17 results about "Micro analysis" patented technology

The invention provides an emission reduction potential mining method and system based on energy and carbon online monitoring data of an industrial enterprise, and belongs to the technical field of carbon emission management.The method comprises the steps that energy consumption data of the industrial enterprise is obtained, and abnormal value detection, abnormal value processing and missing value filling are carried out; in response to the processed data, adjusting threshold boundaries of all grade intervals based on similarity constraints set by an iterative algorithm, determining an optimal clustering number, performing energy consumption grade division and clustering processing, and outputting macroscopic analysis data; mining an energy consumption increasing time node in response to the processed data, extracting a micro energy consumption trend, and outputting micro analysis data; and based on the macroscopic analysis data and the microscopic analysis data, outputting a region-level energy consumption analysis structure comparison result and a unit-level energy consumption data mining result. According to the method, macroscopic and microscopic combination is realized, energy consumption data is automatically analyzed, and decision-making is assisted.

The invention relates to the technical field of geological radar images, in particular to a geological radar image interpretation method, which comprises the following steps of: performing macroscopic analysis on a test result image of an SIR-4000 type geological radar, confirming local anomalies in a dark-color gathering area and a dark-white alternation area, and after circling and communicating, combining an engineering geological survey conclusion and geological target body characteristics to obtain a geological target body interpretation result; classifying and judging abnormities such as a real structural surface and a rock vein; and drawing reverse fine anomalies through microscopic analysis, and mapping the reverse fine anomalies to a three-dimensional diagram contrast feature to eliminate instrument system errors. According to the method, real anomalies and false anomalies can be accurately distinguished, the anomaly attributes and the influence range are clarified, the interpretation pertinence and the construction guidance value are improved, and the tunnel construction safety is guaranteed.

The application discloses a device for micro in-situ characterization of hydrate crystal characteristics of a solid-liquid interface, which comprises a visual reaction kettle, a test sample, a simulated disturbance system, a confocal Raman spectrometer and a constant-temperature control system. The test sample comprises solid wafers arranged oppositely and liquid bridges between the solid wafers. The test sample is placed into the visual reaction kettle, and the temperature and pressure in the visual reaction kettle are adjusted to a low-temperature and high-pressure state. According to different test requirements, a three-axis moving force arm of the simulated disturbance system can drive the solid wafers to move in different directions, so that different degrees of disturbance can be simulated. Meanwhile, the disturbance of fluid injection / extraction, a magnetic field, an electric field and pH value change can also be simulated. In the state of disturbance, micro-analysis information of hydrate structure, components, pore occupancy, hydration number and the like in a natural gas hydrate generation / dissolution dynamic process on the solid-liquid interface can be obtained, so as to provide a theoretical basis for natural gas hydrate accumulation and exploitation.

The present application relates to a kind of low content component energy spectrum analysis method, belong to metallurgical dust resource utilization and material microanalysis technical field.The method includes: preparation sample exposure center section, obtain full section image by scanning electron microscope image splicing, and different analysis regions are divided;Low multiple energy spectrum area scanning is carried out in each analysis region, and the element distribution and zinc content data of multiple positions are obtained;For each analysis region, the arithmetic mean of residual zinc content is calculated as the regional representative value;Based on the characteristic data image of zinc element distribution, high multiple energy spectrum area scanning and point scanning are carried out on the characteristic micro area of zinc element aggregation to analyze the phase form and element composition of residual zinc.The present application realizes the quantitative, zoned, spatial positioning and multi-scale characterization of residual zinc content distribution and occurrence state in metallized pellet by "overall observation-zoned statistics-micro area analysis" progressive analysis, provides more accurate data support for process optimization.

The invention relates to the crossing field of computer vision and material micro-analysis, in particular to an intelligent identification method for a fiber master batch agglomerated structure based on multi-domain feature fusion, which comprises the following steps of: constructing and training a multi-feature fusion network model, and inputting a to-be-detected microstructure image into the trained multi-feature fusion network model; outputting a microstructure image with a rectangular bounding box, wherein a rectangular bounding box region corresponds to an agglomeration structure region of the fiber master batch; compared with traditional manual analysis, the method can effectively improve the analysis efficiency and accuracy, and has wide application prospects in the fields of material science, manufacturing industry and scientific research.

The invention provides a glass insulator detection system based on image analysis detection, and relates to the technical field of image data processing, the system comprises an image acquisition unit, a preprocessing unit, a feature extraction unit, a feature fusion unit and an output unit; according to the invention, after an image acquisition unit is used for acquiring a glass insulator surface image, an image pyramid is constructed from the macroscopic aspect to carry out multi-scale analysis so as to master overall characteristics and multi-resolution information, then, a Canny operator is used for extracting microdefect characteristics of a high-scale sub-image from the microscopic aspect, accurate identification and classification are carried out, and the macroscopic analysis and the microscopic analysis are combined, so that the detection accuracy is improved. By means of the method, defect omission or misjudgment caused by single-scale analysis can be avoided, information of different levels is effectively integrated, rich and accurate data are provided for quality evaluation, the accuracy and reliability of defect detection are improved, and fine evaluation of the surface quality of the glass insulator is achieved.

The invention belongs to but not limited to the technical field of asphalt-aggregate adhesion performance analysis, and discloses an asphalt oxidation functional group and aggregate adhesion action mechanism analysis method and system based on molecular dynamics, and the method comprises the following steps: constructing a multivariable oxidation functional group asphalt molecular model; constructing a refined silicon dioxide aggregate model; building a layered asphalt-aggregate interface system; carrying out geometric optimization, annealing treatment and dynamic simulation on the basis of Materials Studio software; and microscopic analysis of the adhesion performance is realized through interface energy calculation. The method has the remarkable advantages of being high in stability, high in universality, low in cost and the like, overcomes the defects of existing research on micromechanism analysis, and provides a new technical path for correlation research of asphalt aging and adhesion performance degradation.

The application belongs to the technical field of material detection and analysis, and particularly relates to a cross-section sample preparation method of superfine tungsten alloy wire, which comprises the following steps: fixing the tungsten alloy wire on a substrate made of the same material; then using epoxy resin and glass powder to perform hot embedding on the tungsten alloy wire, obtaining an embedded sample after cooling; then grinding and polishing the embedded sample under a pressure of 50-60 MPa; finally using a corrosion solution prepared from hydrogen peroxide, ammonia water and sodium dodecyl sulfonate to corrode the embedded sample, obtaining a tungsten alloy wire cross-section sample. The application uses a substrate made of the same material to fix the tungsten alloy wire, which is convenient for positioning the cross-section and avoids the occurrence of original cell reaction during corrosion; by accurately controlling key process parameters such as grinding pressure and polishing disc rotating speed, the cross-section micro-crack defects are effectively eliminated; by selecting a suitable type of corrosion agent and a suitable corrosion treatment time, a standardized detection sample meeting the requirements of microscopic analysis such as metallographic microscope and scanning electron microscope is prepared.

The invention provides a water plant station light storage construction project investment intelligent evaluation method and system, and relates to the technical field of industrial plant station energy management and investment evaluation, the system comprises a hardware layer and a function layer, and the components of the hardware layer adopt an industrial standard communication protocol to realize data interaction and instruction transmission; the hardware layer comprises a sensor network, a monitoring center, a comprehensive management platform, an algorithm server, an equipment regulation and control center and a central database; the monitoring center is in communication connection with the sensor network and is used for carrying out denoising and missing value complementation preprocessing on the acquired data; a plurality of typical daily load curves are accurately extracted from a high-dimensional noisy water affair load time sequence, and hidden and dispersed load clustering modes are effectively recognized; the matching relation between each typical daily load and the corresponding illumination characteristic is microscopically analyzed, and the overall characteristic and the change rule of the annual load are macroscopically integrated, so that the photovoltaic capacity configuration and the energy storage unit type selection can be ensured to be deeply adaptive to the actual energy demand of the water plant station.

The invention provides a pantograph life prediction method and system combined with wear time sequence analysis, and relates to the technical field of electric locomotive equipment, and the method comprises the steps: collecting a first morphology time sequence data sequence, a second electrical and thermal time sequence data sequence and a third dynamic time sequence data sequence of a pantograph in an operation process, and forming a multi-dimensional time sequence data sequence; inputting the multi-dimensional time sequence data sequence into a pre-trained life degradation index generation model, and outputting to obtain a life degradation index time sequence curve; morphological feature extraction is carried out on the life degradation index time sequence curve through time windows of macroscopic, mesoscopic and microscopic analysis scales, and fusion is carried out to generate a multi-scale feature vector; based on the multi-scale feature vector, the current wear stage of the pantograph is judged; and inputting the multi-scale feature vector into the residual service life prediction model by taking the current wear stage as a condition parameter, and outputting to obtain a residual service life prediction value of the pantograph. And the prediction precision of the residual service life of the pantograph is improved.

The application discloses a macro-microscopic analysis method and system for displacement development based on a sand filling model, and the analysis method comprises the following steps: assembling a simulated reservoir system based on a visual two-dimensional sand filling model; obtaining a displacement image; identifying macroscopic oil saturation; and establishing a microcosmic residual oil regularity evaluation method. The macro-microscopic oil-water distribution image in the displacement process under the reservoir condition can be determined more objectively and comprehensively to measure the development effect of the reservoir, the influence of different development modes under the oilfield condition on the distribution and type of residual oil is optimized, and the injection fluid parameters are guided.

The invention discloses a preparation method of a copper plate EBSD sample, and belongs to the technical field of material microscopic analysis and testing. The method mainly comprises the following steps of sample cutting and fixing, gradient abrasive paper grinding, polishing treatment, cleaning and drying, constant-current electrolysis and post-treatment. According to the method, the mechanical damage layer on the surface layer of the sample can be effectively removed, and the strain-free, scratch-free, clean and flat sample surface is obtained, so that the high-quality EBSD Kikuchi pattern is obtained, and the method is suitable for the fields of metal material interface research, tissue analysis, grain boundary characterization and the like.

The invention relates to the technical field of welding quality monitoring, in particular to a surface acoustic wave filter chip welding quality online monitoring control system. A comprehensive abnormal index is calculated through a prediction analysis mechanism of the welding prediction analysis unit, parameter optimization execution information is generated, the dynamic parameter execution module generates welding optimization parameters to adjust the welding execution unit, a large number of defective products caused by passive response are avoided, welding defects are early warned in advance and actively adjusted, and the welding quality is improved. The welding yield is improved; resonance data and deformation data are comprehensively analyzed through a microanalysis mechanism of the welding microcosmic monitoring unit, and a microcosmic evaluation state is output; and meanwhile, the data storage and visualization module realizes full-process data storage and real-time visualization display, so that the problem that microscopic deformation monitoring is not carried out is avoided, an operator can conveniently master the state in real time, and data support is provided for subsequent process optimization.

The invention relates to the technical field of component detection, and discloses a quantitative detection method for hypoglycemic active components in mulberry leaves, and the method comprises the following steps: carrying out baseline correction on original near infrared spectrum data of a mulberry leaf sample to be detected to obtain standard near infrared spectrum data; carrying out surface micro-analysis on the standard near infrared spectrum data to obtain enhanced spectrum characteristics; correlation wave band optimization is carried out on the standard near infrared spectrum data to obtain a target spectrum wave band; performing correlation calibration analysis on the enhanced spectral characteristics and the target spectral band to obtain blood glucose reducing active ingredient correlation data; carrying out content inversion evaluation on the standard near infrared spectrum data to obtain a predicted content value; performing structured integration on the predicted content value and related data of the hypoglycemic active components to obtain a quantitative detection report of the hypoglycemic active components; the method can improve the quantitative detection efficiency of the hypoglycemic active components in the mulberry leaves.

The invention specifically relates to a method for testing and evaluating the aging performance of a metal oxide resistor disc, and relates to the technical field of performance evaluation of metal oxide resistor discs, and the method comprises the following steps: respectively obtaining a macro-loss value and a micro-loss value after macroscopic and microscopic analysis of a sample, carrying out normalization processing on the macro-loss value and the micro-loss value, and calculating the aging performance of the metal oxide resistor disc; a failure mechanism coefficient is obtained after weighted summation calculation, and a corresponding quality grade is matched based on the failure mechanism coefficient. The method comprises the following steps: constructing a digital twin baseline fusing electricity, a microstructure and thermal characteristics; electrical parameters such as a volt-ampere characteristic curve and a nonlinear coefficient are accurately obtained through standardized testing, microscopic data such as grain morphology and element distribution and thermal characteristic data such as steady-state temperature rise and temperature uniformity are analyzed, and a multi-dimensional reference system is formed; and an accurate reference is provided for aging comparison, and evaluation is ensured to be changed from single-parameter judgment to multi-dimensional comprehensive analysis.

The invention discloses a failure analysis method and system and a defect marking structure, and belongs to the technical field of semiconductors. Cross-scale and cross-equipment seamless precise positioning is realized, and the transition problem from macroscopic positioning to microscopic analysis is perfectly solved through a multi-stage marking system combining'micron-scale defect marking 'and'macroscopic pointing marking' with the assistance of'digital distance marking '; wherein the pointing mark ensures that a general area is quickly found, and the distance mark provides accurate navigation, so that the time for searching defects under high-magnification equipment is greatly shortened, the analysis efficiency is improved by more than multiple times, and the defect loss is effectively avoided.

The present application relates to the technical field of geological exploration, and discloses a method for detecting deep concealed ore deposit by using pico-meter particles. The method comprises the following steps: capturing pico-meter particles from a surface medium sample of a to-be-detected area, obtaining the ultra-microstructure and chemical composition of the pico-meter particles by using an ultra-microscopic analysis technology, judging whether the to-be-detected area has a concealed ore body by summarizing and arranging the obtained data, and further detecting the material composition of the deep concealed ore deposit according to the component and structure information carried by the pico-meter particles. The analysis of the ultra-microstructure and chemical composition of the pico-meter particles can effectively obtain information of a deeper concealed ore body, which is of great significance for detecting an ultra-deep concealed ore body. The present application uses an ultra-microscopic analysis technology to analyze the pico-meter particles, and the obtained information of the deep ore body is more sensitive, more comprehensive, more intuitive, and more conducive to the prediction of the material composition of the deep concealed ore body.