784 results about "Ir microscope" patented technology

The invention discloses a wharf armor block quality detection method, and belongs to the technical field of concrete structure detection.The method comprises the steps that a surface model is constructed through a movable three-dimensional laser scanning device, and a high-risk defect area is recognized; performing encryption scanning and impact echo detection in the high-risk area by adopting a frequency-adjustable dual-mode flaw detection device to generate a three-dimensional defect distribution diagram; a core sample is drilled in the defect area for compressive strength testing, and the material performance is analyzed in combination with a non-contact infrared spectrum; and establishing a dynamic evaluation model, calculating a safety coefficient by integrating surface defects, internal defects and material discreteness data, and generating a three-dimensional visual report. The problems that a traditional detection method is low in efficiency, insufficient in internal defect positioning precision, one-sided in material performance evaluation and the like are solved, full-dimensional detection from the surface to the interior and from the macroscopic level to the microscopic level is achieved through a multi-source data fusion and closed-loop feedback mechanism, and the reliability of structural safety evaluation and the scientificity of maintenance decision are remarkably improved.

The invention provides a farmland disease and pest monitoring method and device and a storage medium, and relates to the technical field of agriculture, the method comprises the steps: dividing a farmland into a plurality of sub-regions, obtaining an infrared spectrum image, and segmenting the infrared spectrum image into corresponding sub-images; and calculating a vegetation stress index based on the reflectivity of the insect pest related characteristic wave band, and screening high-risk sub-regions. Risk plants are randomly extracted in a high-risk area, physiological parameters of leaves are collected, physiological health indexes of the leaves are constructed, and dynamic correction is carried out through an environment regulation coefficient according to environment data. Using the trained network model to identify the risk sample leaf scab, and calculating the scab area index. And finally, fusing the corrected leaf physiological health index and the scab area index, calculating a normalized risk value through a risk assessment function, and dividing the normalized risk value into four risk levels. Through multi-stage fusion of spectral analysis, physiological parameter correction and deep learning scab detection, accurate early warning of diseases and insect pests is realized, and the monitoring efficiency is improved.

The invention discloses a micro-channel aluminum flat tube appearance detection method based on image processing, and relates to the technical field of appearance detection.According to the method, more comprehensive information acquisition is carried out through the multispectral imaging technology in combination with RGB, polarized light, infrared spectrum images and ultraviolet spectrum images, and surface temperature difference changes are detected through infrared spectrums; image preprocessing is carried out through contrast-limited adaptive histogram equalization and bilateral filtering, uneven illumination is inhibited while edge details are kept, the contrast of corrosion defects is improved, details of a low-resolution area are enhanced through a super-resolution reconstruction technology, edge features of a corrosion area are further optimized in combination with a histogram in the gradient direction, and the edge features of the corrosion area are further optimized. The identification capability of small-scale defects is improved; an improved YOLOv3 defect detection algorithm is introduced, multi-scale feature extraction is adopted, and a self-attention mechanism is introduced, so that the model pays more attention to a tiny corrosion area, and meanwhile, the detection precision of a small target is enhanced in combination with focus loss and regression loss.

The invention discloses a method and a system for evaluating the lodging resistance of stalks in a corn flowering period, and relates to the technical field of evaluating the lodging resistance of corn stalks. Comprising the following steps: collecting a plurality of corn plants with known physiological characteristic parameters in the flowering period, performing infrared scanning on the stalk surfaces of the corn plants to obtain an infrared spectrum image, and establishing a sample data set with the physiological characteristic parameters; training a neural network model based on the sample data set, and obtaining a plant physiological feature parameter prediction model by taking the infrared spectrum image as input and the physiological feature parameters as labels; performing infrared scanning on a target plant, and inputting the target plant into the prediction model to obtain a physiological characteristic parameter prediction value; calculating a stem structure stability index by combining geometric structure parameters; calculating an environmental adaptability index according to the environmental parameters; generating a lodging-resistant force index by combining the two, and generating a lodging-resistant force evaluation result according to the lodging-resistant force index. And the accuracy, comprehensiveness, precision and efficiency of evaluation are remarkably improved.

The invention relates to the crossing field of precision agriculture and artificial intelligence technology, in particular to a soil component detection system based on machine learning and infrared spectroscopy, which is characterized in that a soil spectrum is acquired on site through a portable Fourier infrared spectrometer, and after pretreatment, feature vectors are constructed by fusing climate, soil and crop data; the deep learning decision-making module extracts spectral features by using one-dimensional convolution, fuses multi-dimensional information through an attention mechanism, synchronously outputs a fertilization scheme, crop suitability scores and soil improvement measures by a multi-task learning sub-network, and finally, verifies by combining an agronomic knowledge base so as to obtain a fertilization result. According to the method, a comprehensive decision report containing a quantitative fertilization formula, a crop suitability sequence and a soil improvement scheme is generated, precise agricultural guidance is realized, spectral features are automatically extracted by adopting a one-dimensional convolutional neural network, and a multi-modal data fusion and multi-task learning framework is combined, so that the system achieves relatively high precision in the aspect of soil nutrient prediction.

The invention discloses a dissociation rate testing device of a remote plasma source in the field of remote plasma sources. The dissociation rate testing device is characterized in that the remote plasma source is used for dissociating process gas, and a gas outlet is communicated with an etching testing table through a water cooling pipeline; the gas ionization detection device comprises a maintenance gas input control module, an output detection unit and an output calculation unit, the maintenance gas input control module is used for controlling the amount of input maintenance gas, the output detection unit is used for detecting the amount of residual maintenance gas, and the output calculation unit is used for calculating the dissociation rate; the Fourier infrared spectrometer is used for collecting infrared spectrum data of the residual maintenance gas; the RPS control test bench is used for detecting and controlling the air inlet flow, the pressure in the cavity and the cooling water flow of the remote plasma source; the tail gas treatment equipment is used for treating the residual maintenance gas. According to the testing device, full-process monitoring and closed-loop control of the plasma source operation state are realized, and the dissociation rate detection precision and the process stability are improved.

The invention is suitable for the technical field of filters, and particularly relates to a manufacturing method of a linear gradient filter and the linear gradient filter. The manufacturing method of the linear gradient filter comprises the following steps that a glass wafer serves as a substrate, and a lower reflecting layer is plated on the surface of the glass wafer; wherein the lower reflecting layer is formed by alternately stacking a plurality of layers of low-refractive-index materials and a plurality of layers of high-refractive-index materials; coating a resonant cavity layer on one side, far away from the glass wafer, of the lower reflecting layer through a stepped coating process; plating a reflecting layer on one side, far away from the glass wafer, of the resonant cavity layer; and plating a long-wave-pass layer on one side, far away from the glass wafer, of the upper reflecting layer and in an area larger than the central wavelength to obtain the linear gradient filter. Compared with the prior art, the linear gradient filter provided by the invention is better in spectral resolution in a long wave band, smaller in energy difference of channels, excellent in wavelength selection performance and capable of being applied to spectrum applications such as a miniaturized infrared spectrometer and the like.

The invention provides a blood routine noninvasive monitoring patch and system and a detection method, and relates to the technical field of medical detection. According to the blood routine monitoring patch, blood routine monitoring can be carried out by utilizing the patch, a noninvasive detection technical means is adopted, infrared spectrum is utilized to permeate and collect infrared spectrum signals of subcutaneous blood, and after analysis, blood monitoring data is obtained and wirelessly reported to a background server; and blood abnormal signal supervision can be further carried out on the background based on an AI algorithm. Therefore, by the adoption of the blood routine monitoring device, blood routine changes can be monitored in real time by wearing the patch on a patient, invasive operation is avoided, convenience is provided for the patient, and dependence on medical staff is reduced. The patient can carry out blood routine (blood and blood vessel forms) monitoring at home, background feedback can be obtained when abnormity occurs, and intelligent blood routine supervision and detection are achieved.

The invention discloses a femoral artery paracentesis post-operation pressure-controlled hemostasis device and a monitoring intelligent control system thereof, and relates to the technical field of medical apparatuses and instruments, and the femoral artery paracentesis post-operation pressure-controlled hemostasis device comprises an assembly panel; the air bags are arranged at the bottom of the assembly panel; the air pump is arranged at the top of the assembly panel and used for controlling the air bags to contract; the control piece is arranged at the top of the assembly panel and used for controlling the air pump; the Doppler probe is arranged at the bottom of the assembly panel and used for detecting the blood flow speed of the far end of a puncture point, the hemostasis safety and efficiency are remarkably improved through multi-mode intelligent control and a real-time feedback mechanism, the vein area is accurately recognized and avoided in combination with ultrasonic and infrared spectrum analysis of the artery positioning module, the thrombus forming probability is effectively reduced, and the blood flow speed of the far end of the puncture point is detected. The pressure-time optimization model dynamically shortens the compression time (target 3 hours) based on the real-time hemostatic effect, and improves the comfort of the patient while ensuring the hemostatic effect.

The invention discloses an underground gas storage gas monitoring method and system based on optical fiber spectrum analysis. The method comprises the following steps: S1, optical fiber network deployment: arranging a corrosion-resistant optical fiber sensor network in a gas storage; s2, spectral data acquisition: transmitting light with a specific wavelength to a gas chamber at the tail end of an optical fiber through a Fourier transform infrared spectrometer, and acquiring a transmitted spectrum after interaction of gas and light; s3, spectrum decoupling and concentration calculation: based on a peak pattern fitting algorithm of the Lambert-Beer law, decoupling the infrared spectrum characteristics of the mixed gas, matching a standard spectrum database, and calculating the concentration of each gas component; s4, constructing a three-dimensional gas model: according to the position of the optical fiber sensor and the gas concentration data, establishing a gas migration model to predict a leakage position and a movement direction; according to the method, the trend, the component distribution, the concentration change and the potential leakage risk of the gas in the storage are effectively evaluated, and technical support is provided for the safety, the effectiveness and the economical efficiency of operation of the gas storage.

The invention discloses a water environment monitoring method based on image vision, and relates to the technical field of water environments, and the method comprises the steps: continuously shooting water body images of different depths at preset intervals in the vertical direction through an underwater camera; calculating the color difference of two adjacent pictures, and determining a water quality mutation layer; when it is detected that the turbidity in the water quality abrupt change layer interval exceeds a preset threshold value, infrared spectral imaging and ultrasonic ranging cooperative work is switched, the penetrability characteristic of an infrared image and ultrasonic depth calibration data are combined, the layering positioning error is corrected, and the water quality abrupt change layer interval is obtained. Through vertical interval shooting, color difference analysis, infrared and ultrasonic collaborative correction and a dynamic sampling strategy, the precision and efficiency of water quality layered monitoring are improved.

The invention relates to the technical field of grease detection, in particular to a grease detection data analysis system and method based on multi-source data. The method comprises the following steps: acquiring grease multi-source detection data, and performing near infrared spectrum detection feature extraction according to the grease multi-source detection data so as to obtain infrared spectrum detection data; performing signal enhancement processing on the infrared spectrum detection data to generate enhanced infrared spectrum data; carrying out physicochemical index feature extraction based on the grease multi-source detection data so as to obtain physicochemical index data; performing detection anomaly analysis according to the physicochemical index data and the enhanced infrared spectrum data so as to obtain grease anomaly detection data; and performing type division on the grease anomaly detection data so as to obtain grease chemical component anomaly data and grease microorganism anomaly data. Based on the grease detection technology, the abnormal recognition rate and the quality evaluation accuracy of grease detection are improved.

The invention discloses a primary tea grade screening method, a medium and equipment. The method comprises the following steps: acquiring original primary tea information containing a first visible light image, first near infrared spectrum information and first three-dimensional form information according to a preset sampling frequency; performing illumination compensation, geometric correction and multi-source registration processing on the original raw tea information to obtain standard raw tea information; extracting static quality parameters such as strip crimpness from the registered second visible light image, analyzing and acquiring physical and chemical quality parameters from the second near infrared spectrum information, and extracting structural features such as strip compactness from the second three-dimensional form information; finally, grade judgment is conducted through the cascade decision model, and the sorting device is controlled to execute corresponding sorting actions. Fusion analysis and intelligent grading of multi-dimensional features of the raw tea are achieved, and the sorting precision and efficiency are effectively improved.

The invention relates to the technical field of monitoring light supplement adjustment, and discloses a monitoring light supplement automatic adjustment system based on ambient light detection. An ambient light dynamic sensing module of the system captures visible light and infrared spectrum components of a monitoring area in real time through a multispectral sensor array, constructs a light intensity distribution model in combination with illumination intensity historical data, and outputs a theoretical illumination distribution diagram; the spectral feature analysis module compares the theoretical distribution map with the actually measured light field data in a multi-dimensional manner, performs spectral deviation detection and generates a spectral difference feature tensor; the light supplement demand mapping module inputs the tensor into a space illumination analysis network, and generates a light supplement coverage demand thermodynamic diagram and calibrates the boundary of a low-illumination area in combination with a field angle of monitoring equipment and position coordinates of a light supplement lamp; and the self-adaptive light supplement strategy execution module starts a multi-band mixed light supplement mode for a high-demand area according to the thermodynamic diagram, and performs phase cooperative control on adjacent light supplement lamp groups. The system realizes dynamic and accurate adjustment of light supplement, and can effectively adapt to complex ambient light changes.

The invention relates to a three-waveband medium-long wave infrared optical filter and a preparation method, the three-waveband medium-long wave infrared optical filter comprises a front film system, a substrate and a back film system, and the substrate is located between the front film system and the back film system; the front film system is formed by alternately overlapping germanium layers and zinc selenide film layers; and the back film system is formed by alternately overlapping germanium layers and zinc selenide film layers. According to the invention, the light energy of three wave bands of 3780-4000 nm, 4500-4900 nm and 7850-9500 nm is improved, the luminous flux of other wave bands is well inhibited, the technical problem of infrared filtering windows in some special infrared spectrum detection systems is solved, and a new scheme is provided for the application of the infrared spectrum detection systems.

The invention relates to the technical field of component analysis, in particular to an intermediate infrared spectrometer sensor verification system which comprises the following steps: acquiring spatial distribution information of a target sample through an automatic sampling module, and generating a corresponding first feature group based on the spatial distribution information; adjusting an emission wave band and a modulation strategy corresponding to a mid-infrared light source, converting mid-infrared photons into visible light signals, and generating a corresponding second feature group; performing down-sampling processing on the original spectral data to generate a corresponding third feature group; inputting the sparse spectral coefficient into a pre-trained deep learning reconstruction model, and dynamically correcting the reconstruction process in combination with an environment temperature compensation parameter to generate reconstructed spectral data corresponding to high resolution; and analyzing the reconstructed spectrum data, matching a preset substance spectrum database, extracting a characteristic absorption peak position and an intensity ratio of the target substance, and generating a corresponding final analysis result. According to the invention, the intelligence of the sensor verification system can be improved.

The invention relates to the technical field of dielectric materials, and discloses a composite dielectric material and a preparation method and application thereof.The composite dielectric material comprises filler and a base material, the filler is modified barium titanate, and the base material is cyanoethyl cellulose; the modified barium titanate comprises a barium titanate core and a shell, the shell comprises a polymer containing an epoxy group, the core and the shell are connected through a covalent bond, and an infrared spectrum test shows that in an infrared spectrogram of the modified barium titanate, characteristic peaks of the epoxy group and carbonyl respectively exist at 910 + / -10 cm <-1 > and 1720 + / -10 cm <-1 >. Epoxy groups in the modified barium titanate filler can react with hydroxyl groups in a cyanoethyl cellulose matrix material to form covalent linkage, compatibility of the filler and a matrix is improved, meanwhile, a potential well is formed, migration of carriers can be limited, and the composite material has good dielectric performance and energy storage performance.

The invention relates to the technical field of electron microscope sample pretreatment, and discloses a critical point drying control method and device based on online near-infrared monitoring, and the method comprises the steps: obtaining infrared spectrum data, and unmixing the spectrum data to obtain a feature data set of a solvent; outputting a to-be-corrected signal according to the feature data set, correcting the to-be-corrected signal with preset data, and determining a signal feature of the solvent; carrying out dynamic correction on the signal characteristics and a preset characteristic matrix, outputting concentration time sequence data of the solvent, carrying out steady state judgment by combining a preset convergence threshold value, and determining a critical state of the solvent; acquiring control strategy data according to the critical state, analyzing the control strategy data into execution response characteristics, performing multi-channel mapping and correction processing, and outputting a process control signal; and performing control gain correction on the process control signal to finish closed-loop feedback control of the final drying process. The method is based on multi-solvent spectrum sensing and concentration dynamic analysis, and self-adaptive closed-loop control of the critical point drying process is achieved.

The invention provides a micro-nano optical fiber probe for biochemical component detection based on infrared spectroscopy and detection equipment, and a preparation method of the micro-nano optical fiber probe comprises the following steps: S1, selecting one section of a multimode quartz optical fiber as a sensing area, and then removing a cladding layer and a coating layer of the sensing area to expose a bare fiber; s2, dripping a mixed acid solution prepared from hydrofluoric acid and citric acid to the bare fiber to corrode the bare fiber, and then cleaning and blow-drying; immersing the treated multimode quartz optical fiber into a silane coupling agent solution, reacting for 0.5-1 hour, and then drying; s3, immersing the multimode quartz optical fiber treated in the step S2 into a solution containing silver nanoparticles, and reacting for 10-20 minutes to obtain an optical fiber modified by the silver nanoparticles; and S4, sputtering a gold layer on the surface of the silver-modified optical fiber by adopting a vacuum magnetron sputtering technology to obtain the micro-nano optical fiber probe with the silver-gold film layer. After the surface of the silver nanostructure is covered with the gold film, oxygen, water, corrosive molecules and the like are effectively prevented from making direct contact with silver. In addition, both the gold and the silver can support a surface plasmon resonance (SPR) effect, and particularly, the prepared detection equipment can ensure the sensitivity of the SPR.

The invention discloses a method for detecting aspergillus flavus and aflatoxin B1 of crops based on an infrared spectrum. The method comprises the steps of crop sample material collection, rice multi-strain mould infection, spectrum collection, crop sample aflatoxin B1 determination, spectrum pretreatment, correction model construction and model verification. The invention aims at detecting the content of aspergillus flavus infection and toxin B1 thereof in crops by combining a near-infrared spectrometer and a stoichiometric method. The method has the advantages of being simple and easy to implement, high in analysis speed, high in detection precision, low in analysis cost, free of any pollution to the environment and the like, samples do not need any pretreatment, and the method has important practical value for guaranteeing the quality safety of agricultural products and reducing the carcinogenic risk of people.

The invention provides a ventilation system optimization method and system based on the air quality of an industrial aquaculture workshop, and relates to the technical field of environment regulation and ventilation system.The method includes the steps that aquaculture workshop gas is analyzed through an infrared spectrometer, the spectral intensity of the characteristic absorption wavelength of a target pollutant is obtained, and then the accurate concentration of the pollutant is analyzed according to the spectral intensity; and calculating a ventilation parameter reference value and controlling a fan to adjust the air supply amount. An air treatment device in the ventilation system can determine the optimal working time according to the air supply amount so as to decompose pollutants and continuously monitor the concentration of the pollutants at the inlet and the outlet of the device. Subsequently, the air quality deterioration index is predicted by analyzing the concentration change trend, and the current overall purification efficiency is calculated. When the purification efficiency deviates from the range or the deterioration index exceeds the threshold value, the running state of the device is dynamically adjusted and fed back to the system, self-adaptive optimization of the ventilation system of the industrial aquaculture workshop can be achieved, and it is guaranteed that the workshop air quality is stable and the purification efficiency reaches the standard.

The invention discloses a multi-modal image auxiliary diagnosis system based on endometriosis and a medium. The system comprises a determination module, a diagnosis module and a diagnosis module, wherein the determination module is used for determining focus position information, a metabolic activity analysis result and hemodynamic characteristic data according to a near infrared spectrum image, a magnetic resonance image and an ultrasonic image of a human body; the extraction module is used for extracting lesion feature information from different modal images; the optimization and reconstruction module generates a focus multi-modal image according to the focus feature information, processes the focus multi-modal image to obtain a focus three-dimensional model, and obtains a focus morphological feature quantized value based on the focus three-dimensional model; and the diagnosis module inputs the medical record related information, the focus morphological characteristic quantized value, the metabolic activity analysis result and the hemodynamic characteristic data into a preset focus comprehensive diagnosis model, and outputs a diagnosis result of endometriosis. According to the method, the multidimensional information is analyzed through the preset focus comprehensive diagnosis model, and accurate diagnosis of endometriosis is realized.

The invention provides a single-pixel-based visible light infrared fusion compression spectrum imaging system and method, and the method comprises the following steps: 1, carrying out the signal coding of a to-be-detected target signal, and obtaining a coded to-be-detected target infrared signal and a coded to-be-detected target visible light signal; step 2, inputting a to-be-measured target infrared signal into an infrared imaging light path, collecting a medium-wave infrared spectrum intensity value, and inputting a to-be-measured target visible light signal into a visible light imaging light path to collect a visible light spectrum intensity value; step 3, carrying out compressed sampling and reconstruction operation on the visible light spectrum intensity value and the medium wave infrared spectrum intensity value to obtain an infrared hyperspectral image and a visible light hyperspectral image of the target to be detected; 4, performing image fusion according to the infrared hyperspectral image and the visible light hyperspectral image to obtain a fused image with rich details; the fusion imaging of the method has the characteristic of keeping the feature information of the target scene so as to realize high-quality imaging.

The utility model discloses mineral online detection equipment, and belongs to the technical field of mineral detection. The mineral online detection equipment comprises a conveying device for conveying mineral samples and a detection device, the detection device is adjacent to the conveying device, and the detection device comprises a near-infrared light detection module, an X-ray fluorescence detection module and a laser detection module. The near-infrared light detection module is used for irradiating a mineral sample with near-infrared light and generating a near-infrared spectrum, the X-ray fluorescence detection module is used for irradiating the mineral sample with X-ray to generate fluorescence and generate an X-ray energy spectrum, and the laser detection module is used for irradiating the mineral sample with laser and generating a laser-induced breakdown spectrum; the near infrared spectrum, the X-ray energy spectrum and the laser-induced breakdown spectrum are combined for detecting and analyzing various components in the mineral sample. According to the arrangement, the laser-induced breakdown technology has higher detection capability on elements with smaller atomic numbers, and the laser-induced breakdown spectrum and the X-ray energy spectrum are combined, so that the components of a mineral sample are accurately detected.

The invention relates to the technical field of numerical control, in particular to an engraving and milling machine machining track control method based on machine vision. Comprising the steps of receiving an operation task instruction, collecting a reflected light intensity matrix and processing thermal field data, and generating a source data packet; obtaining a fusion heat distribution matrix through the homography matrix; based on the friction thermal response coefficient, a physical tool nose contact area is judged, a dynamic feature mask is generated, the dynamic feature mask and the reflected light intensity matrix are operated, and a monitoring area is output; executing end coordinates are output through least square ellipse fitting, and sensor calibration parameters are inversely transformed into tool nose feedback coordinates; and reading interpolation instruction coordinates, calculating a differential vector, and driving a feed shaft motor to execute displacement compensation. According to the method, deep space-time coupling is performed on the physical cut-off characteristic of the infrared spectrum, the high-frequency texture characteristic of the visible light and the kinematics constraint of a numerical control system, so that a real tool nose hot spot and an optical virtual image are distinguished, and the uniqueness and authenticity of track identification are ensured.

The invention relates to a near-infrared coal quality quantitative analysis model construction method, and belongs to the technical field of coal quality analysis. In order to solve the problems that a traditional coal quality analysis method is tedious in operation, time-consuming, destructive and difficult to meet rapid nondestructive detection requirements, the invention aims to realize rapid, accurate and quantitative detection and analysis of moisture, ash and volatile components in coal by combining a near infrared spectrum analysis technology with a deep learning algorithm. The method comprises the following specific steps: collecting and preparing a coal sample according to the national standard, and measuring moisture, ash and volatile components; the method comprises the following steps: acquiring spectral data by using a near infrared spectrometer, marking the spectral data, performing smoothing, multiplicative scatter correction and normalization processing, and dividing a training set and a test set according to a Kennard-Stone algorithm; and constructing a cascaded integrated deep learning prediction model of CNN-LSTM, and performing training, evaluation and optimization to predict coal quality indexes. According to the method, data are obtained based on a strict national standard experiment method, the accuracy of quantitative analysis of coal quality and the robustness of the model are improved by combining the near infrared spectrum technology with the deep learning model, and powerful support is provided for quality control and reasonable utilization of resources in the coal industry.

The invention relates to the technical field of optical detection, in particular to a notebook computer shell injection molding part surface quality detection method and system, and the method comprises the steps: irradiating a standard part with a preset wave band according to preset parameters through an infrared spectrometer to obtain a first spectrogram, and irradiating the injection molding part with the preset wave band according to the preset parameters to obtain a second spectrogram. Through the synergistic effect of a non-negative matrix factorization algorithm and a Transform fusion model, the problem of spectrum overlapping caused by addition of a flame retardant is effectively solved, through seven-degree polynomial and smooth second derivative processing, the carbonyl peak position measurement error is further reduced, and compared with a traditional Fourier transform infrared spectroscopy, the feature parameter extraction precision is further improved. The number of the carbonyl absorption peaks and the positions of the carbonyl absorption peaks are compared with the standard threshold values twice, so that the problem that some qualified products are mistakenly considered as non-qualified products easily due to deviation during judgment of the carbonyl content of the injection molding shell of the notebook computer is further solved.

The invention discloses a cordyceps sinensis intelligent bionic cultivation control system and method based on the Internet of Things, and the method comprises the steps: collecting the temperature and humidity, gas concentration, illumination intensity and larva behavior data of a cordyceps sinensis matrix through a sensor network, and carrying out the fusion through an edge calculation gateway, and generating a dynamic environment map; constructing a plateau ecological digital twinborn model based on the dynamic environment map in combination with historical meteorological data and a host-strain interaction rule, and generating a staged control time sequence; based on a staged control time sequence, temperature and humidity coupling compensation is carried out through a feedforward-feedback composite algorithm, and actuator linkage is adjusted through fuzzy PID; aI vision is used for identifying a larva infection window period and accurate spraying is carried out, and hypha infection is monitored in combination with a near infrared spectrum to dynamically adjust the CO2 concentration; and constructing an LSTM (Long Short Term Memory) model to predict the growth risk in combination with a dynamic environment map and insect body monitoring data, and actively intervening in an abnormal environment. According to the invention, high-precision, dynamic and intelligent regulation and control of the growth environment of cordyceps sinensis are realized.

The invention relates to the technical field of image enhancement, in particular to a traditional Chinese medicinal material infrared microscopic image enhancement method, which comprises the following steps of: performing two-dimensional Fourier transform on each spectral band image based on input original traditional Chinese medicinal material infrared spectral microscopic image data to obtain frequency domain representation, identifying a target frequency peak value position generated by interference in the frequency domain representation, and obtaining a target frequency peak value; and generating a stripe-removed hyperspectral data cube. According to the method, frequency domain analysis is carried out on an original infrared spectrum microscopic image through two-dimensional Fourier transform, stripe noise generated by light path interference is recognized and eliminated, a stripe-removed hyperspectral data cube is generated, and the input data quality of subsequent component separation is improved. And performing nonlinear decomposition on the spectrum vector after stripe removal, constructing a full-pixel end member contribution weight set, organizing weight values into an original abundance image set in a two-dimensional matrix form by utilizing an end member spectrum index, and realizing spatial decoupling and independent expression of multi-chemical component distribution.

Systems and methods for imaging a body part during a medical procedure e.g., by imaging the body part using at least one image capture unit, where the image capture unit is configured to sense light at least in the IR spectrum; illuminating at least a portion of the body part with light in the infrared (IR) spectrum, using at least one IR light source such that IR illumination of at least one of the at least one IR light source is coaxial with an optical axis of one of the at least one image capture unit; and outputting imagery data emanating from the at least one image capture unit for displaying thereof.