23results about How to "Improve extraction ability" patented technology

The invention provides a coal rock recognition method and system based on image and XRF spectral information fusion, and relates to the technical field of geological rock recognition and classification, and the method comprises the steps: obtaining a to-be-detected coal rock image; inputting the coal rock image into an image detection model to obtain suspicious coal gangue and position information thereof; the method comprises the following steps: performing element testing on suspicious coal gangue, acquiring element information, inputting the element information and a suspicious coal gangue image into a fusion classification model, extracting element features through an element feature extraction module, extracting image features through an image feature extraction module, performing matrix multiplication on the image features and the element features in an initial fusion module, and performing fusion classification on the suspicious coal gangue; and performing deep fusion on the similarity matrix and the image features to obtain initial fusion features, and obtaining a classification result of the coal / coal gangue by using the initial fusion features.

The invention discloses an air conditioner evaporator fin optimization design method which comprises the steps that S1, air conditioner fin multi-dimensional data are collected and preprocessed, and preprocessed air conditioner fin multi-dimensional data are obtained; s2, processing the preprocessed multi-dimensional data of the air conditioner fin into an image, extracting first and second image features, and complementing the first and second image features with the data volume to obtain complemented multi-dimensional data of the air conditioner fin; s3, extracting first, second and third characteristic relations according to the heat efficiency and the gas resistance, and calculating the bending times; s4, multi-dimensional data of the air conditioner fins are screened according to the bending times, the characteristic relation between the notch position and the heat efficiency and the characteristic relation between the notch position and the gas resistance are extracted, and the notch length and the notch depth are calculated; and S5, according to the bending times, the notch positions, the notch lengths and the notch depths obtained through calculation, an air conditioner fin drawing is drawn, and notch machining is conducted. The problems that a traditional air conditioner fin design method excessively depends on artificial experience and data is insufficient can be solved.

The invention discloses a method for predicting SOH based on an HS-MH-MoE model and current, voltage and temperature of a battery. The method comprises the following steps: S1, testing and acquiring current, voltage, temperature and capacity data of the battery; s2, constructing an HS-MH-MoE model, wherein the HS-MH-MoE model comprises a multi-layer multi-head sparse hybrid expert sub-model A two-layer structure is used, each layer comprises three expert sub-models, and each sub-model is a CNN-LSTM model; the CNN automatically learns the spatial correlation of input data, and extracts high-level abstract features; and S3, training and verifying the model. S4, predicting the SOH of the battery: deploying and predicting the model after training; and connecting the three SOH predicted values to a linear regression layer, and outputting a final value. The SOH is predicted based on the CNN-LSTM hybrid expert model HS-MH-MoE and the battery data, the expression ability and prediction precision of the model are improved, the robustness and generalization ability of the model are improved, and the over-fitting risk is reduced.

The invention discloses an extracting agent for extracting and separating nickel cobalt and lithium magnesium, which relates to the technical field of extracting agents and is a composite extracting agent consisting of an extracting agent A and a synergistic extracting agent B, the extraction agent A is tertiary carbon carboxylic acid and salt thereof, the synergistic extraction agent B is composed of a synergistic extraction agent a and a synergistic extraction agent b, the synergistic extraction agent a is naphthalene sulfonic acid and salt thereof, and the synergistic extraction agent b is alcohol R5OH. The invention provides a synergistic extraction system of nickel and cobalt. Tertiary-carbon carboxylic acid with the carbon atom number of 10-20, tertiary-carbon sulfonic acid with the carbon atom number of 10-20 and fatty alcohol with the carbon atom number of 8-16 cooperate with each other. The tertiary carbon carboxylic acid, the tertiary carbon sulfonic acid and the fatty alcohol are matched, so that the tertiary carbon carboxylic acid, the tertiary carbon sulfonic acid and the fatty alcohol have very good selectivity and relatively strong extraction capacity on nickel ions and cobalt ions; and the defects that the water solubility of an existing extraction system is high and strong acid enters a water phase when the strong acid is used for reverse extraction are effectively overcome.

The invention relates to the technical field of building damage assessment, in particular to a global and local clue-combined remote sensing image building damage assessment method, which comprises the following steps of: acquiring pre-disaster and post-disaster initial double-time-phase images of a building in a target area, pre-disaster and post-disaster dual-time-phase images meeting certain pairing conditions are obtained based on the initial dual-time-phase images; performing weight sharing coding on the dual-time-phase image to obtain a multi-scale dual-time-phase feature; obtaining a multi-scale difference enhancement fusion feature based on the multi-scale dual-time-phase feature; based on global and local clues, multi-scale difference enhancement fusion features are connected and fused, and output features are obtained; and predicting a damage assessment result and an error prediction result of the building based on the output features to obtain a final damage assessment result of the building. Therefore, the problems of poor modeling effect, difficulty in application migration, insufficient global-local feature utilization, lack of difficult sample sensing ability and the like caused by small feature difference in related technologies are solved.

The invention discloses a risk prediction model determination method, an application method and a related device, and relates to the technical field of biodiversity, and the risk prediction model determination method comprises the steps: obtaining a data set; the data set comprises landscape diversity risk assessment indexes and risk early warning values of a plurality of continuous years; training a CNN-LSTM model by taking the landscape diversity risk assessment indexes of the previous two years as input and the risk early warning value of the later year as output to obtain a risk prediction model; the CNN-LSTM model comprises two convolution layers, one LSTM layer and three full connection layers. According to the method, the CNN-LSTM model is adopted, the strong extraction capability of the convolutional neural network on the spatial features and the deep analysis capability of the long and short-term memory network on the time sequence data are combined, and an innovative method is provided for predicting the future landscape diversity risk.

The invention discloses a preparation method of strong-flavor high-nutrition tartary buckwheat wine based on tartary buckwheat, which comprises the following steps: in a grain soaking stage, decomposing pectin by pectinase in advance, and steaming with steam after soaking is completed, so that methanol is discharged in grain washing and cooking processes, the methanol content of the tartary buckwheat in a fermentation process is greatly reduced, and the taste of the tartary buckwheat wine is improved. Tartary buckwheat is shelled in a targeted mode, meanwhile, shells and all parts of tartary buckwheat rice are comprehensively utilized, the utilization rate is high, resources are saved, fermentation liquor is prepared from bee pollen and honey, then the fermentation liquor and original pulp liquor are mixed and fermented for preparing the tartary buckwheat liquor, and the tartary buckwheat liquor is high in nutritive value and has black berry fruit fragrance and dry fruit fragrance. The tartary buckwheat fragrance can be better and faster dissolved into the prepared original pulp wine, the production time is greatly shortened, the working procedures are saved, the original pulp wine does not need to be filtered again, and the production process is simpler and more convenient.

The invention discloses a low-flow sewage monitoring method and system based on deep learning, and the method comprises the steps: S1, collecting and preprocessing original sewage data in real time, and obtaining the preprocessed sewage data; s2, combining a mean value and a variance of the preprocessed sewage data to generate feature enhancement data; constructing a noise classifier and denoising the feature enhanced data to obtain denoised sewage data; s3, constructing a gating circulation unit based on attention updating, and extracting a pH value characteristic, a chemical oxygen demand characteristic and an ammonia nitrogen characteristic; s4, extracting ammonia nitrogen fluctuation features, updating ammonia nitrogen features, and correcting ammonia nitrogen data in the denoised sewage data; and S5, carrying out feature fusion on the updated ammonia nitrogen feature, pH value feature and chemical oxygen demand feature, and judging whether industrial sewage is discharged or not. The invention solves the problem that the traditional sewage monitoring method is difficult to deal with the poor monitoring effect caused by low concentration and large fluctuation of low-flow sewage pollution components.

The present disclosure provides a coal-rock identification method and system based on the fusion of image and XRF spectrum information, which relates to the technical field of geological rock identification and classification, and includes: obtaining a coal-rock image to be detected; inputting the coal-rock image into an image detection model to obtain suspicious coal gangue and its position information; performing element testing on the suspicious coal gangue to obtain element information, inputting the element information and the suspicious coal gangue image into a fusion classification model, and then extracting element features through an element feature extraction module, extracting image features through an image feature extraction module, performing matrix multiplication of image features and element features in an initial fusion module to form a similarity matrix, and then deeply fusion the similarity matrix with image features to obtain initial fusion features, and using the initial fusion features to obtain coal / coal gangue classification results.

The utility model belongs to the technical field of vacuum equipment, and particularly relates to a petal-shaped getter for a getter pump, which comprises a getter, the getter is composed of a fixing ring and a titanium-based getter alloy, the getter is petal-shaped and comprises a plurality of elliptical petals, the stability and firmness of the getter pump are improved, and the service life of the getter pump is prolonged. Compared with the prior art, the getter pump can better resist the influence of vibration, impact and other external force, and meanwhile, the air suction surface area of the getter pump is also increased, so that more air is sucked into the pump, the air suction efficiency is improved, the air suction speed is increased, the getter pump can process larger air flow and has higher extraction capacity, and the service life of the getter pump is prolonged. The energy consumption of the pump is reduced, the pressure and load on the pump are reduced, the service life of the pump is prolonged, meanwhile, the speed and pressure of gas entering the pump can be reduced, and the blocking risk caused by suspended solid particles or impurities in the gas can be reduced.

The present invention discloses a low-flow sewage monitoring method and system based on deep learning, including: S1: real-time collection of raw sewage data and pre-processing to obtain pre-processed sewage data; S2: combining the mean and variance of the pre-processed sewage data to generate feature enhancement data; constructing a noise classifier and denoising the feature enhancement data to obtain denoised sewage data; S3: constructing a gated cyclic unit based on attention update to extract pH value features, chemical oxygen demand features and ammonia nitrogen features; S4: extracting ammonia nitrogen fluctuation features, updating ammonia nitrogen features, and correcting ammonia nitrogen data in denoised sewage data; S5: performing feature fusion on the updated ammonia nitrogen features, pH value features and chemical oxygen demand features to determine whether there is industrial sewage discharge. The present invention solves the problem that traditional sewage monitoring methods are difficult to cope with the poor monitoring effect caused by low concentration and large fluctuation of pollutant components in low-flow sewage.

The application relates to the technical field of archaea DNA extraction, in particular to a preparation method and application of auxiliary wall-breaking nanometer magnetic beads for archaea DNA extraction. The method comprises the following steps: (1) constructing an inverse emulsion droplet microfluidic chip: washing, drying and hydrophobic modification treatment are performed on a PDMS base material to prepare a microfluidic chip; (2) preparing an inverse microemulsion system: the continuous phase and the dispersed phase are respectively pumped into the microfluidic chip at a predetermined flow rate, so that Fe 2+ and Fe 3+ undergo a coprecipitation reaction to generate Fe3O4 nanoparticles; (3) maturation crystallization; (4) demulsification separation and washing; and (5) drying to obtain auxiliary wall-breaking nanometer magnetic beads. The method has the beneficial effect of solving the problems in the prior art that the cell wall is difficult to effectively break, the DNA release rate is low, the extraction purity is poor, the repeatability is insufficient and the standardization and continuous preparation are difficult to realize when DNA extraction is performed on archaea samples in extreme environments.

The utility model discloses a knotting machine for garment production, and relates to the technical field of garment production. The knotting machine comprises a base, the top face of the base is fixedly connected with a knotting machine body, the surface of a connecting assembly is fixedly connected with a suction frame, the surface of the connecting assembly is fixedly connected with a light supplementing lamp, and a collecting box is arranged on the inner wall of a collecting assembly. When the surface of clothes is knotted, the overturning assembly can drive the connecting assembly to overturn, the position of the suction frame in the device can be changed, the batting extraction capacity is higher when the suction frame is close to the knotting position, and the extraction capacity is reduced when the suction frame is away from the knotting position. According to clothes made of different fabrics, when different quantities of batting are generated, the suction frame can effectively suck the generated batting, meanwhile, the suction frame can be connected with a light supplementing lamp, and the knotting position can be conveniently observed by processing personnel.

The application discloses a method for extracting antidepression active Euphausia superba oil by adopting subcritical combination solvent, and belongs to the technical field of deep processing of Euphausia superba. The method is a three-stage gradient extraction method of "subcritical butane-acetone-ethanol", and realizes differential separation and enrichment of lipid components by using solvent polarity difference. The first-stage subcritical butane extraction is carried out under low temperature, closed and oxygen isolation conditions, and high-efficiency desolventizing is realized by using reduced pressure gasification, so that the heat-sensitive active ingredients are effectively reserved, and meanwhile, oxidation and metamorphosis of raw materials are avoided, thereby laying a foundation for preparation of subsequent high-quality extracts. The method successfully prepares three Euphausia superba oil products with significant component differences, and especially, the third-stage extract and the enzymatic modification product thereof exhibit excellent antidepression activity. The application realizes functional separation of Euphausia superba lipid components, and provides strong support for development of personalized and diversified antidepression products.

The invention belongs to the technical field of high-performance polymer materials and advanced micro-packaging, and relates to a hyperbranched polyimide embedding material as well as a preparation method and application thereof. The method comprises the following steps: dropwise adding a prepared hyperbranched polyimide solution with an active group as a terminal group into a functional material to be embedded, and carrying out suction filtration and drying to obtain embedded powder; the obtained embedding powder is placed in a supercritical carbon dioxide device, the hyperbranched polyimide embedding material generates a large number of pores by regulating and controlling the temperature, pressure and time and utilizing the swelling, plasticizing and extremely strong permeation and diffusion capabilities of supercritical carbon dioxide to hyperbranched polyimide with active groups, the specific surface area of the material is increased, and the hyperbranched polyimide embedding material is prepared. Finally, the composite material with a porous structure, high embedding rate and well protected activity of functional substances is obtained. According to the hyperbranched polyimide embedding material prepared by the method, the stability of functional substances can be improved, and the compatibility with an addition system can also be improved.

The invention provides a radish posture correction mechanism for a radish harvesting robot, which comprises at least one group of correction mechanical arms, the correction mechanical arms are fixedly connected, and each group of correction mechanical arms comprises a cross beam, a longitudinal beam, a correction motor, a gear box and a correction tail end; two ends of the cross beam are vertically connected with the upper ends of the longitudinal beams; a gear box is arranged at the upper end of the cross beam, the gear box is connected with an output shaft of a correction motor and comprises two output ends, the correction tail end comprises two correction units which are symmetrical and open outwards, the two output ends are connected with main shafts of the correction units respectively, and the rotation directions of the main shafts of the correction units are opposite. The side surfaces of the correction units are fixedly connected with the inner sides of the longitudinal beams; according to the radish pulling mechanism, the radish postures can be corrected before pulling based on the radish postures of different forms, the pulling effect and efficiency of the radish pulling mechanism are improved, and the mechanism is high in adaptability and capable of adapting to radishes of different sizes and varieties and correcting the postures of the radishes.

The invention discloses a behavior recognition method and device based on a video text model and a storage medium, and belongs to the technical field of artificial intelligence. The method comprises the following steps: acquiring video data to be identified, and performing frame extraction on the video data to obtain multiple video frames; inputting the multiple video frames into a preset video text model to obtain a behavior recognition result of the target object in the video data; wherein the video text model comprises an intra-frame attention module, a cross-frame attention module and a video text feature fusion module. According to the method, the time sequence relation between the video frames can be captured, the information between different frames can be integrated, semantic information is provided for behavior recognition, a video text model can comprehensively judge the behavior action of the target object from the two aspects of vision and semantics, and therefore the accuracy of behavior action recognition in the open set video is improved.

The invention relates to an unmanned aerial vehicle multi-scale target detection method based on a DINO framework, and belongs to the field of unmanned aerial vehicle target detection, and the method comprises the steps: constructing a hybrid Transform backbone network which comprises an image preprocessing layer, a first-stage DA-STB structure, a second-stage DA-STB structure, a third-stage DA-STB structure and a fourth-stage CGTB structure; an image to be processed is input into a Patch Partition image preprocessing layer to be divided; sequentially inputting DA-STB structures in the first stage, the second stage and the third stage; inputting the features into a fourth-stage CGTB structure to perform feature enhancement; and inputting the multi-scale features output by the hybrid Transform backbone network into an original Neck module of the DINO model, and outputting a target category and bounding box prediction result. According to the method, the precision and efficiency of multi-scale target detection can be remarkably improved, and particularly, the method has higher target sensing and extraction capability in small target detection and complex background interference environments.

The utility model discloses a liquid baffle device for a crude foil engine, which comprises a flow guide plate and a first cover plate, a second cover plate is arranged above the first cover plate through a plurality of support blocks, the first cover plate and the second cover plate are enclosed to form a gas path acid mist area, the rear side of the second cover plate is lapped with a tank body carrying table, and the gas path acid mist area is lapped with the tank body carrying table. The second cover plate can be adjusted back and forth through cooperation of the limiting holes and the limiting columns, and a third cover plate used for observing the crystallization condition is arranged above the second cover plate. Through the optimized and improved design of the product, a liquid path and a gas path are divided by the first cover plate, an electrolyte and an acid mist area are separated, and a sealed space is formed by matching with the support of the tank body, so that the suction force of the acid mist exhaust system is effectively improved, the acid mist exhaust effect is improved, and the overflow electrolyte is smoother by the guide plate, and the service life of the acid mist exhaust system is prolonged. And the overflow liquid is inhibited and guided to slowly overflow, white points generated on the electrolytic copper foil are effectively reduced, and the beneficial effect of improving the product appearance quality on the surface of the copper foil is achieved.

This invention discloses a WTNet pediatric mandibular wisdom tooth germ segmentation network architecture method, mainly comprising an input enhancement module with a region feature enhancement module and a bone feature separation module. Independent scale-specific feature fusion modules are provided in the tooth segmentation branch and the bone segmentation branch of the bone feature separation module, respectively. For an input and its corresponding ground truth label, the input is first fed into the input enhancement module to generate a mask and supervised using the ground truth label; then, the input and mask are fed into the region feature enhancement module to obtain enhanced input; in the bone feature separation module, the enhanced input is fed into the scale-specific feature fusion modules of the tooth segmentation branch and the bone segmentation branch respectively after passing through a shared encoder, and then fed into the decoders of the two branches. The tooth and bone ground truth labels are used to complete the supervision of the two branches respectively, obtaining tooth segmentation masks and bone segmentation masks. The two masks are fused to obtain the final segmentation result.

The application belongs to the field of image fusion, and discloses a kind of image fusion method based on double decoder, for solving the problem that the feature extraction capability and fusion effect of complex multi-modal image processing of camera shooting of different imaging modes based on deep learning-based image fusion method are poor, the application includes: multi-modal image A1, A2 is extracted through large receptive field feature extraction module, then respectively through two interactive decoding modules, and in the decoding process, the decoding information between two different modal decoding modules is spliced on the channel to interact and fuse, finally reconstructs the fusion image C, calculates the loss of fusion image C and multi-modal image A1, A2, updates network model parameters.The application can effectively realize the fusion of complex multi-modal image, has the characteristics of good feature information extraction, small parameter quantity, high reconstruction precision, strong robustness and the like.

The present invention discloses a question-and-answer method, system, device and medium based on a knowledge graph in a specific field, the method comprising: obtaining data in a target field and constructing a knowledge graph; obtaining a question input by a user, and predicting the number of multiple jumps for the question; if the number of jumps is not zero, based on the knowledge graph and the number of jumps, predicting the relationship path corresponding to the question to construct the initial reasoning chain path of the question; correcting the initial reasoning chain path according to a preset filtering rule, and converting it into a natural language form to obtain a final reasoning chain path; obtaining a plurality of preset instructions, splicing each instruction with the reasoning chain path and the question respectively, and generating a plurality of candidate answers according to the spliced ​​data; wherein different instructions are provided with different specific contents, and the instructions are all used to associate the question and answer with the reasoning chain path; voting on the candidate answers, and returning the answer with the most votes to the user. The present invention improves the accuracy and comprehensiveness of question-and-answer in a specific field.

The present application relates to the technical field of the preparation of polysaccharides, and specifically discloses a method for extracting Tibetan Codonopsis polysaccharides, including: S1, crude extraction; S2, decolorization; S3, separation and purification, and the extraction agent used in step S1 includes choline chloride, and also includes at least one of oxalic acid, levulinic acid and glucose. The product of the present application can be used in the fields of food, medicine and cosmetics, and has the effects of strengthening the spleen and moistening the lungs, replenishing blood and fluids, anti-tumor, lowering blood sugar and anti-oxidation; in addition, the preparation method of the present application effectively increases the yield of Tibetan Codonopsis polysaccharides, promotes the rational use of Tibetan Codonopsis resources and optimizes the quality of Tibetan Codonopsis polysaccharides. The extraction rate of Tibetan Codonopsis polysaccharides can reach more than 27.5%. When the addition amount is 16 mg / mL, the DPPH free radical scavenging rate can reach more than 71%, and the hydroxyl free radical scavenging rate can reach more than 81.5%.