3912 results about "Wave band" patented technology

The invention relates to the technical field of radar signal processing, in particular to a radar target analytical calculation method based on multi-dimensional data fusion and a radar device. Comprising the following steps: deploying a multi-band radar sensor array comprising an X band, a C band and a Ku band in a radar monitoring area; performing pulse compression and Doppler processing on the time domain echo signal, and extracting a time domain feature; spectral analysis is carried out on the frequency domain signals, and frequency domain features are extracted; performing angle estimation on the spatial signals, and extracting spatial features; a dynamic weight distribution model is constructed, a fusion weight is calculated through an adaptive algorithm based on three-dimensional quality indexes of a real-time signal-to-noise ratio (SNR), feature stability (SI) and data integrity (CI), and a joint representation vector containing time domain, frequency domain and space multi-dimensional information is generated. According to the invention, by deploying the multi-band radar sensor array, the recognition capability of the subtle feature difference of the target is improved.

The invention relates to the technical field of remote sensing monitoring, in particular to a remote sensing image ocean green tide monitoring method and system fusing physical prior and spatio-temporal evolution. The method comprises the following steps: acquiring a multi-modal remote sensing monitoring image; performing multi-modal feature extraction on the acquired image, wherein the multi-modal feature extraction comprises spectral reflectivity feature extraction, ocean dynamics feature extraction and feature alignment and unified representation; establishing a physical prior of a green tide characteristic wave band by using an ocean optical radiation transmission model; constructing a dynamic space-time diagram based on the extracted multi-modal features to obtain a node global feature vector and a dynamic adjacency matrix; carrying out adaptive graph convolution feature coding based on physical prior and a dynamic space-time diagram; through fusion of multi-spectral images of multiple platforms such as satellites and unmanned aerial vehicles and ocean dynamic data and combination of atmospheric correction and wave band resampling, consistency processing and high-precision extraction of multi-source features are realized, and comprehensiveness and reliability of green tide feature recognition are remarkably improved.

The invention provides a river water quality monitoring method based on multi-source remote sensing data, and belongs to the technical field of river water quality monitoring. The method comprises the following steps: establishing an inherent optical characteristic model of a river water body based on a Hybrid water body radiation transmission model, optimizing a calculation path by adopting a shortest path algorithm, and carrying out water body optical component inversion by adopting a quasi-analysis algorithm and a generalized inherent optical characteristic algorithm in combination with a multispectral characteristic enhancement model to obtain absorption coefficients of all components; and constructing a multi-band combination index to realize optical coupling effect decoupling, and applying the fine-tuned water quality parameter inversion basic model to a target river area to output a suspended matter concentration distribution diagram, a chlorophyll concentration distribution diagram and a transparency distribution diagram. The technical problem of low precision of remote sensing inversion of water quality parameters caused by mutual coupling of multiple optical active components in a river water body is solved.

The invention provides an unmanned aerial vehicle identification early warning method and system. The method comprises the following steps: acquiring RGB image data, thermal radiation data and spectral data of an unmanned aerial vehicle no-fly zone through a visible light camera, an infrared thermal imager and a multispectral imager; performing transmission preprocessing on the RGB image data, the thermal radiation data and the spectral data, and adaptively adjusting multi-level fusion of a fusion weight based on real-time environmental parameters to generate target fusion data; based on a deep learning model and a tracking prediction algorithm, performing unmanned aerial vehicle identification early warning on the target fusion data, and generating early warning data; and transmitting the target fusion data and the corresponding abnormal event log to a cloud server, and updating the deep learning model by adopting the target fusion data and the abnormal event log. Through cooperative work of a multi-mode sensor, visible light, infrared, multispectral and other wave bands are covered, all-weather and full-scene unmanned aerial vehicle detection is achieved, the fusion weight is adjusted in real time based on real-time environment parameters, and the accuracy of the recognition result under the complex air situation is ensured.

The invention relates to the technical field of image recognition, and further relates to a phenotypic character image recognition and extraction method for efficient corn breeding. The method comprises the following steps: step 1, acquiring an image of the same plant canopy in a test field by using an unmanned aerial vehicle carrying a multispectral sensor in a plurality of different growth periods from a corn jointing period to a mature period, so as to obtain a normalized grayscale image; 2, performing gradient operation on a target wave band based on the normalized grayscale image to obtain significant edge intensity, and generating a character sensitive probability field of a corresponding time phase; and 3, presetting an optimal observation period for each target character, selecting a pixel set which has a probability value not less than 0.5 and is judged to be the character type from an image of a time phase corresponding to the optimal observation period, and sequentially measuring the total projection area, the average connected domain area and the average leaf angle to obtain a comprehensive breeding sequence value of the character. According to the method, high-precision probability identification of the effective phenotype area of the grain-leaf is realized.

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 provides a torreya grandis forest extraction method based on a deep learning network and a multi-temporal remote sensing image, and the method comprises the steps: carrying out the data preprocessing of a remote sensing image, constructing and obtaining a comprehensive feature image of each month, extracting the pixel samples of torreya grandis and non-torreya grandis types, calculating and obtaining a comprehensive class spacing distinguishing capability index of each month, and obtaining a torreya grandis forest extraction result. Obtaining an original wave band feature set of the similar hyperspectral structure; performing feature optimization by using a maximum correlation minimum redundancy algorithm to obtain an optimized waveband feature set; marking torreya grandis and non-torreya grandis areas according to the torreya grandis sample points and the high-resolution remote sensing image, and making classification labels for deep learning; and constructing a space-spectrum multi-scale feature fusion network model, inputting the optimal waveband feature set into a deep learning network for training, and outputting classification results of torreya grandis and non-torreya grandis. According to the method, the multi-temporal remote sensing image can be fully utilized, and the spatial and spectral features are effectively extracted and fused, so that the recognition precision and efficiency of the torreya grandis are improved.

The invention relates to the technical field of image recognition, and discloses a multispectral image and deep learning-based forest disease and insect pest automatic recognition method, which comprises the following steps of 1, carrying a multispectral camera containing a red edge wave band through an unmanned aerial vehicle to obtain a forest region image; 2, calculating a red edge normalized vegetation index of the image; 3, performing time sequence modeling on the red edge normalized vegetation index data of more than five consecutive periods, and inputting a time sequence convolutional network to generate an early lesion probability graph; 4, detecting a pest and disease damage target by adopting a multi-scale adaptive feature pyramid network; 5, outputting a disease and pest distribution thermodynamic diagram; and 6, driving the unmanned aerial vehicle cluster to execute precise pesticide spraying. According to the method, through the high sensitivity of the red-edge wave band to chlorophyll degradation and in combination with sequential convolutional network dynamic modeling, an initial lesion area can be recognized 7-10 days before disease development, the early disease recognition capability is remarkably improved, the disease discovery period is shortened, and large-scale disease diffusion is effectively avoided.

The invention discloses a multi-source data-combined pit-pond water depth calculation and storage capacity calculation method, which comprises the following steps of: acquiring actually measured water depth data, performing data preprocessing, and dividing the data into a training sample point set and a test sample point set; acquiring a multispectral remote sensing image of the reckoning area, and executing image preprocessing; unifying to the same coordinate system; fractal dimensions of each wave band and the combined wave band are calculated, and a water body region is segmented; establishing a mapping relation between the water depth values of the training sample points and the standardized multispectral apparent reflectance; establishing a water depth inversion model, and verifying the water depth inversion model by using the test sample point set; calculating discrete water depth points of the pit pond by using the optimal water depth inversion model; and establishing a minimum energy curved surface to obtain the water storage amount of a single pit pond, and accumulating to generate the total water storage amount. According to the method, actual measurement and remote sensing data are fused, the energy minimization water depth inversion model is constructed, accurate calculation of the water storage capacity of the pit pond is achieved, and the method has the advantages of being high in precision and high in applicability.

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 soil water content inversion method and system based on L-band dual-polarization data and a medium, and belongs to the field of remote sensing technology and environment monitoring. The system comprises an SAR data acquisition module, an optical data processing module, a vegetation scattering separation module, an inversion model construction module, a spatial distribution calculation module and a risk assessment module, wherein the SAR data acquisition module is used for regularly acquiring L-band dual-polarized SAR data of a target area; the optical data processing module assists in identifying and separating vegetation scattering components by calculating vegetation indexes; the vegetation scattering separation module is used for extracting pure soil back scattering signals; the inversion model building module is used for training and optimizing a soil water content inversion model based on the pure soil back scattering signals and field sampling data; the spatial distribution calculation module is used for calculating a spatial distribution diagram of the surface soil water content of the target area; and the risk assessment module is used for carrying out landslide risk grade division and early warning management.

The invention relates to an unmanned aerial vehicle detection countering method, which comprises the following steps of: deploying a multi-dimensional sensing network to carry out full-band instantaneous scanning, synchronously acquiring noise characteristics of radio signals, material characteristics of a terahertz wave band and Doppler effect characteristics, mapping to a virtual twinborn body, reconstructing physical parameters, a communication protocol and a behavior mode of a target unmanned aerial vehicle, performing depth comparison by combining a disguise sample library generated by the antagonism generation network, and generating a holographic target file containing a three-dimensional motion vector, an energy feature and a potential threat intention; a target quantum encryption communication link is interfered through a quantum entanglement signal, a controllable plasma cloud cluster is generated, navigation and image transmission signals of the controllable plasma cloud cluster are selectively attenuated, the controllability of the target is judged, and an uncontrollable target is repelled through coded sound waves; and sending a control instruction containing a biological heuristic obstacle avoidance algorithm in stages, and guiding the control instruction to an intelligent recovery cabin. According to the invention, accurate detection, intelligent identification, safe countering and reliable takeover of the unmanned aerial vehicle are realized, and the airspace safety guarantee capability is improved.

The invention relates to the technical field of spectral analysis, and discloses a method and a system for detecting the concentration of trace metal ions. The method comprises the following steps: acquiring original spectral response data of an electronic smoke sample; carrying out signal separation by utilizing a frequency domain feature extraction technology to obtain an initial spectrum data set; performing background noise filtering and baseline drift correction on the initial data to obtain a refined spectrum data set; constructing a concentration-wavelength mapping relation through absorption peak positioning and characteristic wave band screening on the basis of the refined spectrum data set; carrying out continuous time sequence sampling on the samples based on the mapping relation to generate a concentration dynamic change data set; analyzing a concentration fluctuation rule and an abnormal point in the dynamic data, and generating concentration risk assessment data; and finally outputting a time sequence detection file. According to the method, high-precision dynamic monitoring and intelligent risk assessment of trace metal ion concentration are realized, interference is effectively inhibited, and sensitivity and early warning capability are improved.

The invention discloses an ecological environment detection method and system based on multispectral remote sensing fusion, and relates to remote sensing image processing. The method comprises the following steps: collecting multispectral remote sensing image data of a target area; performing multiband joint atmospheric correction processing on the multispectral remote sensing image according to the scattering coefficient, the atmospheric light value and the transmissivity of each band; performing foreground and background analysis on the corrected multispectral remote sensing image; fusing the vegetation area and the non-vegetation area of each wave band image by adopting different weight strategies to generate a multispectral fusion image; and extracting spectral features of the multispectral fusion image, constructing a standard vegetation spectral feature library, and identifying regions deviating from a standard vegetation spectrum through an anomaly detection algorithm according to the extracted spectral features to obtain various vegetation coverage rates. In view of low vegetation identification precision caused by direct foreground and background division of a multispectral remote sensing image under an atmospheric interference condition, vegetation division is performed after a clear image is obtained, so that the detection precision is improved.

The invention relates to the technical field of agricultural remote sensing measurement, and discloses an unmanned aerial vehicle hyperspectral online calibration measurement method for agricultural remote sensing, which comprises the following steps: acquiring hyperspectral data of a target crop and reference vegetation in the same scene in real time through an unmanned aerial vehicle, and extracting a characteristic wave band reflectivity difference value delta R, according to the comparison result of the time sequence change rate of the delta R and the preset threshold value, crop physiological changes and environmental interference are dynamically distinguished, and online calibration is achieved. A self-consistent calibration mechanism is constructed by means of the time sequence stability of crop spectral characteristics, the illumination mutation influence can be eliminated without depending on an external reference substance, and the accuracy of the calibration result is improved. And meanwhile, a lodging scene is effectively identified through synchronous analysis of a gravity vector and spectrum drift, so that the system can still keep accurate tracking of a real physiological state of a crop under an extreme working condition.

The invention discloses a metal processing detection system and method based on computer vision, and relates to the technical field of defect detection.Rotatable polarization filters are installed in front of a lens of a vision sensor, the polarization filters are arranged at four angles respectively, and four sets of polarization images are collected; calculating the polarization degree of each pixel point on the surface during metal processing by using the gray values of the four groups of polarization images, setting a polarization region interval, and dividing a metal surface reflection region into a specular reflection region and a diffuse reflection region; acquiring images of different wavebands by using a multispectral vision sensor, and constructing a multispectral image set; for the same wave band, the images of different exposure times are synthesized to obtain an HDR high dynamic range image; setting a weight for the HDR image of each wave band, and combining the weighting of the HDR image and the suppression of the polarization degree to carry out multispectral image fusion so as to obtain a final image; and a gray threshold value and an area threshold value are respectively calculated through the average value and the standard deviation, and the metal processing defects are judged by using the two threshold values.

The invention provides a satellite scatterometer Ku wave band observation value rainfall influence correction method, and relates to the field of sea surface wind inversion methods, and the method comprises the steps: obtaining Ku wave band observation data, C wave band data and rainfall rate data of a satellite scatterometer, carrying out wind field matching and wind speed correction under a rain-free condition, so as to obtain an NRCS reference value corresponding to a sea surface real wind speed, and carrying out the calculation of the NRCS reference value; constructing to obtain a scatterometer rainfall matching data set; according to the scatterometer rainfall matching data set, the influence of rainfall on the scatterometer observation value is analyzed, and a Ku waveband rainfall influence conceptualization model is constructed; and calculating rainfall influence conceptualization model parameters and generating a deviation lookup table. According to the invention, on the basis of a classic scatterometer wind field inversion process, the problem of large inversion wind field deviation caused by the influence of rainfall on the observation value of the Ku wave band scatterometer in a medium-low rainfall rate range is solved.

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 relates to the technical field of leaf nitrogen content estimation, in particular to a method for establishing a grassland plant nitrogen content monitoring model based on a remote sensing technology. The method comprises the following steps: controlling an unmanned aerial vehicle platform carrying a narrowband multispectral camera and a linear polarization camera, and collecting a spectral image sequence and a polarization image group at an observation window with a solar zenith angle of 30-40 degrees; extracting a reflectivity value of each wave band in the spectral image sequence, calculating a reflectivity first-order derivative and determining a red edge position offset; extracting horizontal and vertical polarization components in the polarization image group, calculating a polarization degree and constructing a polarization degree difference index; and identifying the center of the fecal spot by searching a pixel with a local maximum polarization degree difference index and red edge position offset blue shift exceeding a preset threshold value. According to the method, excrement spot recognition and remote sensing description of the nitrogen influence range thereof are achieved through double-index complementation of the red edge position offset and the polarization degree difference index.

The invention discloses a seawater quality AI fusion intelligent monitoring system and method based on full spectrum data, and relates to the technical field of seawater quality monitoring. The system comprises a full-spectrum data acquisition module, a water quality initial evaluation and characteristic wave band identification module, an environment adaptive wave band selection module, a weight dynamic distribution and anomaly detection module, a multi-dimensional data fusion and optimization module and an abnormal event response and report module. The system collects original spectral data through a spectral sensor, establishes a basic water quality model after preprocessing, dynamically selects an effective wave band according to real-time environmental parameters, allocates weights, outputs water quality parameters after fusing data of a multi-dimensional sensor, and finally responds to abnormal events and generates a report. The system realizes intelligent and real-time monitoring of seawater quality, and can effectively deal with sudden pollution events and capture water quality fluctuation.

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 a brain wave monitoring visualization method and system, and the method comprises the following steps: obtaining original brain electrical signals of a user at different time points and task scenes, and carrying out filtering, denoising and segmented cleaning to obtain a preprocessing data set; performing frequency domain analysis on the data set, and extracting waveband power distribution; generating thermodynamic diagram data based on the power value, dynamically superposing the thermodynamic diagram data to the visual field of the user through an augmented reality technology, and displaying the activity intensity of the brain area; and accumulating data for a long time to construct a personalized baseline database, comparing the current power with a scene threshold value in real time, generating warning feedback by associating the thermodynamic diagram when the power is abnormal, and optimizing the baseline database. According to the method, the abstract electroencephalogram signals are converted into the visual thermodynamic diagrams, personalized baselines and dynamic feedback are combined, the problems that traditional monitoring data is difficult to understand, depends on group threshold misjudgment and is lack of dynamic optimization are solved, and the intuition, accuracy and practicability of electroencephalogram monitoring are improved.

The present application belongs to the technical field of wireless communications and radar systems. Disclosed are a dual-band fusion and multiplexing frequency source, chips, a radio-frequency transceiver, a system, and a device. The system comprises: a full-function millimeter-wave CMOS 4+1 phased array chip, which is connected to an antenna end, and is used for realizing signal processing and frequency conversion between a millimeter-wave signal and a C-band intermediate-frequency signal, performing phase control and gain adjustment on the millimeter-wave signal, and providing an internal frequency source and a calibration function; a C-band front end, which is connected to the antenna end, and is used for receiving or transmitting a C-band signal; a C-band / intermediate-frequency transceiver multiplexing chip, which is used for performing frequency conversion between a C-band or intermediate-frequency signal and a baseband signal in a multiplexing manner, and performing signal processing and gain control on the C-band intermediate-frequency signal; a dual-band fusion and multiplexing frequency source, which is used for providing a local oscillator signal; and an all-digital control logic circuit, which is used for providing a control instruction. The present application improves the efficiency and performance of dual-band joint communications.

The invention discloses a thermal shock failure prediction and laser repair device for a thermal barrier coating and an application method. The device comprises a multi-physics field coupling simulation analysis module, a thermal shock experiment loading module, an online damage monitoring and failure diagnosis module, a robot laser repair execution module and a central control and data processing core unit. A thermal-mechanical-chemical coupling model is established to predict a coating failure behavior, a high-frequency induction heating and gas quenching system is adopted to simulate an extreme thermal shock environment, a multi-band thermal infrared imager, an ultrasonic probe and a digital image related system are integrated to monitor damage evolution in real time, and in-situ repair of a damaged area is realized based on a six-degree-of-freedom industrial robot. According to the method, the whole-process closed-loop control from failure prediction to repair regeneration is realized, the problems of single function and lack of multi-field coupling analysis and real-time repair capability in the prior art are solved, and the service reliability evaluation precision and the remanufacturing efficiency of the thermal barrier coating are remarkably improved.

The invention provides an intelligent coating method and system for a paint-free suite door. The method comprises the following steps: continuously scanning the surface of a door body through a multi-band optical detection device to obtain reflection intensity data of visible light and near-infrared bands, and synchronously collecting environment temperature and coating temperature data of a coating area; activating the composite electromagnetic shielding structure to absorb external interference, and transmitting the purified multi-source data through a star topology network; carrying out denoising processing on the reflection intensity based on a dynamic modeling algorithm, and carrying out dynamic calibration on the coating thickness model in combination with the real-time temperature parameters to generate a three-dimensional thickness distribution thermodynamic diagram; and the high-degree-of-freedom mechanical arm is driven to execute space trajectory compensation motion, and finally stress balance control in the coating curing process is achieved. According to the technical scheme provided by the invention, accurate control over the coating thickness uniformity and active inhibition of thermal stress in the coating process of the paint-free suite door are achieved, and the coating quality and the process stability are remarkably improved.

The invention provides a hidden camera detection method and system based on image recognition. The method comprises the following steps: scanning an area to obtain an image data set; based on optical characteristics of a hidden camera lens material, reflectivity difference feature vectors under different wavebands are extracted; inputting the feature vectors into a neural network to generate comprehensive feature representation; scanning the same detection point, obtaining a time sequence feature sequence, and analyzing the time consistency of the features; detecting and analyzing the suspected camera area, and calculating a roundness coefficient C as C < gt >; 0.7, judging that geometrical characteristics of the lens are met; determining coordinates of the hidden camera in the three-dimensional space; according to the comprehensive feature representation, the time sequence consistency analysis result and the geometrical shape verification result, calculating the existence probability Pfinal of the hidden camera, wherein Pfinal gt; and when 0.85, outputting a detection alarm signal, and displaying a detection result and three-dimensional positioning information. The detection accuracy can be improved, and the false alarm rate can be reduced.

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 relates to the technical field of multi-water quality analysis, in particular to a full-automatic water quality COD analysis method and device.The method comprises the steps that an absorbance spectrogram and electrochemical characteristics of a target water sample in a first preset wave band are obtained, and an original signal set is generated; obtaining dynamic characteristics of fusion liquid of the target water sample and the potassium dichromate solution in a second preset wave band, and performing characteristic level fusion on the dynamic characteristics and the original signal set to generate a reaction characteristic set; manifold learning and feature optimization processing are carried out on the reaction feature set, and an optimized feature set is generated; performing hybrid neural network modeling processing according to the optimized feature set, and outputting a preliminary COD predicted value; and after the preliminary COD predicted value is corrected, a correction result is verified, and a COD analysis set is generated. According to the invention, multi-source signal data can be efficiently integrated and deeply optimized, and the defects of low analysis efficiency and accuracy caused by insufficient data processing capability in complex water sample analysis in the prior art are overcome.