905 results about "Hyperspectral imaging" patented technology

The invention discloses a method and a system for rapidly screening peanut endophytic mildew, particularly relates to the technical field of nondestructive testing of agricultural products, and is used for solving the problems of false detection and missing detection caused by low contrast and gradient boundary in an initial mildew stage and an internal diffusion sample in an existing detection method. Based on a hyperspectral imaging technology, a filtering kernel size is dynamically adjusted through conjoint analysis of frequency domain energy distribution and spatial local variance, and detail features of a gradient boundary in an image are enhanced; verifying and screening high-confidence candidate regions by adopting spectrum and spatial feature consistency, analyzing and extracting principal component features representing a mildew diffusion trend in combination with a texture direction, and dynamically splicing the principal component features with a spectrum gradient direction to construct a fusion feature vector; quantizing boundary confidence through a Bayesian probability classification model, combining with a neighborhood similarity constraint region growing algorithm to generate a mildewed mark graph with continuous space and consistent features, and introducing a dynamic threshold correction mechanism to adaptively optimize a segmentation standard; and the detection precision and the anti-interference capability of the fuzzy boundary are improved.

The invention discloses a wheat single grain appearance anomaly detection method based on a deep learning model and hyperspectral imaging. The method comprises the following steps: step 1, collecting different types of wheat grain samples; step 2, acquiring hyperspectral image data of the wheat grains by using a visible light-near infrared and short wave near infrared hyperspectral imaging system, and extracting spectral information and image information; step 3, preprocessing the spectral data, and verifying a preprocessing effect; 4, screening spectral characteristic wave bands, extracting texture characteristics and morphological characteristics in combination with a gray-level co-occurrence matrix, and constructing a middle-level data fusion model; step 5, constructing an atlas feature fusion deep learning model to realize high-level fusion of spectrum and image features; and step 6, pixel-level classification is carried out on the hyperspectral image, and a spatial distribution visualization result of the appearance abnormity of the wheat grains is generated. The method has high precision, nondestructive testing and strong generalization ability, and is suitable for wheat quality grading, processing and sorting and storage safety management.

The invention relates to the field of landfill methane emission remote sensing monitoring, and discloses a landfill methane emission flux measuring and calculating method and device and a medium, and the method comprises the following steps: based on regional satellite TROPOMI observation data, carrying out qualitative analysis on annual average methane emission signals of a landfill, and determining a target landfill according to a qualitative analysis result; based on data of a satellite-borne hyperspectral imager, the methane emission flux of the target landfill is quantitatively measured and calculated, and the position of a methane emission hot spot area in the target landfill is determined; and positioning the position of an emission source in the target landfill through an active methane laser detection lens carried by an unmanned aerial vehicle platform, and independently calculating the methane emission flux of the target landfill by adopting an upper and lower section method. Based on the method, the annual average methane emission condition of the landfill can be rapidly and accurately evaluated, the methane emission flux of the whole landfill is obtained, and the internal emission hot spot area of the landfill is positioned.

The invention relates to the technical field of ecological environment monitoring, and particularly discloses a system and a method for monitoring distribution of plant communities along an expressway. An unmanned aerial vehicle carries a hyperspectral imager and a multi-parameter sensor to synchronously obtain vegetation spectral data and environmental parameters of soil salinity, water content, pH value, temperature and humidity; constructing a multi-dimensional data set with space-time matching; an improved end member extraction algorithm is combined with a soil salinity threshold triggering mechanism to realize self-adaptive updating of a dynamic end member library; an environmental constraint adversarial generative network is introduced, virtual end members conforming to ecological characteristics are generated and verified, and the coverage capability of an end member library is improved; performing physiological and ecological dual verification on the end members based on the knowledge graph to form an optimized end member set; and an environmental adaptability weight is introduced in the unmixing process, confidence evaluation and fuzzy logic analysis are combined, a graded credibility vegetation map is output, and accurate identification and spatial positioning of invasive plants and local vegetation are realized.

The invention relates to the technical field of intelligent agricultural equipment, in particular to a tomato disease diagnosis method based on multi-modal data analysis, which comprises the following steps of: 1, synchronously acquiring and preprocessing multi-modal data, synchronously triggering a hyperspectral imaging device and a microscopic camera, respectively acquiring a plant canopy hyperspectral image and a stem microscopic image, and acquiring a plant canopy hyperspectral image and a stem microscopic image; meanwhile, temperature, conductivity and dissolved oxygen environment parameters are continuously collected in the root zone; 2, self-adaptive feature extraction and fusion in the growth stage are carried out, reflectivity correction and leaf segmentation are carried out on the hyperspectral image, and leaf surface spectrum curve features are extracted; step 3, hybrid model construction and space-time analysis: constructing a hybrid model comprising spectrum, microscopy and environment analysis networks, and dynamically adjusting each network weight through a gating network; and 4, generating a disease decision. The method can realize accurate, efficient and real-time tomato disease diagnosis, has high practical value, and can effectively improve the disease prevention and control capability in agricultural production.

The invention relates to the technical field of fruit detection, in particular to a blueberry acidity detection method based on hyperspectral imaging, which comprises the following steps: randomly selecting blueberry samples to be detected, and constructing a reference sample database; acquiring full-band hyperspectral image data of the to-be-detected blueberry sample by using hyperspectral imaging equipment; performing spectrum preprocessing on the hyperspectral image data; eliminating redundant information of redundant wave bands by combining a continuous projection algorithm to obtain a final characteristic wave band set, and extracting a spectral intensity matrix corresponding to the final characteristic wave band set from the hyperspectral image data based on the final characteristic wave band set; constructing a blueberry acidity detection model; and outputting the acidity predicted value of the blueberries. According to the method, the data dimension is greatly reduced, the sensitivity and modeling efficiency of the characteristic wave band are improved, and compared with a traditional destructive detection method, the method has the advantages of being high in speed, free of damage and suitable for large-scale sample detection.

The embodiment of the invention discloses a novel fish acute septicemia detection system based on hyperspectral imaging. The system comprises a data acquisition module, a model detection module and a user interaction module, wherein the data acquisition module acquires hyperspectral image data on the surface of a target fish body; the model detection module carries out diagnosis by taking the hyperspectral image data as input based on a trained deep learning diagnosis model, and the deep learning diagnosis model obtains a pixel space structure and a pixel spectrum sequence of the fish body surface based on the hyperspectral image data; identifying spatial features representing related clinical symptoms based on a pixel spatial structure, and identifying spectral features representing an inositol metabolism condition based on a pixel spectral sequence; and the user interaction module outputs a diagnosis result of the target fish body based on a result output by the model. The characteristic that the aeromonas hydrophila utilizes inositol as a unique carbon source and the map change of typical infection symptoms on the surface of a fish body are combined, and accurate and rapid detection is achieved through hyperspectral imaging and a deep learning algorithm.

The invention relates to the technical field of intelligent forestry management, and particularly discloses a forest farm intelligent comprehensive management and control system, which comprises a space-air-ground collaborative sensing module, a fire danger dynamic modeling module and an intelligent decision execution module. The method comprises the following steps: acquiring temperature field data through multispectral satellite remote sensing, constructing a high-precision terrain model through a laser radar, extracting vegetation features through hyperspectral imaging, extracting a thermal anomaly structure by adopting topological continuous coherence analysis and a Morse-Smal complex method, and establishing an adaptive fire danger model in combination with a quantum annealing optimizer. The system can generate a fire danger thermodynamic diagram in real time and divide risk levels, and drives the unmanned aerial vehicle to perform priority patrol and obstacle avoidance path planning. The problems of difficulty in pseudo hot area identification, low fire danger prediction precision, non-intelligent path planning and the like in a complex environment are solved, and the intelligent level of forest farm fire prevention and control is improved.

The invention discloses a carbon fiber composite material surface modification spraying system and a control method thereof. The system comprises a multi-axis robot, a plasma spray gun, a contact angle measuring probe, a 3D line laser scanner, a hyperspectral imager, an environment sensor and a controller. According to the method, the plasma power and the robot speed are adjusted in real time through contact angle measurement and plasma treatment feedback control, so that the CFRP surface can accurately reach a target value, and the coating adhesive force is improved; 3D line laser scanning and hyperspectral imaging are combined, and the posture, the distance, the wet film thickness and the component uniformity of the spray gun are monitored in real time; based on a prediction model fusing a physical model and a neural network, a self-adaptive fuzzy PID control algorithm is adopted, and the coating flow and the track posture of a spray gun are accurately regulated and controlled in real time. The problems of weak coating binding force, uneven thickness, orange peel, sagging and serious coating waste in traditional spraying are effectively solved, and self-adaptive, high-quality and green spraying of workpieces with complex curved surfaces is achieved.

The invention relates to an indoor lighting rate simulation system for indoor design. The system comprises a dynamic meteorological simulation module which generates a dynamically changing sky model based on real-time meteorological data; the building attribute analysis module analyzes building optical attribute parameters by using the building information model data and the hyperspectral imaging data; the light ray tracing processing module performs light ray tracing based on the dynamic sky model and the building optical attribute parameters to obtain indoor glare distribution data; a reverse parameter optimization module generates window form parameters and sunshade device layout parameters according to the target illumination interval and the indoor glare distribution data; and the real-time visual verification module carries out visual rendering on the window form parameters and the sunshade device layout parameters to obtain an updated lighting rate simulation result for verifying the lighting effect of the design scheme. According to the system, through dynamic meteorological fusion, non-uniform material modeling and real-time optimization feedback, the precision and adaptability of indoor lighting simulation can be improved.

The invention discloses a snapshot type hyperspectral imaging industrial online sorting system which comprises a support assembly, an optical imaging assembly and a motion control assembly. The bracket assembly consists of a bottom plate, and an adjustable bracket and a fixed bracket which are fixed on the bottom plate; the optical imaging assembly comprises a lighting unit, an imaging objective lens and a snapshot type hyperspectral camera, and can synchronously obtain space image information of a measured object and spectral information of a continuous wave band in a single exposure process; the motion control assembly realizes automatic transmission of the measured object through the transmission platform; the data processing module is integrated with an image preprocessing unit, a spectral analysis unit and a spectral database, and is used for end-to-end closed-loop control from original hyperspectral data generation to sorting decision making; the system adopts a metasurface light splitting structure to realize real-time decoding, performs material or category identification by using a pre-trained classification model, and finally automatically generates a sorting decision signal according to an identification result to drive a motion control assembly to complete sorting operation.

The invention discloses a marine plankton hyperspectral imaging detection system, relates to the technical field of spectrum detection, and is used for solving the problem of poor spectrum classification and recognition in a water body environment. According to the method, the hyperspectral image and the environmental disturbance parameters are synchronously acquired, and the disturbance mapping sequence is constructed, so that accurate alignment between the image and disturbance is realized. A disturbance contribution weight matrix is constructed based on a dominant wave band, pixel-level spectrum stripping is carried out, a plankton purification spectrum is extracted, the spectrum purity and the recognition accuracy are improved, and a spectrum abnormal drift region is precisely recognized in combination with derivative spectrum change and image texture features; a coupling relation between the drift region and background disturbance is further established, a dynamic interference factor map is generated, and identification model parameters are dynamically adjusted, so that the identification stability and classification accuracy of the system in a complex interference environment are improved, and the method is suitable for multi-scene marine ecological monitoring.

The invention provides a method for preparing a single-material VMPE recoverable hose through dynamic temperature control multi-layer co-extrusion. The method comprises the steps that infrared temperature measurement modules are installed on all sections of an extruder respectively to construct a dynamic temperature control system; feeding the pretreated raw materials into a corresponding extruder, and adjusting a heating device and a cooling device of a charging barrel of the extruder in real time according to a preset extrusion temperature allowable range through a dynamic temperature control system to obtain a plasticized melt; carrying out bonding treatment on the plasticized melt by utilizing an ultrasonic vibration composite technology to obtain a multi-layer reinforced melt; detecting the thickness of the pipe wall of the multi-layer reinforced melt through a hyperspectral imager and judging whether the thickness of the pipe wall is qualified or not; if not, pipe wall thickness deviation data are fed back to a dynamic temperature control system and an extruder control system to conduct linkage adjustment on extrusion parameters; and if the multi-layer reinforced melt is qualified, cooling shaping and fixed-length cutting treatment are conducted on the multi-layer reinforced melt, the finished recoverable hose is obtained, and the hose forming precision and the recovery performance are effectively improved.

Hyperspectral imaging methods, devices and systems are described that improve the hyperspectral accuracy, and reduce manufacturing costs, by using two images that enable the recovery of hyperspectral images with high fidelity. One hyperspectral imaging device includes one or more imaging lenses, one or more sensors positioned to receive light associated with an object from the one or more imaging lenses, and a spectral filter. The hyperspectral imaging device is configured to capture a first image produced without using the spectral filer and to capture a second image produced with the spectral filter. The first image and the second image have different spectral contents, and the first and the second images are processed using a trained neural network for producing hyperspectral imaging data associated with the first and second images.

The invention relates to the technical field of automatic sorting of agricultural products, in particular to an egg quality intelligent sorting system and method based on multi-modal sensing. The system comprises a multi-mode sensing module, a data processing module and a sorting execution module. The multi-mode sensing module integrates a hyperspectral imaging unit, a voiceprint vibration detection unit and a weight sensing unit, and comprehensively collects egg surface images, vibration frequency and weight data; the data processing module analyzes the image data through deep learning and a data fusion algorithm to detect cracks and blood spots, and fuses multi-modal data to generate features to adapt to characteristic changes of different batches of eggs; the sorting execution module determines the sorting priority according to the fusion features and executes accurate sorting; the system utilizes the synergistic effect of the multi-mode sensing technology to comprehensively evaluate the internal and external quality of eggs, the detection precision is remarkably improved, efficient and intelligent egg sorting is realized, and the automatic sorting technology level of agricultural products is improved.

The invention relates to the field of farmland ecological protection, in particular to a salinization farmland protection forest configuration optimization system and method, and the system comprises a salinization monitoring module, an edge calculation module, a database, a three-dimensional model module, a model optimization module and a configuration scheme optimization module. The salinization monitoring module acquires data through an unmanned aerial vehicle and a hyperspectral imaging spectrometer and determines a soil salinity distribution state, the edge calculation module receives the data and carries out protection forest configuration model calculation, the database stores a calculation model and soil salinity and vegetation growth state data, and the three-dimensional model module generates a three-dimensional spatial-temporal model of forest growth. The model optimization module utilizes deep reinforcement learning to optimize a soil salinity and underground water level simulation model, the protection forest configuration effect is improved, the configuration scheme optimization module generates an optimal protection forest configuration scheme, a complex interaction process is accurately represented, and an efficient and accurate technical means is provided for ecological protection of salinized farmland.

The invention provides a method for carrying out corn quality seed selection by using a hyperspectral imaging technology, and belongs to the technical field of corn quality seed selection. Corn kernels are subjected to spectrum scanning in a wavelength range of 400nm to 2500nm, spectrum data are preprocessed through a continuous wavelet transform algorithm, baseline drift and noise interference are removed, and a high-quality seed selection result is obtained. And accurately extracting spectral characteristic peaks of protein, starch, grease and moisture. And establishing a characteristic peak distribution matrix to record peak intensity distribution, and calculating a characteristic peak intensity weight coefficient and a position offset. And constructing a characteristic peak quality incidence matrix, establishing a numerical mapping relationship between the spectral characteristics and the quality parameters, and obtaining a peak width parameter and a spectral noise level. The spectral quality fusion function is adopted to process the multi-dimensional characteristic parameters, and the comprehensive quality evaluation index and the single quality evaluation index are calculated, so that the technical problem that the multi-dimensional quality characteristics of the corn kernels cannot be accurately identified and accurately graded is solved.

The invention belongs to the technical field of hyperspectral imaging and machine learning, and discloses a plant carotenoid rapid determination method based on hyperspectrum and machine learning. The measuring system comprises a hyperspectral imaging device, a spectrum preprocessing module, a characteristic wavelength extraction module, a machine learning modeling module and a carotenoid content prediction module which are sequentially connected through a data processing unit. According to the method, spectral data of a plant sample is obtained in combination with a hyperspectral imaging technology, key wavelengths are screened through spectrum preprocessing and a feature extraction algorithm, and a carotenoid content prediction model is established by using machine learning models such as random forest regression, partial least squares regression or a gradient elevator. According to the method, rapid and nondestructive detection of the plant carotenoid can be realized without a chemical reagent, the determination precision and efficiency are improved, and the defects of complex operation, long time consumption and strong destructiveness of a traditional detection method are overcome.

The invention relates to the technical field of rice grain detection, in particular to a rice grain mixed-planting rapid nondestructive detection method based on hyperspectral imaging, which comprises the following steps: S1, collecting a rice hundred-grain board hyperspectral image, carrying out preprocessing and region-of-interest segmentation, and extracting a full-wave band spectrum grayscale image of a target region; s2, constructing a multi-channel attention mechanism network model suitable for nondestructive testing of mixed rice grains, and inputting the full-wave band spectrum grayscale image as a data set for classification training; according to the rapid nondestructive testing method for mixed planting of the rice grains based on hyperspectral imaging, spectrum and spatial characteristics of the rice grains are fused, spectrum and spatial characteristic information of the rice grains is enhanced through a multi-channel attention mechanism and a self-attention mechanism module, the classification precision of the rice grains is further helped to be improved, and the detection accuracy of the mixed planting of the rice grains is improved. According to the multi-channel attention mechanism network model provided by the invention, a segmentation module and a classification module are fused, so that the parameter quantity during high-resolution image training can be reduced.

The invention discloses a rapid nondestructive testing method and system for the lipid content and deterioration degree of red pine nuts based on hyperspectral imaging and deep learning, and belongs to the technical field of nondestructive testing of the quality and safety of agricultural and forestry products. The method is provided for solving the problems that an existing method for detecting the lipid content and the oxidation degree in the red pine nut kernels is generally complex in operation process, high in large-batch detection cost, long in consumed time and difficult to achieve detection in the whole storage and transportation process. The method is characterized by comprising the following steps: acquiring original near infrared spectrum data of a pine nut sample through a collected hyperspectrum; determining the lipid reference content truth value and the oxidation deterioration reference degree of the pine nut samples at different sampling times, and establishing a database according to the values; the method comprises the following steps: preprocessing collected original near infrared spectrum data of red pine nuts, and dividing red pine nut sample data collected in different batches into a training set and a verification set; and designing an improved one-dimensional cavity convolutional network based on a dynamic weight distribution module to construct a deep learning model, wherein the deep learning model is used for constructing a deep learning structure suitable for spectral feature analysis of the red pine nuts. And a back propagation algorithm is adopted to train the constructed model, and reverse updating of network weight parameters is realized by minimizing a loss function. When the performance of the constructed model meets the rapid detection requirement, the method is used for efficient and lossless synchronous detection of the lipid content and the oxidative rancidity degree of the red pine nuts.

The invention relates to the field of optical nondestructive testing, and discloses a visualization method for detecting mango quality based on hyperspectrum, which comprises the following steps of: firstly, selecting mango samples, and simulating different mango ripening stages by dividing the mango samples into a plurality of groups of samples; acquiring a high-dimensional spectral image by using a hyperspectral imaging system, and measuring the hardness, the soluble solid content and the titratable acid content; correcting and extracting the high-dimensional spectral image to obtain and optimize the average reflection spectrum of the sample, and establishing a mango maturity classification model and a quality regression prediction model; performing characteristic wavelength contribution degree analysis and screening, and optimizing a quality regression prediction model by adopting a key wave band; the method is applied to each pixel point of a high-dimensional spectral image, a two-dimensional distribution diagram is generated, and mango quality visualization is achieved. The method solves the problems that in the prior art, rapid and lossless judgment of the mango ripening stage cannot be achieved, accurate prediction and visual distribution of key internal quality indexes cannot be achieved, and intelligent grading and quality control of picked fruits are not ideal.

The invention provides an imperfect soybean hyperspectral imaging classification method based on deep learning. Efficient and accurate classification is realized by fusing spectrum and image features. Acquiring spectrum and image data by using a hyperspectral imaging system; the data quality is improved through a preprocessing method; and constructing a dual-channel feature fusion model, and reinforcing key feature extraction in combination with an attention mechanism. According to the method, the classification precision of 95.13% and 94.00% in the visible light-near infrared band and the short wave infrared band is achieved, and compared with a traditional support vector machine and a convolutional neural network method, the classification precision is remarkably improved. The model deeply digs spectral image features through a deep learning network, analyzes the association between the spectral image features and soybean quality, and enhances the robustness and generalization ability of the model. The method has the advantages of being high in precision, rapid in detection, high in adaptability and the like, is suitable for different varieties and large-scale detection scenes, and provides an efficient solution for agricultural product quality monitoring, automatic sorting and agricultural scientific research.

The invention discloses a hyperspectral image super-resolution method and device based on a physical diffusion model. The method comprises the following steps: acquiring a low-resolution hyperspectral image and a corresponding high-resolution multispectral image; extracting edge information and semantic information from the high-resolution multispectral image; and inputting the low-resolution hyperspectral image, the edge information and the semantic information into a physical diffusion model, and reconstructing to obtain a high-resolution hyperspectral image. According to the invention, through a physical information guiding sub-network design, a physical information extraction and fusion method and a physical model guiding sub-network design, a physical mechanism of hyperspectral imaging and a scene physical attribute are successfully and deeply fused into a core iteration process of a diffusion model; the fusion significantly improves the spatial detail definition, spectral fidelity and overall physical consistency of the high-resolution hyperspectral image, and provides more effective physical guidance information and a more robust physical constraint optimization mechanism.

The invention discloses a multispectral and hyperspectral imaging proportion balance optimization method based on satellite remote sensing, and relates to the technical field of balance optimization, and the method comprises the following steps: collecting a first image data set which comprises a low-resolution multispectral image and a hyperspectral image; preprocessing the first image data set, screening out a low-resolution multispectral image from the preprocessed images, improving the spatial resolution of the multispectral image by adopting a super-resolution reconstruction algorithm, and generating a reconstructed multispectral image; fusing the reconstructed multispectral image and the preprocessed hyperspectral image, and performing optimization processing on a fusion result to generate an image with an optimal resolution; and extracting second image data based on the optimal resolution image, and establishing a proportion balance optimization model. According to the method, the data processing time and the imaging cost are accurately calculated, and the image fusion quality evaluation is combined, so that the optimal allocation of the imaging ratio under limited resources is realized.

The invention provides a multi-variety citrus Huanglongbing detection method and system based on hyperspectral imaging. The method comprises the following steps: collecting hyperspectral image samples containing different varieties of healthy and diseased citrus leaves; dividing a region of interest of the hyperspectral image sample, and exporting spectral data of the region of interest; according to the root-mean-square error of the average spectrum curve of the healthy hyperspectral image sample and the diseased hyperspectral image sample, preliminarily screening in the spectrum data to determine the number N of wave band groups; performing secondary screening on the N wave bands to determine M core characteristic wave bands; constructing a discrimination model by using a convolutional neural network, and training the discrimination model by using waveband data of M core characteristic wavebands in the healthy hyperspectral image sample and the diseased hyperspectral image sample; the trained discrimination model is used for predicting the detection result of the Candidatus Liberobacter asiaticum. According to the method, rapid field screening compatible with multiple varieties is achieved, and a standardized technical scheme is provided for digital plant protection of different citrus producing areas.

The invention discloses a hyperspectral image restoration method, which belongs to the technical field of hyperspectral imaging, and comprises the following steps: inputting a single hyperspectral image to be restored into a hyperspectral image restoration network for denoising / super-resolution reconstruction, the features are respectively input into a multi-scale space-spectrum fusion module to obtain branch features, local multi-scale space-spectrum features after dimension raising are obtained through splicing and dimension raising, and then the local multi-scale space-spectrum features are input into a self-adaptive space feature aggregation group to obtain global space-spectrum features; and after the noisy hyperspectral image passes through a convolutional layer, fusing the noisy hyperspectral image with the global space-spectral features to obtain a final de-noised image. The extended denoising method can obtain a super-resolution reconstruction method, and the method comprises the steps: carrying out the up-sampling after the global space-spectrum features are obtained; a low-resolution hyperspectral image to be restored is up-sampled before passing through a convolutional layer. According to the invention, the problems of noise introduction and spatial resolution reduction in the hyperspectral imaging process in the prior art are solved.

The invention relates to an internet-of-things monitoring and early warning system for lake water quality. The internet-of-things monitoring and early warning system comprises a multi-mode intelligent sensor acquisition system, a self-adaptive wireless transmission system, a distributed data storage system, an intelligent data analysis system, a visual interaction system, an intelligent early warning system and a remote intelligent control and repair system. The invention also relates to a monitoring method using the lake water quality Internet of Things monitoring and early warning system. Traditional and innovative sensors are cooperated to comprehensively collect water quality and ecological data, the biosensor accurately detects specific pollutants, the hyperspectral imaging sensor monitors algae distribution and water color change, rich and high-precision data are provided for lake water quality monitoring, the monitoring comprehensiveness and precision are improved, and the water quality monitoring system is suitable for being popularized and applied. The lake ecological protection work is powerfully supported.

The invention provides a compact hyperspectral imager optical system and an imaging method thereof, which are used for solving the technical problems that the overall size of a front telescopic assembly in a traditional hyperspectral imager optical system is difficult to compress, the installation and adjustment difficulty is relatively large, or the incident energy is lost and the space coverage capability is insufficient. And the technical problems of low energy utilization rate and difficulty in improving the sensitivity and the signal-to-noise ratio of the hyperspectral imager of the traditional Dyson type dispersion light splitting assembly are solved. The invention provides a compact hyperspectral imager optical system, which comprises an off-axis two-image-reflection telecentric optical path system based on a free-form surface and a Dyson light splitting system based on a curved surface prism, and realizes high regulation and control capability on light through multiple degrees of freedom of a curved surface shape. Compared with the prior art, the high-spectral imaging device has the advantages that the high-spectral imaging device is simple in structure, high image quality, small distortion and good telecentricity are achieved, meanwhile, the curved prism serves as a dispersion light splitting element, the whole spectrum has high light splitting efficiency, and the signal-to-noise ratio and sensitivity of the high-spectral imaging device can be improved.

The invention discloses a collaborative light regulation and control decision method and system, and relates to the technical field of intelligent regulation and control of plant factory light environments, and the system comprises a plant physiology monitoring unit which is provided with a non-invasive microelectrode array and a hyperspectral imager; and the environment sensing unit is used for collecting illumination intensity, spectrum, temperature, humidity and carbon dioxide concentration data. According to the collaborative light regulation and control decision-making method and system, fusion of multi-source heterogeneous data is realized through a pulse time sequence coding mechanism, and the problem that environmental parameters and plant physiological statuses are difficult to collaboratively analyze in the prior art is solved; based on a dynamic optimization architecture of double-alliance Nash equilibrium, autonomous collaborative decision-making of spectrum parameters can be completed in a short time, and compared with a traditional preset light formula, the response speed is improved; decision effectiveness can be synchronously guaranteed on the virtual model and biological entity level, the light suppression misjudgment rate is reduced, and meanwhile the plant light energy utilization rate is stably improved.

The invention relates to the technical field of hyperspectral imaging, in particular to a skin cosmetic residue grade dynamic monitoring method based on a hyperspectral imaging technology, which comprises the following steps: step 1, acquiring skin surface hyperspectral image data and preprocessing to eliminate environmental interference and noise; 2, compensating and correcting the preprocessed hyperspectral data based on the established skin heterogeneity correction model; 3, extracting multi-dimensional spectral features in the corrected data, and vectorizing correlation model input to predict the residual level; and step 4, outputting a dynamic monitoring result of the cosmetic residue grade for guiding safe use evaluation, and compared with the traditional and prior art, the method solves the problems of long time consumption, high cost, strong subjectivity, low detection precision and the like. By optimizing instrument channels, compensating spectral errors and constructing a quantitative model, rapid nondestructive testing is achieved, the detection accuracy and real-time performance are improved, and reliable technical support is provided for cosmetic safety assessment.