290results about How to "Fully extracted" patented technology

The invention discloses a PPY-YOLO-based steel surface defect detection method and a PPY-YOLO-based steel surface defect detection system. The PPY-YOLO-based steel surface defect detection method comprises the following steps: step 1, preprocessing a steel surface defect image; 2, a neck structure Faster-Neck, a deep learning module C3K2Faster and a spatial pyramid pooling module SPPFSim are introduced into the YOLOv11 network, and a PPY-YOLO target detection model is obtained; step 3, training a PPY-YOLO target detection model; and 4, acquiring a steel image, and inputting the steel image into the trained PPY-YOLO target detection model to obtain the category and positioning information of the steel surface defects. A new model is constructed, the backbone network and the neck network are improved, the backbone network is formed by connecting the input end of the C2PSA module with the output end of the SPPFSim module, and an up-sampling group and a down-sampling group are added to the neck network, so that the model has a relatively good detection effect.

The invention discloses a motor imagery electroencephalogram signal classification method based on multi-scale feature extraction and adaptive feature fusion, which is called EEG-MAFENet for short, and belongs to the field of brain-computer interfaces. The EEG-MAFENet is provided to promote the development of a motor imagery brain-computer interface and solve the problem of low classification accuracy in the prior art. Firstly, multi-scale time domain information and channel space information of electroencephalogram signals are extracted through a multi-scale feature extraction (MFE) module, and a primary spatial-temporal feature sequence is obtained. Next, the most valuable global and local feature information is adaptively fused through an adaptive feature fusion (AFF) module to obtain an advanced fusion time feature vector; and finally, completing a classification task by a classifier module formed based on a full connection layer. Experiments prove that the classification method provided by the invention can more effectively improve the feature extraction and classification performance of the motor imagery electroencephalogram signals.

The invention provides a multi-modal intelligent irrigation decision algorithm based on causal inference, and the algorithm comprises the steps: carrying out the knowledge distillation of a pre-trained image feature extractor based on causal inference, and fully extracting the features of crop time sequence information and image information; according to the method, multi-modal data is considered, and heterogeneous data is deeply fused. Through knowledge distillation of an image feature extractor, the reasoning speed of the model is higher, the prediction accuracy is higher, end-to-end water shortage state prediction can be carried out based on greenhouse environment data, root system environment data and crop images, and a prediction result can be used for guiding crop irrigation and correcting the irrigation frequency and the irrigation amount of crops. And a more intelligent and comprehensive irrigation decision is realized.

The invention belongs to the field of fault diagnosis, and particularly relates to a main shaft bending common characteristic diagnosis system based on a stacking integration method, and the system comprises the following modules: a sensor module which is used for collecting a vibration signal of a main shaft in real time; the data acquisition and processing module is used for converting an analog signal into a digital signal and preprocessing the signal acquired by the sensor; the feature extraction module is used for extracting features from the preprocessed vibration signals; the machine learning diagnosis module analyzes and classifies the extracted features by using stack integration based on a plurality of trained machine learning models, and judges the health state of the main shaft; and the output module is used for outputting the diagnosis result and sending out an alarm signal when abnormality is detected. Real-time monitoring and automatic diagnosis of the main shaft bending fault are achieved, compared with a traditional method depending on expert experience and manual detection, the fault detection efficiency and accuracy can be greatly improved, and misjudgment and missed judgment risks caused by human factors are reduced.

The invention discloses a deep learning method and system for multi-stage classification of embryonic development, and belongs to the field of multi-stage classification of embryonic development, and the method comprises the steps: carrying out the partitioning operation of a preprocessed embryo image, inputting the segmented image into a dual-branch local feature fusion module, and carrying out the local feature extraction, and obtaining a feature map containing fine-grained local features; inputting the feature map containing the fine-grained local features into a deep neural network module for global feature extraction and coding to obtain a final feature map; and inputting the final feature map into a classifier for development stage prediction, and outputting a predicted embryonic development stage. The advantages of the dual-branch local feature fusion module and the deep neural network module are combined to be used for classification of the embryo development stage, local and global information of the embryo image can be fully extracted, the accuracy of the classification stage is improved, and meanwhile a new thought is provided for application of deep learning in IVF.

The invention discloses a hyperspectral and multispectral image fusion method and system based on an improved Transform, and the method comprises the steps: carrying out the up-sampling of a hyperspectral image, and extracting the potential features of the hyperspectral image; multi-scale features of the hyperspectral image and the multi-spectral image are respectively extracted by using a double-branch structure, and global feature extraction is carried out on feature maps through a multi-layer deformable convolution crossover Transform; and adding the extracted features according to the same direction, splicing the features in a channel dimension, and gradually reconstructing a multispectral image through cascade up-sampling. The invention provides a new method based on an up-sampling recursive sub-network and a multilayer deformable convolutional cross Transformer (URSMDCT) for hyperspectral and multispectral image fusion, potential features and multiscale features of the image can be fully extracted, and spectral information and spatial information are effectively fused.

The invention discloses a vertical field large model-oriented high-quality data set construction method and system, and relates to the technical field of text processing, and the method comprises the steps: selecting seed text data, carrying out long text processing, segmenting the seed text data into a plurality of text segments, processing the text segments by using a large model tool to generate seed question and answer pairs, and constructing a seed question and answer pair; auditing and optimizing are carried out according to a preset auditing standard; making an instruction generation data set for the cue word, the text fragment and the seed question and answer pair according to a preset organization rule, performing fine adjustment on a pre-selected base model by using the instruction generation data set, and performing iterative optimization on the large model by testing the performance of the large model after fine adjustment and adjusting and optimizing parameters, thereby obtaining an intelligent extraction large model IELM; and based on the intelligent extraction big model, processing the text data by utilizing a preset script code, automatically generating knowledge question and answer pair data corresponding to the text data, and performing format conversion processing to obtain an instruction data set which is used for a training data set of the vertical domain knowledge big model.

The invention provides a DNA binding protein and RNA binding protein classification method based on ESM-2 and a dual-path neural network, and particularly relates to a staged classification method combining the ESM-2 and the dual-path neural network, which is called DRBP-EDP. In the first stage training, the protein sequence is classified as a nucleic acid binding protein or a non-nucleic acid binding protein; further classifying the nucleic acid binding protein as a DNA binding protein or an RNA binding protein in the second stage of training; meanwhile, a fine data set construction method is further constructed, so that a high-quality protein classification data set is created, through the method, the DRBP-EDP model obtains good performance, the classification accuracy of the nucleic acid binding protein and the non-nucleic acid binding protein in the first stage is 90.03%, and the classification accuracy of the non-nucleic acid binding protein in the second stage is 90.03%. In the second stage, the classification accuracy of the DNA binding protein and the RNA binding protein is 89.56%, a new insight and a new method are provided for protein classification, an innovative tool is provided for research on protein functions, and therefore a new opportunity is provided for development of life science.

The invention provides an oat green extract and a preparation method thereof, and relates to the technical field of plant extraction. The extraction method comprises the following steps: taking immature oat plants, cleaning and quickly freezing; mixing with sodium chloride, and crushing to obtain oat sections; mixing the oat sections with an ethanol solution, performing ultrasonic extraction, and performing solid-liquid separation to obtain filtrate; and filtering, purifying and freeze-drying the filtrate to obtain the oat green extract. According to the method, the immature oat plants are adopted as extraction objects, the extraction method suitable for the immature oat plants is researched, the problem of plant waste caused by the fact that oats cannot be mature in special areas is solved, local resources are effectively and reasonably utilized, and the economic value of the oat resources is maximized. According to the extraction process, effective components of immature plants are dissolved into a solvent through low-concentration ethanol, low-temperature ultrasonic and freezing wall breaking technologies, the natural activity of the effective components in the oat is not changed in the whole process, and the nutritional value of the oat green can be reserved to a greater extent.

The invention provides an insulator defect detection method GAC-YOLO based on YOLOv7, and relates to the field of identification and detection of insulator targets. Comprising the steps that a GSConv module is optimized, and the calculation amount and the memory requirement are reduced while the precision is guaranteed; an ACmix attention mechanism is introduced, convolution and self-attention are fused, the prediction ability of the model is enhanced, and then the detection effect is improved; the centralized feature pyramid is fused, information exchange and integration can be achieved through feature maps of different scales, more comprehensive insulator features are further obtained, and therefore the insulator detection precision is improved. And performing an ablation experiment on each module of the model, and performing a contrast experiment with a mainstream deep learning model to obtain a final insulator detection model. According to the model realized by the invention, on the basis of ensuring the original detection rate of the YOLOv7, the accuracy of the model in insulator defect detection is improved.

The invention belongs to the technical field of image processing, and particularly relates to a remote sensing image super-resolution reconstruction method based on a degradation mechanism. Comprising the following steps: S1, constructing a degradation model, inputting a high-resolution remote sensing image into the degradation model, and obtaining a degraded image corresponding to the high-resolution remote sensing image; s2, constructing a super-resolution network, and inputting the degraded image into the super-resolution network to obtain a reconstructed image; s3, training the super-resolution network by using the joint loss function to obtain a trained super-resolution network; and S4, inputting a degraded image to be reconstructed into the trained super-resolution network to obtain a reconstructed super-resolution image. According to the method, the resolution of the remote sensing image is improved, the quality and processing efficiency of the image are remarkably improved, and the method has high practical application value.

The invention discloses tea polyphenol component detection equipment for jasmine tea and a detection method thereof. The tea polyphenol component detection equipment comprises a spectrophotometer host, and the lower end of the spectrophotometer host is fixedly connected with a bottom support bin. Through the arrangement of the grinding box and the crushing knife rest, in the use process, jasmine tea needing to be detected is placed in the grinding box, then the pressing frame is assembled, through the pressing frame and second filter cloth, the movable space of the jasmine tea is limited, the jasmine tea is made to be close to the crushing knife rest, and therefore the crushing speed is increased; a solvent is placed in the grinding box, separation of tea polyphenol is accelerated through rotation of the crushing knife rest, meanwhile, sufficient extraction of the tea polyphenol can be guaranteed, the solution is directly filtered through the second filter cloth, the solution is taken out through the suction pipe and placed in the cuvette, then the cuvette is placed in the sample chamber, detection can be conducted, overall use is simple, and the detection efficiency is high. The device can directly measure on site, integrates components required by detection into a whole, and is convenient to carry.

The invention belongs to the technical field of wind power short-term prediction, and particularly provides a short-term wind power prediction method of a Bi-MGLSTM model based on VMD decomposition and Bayesian optimization, and the method comprises the steps: preprocessing data; variational mode decomposition is carried out on the wind power data; adding one forgetting gate and one input gate in the front and back LSTM of the BiLSTM model, constructing a Bi-MGLSTM model, and inputting the decomposed modal components into the Bi-MGLSTM model for power prediction; a Bayesian algorithm is used to optimize the hyper-parameters of the Bi-MGLSTM model, and an optimized Bi-MGLSTM model is obtained; predicting by the optimized Bi-MGLSTM model to obtain a prediction result of each modal component, and superposing and reconstructing to obtain a preliminary power prediction result; performing differencing on the preliminary power prediction value and a true value, and performing training and prediction on an error sequence by using the optimized Bi-MGLSTM model to obtain an error prediction result; and adding the power prediction value and the error prediction value to obtain a prediction result. According to the method, the wind power prediction error can be remarkably reduced, and the accuracy of short-term wind power prediction is improved.

The invention provides a construction method and application of an angelica sinensis potion fingerprint spectrum, and belongs to the technical field of drug detection.The construction method includes the steps that firstly, an angelica sinensis potion is used for preparing a test solution, then 0.08-0.12 wt% phosphoric acid aqueous solution (A)-acetonitrile (B) serves as a mobile phase, and the angelica sinensis potion fingerprint spectrum is obtained through high performance liquid chromatography detection, a standard fingerprint spectrum is established and is used for quality evaluation or control in the whole process of research / development / production / clinical application of the angelica sinensis potion. According to the construction method of the fingerprint spectrum of the angelica sinensis potion, main medicinal material components in the angelica sinensis potion can be detected only through high performance liquid chromatography, so that the quality condition of the angelica sinensis potion is reflected more comprehensively.

The invention relates to the technical field of climate event simulation, in particular to an optimization method of climate mode simulation composite extreme wind and rain event frequency based on deep learning, and the method comprises the following steps: obtaining observation data of rainfall and wind speed of a target area and simulation data of a plurality of climate modes; calculating rainfall and wind speed thresholds by adopting a percentile threshold method, and determining the occurrence frequency of a composite extreme wind and rain event; evaluating a climate mode simulation result by using the observation data, selecting the frequency as a characteristic value of the deep learning model, and using the observation frequency as a true value; constructing a multi-layer perceptron neural network, and optimizing model training by fusing a mean square error, a standard deviation ratio and a weighted loss function constrained by a minimum value; and inputting the simulation result of the climate mode into the trained model, and generating the correction result of the composite extreme wind and rain event frequency, thereby improving the simulation precision of the composite extreme wind and rain event frequency, effectively reducing the deviation, and providing support for disaster estimation and decision-making under the climate change background.

The invention discloses a hyperspectral image classification method and a hyperspectral image classification device based on a double-branch multi-scale CNN (Convolutional Neural Network) and a memory enhancement Transform. The method specifically comprises the following steps: step 1, acquiring and preprocessing hyperspectral image data; and step 2, inputting the data after the step 1 into a double-branch multi-scale CNN feature extraction module, and fully extracting spatial features and spatial-spectrum combined features of the hyperspectrum. And step 3, transmitting the data after the step 2 into a memory module, performing detail memory and global memory on the data by MT1, and performing normal memory and important feature memory on the data by MT2. 4, the data obtained in the step 2 and the data obtained in the step 3 serve as input and are transmitted into a memory Transform; and 5, finally, a classification result is obtained through an mlp-head layer. According to the method, rich spatial features and spectral features of the hyperspectrum can be fully combined, and the classification precision and the classification performance of the hyperspectral image are improved.

The invention relates to the technical field of preparation of silibinin, and discloses a rapid extraction and purification device and process for silibinin. A filter disc; the assembling rod is rotationally mounted on the filtering disc, and an extrusion roller used for extruding the scattered raw materials is rotationally mounted on the assembling rod; and a transmission assembly. After the filter disc ascends, the assembling rod is in butt joint with the stirring shaft through the clamping assembly, the stirring shaft rotates to drive the assembling rod to synchronously rotate, the transmission assembly drives the scattering wheels to rotate, the scattering wheels can scatter raw materials on the filter screen, and then the extrusion roller rotates to the position to roll the scattered raw materials; according to the silybin extraction device, a solution infiltrated in a silybum marianum raw material can be extruded after filtration, a solution which is most fully fused with the silybum marianum raw material is fully extracted, the problems that the solution is wasted due to the fact that the solution is directly removed after filtration, and silybin cannot be fully extracted are solved, and the extraction efficiency of the silybin is greatly improved.

The invention relates to the technical field of remote sensing image processing, and discloses a remote sensing ground feature classification extraction network and drawing method in a complex scene, and the method comprises the following steps: S1, obtaining high-resolution satellite remote sensing image data, carrying out the preprocessing of an image, and generating a multi-scene ground feature data set; s2, constructing a two-layer U-shaped network comprising an encoder and a decoder, wherein the two-layer U-shaped network is used for multi-scale feature extraction and ground feature boundary segmentation; s3, inputting the preprocessed remote sensing image into a two-layer U-shaped network, and carrying out ground feature classification and segmentation; s4, performing splicing processing on the segmentation result, and generating a ground feature type spatial distribution map; and S5, performing noise elimination and post-processing operation on the generated distribution map. The multi-scale features are selectively extracted through the feature selection module (FSM), the attention of the network to small target ground features can be enhanced, and the segmentation precision of the small-scale ground features is remarkably improved by selecting the features containing important details.

The invention discloses a Keemun black tea fermentation product, a preparation method thereof and application of the Keemun black tea fermentation product in caenorhabditis elegans anti-aging research. Active components in the black tea can be more fully extracted by using the natural eutectic solvent, and then the active components are further fermented by sequentially using the lactobacillus and the thermophilic thermophilic bacteria, so that the Keemun black tea fermentation product is prepared. The Keemun black tea fermentation product prepared by the invention is applied to caenorhabditis elegans anti-aging research, and adverse effects caused by oxidative stress are relieved through the antioxidant effect of the Keemun black tea fermentation product, so that the antioxidant enzyme activity (SOD, CAT and GSH) of caenorhabditis elegans is remarkably improved, and the metabolite (MDA) is remarkably reduced; and the ROS level in the caenorhabditis elegans is also obviously reduced. Therefore, the Keemun black tea fermentation product prepared by the invention has better antioxidant activity, and can effectively improve the oxidative stress damage of the caenorhabditis elegans, thereby effectively prolonging the life of the caenorhabditis elegans.

The invention relates to the technical field of crude oil cracking, and discloses a method and system for preparing low-carbon olefin and application, and the method comprises the following steps: (1) vaporizing raw oil to obtain a gas phase and a liquid phase; (2) carrying out supercritical extraction and sedimentation on the liquid phase to obtain extracted oil, settled oil and asphalt; (3) performing microwave cracking on the gas phase, the extract oil and water vapor in the presence of an electromagnetic wave absorber to obtain microwave cracking gas; (4) performing catalytic cracking on the settled oil to obtain catalyst cracking gas; and (5) separating the microwave cracking gas and / or the catalytic cracking gas to obtain the low-carbon olefin. According to the present invention, microwave cracking is adopted, and is matched with the supercritical extraction unit and the catalytic cracking unit, such that the problem of easy coking during the cracking reaction of the heavy cracking raw material can be effectively solved, the high low-carbon olefin yield can be obtained, and the operation period can be prolonged.

The invention belongs to the technical field of lasers, and relates to a dual-wavelength slab laser which comprises a laser resonant cavity unit and a slab crystal pump unit. The laser resonant cavity unit is provided with a first light path, and a first total reflection mirror, a slab crystal, a first electro-optical Q switch and a first output mirror are sequentially arranged along the first light path; a second total reflection mirror, a slab crystal, a second electro-optical Q switch and a second output mirror are sequentially arranged along the second light path; the slab crystal pumping device unit is a side pumping or a large pumping, and comprises a semiconductor laser array and an optical waveguide; the shape of the lath crystal is a trapezoid, and the lath crystal is an Nd: YAG crystal; the wavelengths of lasers output by the dual-wavelength slab laser are 1064 nm and 1319 nm respectively. The dual-wavelength slab laser has the advantages of high pumping power and high beam quality, and maximum power output is achieved.

The invention discloses an electromyographic signal gesture recognition method based on adaptive multi-scale double-flow feature fusion, and relates to the technical field of human physiological signal decoding. In order to make full use of modal information in sEMG, when a network is designed, a double-flow architecture is designed to carry out parallel processing on time sequence features and frequency domain features, multi-dimensional information of sEMG signals is fully used, a time sequence feature processing module is responsible for capturing the time dynamic state of the signals, and a frequency domain feature processing module is responsible for capturing the frequency domain features of the signals. The method ensures that the algorithm has agile response capability when identifying rapid actions or short-time changes, a frequency domain feature processing module focuses on frequency domain features of signals, and a proper frequency band is selected through a self-adaptive feature fusion module, so that muscle physiological features reflected in different frequency bands are extracted, and the recognition accuracy of the muscle physiological features is improved. According to the structure, the algorithm not only can effectively cope with electromyographic signal differences among individuals, but also can extract key features beneficial to recognition of complex gestures from frequency domain information, so that the fineness and robustness of gesture recognition are remarkably enhanced.

The invention relates to the field of natural flavone extraction, in particular to a composite extract of ampelopsis grossedentata leaves as well as a preparation method and application of the composite extract. The preparation method of the composite extract of the ampelopsis grossedentata leaves comprises the following steps: step 1, cleaning and crushing the silybum marianum, the ampelopsis grossedentata leaves, the pineapples and the longan to obtain an initial mixed plant; 2, mixing the initial mixed plant with water, and treating under an ultrasonic power condition to obtain an ultrasonic treatment substance; 3, treating the ultrasonic treatment object under a microwave condition to obtain a microwave treatment object; step 4, adding acetobacter aceti, bacillus subtilis and white rot fungi into the microwave treated material, and fermenting to obtain a fermented mixture; and step 5, centrifuging, and collecting supernate to obtain the composite extract of the ampelopsis grossedentata leaves. The composite extract of the ampelopsis grossedentata leaves can improve the extraction efficiency of flavonoid substances, and has remarkable anti-inflammatory and anti-oxidation effects.

The invention relates to the technical field of feeding equipment, and provides a feeding machine which comprises a vibration disc internally storing a magnetic material. The rack is positioned on one side of the vibration disc and is used for providing a mounting point and a supporting point; the transmission mechanism is mounted on the rack and is connected with the discharging end of the vibration disc; the discharging manipulator is connected to the rack; the conveying mechanism is connected with a first vacuum suction cup, and the magnetic materials in the conveying mechanism are extracted and conveyed through the first vacuum suction cup; the discharging mechanical arm is used for laying the magnetic materials on the middle rotating disc through the first vacuum suction cup; and the turnover mechanism is mounted on the rack, is connected with the middle turntable, and is used for turning over the middle turntable and synchronously turning over the magnetic material to enable the magnetic material to be erected. The magnetic material feeding device has the effect that the placing space can be fully utilized during magnetic material feeding.

The invention relates to the technical field of chemical purification, and particularly discloses an extraction and purification method of tetraacetyl phytosphingosine. The invention provides an extraction and purification method of tetraacetyl phytosphingosine. The extraction and purification method comprises the following steps: sample treatment, ethanol extraction, C8 chromatography, PA308 chromatography and PS-5 chromatography. Wherein an eluent used in the steps of C8 chromatography and PA308 chromatography is a 50-60v / v% acetonitrile-water solution; an eluent used in the PS-5 chromatography step is an acetonitrile-water solution with the concentration of 65-85v / v%. According to the extraction and purification method of the tetraacetyl phytosphingosine provided by the invention, the tetraacetyl phytosphingosine in thalli can be fully extracted, and the purity of the tetraacetyl phytosphingosine in the obtained tetraacetyl phytosphingosine extract is greater than or equal to 95%.

The invention relates to the technical field of metal recovery, in particular to a method for recovering metal from coarse nickel cobalt hydroxide. The method for recycling the metal from the coarse nickel cobalt hydroxide comprises the following steps that S1, after the coarse nickel cobalt hydroxide is subjected to ammonia complexing leaching and oxidation in sequence, leachate and manganese slag are obtained; s2, after the leachate is separated through an extraction method, raffinate, a nickel-containing solution and a copper-containing solution are obtained; s3, the raffinate is subjected to ammonia distillation treatment, and cobalt-zinc slag is obtained; s4, the cobalt-zinc slag is subjected to acid leaching, and a zinc-containing solution and cobalt slag are obtained; and S5, the cobalt slag is subjected to reduction leaching, and a cobalt-containing solution is obtained. According to the method for recovering the metal from the crude nickel cobalt hydroxide, the metal in the crude nickel cobalt hydroxide can be fully extracted, and a nickel product, a cobalt product, a copper product, a zinc product and manganese slag which can be sold as a manganese smelting raw material are obtained.

The invention provides a solid beverage with a sleep promoting effect and an identification method thereof, and relates to the technical field of large health food. The beverage is prepared from medicinal and edible traditional Chinese medicinal materials such as radix puerariae, longan aril, mulberries, rhizoma polygonati, jujubes, lilies, wheat, chrysanthemums, red roses with double petals, spina date seeds and liquorice through scientific compatibility, the beverage is prepared by adopting modern pharmaceutical processes such as supercritical CO2 extraction and low-temperature and high-pressure homogeneous extraction, and the quality of a product is controlled and identified through a liquid-phase fingerprint technology. The invention provides a safe and effective product with homology of medicine and food for promoting sleep. By adopting extraction preparation technologies such as supercritical CO2 extraction and low-temperature high-pressure homogenization, the purity and activity of the components are ensured, and the modern pharmaceutical process is advanced; fingerprint analysis shows that the consistency of the relative retention time and the relative peak area of each component is good, and the similarity of chromatograms of all batches of products is more than 0.9998, so that the stability and the consistency of the products are ensured.

The invention belongs to the technical field of biological gel films, and particularly relates to a preparation method and application of an antibacterial gel film based on fish scale gelatin compounded with probiotics. The fish scale gelatin and the probiotics are creatively compounded, and the prepared gel film has good performance; meanwhile, the alcohol-soluble protein, the trehalose and the amino acid are added, so that the fish scale gelatin can form a special space structure, pores are uniform, the structure is compact, and the physicochemical properties of the fish scale gelatin are further improved; the prepared gel film also has a good bacteriostatic effect, and can be applied to storage and preservation of perishable food; the method not only realizes waste utilization of fish scales, but also solves the problem that the prepared product is easy to be infected by microorganisms and spoilage, and achieves a remarkable technical effect. In addition, the invention has the advantages of safety, environmental protection, simple preparation method, easy popularization and application, and very wide application prospect.

The invention discloses an image enhancement and network structure improvement method in a target detection YOLO model, relates to a target detection method, and provides a new deep learning model which comprises three modules: a data enhancement module, a feature extraction module and a detection output module. According to the deep learning model, in target detection, the characteristics of a Copy-Past module, a GAM attention module and a decoupling head module are effectively utilized. The model can enrich the number of samples when the data samples are insufficient; feature information of the input image is fully extracted; and the classification and positioning capabilities are enhanced. For problems existing in detection, the model has high detection capability and robustness. In the detection process of the model, the detection accuracy is improved, the positioning capability is more accurate, and the detection of a small target is also improved.

The invention provides a construction method and application of a fingerprint spectrum of a golden-firewood capsule, and belongs to the technical field of medicine detection.The construction method comprises the steps that a test solution is prepared firstly, then acetonitrile (A)-0.05-0.2 wt% phosphoric acid aqueous solution (B) serves as a mobile phase, the test solution is detected through high performance liquid chromatography, and the fingerprint spectrum of the golden-firewood capsule is obtained; a standard fingerprint spectrum is established by adopting 'Traditional Chinese Medicine Chromatographic Fingerprint Similarity Evaluation System' in 2012 edition; the method is used for quality evaluation or control in the whole process of research / development / production / clinical application of the golden firewood capsules. According to the construction method of the fingerprint spectrum of the golden leaf capsule, main medicinal material components in the golden leaf capsule can be detected only through high performance liquid chromatography, and the quality condition of the golden leaf capsule is comprehensively reflected.