6, improving the KNN to construct a local dynamic quantitative model to predict the concentration; s7, processing low-similarity abnormal data by the global dynamic model, and improving the reliability by combining moving average and abnormal calibration; and S8, introducing reinforcement learning and online gradient descent to adjust parameters in real time to optimize the precision. According to the technical scheme, high-accuracy natural gas detection can be carried out in a complex environment.

The invention discloses a space-space-ground-well integrated carbon dioxide sequestration leakage monitoring method and system, and relates to the technical field of carbon dioxide sequestration leakage monitoring, and the method comprises the steps: carrying out the remote sensing measurement of a target region through a synthetic aperture radar and a satellite, and determining a carbon dioxide sequestration leakage monitoring boundary; performing photogrammetry by using an unmanned aerial vehicle, and constructing a three-dimensional real scene model Surveying the target area on site to obtain surveying and mapping data; based on the key monitoring area, an absorption spectrum and earth surface leakage flux are obtained through infrared spectrum monitoring and vorticity monitoring; gravity monitoring data, storage area underground water parameters, carbon dioxide plume distribution conditions and logging sound wave frequency distribution characteristic graphs are obtained through gravity monitoring, underground water monitoring, optical fiber monitoring and sound wave logging monitoring. And through comprehensive analysis, according to a comprehensive analysis result, whether carbon dioxide leaks is judged, and a carbon dioxide sequestration leakage monitoring result is obtained. The accuracy of carbon dioxide sequestration monitoring can be effectively improved.

The invention relates to the technical field of American ginseng quality detection, in particular to an American ginseng quality detection method and system based on S-transformation and multi-task deep learning. Near infrared spectrum data of American ginseng to be detected are extracted, and time-frequency conversion is performed on the near infrared spectrum data through S-transformation; obtaining a time-frequency characteristic pattern used for representing time-frequency domain information of the near infrared spectrum; and inputting the to-be-detected American ginseng time-frequency feature map into a pre-trained multi-task deep learning model, and identifying the producing area of the to-be-detected American ginseng and predicting the quality index of the to-be-detected American ginseng by using the multi-task deep learning model. The multi-task deep learning model comprises a feature extraction network used for extracting time-frequency features of each detection task of the feature map, a feature interaction network used for enhancing feature complementation between the tasks and performing feature fusion, and a multi-task head network used for executing a production place identification task and a quality index prediction task according to the time-frequency features and outputting the tasks. The method can be suitable for quality analysis and origin classification of small American ginseng samples.

The invention belongs to the technical field of target detection, particularly relates to an infrared weak and small target detection method based on cross-domain feature migration, and provides a novel detection network which is SA-YOLO for short. Firstly, a position guidance enhancement module is designed under the inspiration of a multispectral feature migration idea, and infrared spectral feature response of a weak and small target is effectively enhanced through a triple structure of position perception attention, multi-scale significance fusion and position guidance filtering. Secondly, a spectrum-space sensing module is provided, and a progressive voidage and a double-branch attention mechanism are adopted, so that robustness modeling of multi-scale spatial features is realized. Then, a sensitivity stability loss function is innovatively proposed, and the problem of sudden drop of scale sensitivity of a weak and small target is relieved through dynamic penalty weight and composite normalization. And finally, an infrared small target data set named as ISUAV-D is also collected. Experimental results show that SA-YOLO shows significant advantages in a strong light interference environment and a tiny target detection scene.

The invention discloses a high-precision methane infrared spectrogram intelligent detection method and system, and relates to the field of infrared spectrum determination.The method comprises the steps that multi-wavelength signals of a target detection area are collected, the change process of the multi-wavelength signals is continuously monitored, a change sequence of the wavelength signals is obtained, the change sequence is analyzed, and a methane infrared spectrogram is obtained. Obtaining a first time sequence characteristic of the main absorption wavelength signal relative to the first reference wavelength signal and a second time sequence characteristic of the main absorption wavelength signal relative to the second reference wavelength signal, and determining a judgment threshold according to real-time change states of the first reference wavelength signal and the second reference wavelength signal; and when the change of the main absorption wavelength signal is detected to be earlier than the change of any reference wavelength signal and the change amplitude of the main absorption wavelength signal is greater than a judgment threshold value, calculating the methane concentration value according to the change time difference and the change amplitude ratio of the main absorption wavelength signal and the reference wavelength signal. By implementing the method, the response time of methane leakage identification can be reduced.

The invention relates to an automatic magnesium oxide coating analysis system and method, and belongs to the technical field of coating quality analysis. The method comprises the following steps: acquiring attributes of a magnesium oxide coating, an infrared spectrum and environment temperature and humidity data, and constructing a multi-source feature set; analyzing the distribution dispersion and local enrichment risk of free water based on a dynamic migration model, and analyzing the bonding weakening degree and dissociation risk of bound water based on a chemical stable state model; respectively generating free water treatment process parameters for inhibiting structural damage and combined water treatment process parameters for preventing performance degradation according to the abnormal analysis result and the coating attribute; by performing coupling influence analysis on the two types of process parameters, the balance requirements of the structural integrity and chemical stability of the coating are coordinated, an optimized process scheme is generated, and cooperative regulation is realized. According to the method, accurate identification, independent analysis and cooperative control of the free water and the bound water are realized, and the accuracy, reliability and quality consistency of coating water treatment are improved.

The invention provides a cable insulation aging state evaluation method and device and a detection system. The method comprises the following steps: acquiring near-infrared spectrum data which is a light intensity value of a cable for absorbing near-infrared light; according to the near infrared spectrum data, the crystallinity of the cable is determined, and the crystallinity is the proportion of the volume of a crystallization area of a polymer material of the cable to the material volume of the whole cable; according to the crystallinity and a first preset mapping relation, the aging index of the cable is determined, and the first preset mapping relation is the mapping relation between the crystallinity and the aging index; and when the aging index is greater than a first preset index threshold, controlling the alarm device to give an alarm. The method solves the problem that the aging state of the cable cannot be accurately evaluated in the prior art.

The invention relates to the technical field of data processing, and discloses an automatic control method and system for wastewater desalination treatment based on the Internet of Things. The method comprises the following steps: acquiring a membrane surface image, an interface potential, an infrared spectrum, a transmembrane pressure difference, a feeding flow rate and a flocculation image; calculating a salt gradient attention feature vector according to the salt concentration difference value and the Beckey number; inputting the multi-modal data into a membrane pollution time sequence memory network to obtain a pollution development rate and a flocculant adding proportion; extracting a floc particle size parameter to obtain a flocculation effect score; and constructing a multi-objective function optimization solution to obtain a control instruction. The salt gradient attention mechanism and the membrane pollution time sequence memory network are introduced, and visual image recognition and multi-modal data fusion are combined, so that the problems of inaccurate membrane pollution state evaluation, low intelligent level of a control algorithm and insufficient multi-target collaborative optimization capability in the prior art are solved; the automation level and the treatment efficiency of wastewater desalination treatment are improved.

The invention relates to the technical field of fire safety monitoring, and discloses a linear fire detection method and system based on infrared spectrum analysis and an infrared spectrum detector, and the method comprises the steps: arranging a plurality of infrared spectrum detector nodes; the collected infrared spectrum signals are preprocessed; mapping the preprocessed spectral signal to a pre-constructed low-dimensional feature space; calculating the conditional probability density of the current infrared spectrum signal in various states; judging whether the posterior probability exceeds a set fire judgment threshold value or not; the system comprises an infrared spectrum detection module, a preprocessing module, an embedded mapping module, a discrimination module and a linkage control module. The infrared spectrum detector comprises a spectrum sensing unit, a signal processing unit, a data communication unit and a data uploading interface. According to the method, the technical effects of keeping high sensitivity and low false alarm rate under complex and changeable working conditions are achieved through a combined discrimination model of embedding mapping, kernel density estimation and Bayesian posteriori.

The invention is suitable for the technical field of electric power line inspection, and provides an improved YOLO network-based electric power line metal corrosion detection and region segmentation method, which comprises the following steps of: aligning a collected visible light image and an infrared spectrum image at spatial positions; after carrying out defogging processing on the visible light image, judging whether the defogged image has a corrosion region or not based on the color shift feature and the texture distortion feature; if yes, inputting the visible light image and the infrared spectrum image into an improved YOLO-MA network for processing, and outputting the classification probability and confidence of the corrosion area; inputting the corrosion area with the classification probability and the confidence higher than a preset threshold value into a preset segmentation network, and determining a corrosion level corresponding to the corrosion area; and generating a visual report according to the corrosion level, and superposing the corrosion region bounding box, the segmentation mask and the corrosion level on the original image. The detection efficiency is improved while the omission ratio is reduced, and the real-time monitoring and maintenance requirements of complex industrial scenes are met.

The invention relates to the technical field of molecular simulation, and provides a method for constructing a heavy component average molecular structure of thickened oil, which comprises the following steps: inputting parameters, generating a core aromatic ring unit, constructing an inner-layer aliphatic ring unit, constructing an outer-layer aliphatic ring unit, generating a side chain unit, and storing and outputting structural information. A plurality of molecular structures meeting experimental data are generated at a time through a traversal algorithm thought, composition characteristics of a heavy oil complex mixture are met, more available molecular structures are provided for molecular simulation, the accuracy and efficiency of molecular modeling are improved, the molecular structures completely meeting the experimental data can be rapidly generated, and the method is suitable for large-scale popularization and application. And only the mass spectrometer, the elemental analyzer, the nuclear magnetic resonance hydrogen spectrum and the infrared spectrum data need to be input, so that the experiment cost is reduced.

The invention mainly relates to the technical field of spectral analysis, and provides a white spirit brewing spectral analysis method and system based on a multi-modal large language model in order to efficiently and accurately obtain white spirit brewing information according to multi-modal spectral data of all links of white spirit brewing. The method is characterized by comprising the following steps of: collecting multi-mode spectrum data including gas chromatography data, infrared spectrum data and Raman spectrum data in each link of white spirit brewing; converting the multi-modal spectral data and the natural language instruction into a feature embedding vector which can be recognized by a large language model to train the large language model which is finely adjusted based on professional knowledge in the field of white spirit, and receiving a question of a user in a natural language instruction form by the trained large language model; wine making analysis tasks such as flavor compound identification, key microorganism detection, fermentation state evaluation and base wine quality grading are carried out, and finally analysis results and wine making process optimization suggestions are output in a natural language form, so that the accuracy and efficiency of spectrum data interpretation in the white wine making process are improved.

The embodiment of the invention provides a fire source trend prediction method and device, equipment, a medium and a product. The method comprises the following steps: acquiring multi-source data such as a visible light image, an infrared image and environmental data of a target area, respectively carrying out fire source feature extraction on the visible light image and the infrared image to obtain a visible spectrum feature and an infrared spectrum feature, then fusing the visible spectrum feature and the infrared spectrum feature to obtain a fire source fusion feature, and finally obtaining a fire source fusion feature; space-time coding fusion processing is carried out on the continuous geographic coordinates corresponding to the target area, the fire source fusion features and meteorological features in the environmental data to generate fire source space-time coding features, space-time correlation focusing processing is carried out on the fire source space-time coding features to obtain fire source space-time deepening features, and finally, the fire source space-time deepening features are obtained. And performing multi-scale space-time decoding on the fire source space-time deepening features to obtain a fire source prediction probability graph of the target area. The method is used for achieving the effect of improving the fire source trend prediction precision.

The invention discloses an intelligent monitoring method for casing gas recovery, and relates to the technical field of casing gas detection and analys.The method comprises the steps that casing gas parameters including gas pressure, instantaneous gas volume and gas temperature are collected in real time, and spectrum data are collected through ultraviolet and infrared spectrum sensors; a scattering feature calculation module and a shielding feature calculation module are introduced into a Transformer network, oil mist scattering interference features and shielding interference features are extracted, weight fusion is performed on ultraviolet and infrared spectrum data, interference spectrum hazard values are eliminated, a net absorption spectrum is generated, and concentration is calculated in combination with the Lambert-Beer law to perform safety early warning. Coupling between casing gas parameters is considered, and a long-short-term memory network is adopted to construct a pressure coupling model to correct a pressure threshold value; and the electric control adjustable pipeline is controlled through a PID algorithm to dynamically adjust the drift diameter, so that precise monitoring, safe early warning and stable recovery of the sleeve gas are realized.

The invention relates to an ultraviolet-visible-infrared tri-spectral image adaptive fusion method, which comprises the following steps: acquiring a tri-spectral image of high-voltage equipment as an original image, and preprocessing to obtain a preprocessed image; constructing a feature extraction calculation network to obtain feature vectors of the three-spectrum image; calculating the contribution weight of each image in the fusion result; and carrying out multi-scale weighted fusion on the three-spectrum image, and carrying out post-processing on the final fused image. According to the method, the information integrity and accuracy are remarkably improved, the characteristics of a target under different spectrums can be obtained, the problem that information of a single spectrum is one-sided is solved, and tiny flaws which are difficult to distinguish by naked eyes and are fuzzy in thermal imaging can be accurately positioned in industrial defect detection; the imaging of the visible light spectrum is limited in a severe environment, and the ultraviolet and infrared spectrums can still work in low-light, smog, haze and other scenes; after fusion, the system can stably acquire images in a complex environment.

The invention relates to the technical field of image processing, in particular to a multispectral imaging recognition method and device for mountain tunnel lining corrosion defects. According to the method, an infrared spectrum image is taken as a reference image, image registration is carried out on an acquired visible spectrum image, and a visible spectrum image A and an infrared spectrum image B are decomposed through an NSST function to obtain a low-frequency sub-band and a high-frequency sub-band; the method comprises the following steps of: acquiring a low-frequency sub-band and a high-frequency sub-band, fusing the acquired low-frequency sub-band and high-frequency sub-band to acquire a high-frequency sub-band fused image and a low-frequency sub-band fused image, and reconstructing the low-frequency sub-band fused image and the high-frequency sub-band fused image through NSST inverse to acquire a fused image, so that the fused image has higher definition and contrast in various surface defects, and the image quality is improved. The detail texture information of the visible light image and the thermal radiation information of the infrared image are fully fused, the low-contrast defect features are better highlighted, and high-quality data support is provided for subsequent defect detection.

The embodiment of the invention provides a method for testing the performance of infrared spectrum video equipment, and belongs to the technical field of infrared spectrum imaging. The method comprises the following steps: starting an infrared spectrum video measurement and control platform and infrared spectrum video equipment to obtain a field of view of the infrared spectrum video equipment; selecting an acquisition area in the field of view of the infrared spectrum video equipment; based on the gas required by performance test and the concentration of the gas, filling the gas into a measurement gas chamber of the infrared spectrum video measurement and control platform; adjusting the infrared spectrum video measurement and control platform to simulate an environment required by performance test; collecting a first response value of the infrared spectrum video equipment in the environment; and calculating a performance test result of the infrared spectrum video equipment based on the first response value. The method is used for solving the problem that in the prior art, various possible errors exist in gas performance testing of infrared spectrum video equipment at different concentrations and different temperatures.

The invention relates to the technical field of composite material performance prediction, and discloses a composite material flame retardant property and thermal stability prediction method based on thermogravimetric analysis and infrared spectrum data fusion. The invention aims to solve the problem that it is difficult to effectively fuse multi-modal data to realize quantitative prediction in the prior art. The method comprises the following steps: firstly, collecting thermogravimetric and infrared spectrum combined data, calculating gas transmission lag time, and carrying out forward correction on infrared spectrum data to realize time sequence alignment; then solid-phase thermogravimetric feature vectors and gas-phase infrared feature vectors are extracted, and a fusion feature matrix is generated; extracting solid-phase and gas-phase characteristics respectively by using a dual-channel LSTM network, and performing weighted fusion based on a self-attention mechanism; and finally, synchronously outputting a flame retardant index predicted value and a flame retardant grade classification result through numerical mapping and classification judgment. According to the method, multi-dimensional feature mining of the whole pyrolysis process of the material is realized, and a rapid and accurate digital evaluation means is provided for screening of the flame-retardant material.

The invention belongs to the technical field of physical and chemical index prediction of jerusalem artichoke, and particularly relates to a method for rapidly predicting main physical and chemical indexes in jerusalem artichoke based on a near infrared spectrum technology. The method mainly comprises the steps of pretreatment of a jerusalem artichoke sample, acquisition of physicochemical indexes and spectral data, pretreatment of the spectral data, screening of characteristic wave bands, establishment of a quantitative prediction model, evaluation and verification of model performance and the like. According to the invention, a partial least squares regression (PLSR) model of inulin, protein, total polyphenol and ash content in jerusalem artichoke is established, and an external verification result of the model shows that detection results obtained by a near infrared spectrum and a standard chemical method have no significant difference. Compared with a traditional chemical detection method, the near-infrared technology improves the analysis efficiency and reduces the detection cost on the basis of meeting basic detection requirements, and has an application prospect in rapid prediction of the physical and chemical indexes of the jerusalem artichoke in actual production.

The invention relates to the technical field of image recognition, in particular to an intelligent solid waste recognition system based on multi-modal fusion, which comprises a data acquisition module, a visual guidance module, a recognition module and a cooperative processing module, and is characterized in that the system quickly analyzes surface information such as visible light images, three-dimensional point clouds and infrared spectrums of materials through the visual guidance module; and coding to generate a dynamic vector for guiding a subsequent refined analysis task. The identification module constructs a two-dimensional pixel map on the internal density map according to the guidance of the dynamic vectors, and utilizes a map neural network to deeply analyze local density textures so as to identify high-value components such as an internal circuit board; and the co-processing module performs template matching of the internal components and external surface anomaly detection according to the instruction so as to assess the security risk. According to the method, the external form and internal structure information of the material are deeply fused through the strategy of coarse first, fine second and dynamic guidance, and intelligent recognition of the complex electronic waste is achieved.

The invention relates to a coal mine flame pseudo fire interference suppression method based on time sequence consistency analysis, and belongs to the technical field of coal mine safety monitoring. According to the method, aiming at the problem of high false alarm rate of a flame detection system caused by false fire source interference such as electric welding sparks and mechanical friction in a mine environment, the flame identification accuracy is improved through time sequence consistency analysis. According to the technical scheme, the method comprises the following steps: acquiring an infrared spectrum flame saliency probability graph of continuous image frames in an underground coal mine; extracting a flame candidate region by adopting Otsu adaptive threshold segmentation in combination with morphological optimization and hole filling; and performing time sequence consistency analysis on the continuous frame candidate regions, and calculating the spatial overlapping degree, the morphological stability and the infrared thermal intensity change trend of adjacent frames. According to the method, the false fire false alarm rate is remarkably reduced, the robustness to complex backgrounds and dynamic illumination is enhanced, the method can be independently integrated to an existing infrared flame detection system or used in cooperation with a deep learning model, and the reliability of mine fire monitoring is improved.

The invention relates to the technical field of spectral analysis, in particular to a passive Fourier infrared spectrometer-based temperature drift correction method, which comprises the following steps of: acquiring original interferogram data and temperature sensor data of a passive Fourier infrared spectrometer; and screening features to construct a training sample set, and training a convolutional neural network to obtain a spectral temperature drift correction model of a specific environment temperature. Taking a specific model as a benchmark, inputting adjacent temperature combinations into a benchmark network to calculate a loss value, judging whether the loss value is applicable or not, and obtaining a spectrum temperature drift correction model in a wider temperature range. And inputting temperature and spectrum data in real time, and outputting the corrected light intensity-wavelength to realize temperature drift correction. Through the prediction network fusion strategy and the dynamic time warping algorithm, the calculation amount and the sample data amount of model training are reduced, the adaptability and generalization ability of the model to data under different temperature conditions are improved, and accurate temperature drift correction is achieved.

The invention discloses a method for correcting abnormal points of near infrared spectrum data of gas in transformer oil, which relates to the technical field of infrared spectrum abnormal point correction and comprises the following steps: acquiring a first ordinate threshold and a second ordinate threshold based on a historical normal spectrogram; obtaining a historical offset value based on the first fluctuation threshold, the second fluctuation threshold and the historical abnormal spectrogram; acquiring a gas influence wavelength and a gas non-influence wavelength based on the historical deviation value; acquiring a detection longitudinal axis value based on the unaffected wavelength of the gas and the real-time spectrogram; acquiring an offset difference value based on the detected longitudinal axis value; correcting the real-time spectrogram based on the offset difference value to obtain a final spectrum curve; the method is used for solving the problem that the judgment and correction of the infrared spectrum abnormal points are not accurate due to the fact that the change of the gas content in the transformer oil is not considered in the existing infrared spectrum abnormal point correction technology.