24716 results about "Analytical chemistry" patented technology

The invention relates to a bioelectrochemical sensor used for detecting hydrogen peroxide and a manufacturing method of the bioelectrochemical sensor. The novel bioelectrochemical sensor is a sensor with a three-electrode system, wherein the counter electrode is a platinum electrode, the reference electrode is saturated calomel electrode and the working electrode is gold electrode. The sensor is based on orientation combination of a polypeptide horse radish peroxidase with characteristic sequence, the promotion effect of the polypeptide horse radish peroxidase on the activity of the enzyme and the characteristic of high sensitivity of electrochemical detection methods. The sensitivity of hydrogen peroxide sensors is effectively improved and satisfactory results are obtained.

The present invention relates to the technical field of lithium battery state estimation, and in particular to a fuzzy EKF-AH algorithm-based state of charge (SOC) estimation and correction method for a lithium iron phosphate battery. The method comprises: by taking a certain startup of an energy storage device as a start, a BMS reading an SOC and a state of health (SOH) at the previous shutdown, and on the basis of a standby time, selecting an open circuit voltage (OCV) or an EKF algorithm to correct the SOC; using an EKF-AH algorithm to estimate the SOC, establishing, on the basis of fuzzy control, a fuzzy rule library associated with the SOC and the SOH, and dynamically adjusting a weight and a measurement noise deviation of the EKF-AH algorithm; and calculating estimation differences between the EKF algorithm and an ampere-hour integration method, and if the sum of the estimation differences is greater than a corresponding threshold, issuing an SOH correction warning. The present invention improves the estimation accuracy of the entire life cycle of the lithium iron phosphate battery, and assists the correction of the SOH.

The invention discloses a hydrogen sulfide leakage abnormity monitoring method and system, and relates to the technical field of gas monitoring. A hydrogen sulfide leakage abnormity monitoring system comprises a data monitoring module, an airflow correction module, a twinborn inversion module, an association judgment module, a leakage confirmation module, a linkage control module and a monitoring updating module. According to the invention, the gas flow field correction is carried out on the hydrogen sulfide concentration by constructing the gas flow vector model and calculating the gas diffusion disturbance coefficient, so that the monitoring result is closer to the real gas distribution, and the space precision and reliability of leakage detection are improved; by establishing a double-physical-field digital twinborn model of a battery thermal field and an airflow field, corrected concentration data is introduced into a coupling domain for numerical evolution, and space inversion positioning and diffusion path prediction of hydrogen sulfide concentration abnormity are realized.

The invention belongs to the technical field of calcium hydroxide preparation, and particularly relates to a preparation method of calcium hydroxide with high specific surface area, which comprises the following steps: S1, adding triethanolamine and diethanolisopropanolamine into water, and uniformly mixing to obtain an active agent solution; s2, mixing quick lime with an active agent solution, and carrying out digestion reaction to generate a calcium hydroxide coarse material; and S3, sieving and drying the calcium hydroxide coarse material to obtain the calcium hydroxide with the high specific surface area. The specific surface area of calcium hydroxide can be remarkably increased through the active agent solution, meanwhile, the uniformity and sufficiency of the quick lime digestion reaction are optimized, the calcium hydroxide particle aggregation phenomenon is reduced, and finally the calcium hydroxide product with the high specific surface area and the high activity is prepared.

The invention relates to a preparation method of Pt monatomic nano-enzyme and application of Pt monatomic nano-enzyme in cardiac troponin I. The preparation method comprises the following steps: 1) ultrasonically dispersing cobalt salt, selenium powder, chloroplatinic acid, urea and sodium chloride in an alcohol solvent, heating and stirring until the solvent is completely volatilized to obtain a solid mixture, and grinding the solid mixture into powder; and 2) placing the powder in a heating device, carrying out Joule heat treatment in an air atmosphere, washing a reactant, and carrying out vacuum drying to obtain the Pt monatomic nano-enzyme. Se doping is introduced in the one-step chemical reaction process to construct oxygen vacancies, rapid heating and structural shaping are achieved in combination with Joule heat treatment, the Se-doped Co3O4-loaded Pt monatomic catalyst is obtained, the oxygen vacancies and Se doping sites are synergistically used as anchoring sites, Pt monatomic atoms are effectively fixed, and the loading capacity and dispersity of the Pt monatomic atoms are improved. The obtained material shows excellent POD-like enzyme activity, can be used for constructing a high-sensitivity colorimetric immunoassay system, and realizes rapid and accurate detection of cTnI.

The invention relates to the technical field of biomedical sensing and health monitoring, and discloses a noninvasive glucose detection system. The system comprises an interstitial fluid extraction device, a photoelectric sensing module and a main control circuit which are integrated on a flexible substrate. The interstitial fluid extraction device drives subcutaneous interstitial fluid to migrate to the hydrogel interface layer under the action of reverse ion electroosmosis; the photoelectric sensing module receives interstitial fluid through a hydrogel interface layer, a working electrode of the photoelectric sensing module comprises a Cu monatomic imprinting recognition layer, and imprinting holes formed by Cu monatomic sites are specifically coordinated with glucose molecules. The master control circuit controls sampling and determines the glucose concentration based on the photocurrent signal. Noninvasive sampling and monatomic imprinting photoelectric detection are fused, sampling-detection integration is achieved, and the device has the advantages of being noninvasive, painless, high in selectivity, high in interference resistance, comfortable to wear and the like and is suitable for continuous monitoring of blood glucose.

The invention discloses a water heavy metal concentration simulation and prediction method, which comprises the following steps: integrating original monitoring data, hydrodynamic data, total suspended solids, image remote sensing data and human activity data, and generating a multi-source cleaning sequence data packet; executing cross-modal adsorption capacity estimation by using image remote sensing data in the data packet, inferring particle chemical composition and adsorption isotherm parameters from image textures, and generating a capacity feature packet containing an adsorption capacity upper bound; time-varying travel time is calculated based on the hydrodynamic data and the human activity data, causal alignment is performed on the capacity feature packet and the upstream signal, and a travel time alignment feature packet is generated; and in combination with metal fingerprint parameters, applying an adsorption capacity upper bound as a physical constraint on a form distribution constraint head, explicitly decoupling and predicting the form, and generating a prediction result packet. According to the method, the hydrodynamic physical mechanism and the particle adsorption chemical mechanism are deeply coupled, and the prediction precision and the physical consistency of the model under the unsteady state condition are improved.

The invention relates to a czochralski method-based crystal growth interface shape detection method and device. The method comprises the steps that: a seed crystal temperature T and an interface electromotive force U at the same time in a crystal growth process are collected, and a T-U curve of the interface electromotive force U changing with the seed crystal temperature T is obtained; the change trend of the shape of a crystal growth interface in real time is judged by observing the deviation between the T-U curve and a reference line, wherein the reference line is a straight line passing through the starting point of the T-U curve, and the slope of the straight line is the Seebeck coefficient of the crystal. The invention also relates to a device used by the method. The detection method provided by the invention can detect the change trend of the shape of the crystal growth interface in real time in the crystal growth process, and can be applied to Czochralski method growth equipment for various single crystals such as lithium niobate, lithium tantalate, sapphire, yttrium aluminum garnet and the like.

The invention discloses an automatic calibration method and system for a mining laser gas detector, and relates to the technical field of instrument calibration. The method comprises the following steps: dividing detection beam parameters into measurement beam parameters and reference beam parameters; enabling the measurement light beam parameter to penetrate through the to-be-measured gas area to obtain a measurement signal, and enabling the reference light beam parameter to penetrate through the reference gas chamber to obtain a reference signal; performing photoelectric conversion calculation on the reference signal to obtain a gas concentration detection value; the gas concentration detection value is compared and analyzed with a reference data value according to the central wavelength position of the mining laser gas detector, and a calibration unit is activated according to a comparison result; and automatically correcting the mining laser gas detector, generating a correction parameter set, calculating a measurement signal, and generating a concentration value of the gas to be detected. The technical problems of low calibration efficiency and unstable calibration precision of the mining laser gas detector in the prior art are solved, and the technical effects of realizing automatic calibration of the mining laser gas detector and improving the calibration efficiency and precision are achieved.

The invention relates to a rapid gas delivery system and method for atomic layer etching. The system utilizes a compact gas buffer disposed near the gas distribution unit, enabling faster and more efficient delivery of gas to the chamber. The controller manages the gas flow of the gas buffer to maintain an amount of gas within the gas buffer required to perform a single ALE cycle. When one gas buffer delivers gas to the chamber for a partial ALE step, gas is concurrently delivered to the other gas buffer. The configuration not only can improve the efficiency, but also can save components such as a pressure gauge, thereby saving the cost.

The invention provides a method and a system for detecting gas concentration in an epitaxial growth reaction cavity, computer equipment and a storage medium. The method comprises the following steps: acquiring a reflected light signal, a reference signal and a reaction cavity environment signal; carrying out wavelength calibration and intensity baseline calibration on the reflected light signal according to the reference signal, and generating an experimental absorption spectral line according to the calibrated reflected light signal; according to the reaction cavity environment signal, fitting the experimental absorption spectral line and a target absorption spectral line obtained by querying a preset database, and performing inversion to obtain concentration information of the gas to be detected. According to the invention, through double calibration of a reference channel, an experimental spectral line and a database target spectrum are strictly aligned on a frequency scale and an amplitude scale, interference of linear displacement and baseline fluctuation on fitting is reduced, and quantitative precision is improved; and meanwhile, an environmental condition is introduced as a physical constraint, so that parameter coupling is weakened, the identifiability and robustness of inversion are improved, the dependence on pure empirical correction is reduced, and the stability of detection data is improved.

The invention provides a preparation method of a barrier type antimonide infrared detector. The preparation method comprises the following steps: selecting a gallium antimonide substrate; a gallium antimonide buffer layer, an indium arsenide heavily-doped layer, a class II superlattice absorption layer and an aluminum-arsenic-antimony barrier layer are sequentially grown on the substrate in an epitaxial mode through a molecular beam epitaxy method; mechanically stripping the hexagonal boron nitride two-dimensional material, and transferring the stripped hexagonal boron nitride to the aluminum-arsenic-antimony barrier layer to form a Van der Waals heterojunction and composite barrier layer structure; depositing electrodes by using an ultraviolet lithography technology and a thermal evaporation mode to form one electrode arranged on the gallium antimonide buffer layer and the other electrode arranged on the boron nitride; a large-numerical-aperture micro-lens array is processed on a substrate, and the micro-lens array is composed of gallium antimonide cylinders with a phase modulation function; therefore, the antimonide medium-wave infrared detector with the composite barrier layer can be formed, the quantum efficiency is improved, the order of magnitude of dark current is reduced, and room-temperature medium-wave infrared detection can be realized.

The invention belongs to the technical field of back contact photovoltaic cells, and particularly relates to a back contact photovoltaic cell with high-concentration co-doped regions and a manufacturing method and application thereof.The back contact photovoltaic cell with the high-concentration co-doped regions is characterized in that N-type doped regions and P-type doped regions which are alternately distributed are arranged on the backlight face of an intrinsic amorphous silicon layer, and the high-concentration co-doped regions are arranged between the N-type doped regions and the P-type doped regions; a doping source of the co-doped region comprises doping source phosphorus of the N-type doped region and doping source boron of the P-type doped region, the co-doped region, the doping source of the N-type doped region and the doping source of the P-type doped region form a specific concentration gradient structure, and the specific concentration gradient structure meets the condition that the doping concentration of phosphorus contained in the co-doped region is greater than that of phosphorus contained in the N-type doped region; the doping concentration of boron contained in the co-doped region is greater than the doping concentration of boron in the P-type doped region. The carrier transport and collection efficiency is optimized, the fill factor and open-circuit voltage are improved, the cell conversion efficiency and stability are improved, the preparation process is simple, and multiple times of opening etching are not needed.

The invention relates to a data analysis technology, and discloses a spectral feature and artificial intelligence fused grain heavy metal detection method and system.The method comprises the steps that a near-infrared diffuse reflection spectrum, a laser-induced breakdown spectrum and a surface enhanced Raman spectrum of a grain sample are obtained, a matrix accumulation area thermodynamic diagram is constructed according to the near-infrared diffuse reflection spectrum, and the matrix accumulation area thermodynamic diagram is obtained; the method comprises the following steps: optimizing a laser-induced breakdown spectrum based on a thermodynamic diagram of a matrix accumulation area, carrying out dual-channel spatial-temporal feature extraction on a surface enhanced Raman spectrum and the optimized laser-induced breakdown spectrum to obtain a fusion feature vector, carrying out weighted fusion optimization on the fusion feature vector based on a signal-to-noise ratio of multispectral data to obtain an optimized fusion feature vector, and finally obtaining a fusion feature vector. And carrying out migration adaptation on a pre-trained grain detection model by using a pre-marked new grain sample, and carrying out heavy metal content identification on the grain sample by using the adaptive grain detection model according to the optimized fusion feature vector to obtain a heavy metal detection result. The precision of grain heavy metal detection can be improved.

The embodiment of the invention provides an anti-dazzle low-reflection optical film and a preparation method and application thereof, and relates to the field of optical films. The anti-dazzle low-reflection optical film comprises a base material layer, an anti-dazzle layer and a low-reflection layer which are sequentially stacked from bottom to top, the low-reflection layer contains low-reflection particles, and the anti-dazzle layer contains organic anti-dazzle particles and inorganic particles. The mass concentration of the organic anti-dazzle particles close to the base material layer is larger than the mass concentration of the organic anti-dazzle particles close to the low-reflection layer, the mass concentration of the inorganic particles close to the base material layer is smaller than the mass concentration of the inorganic particles close to the low-reflection layer, and the refractive index of the low-reflection particles is larger than the refractive index of the inorganic particles and smaller than the refractive index of the organic anti-dazzle particles. Seen from the direction from the low-reflection layer to the base material layer, the refractive indexes of all the layers are gradually increased. According to the anti-dazzle low-reflection optical film and the preparation method and application thereof, the optical film achieves high haze and low reflectivity and can be applied to a strong light environment.

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

The invention discloses a method and device for identifying organic matter pores in a shale electron microscope scanning image, and the method comprises the steps: obtaining a shale scanning electron microscope original image, carrying out the manual marking, and generating an original image and mask image data set; performing synchronous data enhancement on the data set and dividing a training set and a verification set; constructing an improved U-Net model containing a multi-scale feature fusion input module, a residual attention module and a wavelet domain feature enhancement center module; training the model by adopting a dynamic mixed loss function and an AdamW optimizer; and verifying the model through an average intersection-to-union ratio, a pore Dice coefficient and a pore error, and identifying the pores of the image to be processed after reaching the standard. The device correspondingly comprises an image acquisition annotation module, a data preprocessing module, a model construction module, a training module, a verification module and a pore recognition module. According to the method, full-automatic identification is realized, the multi-scale pore segmentation precision and the low-porosity sample adaptability are improved, and the efficient characterization requirement of the shale gas reservoir is met.

The invention relates to the technical field of vanadium pentoxide preparation, and particularly discloses a preparation method of high-purity vanadium pentoxide, which comprises the following steps: S1, vanadium slag pretreatment: carrying out microwave activation on vanadium slag, and then carrying out ball milling and roasting to obtain clinker; the sulfuric acid-oxalic acid composite acid and the clinker are mixed and leached, the pH is adjusted after leaching is completed, and vanadium-containing leachate is obtained through filtering; s3, vanadium enrichment: heating the vanadium-containing leachate, adding a sodium carbonate solution while stirring, adjusting the pH value, preserving heat and curing, and then carrying out hot filtration to obtain vanadium-rich filter residues; s4, vanadium purification: mixing the vanadium-rich filter residues with a sodium hydroxide solution, stirring, introducing compressed air while stirring, and carrying out hot filtration after the reaction is finished, so as to obtain vanadium-rich filtrate; and S5, calcining: carrying out two-stage calcining on the ammonium metavanadate precursor to obtain high-purity V2O5. The preparation method is high in vanadium recovery rate, and the purity of the prepared V2O5 is 99% or above.

The invention provides a lithium iron phosphate-based positive electrode material and a preparation method thereof, a positive plate and a battery, the lithium iron phosphate-based positive electrode material comprises a lithium iron phosphate matrix and a carbon material existing on the surface of the lithium iron phosphate matrix, and the lithium iron phosphate-based positive electrode material comprises mesopores and macropores. The conductivity and the ion transmission performance of the lithium iron phosphate-based positive electrode material can be both improved, and the energy density and the rate capability of the battery are improved.

The invention discloses a liquid chromatography detection method and system for eye drops, and relates to the technical field of chromatographic detection.The liquid chromatography detection method comprises the steps that liquid chromatography analysis is conducted to obtain an original chromatographic signal; performing adaptive baseline correction on the original chromatographic signal to obtain a corrected chromatographic signal, synchronizing the corrected chromatographic signal to a chromatographic peak identification analysis model for identification to obtain a plurality of chromatographic peaks including principal component peaks and specific impurity peaks, and performing calculation to obtain a first integral area of the principal component peaks and a second integral area of the specific impurity peaks; the preset correction factor is combined with the first integral area and the second integral area to automatically carry out quantitative calculation on the eye drops, and a liquid chromatogram detection report of the eye drops is generated. The technical problems that in the prior art, main component peak and impurity peak recognition and quantification depend on manual processing in the eye drop liquid chromatogram detection process, and accuracy and consistency are insufficient are solved, and the technical effects that the accuracy and automation level of eye drop liquid chromatogram detection are improved, and it is guaranteed that main component and impurity quantification results are stable and consistent are achieved.

The invention discloses a transformer insulating oil chromatographic data abnormity identification method, and relates to the technical field of data identification, and the method comprises the following steps: carrying out dissolved gas analysis on an insulating oil sample through a gas chromatograph to obtain a chromatographic original response curve; performing baseline correction and de-noising processing on the chromatographic original response curve, positioning each gas peak based on a first-order derivative method and a second-order derivative method, and extracting chromatographic peak height and chromatographic peak area parameters; according to the extracted chromatographic peak height and chromatographic peak area, three Duval fault ratios are calculated, and the ratio of the total soluble gas concentration to each gas concentration threshold value is calculated; combining into a multi-dimensional diagnosis feature vector; according to the method, the chromatographic peak shape of the insulating oil sample is subjected to multi-stage baseline correction and de-noising treatment, and the gas peak position is accurately positioned based on the first-order and second-order derivative methods, so that the high precision of chromatographic peak height and chromatographic peak area extraction is ensured, the accuracy of dissolved gas concentration calculation is improved, and the misjudgment risk caused by noise or baseline drift is reduced.

The invention discloses a zinc-bromine flow battery multifunctional negative electrode electrolyte and a preparation method thereof, and belongs to the technical field of electrochemical energy storage, and the method comprises the following steps: uniformly stirring and mixing a solvent, deionized water and a cosolvent, adding an electrolyte, stirring until the electrolyte is completely dissolved, then adding a supporting electrolyte and a negative electrode additive, stirring until the supporting electrolyte and the negative electrode additive are completely dissolved, and obtaining the zinc-bromine flow battery multifunctional negative electrode electrolyte. The multifunctional negative electrode electrolyte of the zinc-bromine flow battery is obtained; the negative electrode cosolvent is dimethylformamide; and the negative electrode additive is at least one of saponin and cinnamyl aldehyde. Saponin molecules are adsorbed on the surface of the zinc electrode, and electronegative carboxyl groups of the saponin molecules form an electrostatic repulsion net in an electric double layer, so that zinc ions are forced to be uniformly dispersed, local ion concentration hot spots are eliminated, and dendritic crystal nucleation is inhibited from the source. In cinnamyl aldehyde, the strong electronegativity of oxygen atoms and the electronegativity of aldehyde oxygen are far higher than those of hydrogen, so that electrons are strongly attracted, adjacent carbon atoms are positively charged, polar adsorption sites are formed, the hydrogen evolution reaction is effectively inhibited, and the stability and the cycle life of the battery are greatly improved.

The invention relates to the technical field of oil stain detection, and discloses an oil-filled equipment oil stain detection method based on fluorescence reflection. The method comprises the following steps: carrying out cleaning pretreatment on a to-be-detected surface; an ultraviolet light source is utilized to excite the oil stain to generate fluorescence; collecting a fluorescence image through an imaging device with a filter; performing noise reduction and edge processing on the image to extract an oil stain area; and finally judging the existence and type of oil stain and estimating the thickness according to image features. According to the invention, the fluorescence characteristic of the oil stain is utilized, no fluorescent agent needs to be added, and pollution-free, high-precision and high-efficiency non-contact detection is realized.

The application discloses a high-energy-density lithium ion battery, which comprises a positive electrode sheet and a negative electrode sheet, the positive electrode sheet comprises a positive electrode active material, the positive electrode active material comprises a lithium iron phosphate material and a lithium-rich material, the mass ratio of the lithium-rich material in the positive electrode active material is 0.2%-8%, and the chemical formula of the lithium iron phosphate material is Li 1+x Fe 1‑ y M y (PO4) 1+z , 0<=x<0.1, 0<=y<0.01, 0<=z<0.04, M comprises at least one of Ti, V, Zr, Nb, Mg, Mo, W and Al, the lithium-rich material comprises at least one of LFO and LNO, the chemical formula of the LFO is Li 5+a Fe 1‑b Z b O 4+c , 0<=a<0.5, 0<=b<0.01, 0<=c<0.03, Z comprises at least one of Ti, V, Zr, Nb, Mg, Mo, W and Al, and the chemical formula of the LNO is Li 2+d Ni 1‑ e Y e O 2+f , -0.5<=d<=0.5, e>=0, -0.5<=f<=0.5, Y comprises at least one of Ti, V, Al, Mg, Mn and Fe, and the negative electrode active material comprises a silicon negative electrode material, the mass ratio of the silicon negative electrode material in the negative electrode active material is 1%-50%. The application has the advantages of improving the energy density, rate performance and cycle performance of the lithium battery.

The invention provides embedded metal fullerene and a preparation method thereof. The preparation method comprises the following steps: mixing a metal source and graphite powder, and filling a hollow graphite rod with the mixture to prepare an anode; the electrode is installed at the anode end of an electric arc furnace, and on-line activation is conducted through anode and cathode short-circuit current under the inert atmosphere; then carrying out arc discharge under specific air pressure and current, and collecting generated carbon ash; performing ultrasonic extraction by using an organic solvent to obtain a fullerene crude extracting solution; and finally, performing multi-stage separation and purification by high performance liquid chromatography, and performing primary and fine separation by sequentially adopting preparative and semi-preparative chromatographic columns to obtain a high-purity product. According to the method, a complete technical path capable of efficiently and controllably preparing the ultra-high-purity embedded metal fullerene is formed through a synthesis process integrating online activation and accurate atmosphere regulation and control and a multi-stage gradient chromatography purification strategy, and a solid foundation is laid for application of atomic-scale accurate materials in the frontier fields of quantum devices and the like.

The invention discloses a correction method for saturation spectral line intensity in a laser spectrum, and relates to the technical field of spectral data analysis, the method comprises the following steps: identifying saturation characteristics in the spectrum, determining a saturation point based on an intensity threshold, and recording the original intensity of the saturation point; determining a target spectrum peak wavelength range containing a saturation point; eliminating saturation points from the wavelength range of the target spectrum peak, and retaining unsaturation points; fitting the unsaturated point through a nonlinear function to obtain the maximum fitting strength; if the maximum fitting strength is lower than the original strength, performing strength correction by adopting a linear extension mode by taking an unsaturated point as a reference, and otherwise, performing strength correction directly based on a fitting model; and outputting the corrected spectral intensity data, and calculating the maximum intensity of the spectral peak and the integral area of the spectral peak. According to the method, the accuracy of saturation judgment and the reliability of a correction result are improved, and a direct intensity data basis and core derivative parameters are provided for quantitative analysis.

Methods of characterizing a sample comprising one or more organic compounds are provided, comprising: receiving a X-ray diffraction spectrum for the sample, and inputting the X-ray diffraction spectrum for the sample into a deep learning model trained using a contrastive learning loss to produce latent space representations such that pairs of input X-ray diffraction spectra assigned a first binary label have latent space representations that are closer to each other than pairs of input X-ray diffraction spectra assigned a second binary label Related methods, products and systems are also described.

The invention relates to a soybean detection method and device based on a near infrared spectrum and a storage medium, and belongs to the technical field of spectral analysis. Index values of various classification indexes are extracted based on a sample near infrared spectrum of a soybean sample; determining the type of the soybean sample based on the index values of the various classification indexes; preprocessing the near infrared spectrum of the sample based on a preprocessing model corresponding to the type of the soybean sample to obtain a processed sample spectrum; detecting the processed sample spectrum based on a soybean detection model corresponding to the type of the soybean sample to obtain soybean detection parameters; the prediction accuracy and stability can be improved, the experience requirements on operators are reduced, and industrial application is easier.

The invention provides a carbon emission management monitoring method and device for rice planting in a cold region, and relates to the technical field of carbon emission management monitoring. Vertical three-layer parameters of a soil profile, namely the surface methane concentration, the middle oxidation-reduction potential and the deep organic acid concentration, are collected; the method comprises the following steps: mapping a middle-layer potential into an inhibition coefficient according to a microbial electrochemical principle, further calculating potential uplink electron flux and actual downlink electron sedimentation intensity, identifying an electron transport chain breakage risk through a residual error of the potential uplink electron flux and the actual downlink electron sedimentation intensity, and carrying out nonlinear fusion on the electron transport residual error and environment weights of all layers by adopting a p-norm model. Through the risk amplification coefficient, the superlinear conduction of the local risk to the whole system is realized, the dominant risk layer is accurately positioned based on the comprehensive weight, the differential field management strategy is automatically generated, and finally the closed-loop intelligent management normal form is constructed, and the accurate control of the carbon emission based on the mechanism model is realized.

The invention relates to the technical field of image data processing, in particular to a water-soluble fertilizer dissolution rate visual measurement method and system, and the method comprises the steps: obtaining a sequence image of a dissolution process; performing multi-scale frequency domain analysis on the image, and calculating a scale convergence index by using the deviation between a local phase and a weighted average phase; a spectral sharpness factor is calculated according to the ratio of the high-frequency energy response to the low-frequency energy response; fusing the scale convergence index and the spectral sharpness factor to obtain a solid boundary probability; segmenting the particle region based on the probability and converting the particle region into physical mass, and differentiating a mass change sequence to obtain a dissolution rate. By fusing the phase consistency and the frequency domain energy distribution characteristics, the physical entity edge and the optical refraction halo are effectively distinguished, the refraction interference problem during high-concentration dissolution is solved, and the dissolution rate measurement accuracy is improved.