20 results about "Band ratio" patented technology

The invention discloses an engine oil carbon deposition sampling detection method and system based on multispectral imaging, and the method comprises the steps: carrying out the collection of a to-be-processed multispectral image of an engine oil sample under a multispectral light source, and carrying out the preprocessing of the image to obtain a to-be-processed image; performing dark current correction on the to-be-processed image according to a band ratio method, and enhancing the signal intensity of the to-be-processed image; calculating the spectral reflectivity of the to-be-processed image according to the signal intensity of the to-be-processed image; extracting a three-dimensional feature tensor in the to-be-processed image according to the spectral reflectivity; segmenting the to-be-processed image according to the three-dimensional feature tensor, and screening a carbon deposition region; calculating a carbon deposition probability value of each pixel point in the carbon deposition area according to the spectral reflectivity; and setting a carbon deposition threshold value according to the carbon deposition probability value for judgment so as to judge whether carbon deposition exists in the engine oil sample or not. The similarity between the sample engine oil spectral reflectivity and the reference engine oil is reflected, and a possible carbon deposition area is found, so that the carbon deposition defect is accurately judged and identified.

The invention provides a method and system for detecting impurities on the surface of a processed aluminum plate, and belongs to the field of impurity detection.The method comprises the steps that S1, a multiband light source system is used for sequentially irradiating the surface of the processed aluminum plate, and a multispectral image is obtained; s2, preprocessing the multispectral image to obtain a processed multispectral image; s3, performing feature extraction on the processed multispectral image to obtain waveband ratio features and impurity principal component features, and performing dynamic feature selection based on aluminum plate processing conditions to obtain aluminum plate surface impurity features; s4, carrying out pixel-level segmentation and classification by adopting a lightweight deep learning model to obtain the types of impurities on the surface of the aluminum plate; and S5, performing impurity evaluation by adopting the impurity evaluation model to obtain an impurity severity score, and generating an impurity distribution visualization graph. According to the method, the multiband light source system is used for acquiring the multispectral image, and accurate identification of various impurities is realized through extraction of band ratio features and principal component features.

The invention relates to the technical field of reducer vibration noise optimization, and discloses a planet row coaxial reducer vibration noise optimization method, which comprises the steps of obtaining initial assembly parameters of a reducer, collecting load measurement signals of planet wheels of the reducer under an order frequency sweeping working condition, converting the load measurement signals into equivalent meshing loads based on a calibration curve, and calculating the equivalent meshing loads according to the equivalent meshing loads. Calculating a load imbalance coefficient according to the equivalent meshing load, and taking a peak value of the load imbalance coefficient as a first evaluation index; shell radial acceleration and sound pressure signals are synchronously collected under the order frequency sweeping working condition, the side-band ratio is calculated, and the peak value of the side-band ratio is taken as a second evaluation index; and performing diagnosis shunting based on the first evaluation index and the second evaluation index, determining an assembly parameter which is adjusted preferentially, executing iterative adjustment, and outputting a final assembly parameter after iteration is completed. According to the method, uniform load unbalance caused by assembly errors and side band amplification caused by geometric eccentricity can be effectively separated and solved, and vibration and noise of the speed reducer are reduced fundamentally.

The invention provides an unmanned aerial vehicle inspection identification method and device for a power transmission tower, and relates to the technical field of electric power facility health monitoring. Polar coordinates of stay wire endpoints are obtained through unmanned aerial vehicle laser ranging, the length of the stay wire is calculated, and the deformation abnormity degree is evaluated; on the basis of a five-channel hyperspectral imaging system, orthogonal route scanning imaging is utilized, an insulator is positioned in real time in combination with a CNN algorithm, and the state of the insulator is analyzed through a UV wave band radiation value, a discharge gradient and a temperature difference; extracting reflectivity data of a wave band of 550-700nm, and calculating a corrosion area proportion by adopting a wave band ratio method; and evaluating the health state of the iron tower by combining the stay wire deformation, the insulator abnormal state and the corrosion degree. According to the method, multi-source data and an intelligent algorithm are fused, high-precision detection of stay wire deformation, insulator defects and corrosion area is realized, the problems of low efficiency and single parameter of traditional inspection are solved, and automation and reliability of state evaluation of the power transmission tower are remarkably improved.

The invention relates to the technical field of algal bloom automatic identification, and discloses an algal bloom automatic identification method and system based on multispectral information, and the method comprises the steps: firstly carrying out the standardization processing and mosaic fusion of an original multispectral image, and obtaining high-quality reflectivity data; and then, by calculating a water body-vegetation absorption index and performing intelligent extraction, a water body range is accurately defined, and non-water body interference is eliminated. The potential algal bloom area in the water body is subjected to self-adaptive preliminary screening through the band ratio index, the classification range is narrowed, and the efficiency is improved. And finally, extracting multi-dimensional feature vectors including spectral reflectivity, waveband ratio index, yellowness index and reflection valley depth from the potential algal bloom areas, and inputting the multi-dimensional feature vectors into a pre-trained machine learning classifier model for deep learning and pattern recognition. Therefore, the limitation that a traditional method only depends on a single or few empirical indexes is overcome, more complex and fine spectral characteristics of algae blooms can be captured, and high-precision classification and discrimination are achieved.

Embodiments herein provide a method and system for a vicarious calibration of optical data from satellite sensors for urban scene flat fields. Identifying test sites automatically in the urban scene helps in vicarious calibration or on-board calibration of the hyperspectral / multispectral image. An internal average relative reflectance is calculated to get a relative reflectance of the image. Band ratios for various pixels is determined to assess flatness of the spectrum. Flat field candidates are identified from the various pixels having average band ratio nearing zero and a morphological technique is applied to determine a flat field. Finally, the image is calibrated vicariously based on the determined flat field as a test site. The on-board calibration of the remote sensing image may lead to a faster way to get the reflectance image of the scene, with the help of the calibration constants.

The invention belongs to the field of intelligent power distribution systems, and provides a design method and system for an online harmonic monitoring system of a power system, and the method comprises the following steps: collecting a voltage signal of a transformer in a transformer substation in real time through a voltage transformer; performing frequency domain analysis on the acquired voltage signal by applying Fourier transform, and extracting each frequency component in the signal; setting a time window and a time interval, obtaining the frequency corresponding to the frequency component with the maximum amplitude in each time interval, and recording the frequency as the highest frequency; obtaining a harmonic super-harmonic frequency difference through the highest frequency, and obtaining a harmonic violent wave band time window through the harmonic super-harmonic frequency difference; and obtaining a harmonic violent band ratio through the harmonic violent band time window, and monitoring the operation condition of the power system through the harmonic violent band ratio. According to the design method provided by the embodiment of the invention, the problem can be quickly detected at the initial stage of harmonic pollution, and the harmonic filter is timely started or the load distribution is adjusted, so that the stability of the power system is ensured.

This invention provides a method, apparatus, and device for identifying water bodies on a single orbit of a polar-orbiting meteorological satellite, comprising: acquiring first-band data, first-positioning information data, and second-band data, second-positioning information data, and product data at a first spatial resolution from a polar-orbiting meteorological satellite; sampling the data at the second spatial resolution to obtain second-band data, second-positioning information data, and product data at the first spatial resolution; acquiring high-quality binary classification data; training a decision tree model based on the first-band data, second-band data, normalized index data, band difference data, and band ratio data at the first spatial resolution corresponding to high-quality water body pixels and high-quality non-water body pixels within the target area, to obtain a target decision tree model; and obtaining the single-orbit water body identification area based on the target decision tree model. The solution of this invention accurately identifies water bodies in complex background conditions, improving monitoring accuracy and applicability.

This invention provides a method and device for UAV inspection and identification of power transmission towers, belonging to the field of power facility health monitoring technology. The invention obtains the polar coordinates of the guy wire endpoints and calculates the guy wire length using UAV laser ranging to assess the degree of deformation anomaly; based on a 5-channel hyperspectral imaging system, it utilizes orthogonal flight path scanning imaging combined with a CNN algorithm to locate insulators in real time, and analyzes the insulator condition using UV band radiation values, discharge gradients, and temperature differences; it extracts reflectivity data in the 550-700nm band and calculates the corrosion area ratio using the band ratio method; and it comprehensively assesses the tower's health status by considering guy wire deformation, insulator anomalies, and corrosion degree. This method integrates multi-source data and intelligent algorithms to achieve high-precision detection of guy wire deformation, insulator defects, and corrosion area, solving the problems of low efficiency and single parameters in traditional inspections, and significantly improving the automation and reliability of power transmission tower condition assessment.

The present application relates to the technical field of automatic identification of algal blooms, and discloses a method and system for automatic identification of algal blooms based on multispectral information. The method first performs standardization processing and mosaic fusion on the original multispectral images to obtain high-quality reflectance data. Subsequently, the water body range is accurately defined by calculating the water-vegetation absorption index and performing intelligent extraction, eliminating non-water body interference. The potential algal bloom areas in the water body are adaptively screened using the band ratio index to narrow the classification range and improve efficiency. Finally, multidimensional feature vectors including spectral reflectance, band ratio index, yellowness index and reflection valley depth are extracted from these potential algal bloom areas, and are input into a pre-trained machine learning classifier model for deep learning and pattern recognition, thereby overcoming the limitations of traditional methods that rely only on a single or a few empirical indices, and being able to capture the more complex and subtle spectral characteristics of algal blooms, achieving high-precision classification and discrimination.

The application discloses a vegetation reflectance spectrum measurement method and system for forest monitoring, acquires original spectrum collection signals of a target area of a forest and carries out canopy penetration attenuation correction to establish a benchmark reflectance spectrum set, identifies a forest canopy projection area and atmospheric scattering interference of the benchmark reflectance spectrum set to form a ground true reflectance spectrum, carries out red edge feature positioning and band ratio analysis based on the ground true reflectance spectrum to form a vegetation reflectance spectrum index, carries out multi-time spectrum difference detection on the vegetation reflectance spectrum index and removes phenology interference to determine a change confidence interval, screens abnormal spectra based on the change confidence interval and carries out forest health grade labeling after correction by a spectrum evolution coefficient to output forest coverage change measurement results. The application can effectively eliminate the interference of forest canopy shadow and atmospheric scattering, distinguish normal phenology fluctuation from real health abnormality, and provide reliable spectrum data support for forest vegetation state monitoring.

The present invention discloses a remote sensing inversion method for suspended particulate matter in high-turbidity water bodies applicable to high-resolution satellites, which relates to the technical field of suspended particulate matter monitoring. The method comprises the following steps: collecting water suspended particulate matter concentration data and corresponding water remote sensing reflectivity data, pre-processing the data to obtain equivalent remote sensing reflectivity data; constructing a segmented water suspended particulate matter concentration inversion model; using the remote sensing reflectivity of the red band as the segmentation object, and determining the coefficient R 2 The optimal segmentation threshold was calculated using the root mean square error (RMSE) as model accuracy evaluation metrics. The final segmentation model coefficients for the segmented suspended particulate matter concentration inversion model were obtained using the collected data through least squares fitting. The segmented suspended particulate matter concentration inversion model was then evaluated. This paper employs a segmented modeling strategy, combining a semi-analytical model with an empirical model and using weighted band ratios as model input, significantly improving the accuracy and universality of the inversion algorithm.

The application provides a methane anomaly inversion false positive removal method and system suitable for a short-wave infrared spectral imager, which utilizes spectral indexes, i.e., a normalized water index, a solar panel probability index, and a plastic greenhouse and artificial lawn probability index to identify water bodies, solar panels, plastic greenhouses, and artificial lawns; utilizes a constant color space combined with a threshold method to identify shadows; utilizes a threshold method and a band ratio method to identify clouds; and finally obtains an interference ground feature mask to reduce false positives when using a matching filter algorithm and a short-wave infrared satellite remote sensing image to invert methane concentration anomalies. Thus, the application helps to realize accurate, rapid, and low-cost satellite remote sensing of methane concentration anomalies, and lays a key foundation for identifying and quantifying methane emission sources.

The present invention provides a method and system for detecting the freshness of an object. The method comprises: obtaining an optimal band ratio feature and a texture feature of a hyperspectral image of the object to be detected; and determining the freshness of the object to be detected based on the optimal band ratio feature and the texture feature. The system executes the method. The present invention utilizes the optimal band ratio of the hyperspectral image of the object to be detected to obtain the texture feature of the optimal band ratio image. Based on the optimal band ratio feature and the texture feature, the method enables rapid nondestructive testing of the freshness of the object to be detected, such as crop seeds, significantly reducing the difficulty and cost of developing rapid nondestructive testing equipment.

The application provides a kind of processing aluminum plate surface impurity detection method and system, belong to impurity detection field, including S1, using the multi-band light source system is sequentially irradiated to processing aluminum plate surface, obtains multispectral image;S2, the multispectral image is pretreated, obtains the multispectral image after processing;S3, the multispectral image after processing is extracted, and band ratio feature and impurity principal component feature are obtained, based on aluminum plate processing condition is dynamically selected, and the aluminum plate surface impurity feature is obtained;S4, using lightweight deep learning model is pixel level segmentation and classification, and the aluminum plate surface impurity class is obtained;S5, using impurity evaluation model is impurity evaluation, and the impurity severity score is obtained, and generates impurity distribution visualization chart.The application uses multi-band light source system to obtain multispectral image, through the extraction of band ratio feature and principal component feature, realizes the accurate identification to multiple impurities.

The invention discloses a shallow lake phycocyanin concentration OLCI image remote sensing inversion method, and belongs to the technical field of environmental science and remote sensing monitoring. The method comprises the following steps: firstly, selecting a research range, and acquiring a large amount of actually measured PC concentration data in different periods through lake field sampling; secondly, satellite image acquisition and satellite-ground matching are carried out, a satellite-ground matching data set is obtained, a water body classification method based on a decision tree framework is adopted, fine classification is carried out on different driving types, and water bodies are divided into four types; then, based on a classification result, independently constructing a phycocyanin PC inversion model for each type of water body; and finally, performing inversion and detection on the phycocyanin concentration of the shallow lake by using the phycocyanin prediction model corresponding to each type of water. The method is high in timeliness and simple and convenient to operate, and has a wide business application prospect; a baseline ratio algorithm in the prediction model combines the advantages of a band ratio algorithm and a baseline algorithm in a traditional PC inversion algorithm, generalization and precision are improved, and the baseline ratio algorithm is the key for successful classification of water and successful construction of the model.

The invention discloses a sub-band and sub-band power adjustment method for multiple users in a DTP satellite multi-beam overlapping coverage area. Reasonable user transmitting sub-band and receiving sub-band measurement periods are respectively set by respectively measuring power attenuation conditions of a user transmitting sub-band and a user receiving sub-band. Furthermore, according to the power attenuation condition of the transmitting sub-band or the receiving sub-band of the user in the measurement period, the user needing to be adjusted and the adjustment priority are determined. For a plurality of beams where the current user to be adjusted is located, a candidate beam set is determined according to whether the residual power of each beam and the number of idle sub-bands meet the total sub-band power of the adjusted user and the number of sub-bands occupied by the user or not, and finally, according to the power-band ratio maximization principle of the residual power of the beams and the idle sub-band set, the candidate beam set is determined. And selecting an optimal adjustment beam for the user from the candidate beam set, and allocating the corresponding sub-band and the power of each sub-band for the user in the optimal adjustment beam, so that the signal bandwidth and power allocation advantages of the DTP satellite by taking the sub-band as the granularity are exerted.

This application relates to the field of metal mining technology and proposes a method, apparatus, equipment, and medium for estimating the heavy metal content in soil. The method includes: collecting soil samples from a target area and determining the content of multiple heavy metal elements; acquiring visible-near-infrared hyperspectral reflectance data and X-ray fluorescence spectral data of the soil samples; calculating the competition coefficients between heavy metal elements using the L-V competition model to determine the set of competing elements for the target heavy metal element; constructing a multi-objective optimization function based on the band importance of random forests, with the goal of maximizing the spectral response differences of the target heavy metal element and minimizing the spectral response differences of its competing elements; combining wavelength spacing constraints; using the NSGA-II algorithm to select the optimal set of band ratio features; training a machine learning regression model; and estimating the heavy metal content in the soil of the target area. This scheme significantly improves the inversion accuracy and stability of the model.

The invention belongs to the technical field of water depth inversion, and particularly relates to a water depth inversion method based on multispectral remote sensing, which comprises the following steps: S1, preprocessing remote sensing image data, and extracting image reflectivity; screening the actually measured water depth data, and ensuring that the actually measured water depth data is unified with a space coordinate system of the remote sensing image; s2, dividing a training set and a test set through stratified sampling; s3, selecting a feature with a high Pearson's correlation coefficient as a water depth inversion factor; s4, on the basis of the selected water depth inversion factors, a random forest model, an XGBoost model and a fusion model combining a band ratio and a random forest algorithm are constructed to serve as water depth inversion models; and S5, obtaining predicted water depth data through the water depth inversion model, and drawing a water depth distribution diagram of the research area. The method is high in model robustness, can adapt to a complex environment, is high in inversion precision, carries out the precision evaluation and comparison of a plurality of models, provides a scientific basis for a user to select an optimal inversion result, and improves the reliability and credibility of the result of the whole method.

The invention discloses a target flight event prediction method based on an infrared dual-band ratio, and the method comprises the steps: S1, carrying out the time dimension alignment of target medium-wave infrared time sequence data and target short-wave infrared time sequence data obtained by detectors with different sampling frequencies; s2, calculating the ratio of the medium-wave body infrared radiation data to the short-wave body infrared radiation data; s3, taking the augmented target dual-band radiation ratio data and the augmented artificial prior feature data as input data; and S4, carrying out joint training on the input data by adopting a multi-channel long-short-term memory network model to obtain a target flight event prediction result. According to the method, the dual-band ratio information and the artificial features are jointly output to the multi-channel neural network, abnormal activities of the flight target can be found in advance, non-cooperative target threat judgment and prediction are achieved, and a multi-level defense cooperation mechanism is supported.