4383 results about "Leaf blade" patented technology

The invention relates to the technical field of agricultural science and technology, in particular to a leaf disease and pest identification method and system based on image processing, and the method comprises the following steps: setting multispectral and thermal imaging equipment, collecting multispectral images and thermal imaging data of a target frequency band, deleting images with insufficient definition and data with excessive noise, obtaining optimized multispectral images, and obtaining the leaf disease and pest identification result. And marking and classifying the data according to the collected date to form a time sequence image data set. According to the method, the network parameters are dynamically adjusted to reflect the state of each stage of leaf growth, so that the pest and disease damage prediction is not only based on static data, but also reflects the actual change of plant growth, and the real-time application value and accuracy of the prediction model are improved; the introduction of state transition learning enables the transition from health to each stage of pest and disease damage to be predicted more accurately, carries out early diagnosis and timely intervention, improves the recognition precision of the specific position of the pest and disease damage, can carry out local processing, and effectively improves the pertinence and economic efficiency of the processing.

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.

An embodiment of an improved fishing lure comprises a fishing lure body having an approximately flat surface on the top side or bottom side of the body and a blade statically affixed to the approximately flat surface. The lure body may include a recess having a wall and a base, wherein the approximately flat surface is at the base of the recess, and wherein around a portion of the perimeter of the recess at least one lip extends inward from the wall of the recess. The blade is preferably disposed in the recess and below the at least one lip. In some embodiments, the approximately flat surface has a float angle between about 30 degrees and about 90 degrees relative to horizontal, and more preferably between about 35 degrees and about 45 degrees relative to horizontal.

The invention relates to the technical field of agricultural intelligent monitoring, in particular to a crop growth detection system and method based on machine vision, and the system comprises an image collection module, a morphological feature capture module, a growth trend judgment module, an abnormal region marking module and a state information output module. According to the method, crop images are collected through a multi-angle camera, leaf contours, stem bending and plant spacing are extracted, multi-dimensional modeling of morphology is realized, structural change identification is enhanced, time sequence comparison of key morphological characteristics is realized, identification precision and time efficiency are improved, leaf and stem change trends are continuously analyzed, offset is quantified, and growth abnormity is early warned in advance; health degradation identification is combined with fluctuation area marking, dynamic monitoring is achieved, high-risk positioning is carried out on a time overlapping area, time-space locking is enhanced, precise management is assisted, the process is from image analysis to abnormal focusing, an information reasoning chain is established, and the monitoring precision and response efficiency are remarkably improved.

The invention belongs to the technical field of image processing, and discloses a grape leaf AI grading processing method and system based on computer vision. Comprising the following steps: acquiring image data of grape leaves, and performing variety label marking and image optimization to obtain a clear leaf image data set; sequentially performing feature extraction and feature fusion on the clear leaf image data set to obtain fused disease condition features and fused growth condition features; constructing a leaf evaluation framework capable of being adjusted in real time based on the fused disease condition features and the fused growth condition features; performing image evaluation on the clear leaf image data set by using a leaf evaluation framework to obtain a disease degree evaluation index and a growth condition evaluation index, and performing disease degree grading and growth condition grading on the clear leaf image data set based on the disease degree evaluation index and the growth condition evaluation index; obtaining a grape leaf grading result; accurate grading of the grape leaves is achieved, and the industrial production efficiency is improved.

The invention relates to the technical field of ecological environment monitoring and forest remote sensing, in particular to a forest plant stoichiometric characteristic spatial-temporal change intelligent analysis method, which comprises the following steps: S1, acquiring spectral reflectivity and element concentration values of plant leaves in a target area; s2, performing space-time registration processing; s3, constructing a three-dimensional space-time matrix, and generating a space-time cube with a dynamic weight identifier; s4, calculating the space-time gradient variation of the carbon-nitrogen-phosphorus ratio in each space unit; s5, performing feature decoupling on the space-time gradient variation; and S6, generating a stoichiometric characteristic three-dimensional atlas with altitude-time double coordinate axes. According to the method, by constructing the multi-factor driven three-dimensional space-time analysis model, decoupling analysis of long-term trend and short-term fluctuation of forest plant stoichiometric characteristics is achieved, the element flux mutation threshold value at the terrain turning position is accurately recognized, and the dynamic monitoring capacity of the ecological process is effectively improved.

The invention belongs to the field of agricultural information perception, and relates to a plant leaf scab segmentation method and system based on RGB-D cross-modal fusion, and the method comprises the steps: collecting a color image and a depth image of a crop through a synchronous collection device; registering the color image and the depth image to obtain a registered color image and a corresponding registered depth image; constructing an initial segmentation network, and performing model training on the initial segmentation network; the initial segmentation network comprises a cross attention-based feature aggregation module and an attention-guided adaptive feature fusion module; inputting the registered color image and the registered depth image into a trained segmentation network for segmentation to obtain a segmentation result; through heterogeneous data fusion of a depth sensor and a visible light camera, a multi-dimensional feature system covering two-dimensional textures and three-dimensional deformation is constructed; in combination with a cross-modal feature complementation mechanism, the recognition robustness of weakly dominant diseases and insect pests is enhanced, and the segmentation precision in a complex illumination and branch and leaf shielding scene is significantly improved.

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 garden monitoring, in particular to a landscaping monitoring system and method, and the system comprises a collection module which is used for obtaining multi-angle images of a garden in stages, and selectively collecting the images according to a correlation judgment result; the analysis module is used for extracting vegetation indexes, disease targets, leaf health features and falling object state features; the identification selection module is used for generating a dynamic acquisition strategy according to the relevance abnormal index and triggering the acquisition module to directionally acquire the second-level image; the weighted fusion module is used for performing adaptive weighted fusion on the multi-source features and calculating a comprehensive wilt risk level; the early-warning module is used for generating early-warning signals and early-warning grades, the view limitation of traditional single-view-angle monitoring is broken through, large-range vegetation vitality abnormal areas can be rapidly screened, detail features of trunk diseases, low leaf withering and microscopic disease spots can be accurately captured, and the comprehensiveness and reliability of early withering recognition are remarkably improved.

The invention relates to the technical field of artificial intelligence, in particular to a pest and disease identification method and system based on image processing and a medium, and the method comprises the steps: obtaining an original image of a plant leaf, and carrying out the preprocessing of the original image, and obtaining a preprocessed image; performing scab region segmentation on the preprocessed image by using an improved U-Net model to obtain a contour and a position of a scab; carrying out feature extraction on the segmented scab region, extracting deep semantic features through a pre-trained ResNet-50 network, and carrying out splicing fusion on the deep semantic features and color features and shape features of the scab to form a comprehensive feature vector; inputting the comprehensive feature vector into an integrated classifier based on XGBoost to carry out disease and insect pest type identification, and outputting disease and insect pest types and corresponding probabilities; the accuracy of pest and disease prediction can be improved.

The invention relates to the technical field of intelligent agriculture, in particular to an intelligent disease and pest monitoring and prevention system which comprises an environment self-adaption module, a spectrum collection module, a dynamic threshold value analysis module, a prevention decision module, an execution terminal and a data storage module. A polarization filter is adopted to suppress reflection of light on the blade surface to obtain a spectrum fusion image; the dynamic threshold value analysis module is used for establishing a dynamic proportion threshold value of the insect spot area and the medicinal material biomass by adopting a U-Net network in combination with a growth cycle recognition algorithm so as to obtain graded insect pest evaluation parameters; and the prevention and control decision module is used for combining the graded insect pest evaluation parameters with the environmental parameter set and generating a prevention and control scheme by adopting a pesticide application decision tree model based on northern climate characteristics. According to the invention, through dynamic environment adaptive adjustment and northern climate optimization pesticide application decision, efficient and accurate prevention and control of diseases and insect pests in a complex agricultural environment are realized.

The invention discloses a plant leaf imitating hydrogel composite material and a preparation method thereof, and belongs to the field of bionic composite materials. The preparation method comprises the following steps: firstly, preparing printing paste by compounding a coloring agent, printing a fabric to obtain a green fabric capable of simulating spectral characteristics of green peaks and red edges of plant leaves, and simulating fence tissues and skeletons in a leaf structure by using the green fabric; then taking a biological material polyvinyl alcohol (PVA) as a base material, adding a monomer with strong hygroscopicity, a cross-linking agent and an initiator, and blending to prepare a hydrogel prepolymer solution; and finally, pouring the hydrogel prepolymer solution into a mold containing the green fabric for copolymerizing, so that hydrogel is directly formed on the surface of the fabric and in pores, and the plant leaf imitating hydrogel composite material is obtained. The material gets rid of dependence on moisture absorption salt, and has the advantages of stable water absorption and simulation effects, good durability and the like.

The invention relates to the technical field of intelligent recognition, and particularly discloses a municipal greening nursery stock disease and pest recognition method and system based on the Internet of Things, and the method comprises the steps: carrying out the leaf surface state feature extraction of a nursery stock leaf image collected by a camera based on a computer vision technology and an artificial intelligence technology; and leaf surface semantic feature information focused on the pest and disease damage area is obtained, so that automatic identification of pest and disease damage types is realized. Therefore, manpower resources can be saved, and the efficiency and accuracy of seedling disease and pest detection are improved.

The invention belongs to the technical field of agriculture, particularly relates to a cranberry water demand dynamic sensing system and a method thereof, and aims to solve the problems that an existing traditional irrigation method is often based on a fixed time table or simple soil humidity measurement, the actual water demand condition of cranberries cannot be timely and accurately reflected, and insufficient irrigation or excessive irrigation is easily caused. In order to solve the problems that in the prior art, water resources are wasted, and the growth and yield of cranberries are possibly influenced, the invention provides the following scheme: the system comprises a monitoring module, a control module and a display module, wherein the monitoring module comprises stem flow rate, leaf water potential, soil water content at different depths and positions, air temperature and humidity, illumination intensity and wind speed data; according to the cranberry water demand monitoring system, accurate monitoring, real-time evaluation and scientific prediction of the cranberry water demand condition can be achieved, then a reasonable irrigation decision and environment regulation and control are guided, and the irrigation efficiency and the water resource utilization efficiency are improved.

The invention relates to the technical field of fish protection in water conservancy projects, and in particular to a fish passing system suitable for a low-head overflow dam and an operation method thereof, comprising a lock chamber, an upper lock head and a lower lock head arranged at two ends of the lock chamber, gates being installed on the gate heads, and a diversion port being opened on the gates; a main rotating blade is fixedly arranged near the diversion port in the lock chamber, reciprocating components are symmetrically arranged on both sides of the main rotating blade, the reciprocating components include a rotating rod and a guide rod arranged in parallel, and the main rotating blade is connected to the rotating rods of the reciprocating components on both sides through a transmission component; a reciprocating plate is arranged on the rotating rod, the reciprocating plate is slidably sleeved on the guide rod, a rotatable flipping rod is arranged on the reciprocating plates opposite to each other on both sides, and a disturbance frame is fixed on the flipping rod; the flipping rod drives the disturbance frame to flip together, so that the disturbance frame is switched between a horizontal state and a vertical state; the water flow at the diversion port is subjected to power conversion, the reciprocating plate on the rotating rod is driven to reciprocate linearly, and the migratory fish is driven to move upward to the upstream waters by automatically flipping the disturbance frame.

The invention relates to the technical field of aviation turboshaft engines, and provides a method for evaluating the influence of sand dust erosive wear on the performance of a turboshaft engine, which comprises the following steps: calculating the amount of sand dust sucked into the turboshaft engine; calculating and solving an air flow field in the air compressor channel and a movement track of sand dust particles by adopting computational fluid mechanics software; establishing a mathematical model of erosive wear of the air compressor blade of the turboshaft engine caused by sand dust; on the basis of the motion trail and the mathematical model, the blade profile of the blade after abrasion is obtained, the airflow field in the air compressor channel after abrasion is further calculated and solved, and the reduction amount of the efficiency of the air compressor after sand dust erosion abrasion is obtained; and on the basis of the reduction amount of the compressor efficiency, engine overall performance calculation software is adopted to calculate and evaluate the engine performance parameter change condition. According to the method, a mathematical model of compressor blade abrasion is constructed, the turbine shaft engine performance degradation evaluation method is provided, and quantitative analysis and evaluation of turbine shaft engine performance degradation in the sand and dust environment are achieved.

The invention discloses a hyperspectral technology-based inversion method for SPAD values and nitrogen concentrations of leaves in the whole growth period of rice. The hyperspectral data of the leaves of the rice are obtained by means of an ASD Field Spec4 portable ground object spectrometer in the initial differentiation period (PI), the heading period (HD), the middle filling period (MF) and the mature period (MA) of young ears; measuring the SPAD value of the rice as the chlorophyll content by using an SPAD-502 chlorophyll meter; determining the nitrogen concentration of the rice leaves by adopting a Kjeldahl method; performing correlation analysis on the hyperspectral data of the leaves, the nitrogen concentration and SPAD, and screening sensitive wavebands. According to the method, the hyperspectral characteristic wave band of the rice leaf is extracted through a correlation analysis method, so that the redundancy of full-spectrum data modeling is reduced, data are optimized, an inversion model of the nitrogen concentration and SPAD value of the rice leaf is constructed based on linear regression (LR), random forest (RF), support vector regression (SVR) and gradient boosting (GBRT), and the nitrogen concentration and SPAD value of the rice leaf are obtained. The optimal machine learning algorithm with high precision and good stability is obtained, and a scientific basis can be provided for non-destructive real-time leaf color monitoring and precise fertilization control of indica rice in the South China.

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

The invention relates to the technical field of agricultural management, in particular to a smart farm environment regulation and control method and system based on big data analysis, and the method comprises the following steps: obtaining an instantaneous temperature value of the upper surface of a leaf and an instantaneous temperature value of the lower surface of the leaf through an infrared temperature sensor, obtaining an instantaneous value of stomatal conductance through a stomatal conductance sensor; and acquiring a water vapor pressure difference value of the air cavity through a water vapor pressure sensor, aligning a time sequence by adopting a multi-source data fusion algorithm, eliminating noise, and generating a dynamic physiological data set. According to the method, the leaf temperature difference, the air hole conductance and the air cavity water vapor pressure difference are collected, a real-time dynamic data set is constructed, a temperature difference amplification signal is extracted, transpiration enhancement triggering, wind speed increasing, water mist particle size decreasing and wind speed and particle size linkage regulation and control are analyzed in combination with the trend, a regulation and control weight is constructed by introducing multiple regression analysis, and the wind speed and the particle size are dynamically regulated according to the weight. A leaf temperature difference regression base line is guaranteed, the regulation and control accuracy and physiological adaptability are improved, and the environment self-adaptive regulation capability is enhanced.

Seed treatment systems with a mixing reservoir including a tank, a circulation mechanism positioned within the tank, and a controller coupled to the circulation mechanism, where the circulation mechanism includes a plurality of vanes configured to rotate about an axis within the tank, and where the controller is configured to mix seed treatment components within the tank in one or more mixing cycles by rotating the plurality of vanes within the tank, each mixing cycle including a first mixing operation in which the plurality of vanes rotate in a first direction about an axis for a first time period, and a second mixing operation in which the plurality of vanes rotate in a second direction about the axis opposite to the first direction for a second time period. Methods of use of the disclosed systems, e.g., for treating plant seeds, and agricultural compositions produced thereby are also provided.

The invention relates to the field of the centrifugal fan industry, in particular to an impeller with multi-element coupling bionic blades and a centrifugal fan, the impeller comprises a front disc and a rear disc, an exhaust inlet is formed in the middle of the front disc, a plurality of blades are arranged on the rear disc in a surrounding array mode, and the blades comprise long blades and short blades which are evenly distributed at intervals; the long blades and the short blades are all metal plate type single-arc multi-element coupling bionic blades. According to the impeller with the multi-element coupling bionic blades and the centrifugal fan, according to the principle of a bionic flow control technology, the front edge of the long blade adopts a streamline structure characteristic of simulating the outer side edge of a shark tail fin, and the tail edge of the blade adopts a typical arc tooth structure characteristic of simulating the feather front edge of a tenon wing; the common problems of small flow, low pressure, low efficiency and large noise of a centrifugal fan in the prior art are solved.

The invention relates to a rice growth process three-dimensional scene construction method based on a crop growth model and a rendering engine, and the method comprises the following steps: obtaining plant density, distribution, leaf inclination angle distribution, a leaf spectrum, a canopy spectrum and substrate reflectivity through a field experiment, and constructing a single-plant rice 3D model; coupling a crop growth model, and describing vegetation canopy reflection characteristics through canopy structure parameters and leaf BRDF to simulate canopy reflection factors; respectively calculating a water body scattering phase function and a water surface BRDF according to the water body biological optical model and the wave water surface reflection model; generating a three-dimensional water body volume model through an AVRM model; blender software is combined with a LuxCoreRender rendering engine to couple incident radiation, vegetation canopy reflection factors, water optical attributes and other related input parameters, and a real rice scene is constructed. According to the method, the high-precision three-dimensional rice scene model is established, important technical support is provided for improving the precision and efficiency of rice remote sensing radiation modeling and further for agricultural remote sensing and wetland monitoring, and the method has popularization and application prospects.

The invention belongs to the technical field of welding, and particularly relates to a positioning device for welding a fireproof valve, which comprises a base, one side of the top of the base is provided with a moving mechanism, the top of the moving mechanism is provided with a supporting seat, and the other side of the top of the base is connected with a fixing seat through a bolt. Inserting grooves are formed in the opposite sides of the fixing base and the supporting base correspondingly, fixing grooves are formed in the positions, located at the bottoms, of the two opposite sides of the inserting grooves, storage grooves are formed in the positions, close to the tops, of the two opposite sides of the inserting grooves correspondingly, and through openings are formed between the bottoms of the storage grooves and the tops of the fixing grooves. A fixing mechanism is arranged among the inserting groove, the fixing groove and the containing groove. According to the welding device, the blades and the rotating shaft can be effectively fixed through cooperation of the pressing mechanism and the fixing mechanism, and the situation that due to the poor fixing effect of the blades and the rotating shaft during welding, the positions of the blades and the rotating shaft deviate during welding, and welding of the blades and the rotating shaft is affected is prevented.

The invention discloses a movable forestry fertilizer applicator, and particularly relates to the technical field of forestry fertilizer application devices.The movable forestry fertilizer applicator comprises a movable forestry fertilizer application device body, and when the movable forestry fertilizer application device body is moved to a fertilizer application site, fertilizer application operation can be conducted on leaves and roots of trees according to different fertilizer applied to the trees to be fertilized; a driver is started, so that a threaded transmission column is controlled by the driver to rotate, an adjusting plate is meshed, so that the adjusting plate slides and descends on the side face of a transmission rod, the adjusting plate is moved to the ground, and the descending distance is adjusted according to the depth of the adjusting plate; the lifting column is started to control the fertilizing pipe to drill and descend the ground, and the outer ground breaking column rotates so that the fertilizing pipe can be attached to the root of the tree to fertilize the tree.

The invention discloses a fan blade diagnosis method and device based on the voiceprint perception technology and a product, and the method comprises the steps: firstly obtaining a sound detection signal of a target fan blade, and then carrying out the feature extraction of the sound detection signal, and obtaining a corresponding feature vector; then, based on the feature vector, extracting an effective blade sound signal from the sound detection signal; after the extraction of the effective sound signals is completed, voiceprint feature extraction processing can be carried out on the effective blade sound signals to obtain corresponding voiceprint features; thirdly, constructing a blade damage detection model which takes voiceprint features as input and takes damage detection results as output; on the basis, the voiceprint features of the effective blade sound signals can be directly input into the model, so that the damage detection result of the target fan blade can be quickly obtained; therefore, blade detection is carried out based on the voiceprint technology, the cost is low, and the method can adapt to the complex field environment of the fan.

The invention discloses a method for predicting plant photosynthetic ability by using plant leaf traits, and relates to the technical field of agricultural science, and the method comprises the following steps: obtaining image data of a target plant leaf in a natural state through a high-resolution imaging device, and synchronously recording environment parameter information during collection; extracting multi-dimensional feature parameters of the leaf based on the image data, wherein the multi-dimensional feature parameters comprise a color index, a texture feature, an area, a thickness estimation value and an edge contour form; and carrying out stress feature identification on the character data by utilizing an association rule of the environmental parameters and the leaf characters. According to the invention, by introducing a stress identification and data correction mechanism, the accuracy of photosynthetic capability prediction is improved; the method has good environmental adaptability and crop universality, and is suitable for various agricultural scenes; and a prediction feedback closed-loop system is constructed, continuous optimization and intelligent adjustment of the model are realized, and the stability, reliability and practical application value of the system are remarkably enhanced.

The invention discloses a multi-sensor fusion accurate spraying control system and method, and relates to the technical field of fruit tree planting, and the system comprises a data collection module, a fruit tree analysis module, a leaf analysis module, a pesticide analysis module and a pesticide spraying module. The environmental factors comprise wind power data and light data during pesticide spraying, the fruit tree data comprise distribution data of fruit tree leaves and fruit tree leaf image data, and the spraying equipment data comprise an equipment inclination angle and a distance between equipment and a fruit tree; according to the technical key points, in the spraying fertilization process, leaves on fruit trees are analyzed, healthy leaf values are calculated, whether the fruit trees are healthy or not is judged according to the healthy leaf values, a fruit tree treatment scheme is analyzed when the fruit trees are abnormal, spraying fertilization and pesticide spraying are combined, the using effect is good, and the good using prospect is achieved.

The invention discloses a method for increasing the phenol yield through catalytic pyrolysis of a decommissioned fan blade, and relates to the technical field of waste composite material resourceful treatment.The method comprises the steps that decommissioned fan blade fragment materials obtained after crushing and magnetic separation are subjected to multispectral imaging, multispectral data are obtained, and pretreatment is conducted; performing spectral feature extraction on the preprocessed multispectral data by using a convolutional neural network; identifying and separating the fragment materials based on spectral characteristics and automatic separation equipment; carrying out activation and carbonization treatment on the separated balsa wood fragments to prepare a carbon-based catalyst; carrying out catalytic pyrolysis on the separated fragment material by utilizing an automatic adjusting reactor and a carbon-based catalyst; collecting a product generated by catalytic pyrolysis, and carrying out qualitative and quantitative analysis on phenols to obtain a qualitative analysis result and a quantitative analysis result. According to the method, through a mode of combining multispectral imaging and the convolutional neural network, different types of composite materials are accurately identified and classified.

The invention relates to a comprehensive performance verification method for a wear-resistant layer of a turbine blade, and belongs to the technical field of turbine blades, the comprehensive performance verification method comprises a melting performance test, a pressure-resistant performance test, a wear-resistant performance test, a high-temperature hardness test and a tension shear test, and the melting performance test is performed on a welding rod made of a first material; the first wear-resistant layer made of the first material is subjected to a pressure resistance test, the second wear-resistant layer and the third wear-resistant layer made of the first material are subjected to a wear resistance test, the fourth wear-resistant layer made of the first material is subjected to a high-temperature hardness test, and the blade crown wear-resistant layer made of the first material is subjected to a tension shear test. The device has the beneficial effects that a fusion performance test, a pressure resistance test, a wear resistance test, a high-temperature hardness test and a tension shear test are carried out on the welding rod made of the first material, so that the comprehensive performance test on the welding rod made of the first material is realized, and whether the welding rod made of the first material can be used for processing a blade or not is judged; and the machining quality and safety of the blade are ensured.

The invention discloses a multi-layer liftable three-dimensional planting shed frame which comprises a planting shed frame body, the planting shed frame body comprises a mounting shell, one side of the mounting shell is fixedly connected with a supporting top plate with a built-in light supplementing lamp, and the top of the supporting top plate is fixedly connected with a water storage tank; the invention belongs to the technical field of agricultural planting, and aims at solving the problems that in the prior art, when vegetables on the upper layer grow flourishing, illumination of the vegetables on the lower layer is prone to being shielded, and consequently leaves of the vegetables on the lower layer become yellow and grow slowly due to insufficient illumination. The technical effects are that through the arrangement of the layer height circulation adjusting mechanism, the upper layer vegetable planting rack can be automatically and regularly adjusted to the lower layer and the lower layer vegetable planting rack can be automatically and regularly adjusted to the upper layer in the vegetable growth process, so that the normal growth of the lower layer vegetables is prevented from being influenced by the fact that the vegetables always located on the upper layer shield light; and the illumination uniformity of each layer of vegetable planting frame is improved.