2602 results about "Wave band" patented technology

The invention relates to the field of river pollution treatment, and discloses a water quality treatment method, system, equipment and medium based on a remote sensing inversion technology, and the method comprises the following steps: S1, collecting a hyperspectral remote sensing image of a to-be-monitored water body, the hyperspectral remote sensing image comprising chlorophyll concentration, suspended matter concentration and dissolved oxygen; s2, based on the hyperspectral remote sensing image, preprocessing the hyperspectral remote sensing image, including geometric correction, atmospheric correction, noise removal and waveband fusion, to obtain a preprocessed image meeting an inversion precision requirement; and S3, inputting the preprocessed image into a water quality inversion model, and obtaining pixel-level chlorophyll, suspended solids and dissolved oxygen concentration data based on physical radiation transmission. By combining the hyperspectral remote sensing image and the water quality inversion model, large-range, real-time and high-precision water quality monitoring and pollution zoning are realized, model parameters are dynamically updated, and the timeliness and precision of water quality treatment are improved.

The invention discloses a method and a system for reflecting a growth state by a spectral curve of a plant leaf under salt mist stress and a storage medium, and relates to the field of plant growth monitoring. Processing the collected leaf image, extracting a leaf main body area, removing an interference part, and matching the filtered original spectral data to obtain target spectral data of the leaf main body area; the method comprises the following steps: combining target spectral data of a leaf main body area with leaf moisture content data, constructing a spectral analysis model by adopting a partial least square method, taking the leaf moisture content as a covariable into the spectral analysis model, peeling off the interference of leaf moisture change on the spectral data, and calculating the leaf moisture content. And finally obtaining a target spectrum curve of the plant leaf under the salt mist stress. A characteristic wave band corresponding to the target spectrum curve is extracted, a plant growth index is constructed according to the characteristic wave band, and the plant growth index is used for reflecting the growth health state of the plant. The problems that traditional monitoring is high in subjectivity, insufficient in precision and low in efficiency are solved.

The invention belongs to the technical field of radar wave-absorbing materials, and discloses a double-sided wave-absorbing broadband stealth composite material and a preparation method thereof. The broadband stealth composite material with the double-sided wave absorbing function comprises a non-metal-based composite material body and at least five metamaterial layers arranged in the thickness direction of the non-metal-based composite material body at intervals. The number of the metamaterial layers is an odd number, a middle metamaterial layer is arranged in the middle of the metamaterial layers in the thickness direction, and the rest metamaterial layers are symmetrically distributed on the two sides of the middle metamaterial layer; the metamaterial layer comprises conductive pattern units which are periodically arranged; the relative dielectric constant of the nonmetal-based composite material body is not greater than 10; the double-sided wave-absorbing broadband stealth composite material does not contain a metal reflecting layer. When electromagnetic waves with the frequency of 1-18 GHz vertically enter the material, the transmissivity of the material is small (smaller than-13 dB, namely smaller than 5%), and meanwhile the material has low reflectivity in a broadband. And the preparation method is simple to operate and easy to realize large-scale production.

The invention discloses a crop disease and insect pest image recognition algorithm based on dynamic adaptive multispectral fusion Transform, and belongs to the crossing field of agricultural information technology and computer vision. The objective of the invention is to solve the problems of insufficient multi-spectral feature fusion, poor complex background adaptability and insufficient precision in traditional recognition. Acquiring pest and disease damage images of crops in different wave bands (visible light, near-infrared light and the like) to construct a data set; through a dynamic adaptive fusion module, spectral weight distribution is learned in real time based on an attention mechanism, weights are adjusted according to spectral response differences of disease and insect pest areas, and accurate feature aggregation is achieved; the fusion features are input into an improved Transform model, a self-attention mechanism of crop semantic priori knowledge is introduced, focusing of key features of diseases and insect pests is enhanced, and background interference is inhibited; and finally outputting the disease and pest category and confidence. According to the method, through dynamic fusion and Transform cooperation, the recognition accuracy and robustness in a complex scene are improved, support is provided for early warning and prevention of diseases and insect pests, and the application value is remarkable.

The invention discloses supercritical fluid foaming polyimide wave-absorbing foam and a preparation method thereof.The thermoplastic polyimide is prepared by the steps that aromatic dianhydride, aromatic diamine and aliphatic diamine serve as comonomers, polyimide acid is generated through solution polymerization, and then high-temperature cyclization is conducted; wherein the mole number of the aliphatic diamine accounts for 10-50% of the total mole number of the diamine, and the free volume and glass transition temperature of a molecular chain are effectively regulated and controlled by introduction of the aliphatic diamine; the wave-absorbing foam is prepared by taking polyimide as a matrix, doping 1-20wt% of wave-absorbing filler, and foaming for 1-3 hours in a supercritical carbon dioxide or nitrogen environment at the temperature of 180-220 DEG C and the pressure of 9-25MPa, through molecular structure design, the adsorption capacity and impact resistance of polyimide to carbon dioxide are remarkably improved, and the prepared foam has the advantages of light weight, high porosity, uniform foam structure and excellent heat resistance, can realize effective electromagnetic wave absorption in an X wave band, and is suitable for the fields of aerospace stealth and electromagnetic compatibility protection.

The invention discloses a light aging test instrument, which includes a test cavity, a bottom baseplate, the top cover board which is fixed on the bottom and the top of the test cavity respectively. A lamp socket which fitted with a lamp is right on the centre of the base plate, the photo permeable holes are set at the same height on the side wall of the test cavity. A visual light narrow wave band filter sheet is in the photo permeable hole, and the sample trough is fitted on the photo permeable hole. The test instrument has a compact structure, easy to disassemble and assemble, inflexible for employ, with low cost of the production.

The invention discloses a process and a device for continuously synthesizing vitamin D3 through photocatalysis, and relates to the technical field of process on-line monitoring and intelligent control. Photochemical reaction requirements under raw materials with different concentrations are matched through multi-band illumination weight dynamic distribution; when the concentration of the 7-dehydrocholesterol fluctuates, the illumination ratio of 290nm to 305nm wavebands is adjusted in real time by a reinforcement learning algorithm, so that the conversion of a pre-vitamin D3 intermediate is more efficient, and the competitive consumption of a by-product path is reduced; the reaction retention time is further optimized through synchronous flow rate adjustment, and local excessive irradiation caused by sudden concentration increase is avoided; the environment temperature and humidity change is incorporated into a state vector, and the control module generates a compensation instruction accordingly; and if the humidity changes suddenly, pulse type flow compensation is triggered to counteract reaction efficiency attenuation. A closed-loop response mechanism reduces the dependence on physical temperature control equipment, and is especially suitable for mobile production scenes.

The invention discloses an insulator pollution intelligent identification method and system based on a multispectral laser radar, and belongs to the technical field of external insulation state monitoring and intelligent operation and maintenance of a power system, and the method comprises the steps: S1, employing a multi-wavelength laser radar which comprises at least three emission wavebands and comprises 1550 nm to scan an insulator, and selecting at least three wavebands from {355 nm, 405 nm, 532 nm, 905 nm, 1064 nm and 1550 nm}; distance gating is set according to the measuring range, and the width of a time window is 5 ns to 50 ns so as to suppress corona and background interference; through double calibration of an airborne reference sheet and a ground polarization datum plate, fitting is carried out after registration and calibration, angle response correction is carried out on a DPR, and meanwhile, the angle sensitivity of a weight is constrained in complex index solution, so that the relative fluctuation of an SGT under different visual angles is controlled within 3%, and the relative fluctuation of a DPR is controlled within 3%. Therefore, the problem of inconsistent identification results caused by attitude change and incident angle difference is solved.

The invention discloses a primary and secondary mirror combined detection system and method for a beam shrinking system, and belongs to the technical field of optical detection.The method comprises the steps that a first micro-spherical mirror and a second micro-spherical mirror are arranged, reflection light spots are captured through an interferometer, the position offset is calculated, and a first control instruction for adjusting the pose of a secondary mirror is generated; a dual-band light source is integrated in a detection light path, wavefront data and alignment monitoring data are collected, and temperature drift is corrected to obtain effective combined wavefront; an error separation model is constructed based on the primary mirror compensator parameter and the Zernike aberration coefficient, and a primary mirror surface shape error component and a secondary mirror offset error component are decoupled and separated by using a preset aberration coupling coefficient; feeding the error component back to a polishing process and a displacement table for closed-loop iterative correction until the RMS value and the PV value of the effective combined wavefront meet a preset imaging quality index; through precise positioning, error correction and decoupling separation, the precision and efficiency of primary and secondary mirror combination detection are improved, and targeted correction is realized.

The invention relates to the technical field of image processing, and discloses an image correction method and system for binocular diffraction AR intelligent glasses. The method comprises the following steps: calculating a dispersion response parameter of a diffractive optical element in the AR glasses in a visible light wave band; respectively carrying out dispersion distortion detection on the left eye image and the right eye image in the AR glasses to obtain a left eye dispersion distortion feature and a right eye dispersion distortion feature; interpupillary distance change data of a user is obtained, effective aperture offset calculation is carried out on the diffractive optical element, and interpupillary distance compensation parameters are obtained; performing coupling calculation on the left eye dispersion distortion feature, the right eye dispersion distortion feature and the interpupillary distance compensation parameter to obtain a binocular dispersion correction matrix; and performing dispersion compensation on the left eye image and the right eye image to obtain a dispersion correction image, and outputting the dispersion correction image to a display module of the AR glasses. The method can respond to the pupil distance change of the user in real time, automatically adjust dispersion correction parameters, realize accurate and comprehensive dispersion correction, and ensure the stability of dispersion correction performance.

The invention relates to the technical field of remote sensing measurement, in particular to a power transmission line tree high-risk assessment method and device based on satellite remote sensing and a medium. And based on the result and line data, calibrating a safety distance, combining terrain elevation correction, identifying the tree, calculating the height of the tree and the clearance distance of the line, comparing the clearance distance with a safety standard, dividing high, medium and low risk levels, and generating a high-risk assessment result of the power transmission line tree. According to the method, the vegetation distribution is described by extracting the spectral features of the remote sensing image, the dynamic safety interval is constructed in combination with the position of the power transmission line and the vegetation distance, the model is established by fusing terrain and crown elevation data, clearance parameters are quantitatively calculated to generate the risk level, the complex terrain adaptability and the risk point automatic identification precision are improved, and the early warning reliability is improved. And intelligence and decision-making accuracy of power transmission line inspection are promoted.

The invention provides an underground water environment monitoring system and method, and the method comprises the steps: carrying out the three-dimensional fluorescence spectrum full-wave band scanning of a known type of pollution source reference sample in a target monitoring region, and obtaining a three-dimensional fluorescence spectrum data set; performing characteristic analysis on the three-dimensional fluorescence spectrum data set to obtain fluorescence characteristic components, and determining a characteristic excitation wavelength corresponding to each fluorescence characteristic component; the classification contribution degree of each characteristic excitation wavelength is evaluated, and a characteristic excitation wavelength combination during source tracing of the pollution source is obtained through screening; performing fixed wavelength scanning on the to-be-detected underground water sample in the target monitoring area according to the characteristic excitation wavelength combination, and determining the initial type of the pollution source; and determining the relative contribution degree of the type of the pollution source of the to-be-detected underground water sample, simulating the migration trend of the pollution plume according to the underground water migration and diffusion model, and generating the position of the pollution source of the to-be-detected underground water sample. By adopting the scheme of the application, the pollution source of the underground water can be quickly identified and positioned at low cost.

The invention provides a near-infrared model cross-device application method based on multi-source fusion optimization, and aims to solve the problem that various neural network models constructed by a high-precision near-infrared spectrometer cannot be applied to portable devices when being migrated to the portable devices. And the model performance is reduced due to differences of an optical system structure, a wavelength range, a signal-to-noise ratio, an instrument response characteristic and the like. According to the method, the near infrared spectrum data and the conventional quality parameters are subjected to feature level fusion, and the cross-equipment spectrum deviation is calibrated and compensated by using the stability of the conventional quality parameters, so that efficient knowledge migration from high-precision equipment to portable equipment is realized in the fused feature space. The problem of cross-device feature dimension mismatching is solved through a stable feature importance evaluation-based wave band alignment method; a small sample optimization strategy and a hyper-parameter automatic search technology are combined, so that the transfer learning effect is improved, the performance of a portable equipment detection model is remarkably enhanced, and meanwhile, the modeling cost is greatly reduced.

The invention discloses a temperature and gas detection system and method based on an ultraviolet to near infrared spectrum, relates to the technical field of spectral measurement, and solves the technical problems that the space in a smelting furnace is occupied and the complexity of the system is increased due to the fact that two sets of independent equipment are adopted for temperature measurement and environment monitoring in the smelting process respectively in the prior art. The method comprises the following steps: acquiring spontaneous radiation light of a to-be-detected target; carrying out dispersive light splitting on the spontaneous radiation light to obtain spectral data of a full wave band from ultraviolet light to near-infrared light; performing denoising processing on the spectral data to obtain a spectrum of the to-be-detected target; obtaining temperature data according to the spectral analysis of the to-be-detected target; calculating based on the spectrum and a pre-established spectrum database to obtain gas concentration; the temperature of the to-be-measured target can be accurately measured, and the measurement accuracy is improved.

The invention relates to the technical field of radar echo denoising, in particular to a radar echo signal filtering noise self-adaptive suppression method and a radar echo signal filtering noise self-adaptive suppression system. Acquiring radar station position and beam pointing data, and synchronously acquiring forest vegetation data and environment interference data of a beam pointing target area to form radar environment data; the radar environment data are identified through the multi-band decision model, and multi-modal waveform parameters are obtained; based on the multi-modal waveform parameters, radar equipment is controlled to perform periodic detection on the target area, and radar echo data are obtained; the method comprises the steps of identifying radar echo data and radar environment data, extracting dynamic spectrum characteristics in the radar echo data, performing clutter suppression processing on the dynamic spectrum characteristics by using the radar environment data, calculating the confidence probability that an early fire exists in a target area, and executing space-time consistency joint verification; according to the invention, fire early warning is carried out through the confidence probability of the fire.

The invention provides an all-dielectric film system structure with visible near-infrared low transmission vegetation simulation, intermediate infrared high reflection and microwave high transmission. The device sequentially comprises, from top to bottom, a visible near-infrared simulated vegetation layer which is composed of first to fourth dielectric layers and is configured to simulate vegetation reflection spectrum characteristics in a wave band of 380-2500 nm and has a low transmission characteristic; the intermediate infrared high-reflection layer is formed by alternately stacking a first dielectric layer and a third dielectric layer, and is configured to form high reflection in a wave band of 3-14 microns; and a substrate layer; the thickness of the whole film layer is far smaller than the microwave wavelength, so that the film system keeps high transmission in the microwave band. According to the invention, the amorphous InSb material with high loss characteristic in visible near-infrared light is introduced, so that the average transmissivity of 1500-2500 nm is reduced to 0.095 while high-precision simulation of the vegetation reflectivity spectrum is realized, thereby realizing simulation of low transmissivity of the wave band vegetation.

The invention relates to a method for verifying a Mars sky radiance simulation result based on a Mars real-shot photo, and belongs to the technical field of Mars sky background radiance. Comprises: image acquisition; performing radiation calibration; correcting dark current; performing flat field correction; converting a radiation value; and geometric correction. The Mars sky radiance simulation method breaks through the dependence of an existing model on idealized hypothesis, performs dynamic verification by using actual shot image data, remarkably improves the authenticity and reliability of a Mars sky radiance simulation result, and effectively solves the problem of systematic deviation under a specific wave band; by constructing a complete process from image acquisition, radiation calibration, geometric modeling to brightness inversion and comparison verification, dynamic feedback and closed-loop correction of a theoretical radiation model are realized, model adaptability is improved, and fine modeling under multi-temporal and multi-meteorological conditions is supported; the method can be applied to an existing Mars rover navigation camera and an image processing link of the Mars rover navigation camera, does not need additional modification, has good engineering practicability and task compatibility, and is convenient to popularize and apply.

The invention relates to the technical field of fault detection, in particular to a power distribution network system fault monitoring method and system based on multi-source information, and the method comprises the following steps: based on the operation state data of a power distribution network, extracting voltage, current and equipment states, recognizing and standardizing abnormal events, extracting timestamp recognition wave bands and propagation paths, and carrying out the statistics of type evaluation weights. According to the method, logical and sequential analysis is carried out on the multi-source operation data of the power distribution network, association recognition and hierarchical collection of abnormal events are achieved, abnormal association judgment and propagation rule recognition are enhanced, and the fault propagation and collection efficiency is improved. The fault data analysis and presentation mode is optimized, the fault traceability and path tracking accuracy is improved, abnormal wave bands are dynamically divided, priorities are sequenced, misjudgment and omission are reduced, accurate recognition of key nodes and influence ranges is ensured, and the power grid abnormity monitoring and risk early warning capacity is enhanced.

The invention provides a short-wave multi-mode hollow-core anti-resonance optical fiber and a preparation method thereof, and relates to the technical field of optical fibers, and the short-wave multi-mode hollow-core anti-resonance optical fiber comprises an outer sleeve and a plurality of anti-resonance units which are arranged along the circumferential direction of the inner wall of the outer sleeve in an array manner; a central cavity enclosed by the plurality of anti-resonance units forms a fiber core area; the anti-resonance unit comprises at least three layers of anti-resonance tubes with different radiuses and two supporting rods arranged between two adjacent layers of anti-resonance tubes; wherein the two supporting rods in each layer are oppositely and obliquely arranged in pairs by taking the radial direction of the optical fiber as a symmetry axis, and the supporting rods on the same side are sequentially arranged along the same radius of the anti-resonance unit. Therefore, the waveguide filter has the advantages of being suitable for the 850 nm wave band, and being capable of supporting more high-order guided modes while giving consideration to low loss.

The invention belongs to the technical field of remote sensing, and discloses a wolfberry planting distribution high-precision remote sensing monitoring method based on multi-temporal fusion and spectral feature optimization. According to the method, a multi-temporal and multi-spectral satellite image is used as a data source, and preprocessing and multi-temporal image fusion are firstly carried out; the method comprises the following core steps: establishing a time sequence characteristic curve according to a unique phenological period (such as bare soil characteristics in a dormancy period and high vegetation coverage in a rapid growth period) of wolfberry; in a spectral domain, screening out a characteristic spectrum dimension combination with the highest discrimination degree between the wolfberry and other crops through a characteristic wave band optimization algorithm (such as vegetation index difference degree and red edge characteristics); and in combination with an object-oriented classification or deep learning classification model, constructing a space-time coupling classifier, and performing high-precision extraction and distribution mapping on the Chinese wolfberry planting region. The method can effectively solve the problem of confusion classification of Chinese wolfberry and similar ground features (such as other shrubs and orchards), and realizes rapid and accurate monitoring of Chinese wolfberry planting area and spatial distribution.

The invention relates to the technical field of soil moisture content monitoring, and discloses a method and a system for accurately monitoring soil moisture content through remote sensing inversion of an unmanned aerial vehicle. Acquiring original spectral data of different soil types and environmental conditions through a spectral sensor to obtain an initial multispectral image data set; dividing soil types by using a support vector machine classification method according to pixel reflection characteristics to form a soil type grouping set; calculating a multispectral band reflectivity variance for each group, and screening candidate bands to construct a set; processing the candidate wave band set through a random forest regression model, adjusting the weight in combination with an environment variable, and performing iterative screening to form an optimized wave band combination sequence; extracting spectral features of the optimized wave band, and carrying out weighted fusion on the soil variation region to output a processed image; and calculating the soil moisture content by using a linear inversion model based on the pixel reflectivity of the processed image, and outputting a distribution diagram when the deviation reaches the standard. The method is suitable for different soil and environments, the wave band is accurately selected, the precision is improved, and high-quality data is provided for agricultural and environmental governance.

The invention discloses a high and cold slope disaster body segmentation method fusing optical and SAR images, and the method comprises the following steps: 1, carrying out the snow coverage normalization processing based on the visible light green light wave band and the short wave infrared wave band of an optical image, and generating an ice and snow mask through an expectation maximization algorithm and a Gaussian mixture model; 2, preprocessing the optical image and the SAR image; step 3, constructing a high and cold slope disaster body segmentation model based on a Transform network; 4, training the segmentation model by using Dice loss and cross entropy loss in a combined manner, and freezing parameters of an image encoder in the training process; and step 5, inputting the high and cold slope remote sensing image into the trained segmentation model, and outputting a segmentation result of the slope disaster body by the segmentation model. According to the method, the problems of large ice and snow interference and poor multi-source data collaboration in high and cold slope disaster body segmentation are solved, and the segmentation precision and the engineering applicability are improved.

The invention discloses a physical information neural network hyperspectral band selection method for target identification, which uses physical information SDI as prior information to be combined with a channel attention mechanism added in a deep neural network to guide band selection, and evaluates the advantages and disadvantages of band weights through a reconstruction module after the weights are generated. Wave band selection is carried out accordingly; according to the hyperspectral image target recognition waveband selection method based on the physical information neural network, compared with a traditional method, the process is simplified and the precision is improved through end-to-end learning, the utilization of spatial context information can be enhanced by adding an attention mechanism, and the model has interpretability by adding prior SDI information.

The present application relates to a kind of on-chip photo-thermal gas sensor and on-chip photo-thermal gas sensor assembly, the refractive index of the upper waveguide material layer of sensor ridge waveguide layer is greater than the refractive index of lower waveguide material layer, can support TM mode pump light and TE mode probe light simultaneously. That is, both high absorption of pump light and weak influence of probe light on the gas to be measured are considered, and the detection accuracy of the gas is improved. Moreover, a simpler waveguide structure is used, and the waveguide preparation process is simple, and the waveguide loss is relatively low. Compared with the photo-thermal interference method in optical fiber sensing, the interaction between pump light and the gas to be measured is greatly improved, the length of the detection waveguide is greatly shortened while ensuring high sensitivity, and the device size can be reduced to micrometer level. In addition, the waveguide structure of the on-chip photo-thermal gas sensor disclosed in the present application is prepared from chalcogenide glass, which has more obvious advantages for gas detection in the mid-infrared waveband.

The invention provides a random forest shallow sea sediment classification method based on multi-temporal remote sensing image fusion, and relates to the technical field of sediment information extraction. Comprising the following steps: 1, collecting and preprocessing multi-temporal image data to obtain remote sensing reflectivity; 2, the water depth of each single-time-phase image is inverted, and the optimal water depth is obtained; 3, calculating bottom reflectivity characteristics of blue and green wave bands based on the optimal remote sensing image; 4, respectively calculating topographic features and spectral features based on the optimal water depth and the optimal remote sensing image; and 5, in combination with the bottom reflectivity features, the topographic features and the spectral features, carrying out random forest feature optimization and classification model training, and generating a substrate classification result. On the basis, the method solves the problems that an existing remote sensing image substrate classification method is insufficient in feature consideration, noise in a single-time-phase image can cause low classification precision, and therefore negative effects can be generated on accurate acquisition of substrate information.

The invention discloses a ceramic substrate surface defect intelligent identification method and system based on multispectral imaging, and the method comprises the following steps: synchronously collecting multispectral images under a plurality of visible light and near-infrared wavebands, constructing a multispectral image stack, and completing dark field deduction, registration and geometric correction processing; inputting the crack sensitive wave band combination image into a CrackFormer crack identification network, extracting slender structure characteristics and positioning a crack area; inputting the edge enhancement graph into an improved BDCN edge detection network, extracting the contour structure of the ceramic substrate, and identifying the position of a contour gap; a chromatic aberration defect area is extracted through brightness difference calculation; and fusing the identification results of the three types of defects, and outputting a defect mask pattern and structured defect information. According to the method, the multi-band image and the deep learning model are fused, and the accuracy and adaptability of ceramic substrate defect detection are improved.

The invention relates to the technical field of target detection and identification, and discloses a low-altitude target identification system and method combined with multi-band signal processing. Comprising the steps of establishing a cross-frequency implicit vector model and constraining a geometric scale, a scattering center and rotor harmonic waves through synchronous acquisition of an L-band radar, a Ku-band radar and a millimeter-wave radar, and realizing unified characterization of multi-frequency signals; receiving a global satellite navigation system reflection signal, communication channel state information or a very high frequency / ultrahigh frequency passive radar signal to form prior discrimination, and triggering high-frequency radar short-time imaging; carrying out propagation correction on millimeter wave, terahertz and infrared signals in combination with a meteorological profile and generating an environment label; determining a sampling window by using the region of interest and rotor phase prediction, and optimizing a residence strategy; harmonic waves are extracted from radar and acoustic signals, robust pairing is carried out, and a cross-domain consistency score output category is calculated. The method realizes multi-frequency multi-source cooperative processing, has environment self-adaption and closed-loop feedback capabilities, and can improve the precision and stability of low-altitude target identification.

The invention discloses a PVC film coating uniformity detection method based on spectral analysis, and relates to the technical field of optical detection, and the method comprises the following steps: S001, adopting a controllable incident illumination system with a multi-angle rotation function to carry out continuous angle scanning on the surface of a PVC film, collecting reflection spectrum intensity data in a corresponding wave band range according to different incident angles, and generating a spectrum data matrix containing interference characteristics; and S002, carrying out continuous wavelet transform processing on the spectral data matrix, carrying out multi-scale local decomposition on a spectral response signal, extracting and separating a periodic interference characteristic signal, and obtaining a purified spectral matrix after interference reduction. According to the method, through multi-angle spectrum collection and interference feature stripping, accurate identification of a coating thickness abnormal area and combination of strength indexes, spraying parameters are regulated and controlled, a detection-compensation-reinspection closed-loop mechanism is constructed, interference misjudgment is effectively avoided, and the accuracy and regulation and control efficiency of uniformity detection of the PVC film coating are improved.

The invention relates to a dual-waveband medium-long wave infrared optical filter based on a germanium / zinc sulfide asymmetric film system and a preparation method of the dual-waveband medium-long wave infrared optical filter. The dual-waveband medium-long wave infrared optical filter comprises a front film system, a substrate and a back film system, the front film system and the back film system are formed by alternately overlapping germanium layers and zinc sulfide film layers; the average transmittance of the infrared optical filter in the wave band of 3800 nm to 4800 nm and the wave band of 8000 nm to 10000 nm is not lower than 91%. According to the two-waveband medium-long wave infrared optical filter disclosed by the invention, 3800 to 4800 nm amp is realized; according to the infrared filtering window, the light energy of medium waves and long waves of 8000-10000 nm is improved, the luminous flux of other wavebands is well inhibited, the technical problem of the infrared filtering window in some special infrared spectrum detection systems is solved, and a new scheme is provided for infrared application.

The invention discloses a transformer substation insulator pollution positioning method, device and system fusing a multispectral image and a laser radar point cloud, and belongs to the technical field of power equipment multispectral detection and intelligent operation and maintenance. The method comprises the steps that a multispectral imaging device and a laser radar device are arranged in a measurement area facing the outdoor insulator of the extra-high voltage transformer substation, and the laser radar device is controlled to collect three-dimensional point clouds of the insulator and a surrounding structure of the insulator, controlling the multispectral imaging device to acquire multispectral images of the outer insulation surface of the insulator under a plurality of working wavebands; performing spatial registration on the three-dimensional point cloud and the multispectral image based on a calibration plate and geometric constraints, and mapping multispectral spectral information to point cloud sampling points; by introducing a multispectral three-dimensional point cloud and a virtual cleaning operator and combining multi-view joint optimization under an umbrella skirt structure parameter model, the problem that pollution assessment is unstable and not fine due to a single-view image or a simple intensity threshold value in the prior art is solved; and a reliable quantitative basis is provided for substation insulator electrical risk assessment and intelligent operation and maintenance decision.