2844 results about "Infrared" patented technology

The invention relates to the field of soil spectrum analysis, in particular to a farmland soil environment detection method and system. The method comprises the following steps: firstly, analyzing fluctuation characteristics of near infrared spectrum curves of a plurality of soil samples in a preset window of a target wavelength to obtain undisturbed degrees of the near infrared spectrum curves in the target wavelength; fitting spectral data points of the near infrared spectral curve, and obtaining feature retention degrees of the extreme points on the fitted curve according to the difference of fitting absorbance of adjacent extreme points on the fitted curve, the absorbance of each wavelength and the difference of the fitting absorbance and the wavelength distribution of the extreme points on each fitted curve; filtering the spectral data points of the target curve based on the non-interference degree and the feature retention degree to obtain a filtering curve; according to the method, the interference information in the near infrared spectrum curve is removed, the existing key information is reserved as much as possible, and the precision of farmland soil environment detection is improved.

The invention relates to the technical field of American ginseng quality evaluation, and discloses an American ginseng quality evaluation method based on multi-task deep learning, a computer readable storage medium and a computer program product. The method comprises the following steps: collecting near infrared spectrum data of each American ginseng sample; determining the ginsenoside content of each American ginseng sample to obtain label data of each American ginseng sample; according to the collected near infrared spectrum data and the obtained label data, constructing and training an American ginseng quality detection model; according to the constructed American ginseng quality detection model, producing area tracing and ginsenoside content prediction are carried out on American ginseng to be evaluated. According to the method, the improved multi-task deep neural network is constructed, so that the feature extraction capability is improved, nondestructive origin traceability and total ginsenoside content prediction of American ginseng can be realized at the same time, the detection accuracy and efficiency are improved, and the defects of a traditional method in the aspects of real-time performance and nondestructive detection are overcome.

The invention provides a vehicle-mounted soil nutrient detection method and device based on soil spectral data classification, and relates to the technical field of soil detection.The method comprises the steps that soil near infrared spectral data of soil of a current site in a farmland area to be detected is collected through vehicle-mounted equipment; then obtaining first spectrum difference information and second spectrum difference information based on the soil near infrared spectrum data; further determining the similarity degree of the near infrared spectrum data of the soil; dividing the soil near infrared spectrum data into a plurality of different soil near infrared spectrum data subsets through a spectral clustering algorithm, and performing soil nutrient detection through a farmland soil nutrient detection model. According to the vehicle-mounted soil nutrient detection method based on soil spectral data classification, the total nitrogen content and the organic matter content of the soil can be accurately output in real time, and the detection effect of soil nutrient detection is improved.

The invention relates to a near-infrared coal quality detection method based on multi-dimensional data fusion so as to improve the coal detection precision. According to the method, a modeling sample set containing multi-dimensional data of the producing area of a coal sample, granularity characteristics, the distance between the coal sample and a probe, air humidity and environment temperature is constructed, near infrared spectrum data of the coal sample is collected, spectrum preprocessing, feature extraction and data fusion are carried out, and finally a quantitative analysis model is constructed to predict the coal quality characteristics in a high-precision mode. According to the method, a one-dimensional convolutional neural network (1d-CNN) technology is adopted, automation and intelligence of coal quality detection are realized, and the detection efficiency is improved. In addition, accurate data of moisture, ash content and volatile components in the coal sample are obtained strictly according to a national standard experimental method, and the representativeness of the sample and the reliability of the data are improved. Noise interference is effectively reduced through the preprocessing step, and scattering influence is corrected. The method provides powerful technical support for quality control, reasonable utilization of resources and the like in the coal industry.

The invention belongs to the technical field of photovoltaic cables, and particularly relates to a photovoltaic cable extrusion control method. The method comprises the following steps: S1, high-frequency data acquisition: introducing temperature-pressure-speed model data to obtain dynamic extrusion temperature, melt pressure and extrusion speed parameters in an extrusion process; an infrared on-line diameter measuring instrument is introduced to acquire real-time data of the outer diameter of a cable insulating layer and the outer diameter of a sheath layer; establishing an influence intermediate value, wherein the influence intermediate value comprises heat conduction delay time, melt flow delay time and equipment response delay time; establishing a traction speed-melt pressure-temperature-predicted outer diameter quaternary coupling model; according to the method, a quaternary coupling model is established by combining real-time data of the outer diameter of an insulating layer and the outer diameter of a sheath layer on the basis of a speed-temperature-pressure acquisition model, and the problem that repeated vicious cycle adjustment is easily caused by passive logic of'adjustment after waiting for deviation exceeding the standard 'is solved.

The invention discloses an artificial intelligence-based refining process efficiency optimization and real-time temperature control method and system, and the method achieves the efficient optimization and precise temperature control of a refining process through the innovation of four steps: 1, breaking through the limitation of traditional single-point monitoring, and deploying a multi-modal sensing array and a feed port near infrared spectrum sensor according to three-dimensional coverage; process-raw material two-dimensional data are collected in real time and associated, and the problem of data islands is solved; 2, innovatively constructing a'process-raw material 'two-dimensional feature vector, predicting a finished product rate by adopting an XGBoost model, and judging a dynamic power adjustment demand in combination with a threshold value to replace a traditional experience decision; thirdly, on the basis of the value output by the DQN model, an optimal dynamic temperature curve is calculated in a staged mode, and then the problem of fixed curve stiffness is solved through a generated precise power instruction; and fourthly, a three-level aging standard reaching mechanism is created for the first time, and an effect closed loop is formed corresponding to a differentiated scheme of model strengthening, local compensation and cross-furnace repairing.

The invention discloses a multi-mode fruit sugar degree nondestructive testing method, device and system and a medium, and the method comprises the steps: preprocessing an input fruit near infrared spectrum image and a fruit visible light image to obtain a one-dimensional near infrared spectrum input image tensor and a two-dimensional visible light input image tensor; a pre-trained multi-modal deep learning fusion model is utilized to obtain the predicted fruit sugar degree, and the multi-modal deep learning fusion model is pre-trained to establish an input near infrared spectrum input image tensor and a visible light input image tensor. The method comprises a spectral feature extraction network, an image feature extraction network, a multi-modal feature fusion network and a regression prediction network. The objective of the invention is to solve the problem of limited prediction precision caused by the fact that single spectral information is susceptible to noise, illumination conditions, peel thickness, water content and other factors, and improve the accuracy of fruit sugar degree nondestructive testing.

The invention discloses a method for realizing spectrum reconstruction based on spectrum physical calibration, and belongs to the technical field of infrared spectrum. The method comprises the following steps of S1, data acquisition, S2, spectrum-electricity joint data preprocessing, S3, spectrum reconstruction neural network construction, S4, model training, S5, multi-objective optimization and S6, model testing. In the constructed spectrum reconstruction neural network, the neural network is optimized by combining the spectrum physical calibration error and the multi-objective optimization algorithm, and the reconstruction precision and efficiency of the spectrum are improved.

The invention relates to the technical field of pot body temperature monitoring, and discloses a pot body surface temperature real-time monitoring system based on an infrared sensing technology. The system comprises a temperature data acquisition module, a feature extraction module, a temperature safety analysis module, a safety temperature range construction module, a deviation analysis module, a communication adjustment module and a temperature uniformity analysis module. The temperature data acquisition module combines the temperature data acquired by the infrared sensor and preset environment parameters to generate a dynamic temperature distribution model; the feature extraction module processes the model to obtain a compression temperature feature flow; a temperature safety analysis module generates a temperature control decision priority atlas according to the temperature control decision priority atlas; the safe temperature range building module builds a safe temperature corridor according to the atlas and generates a control strategy; the deviation analysis module analyzes the temperature deviation to generate abnormal mark data; the communication adjustment module dynamically allocates bandwidth transmission data; and the temperature uniformity analysis module detects temperature uniformity fluctuation and judges whether optimization control is performed or not. The system realizes precise monitoring and intelligent control of the temperature of the pot body.

The invention discloses a hospital basement ventilation lighting lamp and an energy-saving management and control system.The hospital basement ventilation lighting lamp comprises a lighting unit, a ventilation unit and an energy supply unit, the lighting unit comprises a light-transmitting plate and an LED lamp set, the ventilation unit comprises a motor and a fan, and the energy supply unit comprises a storage battery pack and a standby power supply. The ventilation lighting lamp and energy-saving management and control system for the hospital basement comprises a lighting module, a grouping module, a sensor module and a control module, the grouping module divides the basement into a plurality of control areas, the sensor module comprises an infrared temperature sensor, and the control module comprises a controller and an actuator. The problems that when constructors conduct construction, light of the whole construction area is turned on, but single-time construction only aims at a small area, so that energy waste of an unconstructed area is caused, meanwhile, the health of the constructors is easily affected due to the fact that the quality of air in a basement is low, and heating is easily caused due to the fact that illumination time is too long are solved.

The invention relates to the technical field of composite materials, in particular to a heat and sound insulation PVB intermediate film and a preparation method thereof. The problem that a PVB intermediate film is insufficient in heat insulation and aging resistance is solved. The preparation method comprises the following steps: pre-plasticizing PVB resin, shearing and mixing the pre-plasticized PVB resin and silane coupling agent modified enhanced nano cellulose, sequentially adding additives such as a plasticizer, molybdenum trioxide loaded lanthanum hexaboride, a composite light stabilizer and modified cerium oxide, and carrying out tape casting and hot pressing treatment to obtain the PVB intermediate film. Wherein the modified lanthanum hexaboride enhances infrared ray shielding, the composite light stabilizer and the modified cerium oxide synergistically improve the aging resistance, all the components are subjected to substance cooperation and process cooperation, comprehensive improvement of heat insulation, sound insulation and aging resistance of the PVB intermediate film is achieved, and the PVB intermediate film is suitable for the field of high-strength building glass.

A radiative cooling device that is in a state in which a radiative surface is colored is provided. A radiative cooling device CP includes an infrared radiative layer A that radiates infrared light IR from a radiative surface H, a light reflective layer B that is disposed on the side opposite to the radiative surface H with respect to the infrared radiative layer A, and a color portion X. The infrared radiative layer A is a resin material layer J that has a thickness adjusted so as to emit a heat radiation energy greater than an absorbed solar energy in a wavelength range from 8 μm to 14 μm, and the color portion X contains a colorant that absorbs light in the visible range.

The invention discloses an embedded fiber grating sensor self-calibration strain monitoring system and method.The system comprises a fiber grating sensor, an optical fiber demodulator and a stress buffering type self-calibration mechanism, and the fiber grating sensor is embedded into a propeller blade prefabricated mounting groove through a metal embedded part and a gradient modulus adhesive layer; the plurality of fiber grating sensors are symmetrically distributed in a propeller blade prefabricated mounting groove in pairs, and infrared rays of each pair of symmetrical fiber grating sensors are converged at the circle center of a propeller hub to form a dynamic reference point; the optical fiber demodulator is integrated on the propeller hub and is connected with the plurality of optical fiber grating sensors; the stress buffering type self-calibration mechanism integrates a double-shaft flexible hinge and a shape memory alloy, and interface stress buffering and fiber grating sensor position collaborative self-calibration are achieved. The interface stress is effectively reduced through the gradient modulus glue layer, and the shape memory alloy drives the double-shaft flexible hinge to efficiently correct the offset of the fiber grating sensor in real time and buffer the vibration.

The invention provides a high-frequency band-pass radar and infrared dual-stealth optical transparent metamaterial, and aims to overcome the defect that an existing stealth material cannot have optical transparency, infrared low emission, microwave broadband absorption and high-frequency band-pass at the same time. The antenna is formed by periodically arranging a plurality of structural units, and each structural unit sequentially comprises a low infrared emission layer, a first transparent dielectric substrate, a radar wave absorbing layer, a second transparent dielectric substrate and a frequency selection wave-transparent layer from top to bottom; the low infrared emission layer and the radar wave absorbing layer are respectively composed of a square patch array made of a transparent conductive material and a low duty ratio circular ring; the frequency selection wave-transparent layer is composed of a square ring aperture type metal mesh. Optical transparency, infrared stealth and radar stealth are integrated, meanwhile, the band-pass performance is achieved in the high-frequency guidance wave band, and the high application prospect is achieved in the field of actual engineering.

The present invention relates to a photovoltaic support for a solar power station used in the field of converting infrared radiation, visible light or ultraviolet light to generate electric energy. The photovoltaic support comprises a support leg, a tripod rotatably mounted on the upper end of the support leg and a photovoltaic panel receiving frame hinged on the left end of the tripod. A support controller is fixedly mounted on the upper end of the tripod. The above-mentioned stabilizing sleeve, adaptive stabilizing group and photovoltaic support control system are used in combination to effectively improve the installation stability and portability of the photovoltaic support, improve the installation efficiency and installation automation of the photovoltaic support, and effectively promote the adaptability of the photovoltaic support to the installation environment, effectively promote the versatility of the photovoltaic support, and can be effectively applied to wasteland plains, deserts, large reservoirs and other environments, thereby reducing the construction cost of the solar power station, reducing cost losses, effectively reducing the subsequent maintenance difficulty and maintenance cost of the photovoltaic support, promoting the reuse rate of the photovoltaic support, and effectively promoting the economic adaptability of the photovoltaic support.

The invention discloses an infrared heat pump drying machine, which comprises a drying chamber, a plurality of infrared heat pumps, a plurality of infrared heat pumps, a plurality of infrared heat pumps, a plurality of infrared heat pumps, a plurality of infrared heat pumps, a plurality of infrared heat pumps, a plurality of infrared heat pumps and a plurality of infrared heat pumps, and is characterized in that the infrared heat pumps are arranged on the infrared heat pumps; the heat pump drying system comprises a heat pump drying outer box, an evaporator, a compressor and a condenser, the evaporator, the compressor and the condenser are arranged in the heat pump drying outer box, humid air in the drying chamber enters the heat pump drying outer box through an air outlet and an air return pipe, and the humid air is conveyed back to the drying chamber through an air supply pipe and an air supply outlet after being dried, dehydrated and heated by a heat pump; and the infrared drying system comprises a pair of infrared drying racks and a pair of carbon fiber infrared radiant panel groups. The invention further discloses an infrared heat pump drying method. Infrared drying and heat pump drying can be combined for use, the rapid heating characteristic of infrared rays can be utilized, heat can be recycled through a heat pump system, and the dual purposes of energy conservation and high efficiency are achieved.

The invention relates to the technical field of petrochemical engineering, in particular to a method and equipment for predicting the yield of crude oil cut distillate oil, a medium and a product. According to the method, near infrared spectrum data of to-be-predicted crude oil is obtained, a corresponding key spectrum variable is determined, the key spectrum variable is input into a target prediction model corresponding to the type of the to-be-predicted crude oil, and the cut distillate yield output by the model is obtained. Thus, interference of other parameters on prediction accuracy is reduced through the key spectral variables with high correlation degree with the cut distillate yield, and the exclusive prediction model is constructed for different types of crude oil, so that interference of heterogeneity between crude oil types on prediction accuracy is reduced, the adaptability of the prediction model to different crude oil characteristic changes is improved, and the prediction accuracy is improved. Therefore, the prediction model can rapidly and accurately predict the yield of the cut distillate oil of the crude oil, and the prediction accuracy and prediction efficiency of the yield of the cut distillate oil are improved.

The invention relates to the technical field of Brassica rapa polysaccharide detection, and discloses a quantitative detection method of Brassica rapa polysaccharide based on near infrared spectroscopy. The method comprises the following steps: acquiring near infrared spectrum data of a brassica rapa sample, and dividing detection stages by combining spectral characteristics to obtain a plurality of detection stage identifiers; performing differential mapping on the spectrum load spectrum according to the identifier to obtain an optical parameter set of each stage; constructing a staged near infrared spectrum quantitative model by using the set; performing importance weighting on the original spectral data according to the identifier to generate optimized spectral data; and extracting polysaccharide related characteristics from the optimized spectral data through a staged model, and outputting a predicted value of the content of the Brassica rapa polysaccharide. According to the method, through stage division, differential mapping and data weighting, the spectral data utilization rate and the model suitability are improved, a targeted technical path is provided for quantitative detection of Brassica rapa polysaccharide, and the method can be used for quality control of Brassica rapa related products.

A sight system based on a combination of infrared imaging, low-illumination imaging, and an LED sight light dot, comprising an LED graphics chip (20), a lens (3), a beam splitter (30), a display screen (31), a thermal imaging core assembly (13), a low-light night vision camera module (48), and a main control board (51). The thermal imaging core assembly (13) is electrically connected to the main control board (51), the low-light night vision camera module (48) is electrically connected to the main control board (51), the thermal imaging core assembly (13) and the low-light night vision camera module (48) are both electrically connected to the display screen (31), the main control board (51) is electrically connected to the LED graphics chip (20) and the display screen (31), separately, and light emitted from the LED graphics chip (20) and the display screen (31) is incident on the lens (3) after passing through the beam splitter (30).

The invention relates to a multi-mode-based pit mud quality detection method, device, equipment and medium, and the method comprises the following steps: acquiring near infrared spectrum data NIR and mid-infrared spectrum data MIR of a pit mud sample to obtain original spectrum data of the pit mud sample, and preprocessing the original spectrum data to obtain standard spectrum data; dynamically adjusting a data fusion mode of NIR and MIR by adopting a multivariate statistical analysis algorithm based on the standard spectral data, and further constructing an NIR-MIR fusion model; carrying out spectral analysis on the pit mud sample based on an NIR-MIR fusion model so as to extract key component information in the pit mud sample; and based on the key component information, according to a preset multi-dimensional evaluation strategy, carrying out comprehensive evaluation on the pit mud sample, and generating a quality evaluation result. The pit mud detection device has the effect of improving pit mud detection efficiency.

The invention provides a brain function evaluation method and system based on fNIRS and eye movement feature fusion, and the method comprises the steps: testing a to-be-tested person under a pre-constructed social scene normal form, and obtaining test data which comprises functional near infrared spectrum data and eye movement data; extracting time dynamic characteristics of brain function activation and brain function network space organization mode characteristics from the functional near infrared spectrum data, and constructing a near infrared brain function characteristic set; for a near-infrared brain function feature set corresponding to the functional near-infrared spectrum data and an eye movement space-time feature set corresponding to the eye movement data, constructing features in each near-infrared brain function feature set and features in the eye movement space-time feature set into feature pairs, and calculating a correlation coefficient of each feature pair, feature fusion and screening are realized based on correlation coefficients; and inputting the screened features into a classification evaluation model to obtain the severity of the behavioral dysfunction and a brain function analysis value.

The invention provides a near infrared spectrum rapid evaluation method for the protein content of brown rice. The method comprises the following steps: acquiring optical signals of different varieties of brown rice under a near-infrared band and physicochemical indexes measured according to a traditional method, preprocessing the acquired optical signals by utilizing a smoothing (Savitzky-Golay) method, and constructing a brown rice protein content evaluation model based on machine learning and a characteristic band selection algorithm; effective prediction of the protein content of the brown rice is realized only through optical characteristic parameters. According to the method, the protein content of the brown rice can be predicted only by collecting the near infrared spectrum of the brown rice, and in food processing enterprises, the technology can remarkably improve the efficiency and stability of product quality monitoring, support real-time online detection and provide an efficient and rapid quality control scheme for the enterprises, so that the product consistency is improved, the waste of raw materials is reduced, and the production cost is reduced. The production benefit is improved.

The utility model provides a thickness control tool for roadbed filling, which belongs to the technical field of roadbed filling and comprises a base, a vertical rod is fixedly connected above the base, and scale marks are arranged on the vertical rod. The sliding sleeve is arranged on the vertical rod in a sliding and sleeving mode, and a positioning mechanism used for being fixed to the current height is arranged on the sliding sleeve; the infrared ray equipment is arranged on one side of the sliding sleeve, and a rotating mechanism used for adjusting the rotating angle of the infrared ray equipment is jointly connected between the sliding sleeve and the infrared ray equipment. When in use, the device is placed aside, and the accuracy of the filling thickness is ensured by observing infrared rays during construction; in this way, filling construction of workers can be prevented from being hindered, meanwhile, the area of infrared rays emitted by the infrared ray equipment is wide, and the tool can be prevented from being frequently moved. And the infrared ray equipment can be of an angle-adjustable structure through a rotating mechanism, the filling gradient can be further controlled, and the application range is wider.

An inspection system detects defects within a component using eddy current thermography. An infrared camera detects heat from a defect within the component generated by the eddy current interfering with the defect. The eddy current is generated in the component by a magnetic field applied to the component by an inductor assembly. The inductor assembly includes a magnetic core having magnetic core material and two arms, each arm including a coil to generate the magnetic field. The inductor assembly is configured to apply the magnetic field at an angle offset from a horizontal axis of the magnetic core such that the eddy current flows at an angle within the component relative to the offset angle. Further, the magnetic core material can be shaped to apply the magnetic field at the offset angle or to accommodate complex component shapes.

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

The invention relates to a preparation process and method for coating tungsten-doped vanadium dioxide nano-particles on the surface of silicon dioxide in a self-adsorption mode and application of the tungsten-doped vanadium dioxide nano-particles. The process sequentially comprises the steps of pretreatment of silicon dioxide and tungsten-doped vanadium dioxide particles and preparation of a core-shell structure formed by self-adsorption of the silicon dioxide and the tungsten-doped vanadium dioxide particles. The method comprises the basic operation procedures of grinding silicon dioxide microspheres, treating tungsten-doped vanadium dioxide particles with deionized water, setting stirring parameters and centrifugal parameters and the like. The invention also relates to application of the tungsten-doped vanadium dioxide and silicon dioxide core-shell structure prepared by using the self-adsorption method in adjustable transmittance and adjustable radiation capability in the fields of intelligent window energy-saving glass and radiation refrigeration. The problems that a traditional vanadium dioxide nanoparticle film is low in visible light transmittance, poor in near-infrared modulation capacity, adjustable in infrared radiance and the like are solved.

An optical filter includes: a substrate; a dielectric multilayer film (I) laid on or above one main surface of the substrate; and a dielectric multilayer film (II) laid on or above the other main surface of the substrate, in which the substrate includes a near-infrared ray absorbing glass and a resin film, the resin film includes a resin and a dye (NIR1), and the optical filter satisfies specific spectral characteristics.

The invention discloses double-sided coated glass. The double-sided coated glass comprises a glass substrate, a first coating layer and a second coating layer, the first coating layer is arranged on the front surface of the glass substrate and sequentially comprises an anti-reflection layer, an ultraviolet reflection layer and a middle and far infrared reflection layer from the glass substrate to the outside; the second coating layer is arranged on the back surface of the glass substrate and sequentially comprises a near-infrared reflecting layer and a near-infrared regulation and control layer from the glass substrate to the outside, and the near-infrared regulation and control layer is used for regulating and controlling the reflectivity of the near-infrared reflecting layer. According to the design of the front anti-reflection layer of the double-sided coated glass, through continuous transition of the particle size and the refractive index of nanoparticles, the visible light reflectivity is remarkably reduced, and the light transmittance of the glass is kept to be 85% or above and is superior to that of existing composite coated glass; meanwhile, through the synergistic effect of the ultraviolet reflecting layer, the middle and far infrared reflecting layer and the back near-infrared reflecting layer, the ultraviolet blocking rate can be larger than or equal to 99%, the comprehensive infrared blocking rate can be larger than or equal to 90%, and the technical problem that light transmission and blocking performance of traditional glass are difficult to consider at the same time is solved.

The invention provides a bottle sorting and data monitoring method and system and bottle recycling equipment. The method comprises the following steps: collecting a visible light image and near infrared spectrum data of a bottle target, and preprocessing the collected visible light image and near infrared spectrum data; extracting a fusion spectrum feature vector of the pre-processed visible light image and the near infrared spectrum data, and generating a feature matrix for material classification; inputting the feature matrix into a lightweight convolutional neural network model deployed in an edge calculation unit, and obtaining a recognition result of the material and form of the bottle; a sorting control instruction is generated based on the recognition result, and motion space state representation is constructed in combination with current state information of the mechanical arm; and inputting the spatial state representation into a path optimization model based on deep reinforcement learning, and outputting and executing an optimal sorting path and a coordination action sequence. According to the method, the precision and robustness of bottle identification can be improved, the recycling efficiency of renewable resources can be improved, and the recycling cost can be reduced.

The invention provides a food component analysis method and system based on a near infrared spectrum. The method comprises the following steps: acquiring a near infrared spectrum signal through a food sample, and generating a sample near infrared spectrum signal by adopting an intelligent sampling method; based on the near infrared spectrum signal of the sample, automatically focusing a key area by using an attention mechanism enhanced convolutional neural network, and combining a spectrum characteristic selection technology to screen spectrum characteristics, and generating sample component and content information; based on the sample component and content information, evaluating the human body metabolism influence degree by applying a gradient boosting decision tree algorithm, analyzing a biological mechanism by combining a bioinformatics analysis technology, and generating a sample influence evaluation report; and based on the sample influence evaluation report, adopting natural language processing to generate a food component analysis report. According to the technical scheme provided by the invention, high-precision and automatic food component analysis is realized, the analysis accuracy and efficiency are improved, and an influence mechanism of food components on human metabolism is deeply analyzed.