651 results about "Energy spectrum" patented technology

The invention provides a neutron dose evaluation method and system based on mobile phone neutron activation product analysis, and the method comprises the steps: constructing an equivalent medium model for replacing a real mobile phone, and carrying out the neutron energy spectrum solving through the mobile phone activation product. The neutron energy spectrum can be reconstructed on site in daily scenes without professional equipment, such as villages, streets and shopping malls, so that the uncertainty of individual dose is reduced to a level equivalent to that of a laboratory spectrometer, the dependence on a multi-ball spectrometer, a TOF spectrometer and professionals is thoroughly eliminated, rapid and accurate measurement of the neutron energy spectrum and large-scale population dose evaluation are realized, and the accuracy of the neutron energy spectrum is improved. And a basis is provided for delimiting the influence range of the nuclear event, judging the event level of the nuclear event and formulating a hierarchical medical rescue scheme.

The invention discloses a distinguishing table type X-ray spectrometer and an X-ray absorption spectrum testing method, and relates to the technical field of spectral measurement. The high-resolution table type X-ray spectrometer comprises a sample signal source which is located in a Dowland circle and is used for generating X-rays; the exponential crystal is a bent crystal, the surface shape of the exponential crystal is defined by an exponential equation, so that different points on the curved surface of the crystal have different curvature radiuses and correspond to Rowland circles with different radiuses, each point on the exponential crystal is tangent to the corresponding Rowland circle, and the exponential crystal is used for performing monochromatization and focusing on the X-ray incident from the sample signal source; and the detector is positioned on a surface formed by the corresponding imaging points on each Rowland circle, and is used for receiving the X-rays which are monochromatized and focused by the index crystal and outputting corresponding light intensity signals. Therefore, through the cooperation of the index crystal and the multi-Rowland circle structure, the X-ray absorption spectrum test with high light receiving efficiency, high spectral resolution and wide energy spectrum range can be realized, and the comprehensive performance of the desk type spectrometer is improved.

The invention relates to the technical field of high-voltage electric appliance detection, in particular to a device and method for detecting the vacuum degree of a vacuum arc-extinguishing chamber, and the method comprises the steps: arranging a field emission cathode on one metal end cover of the vacuum arc-extinguishing chamber; an anode is arranged on the metal shielding cover close to the field emission cathode in the vacuum arc-extinguishing chamber; the high-voltage direct-current power supply is connected to the field emission cathode and the anode and is used for applying voltage to the electrodes so as to drive the field emission cathode to emit electron beams to impact the anode; the X-ray energy spectrum detector directly faces the anode, the X-ray energy spectrum detector is used for measuring bremsstrahlung X-ray energy spectrum generated when the electron beam impacts the anode, an electron emission source is actively arranged in the arc extinguish chamber, and the distribution of the X-ray energy spectrum generated by the electron emission source is measured. And high signal-to-noise ratio, high-precision and high-sensitivity vacuum degree quantitative detection is realized by utilizing the strong correlation between the energy spectrum form and the internal gas pressure.

The invention relates to the technical field of electron energy measurement, and discloses an electron spectrometer and an electron energy spectrum measurement method and system.The electron spectrometer comprises a magnetic deflection unit used for receiving an incident to-be-measured electron beam, generating a magnetic field and applying the magnetic field to all to-be-measured electrons in the to-be-measured electron beam; the scintillator fluorescent screen is arranged at the downstream of the magnetic deflection unit and used for receiving the deflected electrons to be detected, and the electrons bombard the scintillator fluorescent screen and generate light spots; the optical acquisition unit is used for acquiring a light spot image on the scintillator fluorescent screen; and the signal processing unit is used for processing the light spot image so as to identify the spatial position information of the light spot in the light spot image and convert the spatial position information into electronic energy data. The method has the beneficial effects that the unification of high resolution and high measurement efficiency is realized, and the problem of signal accumulation under a high counting rate of a traditional direct measurement method and the inherent defect of low efficiency of a scanning type magnetic spectrometer are overcome.

The invention provides an anomaly diagnosis method for a nuclear power plant gamma energy spectrum continuous monitoring process, and the method comprises the following steps: S1, determining at least one target gamma characteristic peak which needs to be monitored under the current monitoring condition of a nuclear power plant, and calculating the anomaly of the target gamma characteristic peak based on the performance index of a gamma spectrometer and the expected energy of the target gamma characteristic peak; setting a parameter allowable range of each target gamma characteristic peak; the parameter permissible range at least comprises a peak center energy permissible range and a resolution permissible range; s2, acquiring a gamma energy spectrum output by the gamma spectrometer, and processing the energy spectrum to obtain actual measurement parameters of a target gamma characteristic peak; the measured parameters at least comprise a peak center energy measured value and a resolution measured value; s3, the actually measured parameters are compared with the corresponding parameter allowable ranges; and S4, if the actually measured parameter of any target gamma characteristic peak exceeds the corresponding parameter allowable range, determining that the state of the gamma spectrometer is abnormal. Automatic and real-time abnormity diagnosis of the monitoring state of the gamma spectrometer is realized.

The invention discloses a diamond microstructure precision grinding system and method with visual positioning, and the method comprises the following steps: collecting a microscopic visual image, calibrating a visual coordinate system and a machine tool coordinate system, and building a unified positioning coordinate system; extracting composite visual features, constructing an energy field, performing adaptive partitioning, and generating risk partitioning codes; the risk partition codes are input into a double-flow network, grinding signals are input into grinding branches, and parameter tracks are generated; precisely grinding according to a segmented parameter track, reducing the cutting depth and the speed in a high potential energy area, and increasing the speed in a low potential energy area; after grinding, obtaining a three-dimensional shape, performing error analysis and energy spectrum generation, and mapping to risk partition coding; and according to the error energy and the energy field, the grinding priority is judged, and high-priority partitions are selectively ground and remeasured. According to the method, the visual potential energy field is constructed and the improved double-flow neural network is fused, so that accurate positioning, self-adaptive grinding control and high-precision micro-area grinding of the diamond microstructure are realized.

The invention discloses a transformer abnormity identification method based on voiceprint feature analysis, and belongs to the field of power equipment state monitoring and intelligent diagnosis. The method comprises the following steps: firstly, analyzing an iron core acoustic mechanism based on a magnetostrictive effect, and establishing a three-dimensional model through finite element simulation to obtain vibration and sound field characteristics; in a complex substation environment, a hybrid noise reduction method combining density peak clustering and a CEEMDAN-wavelet threshold is provided, and the signal-to-noise ratio is effectively improved. Then extracting Mel-frequency cepstrum coefficients (MFCC) and spectrum features, and performing local linear embedding (LLE) dimension reduction to form a compact feature set; in the recognition stage, a convolutional neural network framework is designed, specifically, a spectrogram and an energy spectrum are modeled through a two-dimensional CNN, an MFCC tensor obtained after dimensionality reduction is modeled through a three-dimensional CNN, and accurate diagnosis of mechanical faults such as core looseness is achieved. The method has the advantages of being non-contact, anti-noise and high in recognition precision, and real-time diagnosis and early warning of mechanical abnormity of the transformer can be achieved under complex working conditions.

The invention relates to the technical field of data processing, in particular to an AI large model multivariate data fusion method and system based on multiple modalities, and the method comprises the steps: collecting multi-source data; performing classification abnormity judgment; abnormal type identification; calculating severity; switching fusion modes; and outputting an alarm. According to the method, parameters such as the spindle temperature, the rotating speed fluctuation rate, the vibration acceleration, the current load, the acoustic emission energy spectrum peak value, the cutting force variance and the cooling liquid metal particle concentration are acquired and fused step by step in multiple dimensions in the machining process of the numerical control machine tool, so that the running state of the machine tool is dynamically sensed from multiple angles; furthermore, the physical essence of tool wear and material peeling is revealed through the coupling relationship between the cutting force variance and the metal particle concentration, and meanwhile, the dynamic stability change of the machine tool structure is captured by combining the spindle rotating speed fluctuation ratio; the problem that state monitoring is not accurate in the numerical control machining process due to data isolation and fusion strategy staticization is effectively solved.

The invention provides a boiler heating surface pipe wall leakage detection method, and belongs to the technical field of industrial intelligent detection based on machine learning. According to the method, a heterogeneous sensing architecture combining adaptive signal decomposition optimized by an improved sparrow search algorithm and acoustic array positioning corrected by a physical field is adopted, and complementary features are extracted from time domain statistical features and a frequency domain energy spectrum through a specially designed double-flow space-time attention fusion neural network; therefore, the weak leakage signals in the strong noise environment can be captured and identified. Based on an identification result, a hearth three-dimensional temperature field distribution model is used for carrying out gridding correction on a sound wave propagation path, a nonlinear positioning equation set is constructed, and three-dimensional space sub-m-level accurate positioning of a leakage sound source is realized without manual intervention; the problems of false alarm and missing alarm caused by background noise interference and positioning deviation caused by temperature gradient in traditional acoustic monitoring are thoroughly solved.

The application provides a negative electrode material and a preparation method thereof and a secondary battery, and relates to the technical field of battery materials. The negative electrode material comprises a silicon-based inner core and a carbon layer coated on the surface of the silicon-based inner core, wherein the silicon-based inner core comprises nanosilicon and a silicate containing a metal element M; the negative electrode material is subjected to section analysis and energy spectrum analysis, and meets the conditions of k1<=10, k2<=5 and 0.1 The preparation method of the negative electrode material comprises the following steps: heating and evaporating pre-disproportionated silicon monoxide material and M metal source material to obtain silicon monoxide gas and metal source gas; mixing and condensing the two kinds of gas to obtain an inner core material; and performing carbon coating treatment to obtain the negative electrode material. In the negative electrode material prepared by the application, the metal silicate effectively separates the nanosilicon domain and the silicon oxide domain, and is uniformly distributed, and the negative electrode material has high initial efficiency and excellent cycle performance.

The invention provides a coastal erosion rate prediction method, and belongs to the technical field of coastal erosion, and the method comprises the steps: building a three-dimensional seabed grid model, solving a hydrodynamic field through depth average simplification and GPU parallel calculation, and calculating sediment flux distribution based on shear stress discrimination and a high-order windward format. Outputting an erosion mode category and a local erosion strength coefficient by using a coastline erosion feature recognition model comprising a Josephh ring screening layer and a self-organizing mapping projection layer, starting adaptive grid encryption when the local erosion strength exceeds a threshold value, and calling a corresponding parameter set according to an erosion mode to calculate a seabed elevation change and a coastline erosion rate; and a wave energy spectrum reconstruction algorithm is adopted to generate future wave sequence cycle prediction, so that the technical problem that calculation precision and calculation efficiency are difficult to consider in coast erosion rate prediction under a complex wave power condition is solved.

The invention discloses an intelligent regulation and control method and system for the environmental comfort degree of a central air conditioner terminal, and relates to the technical field of air conditioner regulation and control. The intelligent regulation and control method comprises the following steps that a pressure fluctuation track generated by aerodynamic disturbance is collected in the operation process of a draught fan, and the pressure fluctuation track is converted into energy spectrum distribution in a time sequence; establishing dynamic energy representation of the fan frequency; based on dynamic energy representation of fan frequency, a sound vibration field is projected in a spatial range, excitation response of inherent frequency is synchronously recorded through a room structure sensing array, and instantaneous energy coupling fingerprints of a room structure are formed. Through dynamic energy characterization and structural response coupling analysis, real-time identification and quantitative control of the standing wave noise risk are realized, a phase drift mechanism is introduced at a risk critical point, a fan disturbance phase is actively adjusted, standing wave superposition is effectively blocked, and acoustic safety and operation stability of a tail end environment are improved.

The invention discloses an electric power aerial patrol operation data mining method, and belongs to the technical field of intelligent patrol. Coordinate values and temperature data of target equipment are synchronously collected through an infrared thermal imager, a visible light camera and a vibration sensor carried by power equipment, infrared coordinates and visible light coordinates are projected to the same geographic coordinate system through affine transformation, space registration is achieved, continuous wavelet transformation is conducted on collected vibration signals, and the temperature of the target equipment is obtained. The method comprises the following steps: extracting vibration feature vectors including global vibration intensity, wavelet energy spectrum and wavelet entropy, calculating the weight of each feature through image quality, carrying out weighted fusion to calculate a state index representing the health state of equipment, judging the state index according to a dynamic threshold value, judging that the equipment is abnormal if the state index exceeds the threshold value, and judging that the equipment is abnormal if the state index does not exceed the threshold value. And a feedback mechanism is triggered to collect data again to monitor the trend, otherwise, the data is stored for subsequent analysis. According to the invention, the precision and reliability of abnormal state identification in electric power aerial patrol operation and the robustness to a complex field environment are significantly improved.

The invention discloses a material classification method and device based on an ultraviolet electron spectrum, a medium and a product, and relates to the technical field of material surface science and data analysis. The method comprises the following steps: acquiring ultraviolet light electron energy spectrum data of a target material to be analyzed, and extracting a first spectrum curve from the energy spectrum data; obtaining a conductor characteristic identification result of the target material according to the first spectrum curve; under the condition of determining that the target material does not have the conductor characteristics based on the conductor characteristic identification result, extracting a second spectrum curve in a preset valence band top energy interval extending towards the negative energy direction by taking the Fermi level as a starting point from the ultraviolet light electron spectrum data, and performing multi-dimensional characteristic fusion on the second spectrum curve, generating a semiconductor characteristic identification result of the target material based on the fusion result; and determining a classification result of the target material according to the semiconductor characteristic identification result. According to the scheme, the conductive attributes of the target material can be quickly and accurately classified.

The invention discloses an electric power intelligent monitoring system based on multi-device cooperative linkage and an operation and maintenance method, and belongs to the technical field of electric power intelligent monitoring and operation and maintenance. Radiation data and historical decay records are collected through a sensor, a linear regression algorithm is adopted to fit and establish an energy spectrum purity attenuation model, and the dose loss proportion is calculated in real time; carrying out dynamic compensation on beam intensity and irradiation time based on the proportion, fusing real-time dose monitoring data by utilizing a Kalman filtering algorithm, and optimizing a beam intensity value to suppress noise interference; the irradiation time is further adjusted according to whether the dose loss exceeds a threshold value or not, and a dynamic compensation combination is formed; the effectiveness of the combination in each stage of the source life cycle is verified through the simulation module, and the verified compensation parameters are updated to the control system in real time, so that closed-loop regulation and control are realized. According to the invention, the whole process of dose output in the irradiation process is accurately and stably controlled, and the consistency and reliability of the irradiation process are remarkably improved.

The invention provides a gamma-ray energy spectrum analysis method and system based on a deep learning physical information network, and relates to the technical field of gamma-ray energy spectrum analysis. The method comprises the following steps: acquiring sample data containing an energy spectrum and corresponding analysis data, and preprocessing the sample data; constructing an energy spectrum analysis model based on a multi-scale convolutional neural network, an attention mechanism module and a physical information neural model; training an energy spectrum analysis model by using the preprocessed sample data to generate a target energy spectrum analysis model; and inputting the to-be-analyzed energy spectrum into the target energy spectrum analysis model, and outputting an analysis result. By constructing a multi-module collaborative energy spectrum analysis model, accurate decomposition of energy spectrum multi-scale features, efficient interaction of cross-feature information and collaborative prediction of multiple parameters are achieved, the problems that spectrum peak overlapping is difficult to separate, weak peaks are difficult to recognize, and the anti-jamming capability is weak in a traditional method are solved, and the precision, efficiency and automation degree of energy spectrum analysis are improved.

The invention relates to a binding material single particle local spatial evolution analysis method, and belongs to the technical field of civil engineering binding material microstructure characterization. The method comprises the following steps: acquiring back scattering (BSE) and characteristic X-ray energy spectrum (EDS) images of a sample; selecting a target gel particle based on the BSE image and intercepting a local area; identifying the reaction edge of the target gel particle; performing equal-width stepped strip division inwards and outwards by taking the reaction boundary as a starting point, and storing the mask; carrying out image operation on adjacent masks to extract strips, and carrying out statistical analysis on gray feature and element feature changes of each strip; according to the method, surrounding phase interference is avoided through local analysis, whole-process analysis is realized by adopting unified image processing software, a programming basis is not needed, the technical threshold is reduced, the operation process is simplified, the uniformity of strip division and the reliability of an analysis result are ensured, and the method is suitable for large-scale popularization and application. And a simple, convenient and effective technical means is provided for revealing the reaction mechanism and quantitative reaction activity of the cementing material.

The invention discloses a planar turbulence prediction method based on a hybrid prediction model, and relates to the technical field of cross of computational fluid mechanics and artificial intelligence, and the method comprises a sensor point selection method based on a multi-index assignment strategy, and is used for maximizing information acquisition from sparse deployment. A convolutional long short-term memory network, a multi-layer perceptron and a mixed deep learning model of a Fourier neural operator are fused, Conv-LSTM and MLP are used for capturing local nonlinear spatial-temporal features, and FNO is used for modeling global dependence and correcting boundary errors; a physically constrained training framework ensures the physical rationality of a prediction result by introducing undivergence,-5 / 3 energy spectrum slope and other constraint terms into a loss function; under the condition that only a small number of measuring points are used for input, the plane turbulent wind field can be reconstructed in real time in a high-precision and high-fidelity mode, prediction errors are remarkably reduced, the boundary effect problem is solved, the physical credibility of prediction results is guaranteed, and the method has important application value in the fields of wind engineering, weather forecast and the like.

The invention belongs to the technical field of circuit board detection, and particularly relates to a method for detecting, identifying and analyzing residues in a circuit board cavity, which comprises the following steps of: positioning suspected residual areas according to the gray difference of an X-ray image, generating a space coordinate set of the suspected residual areas, screening out a to-be-analyzed area set by combining chemical chromogenic reaction intensity and X-ray contour features, and analyzing the to-be-analyzed area set. The method comprises the following steps of: obtaining a plurality of regions, dividing the regions into a plurality of subsets according to spatial diffusion characteristics, obtaining element and functional group data of each region through energy spectrum and spectral analysis, analyzing candidate residue categories, and dynamically adjusting subset division based on the consistency of the candidate categories in the subsets until the consistency requirement is met. And finally, outputting a specific residue category for each region according to a division result. According to the method, dynamic subset division is carried out according to the space congruency and the contour features, systematic distinguishing and classification of diffusion type residues and attachment type residues are achieved, and the accuracy of category judgment is improved.

The invention discloses a hybrid antenna array direction of arrival estimation method based on noise marginalization SBL. The method comprises the following steps: initializing system parameters and a sampling grid set; establishing a hybrid antenna array receiving signal model, and initializing a hybrid beam forming matrix into a block diagonal matrix meeting constant modulus constraint; constructing a hierarchical probability model under a Bayesian framework, and introducing a noise precision parameter into signal prior; integral operation is carried out on the noise precision parameter, so that the signal posterior distribution is converted into student t distribution; an objective function is constructed, an expectation maximization algorithm is adopted to iteratively update a signal energy spectrum, and the process does not involve noise parameter estimation; an alternating iteration strategy is adopted, the signal energy is fixedly updated through inner circulation, and optimization is performed through a gradient descent method after outer circulation is fixed; after convergence, constructing a target function of off-grid estimation by reconstructing a covariance matrix; and searching the off-grid direction of the maximized objective function near a spectrum peak to obtain a final DOA estimation value.

The invention provides a ground simulation test method and device based on ground irradiation source combination parameter optimization. The method comprises the steps that an in-orbit space irradiation environment is simulated, an orbit environment particle energy spectrum of a to-be-tested space orbit is obtained through calculation, and an in-orbit PKA distribution spectrum in a to-be-tested material is calculated based on the orbit environment particle energy spectrum; based on the obtained on-orbit PKA distribution spectrum, determining optimal combination parameters of one or more groups of ground irradiation sources through a specified optimization algorithm and fitting calculation, so that a PKA energy spectrum generated by the ground irradiation sources in the to-be-measured space material is matched with the on-orbit PKA distribution spectrum, and an optimal fitting energy spectrum is obtained; and calculating and determining a critical dose rate, and calculating to obtain an acceleration fluence and a maximum acceleration multiplying power for guiding a ground acceleration test so as to carry out a ground simulation test of the to-be-tested space material. According to the application, the combined parameters of the ground irradiation source are optimized, and a more accurate ground simulation test is realized.

The invention provides a mine sample analysis method and system based on spectrum correction, and relates to the technical field of mine component analysis. The method comprises the following steps: firstly, preprocessing a first mine sample, respectively obtaining an XRF energy spectrum and a gamma energy spectrum, then, carrying out fusion processing on the two energy spectrums to obtain a first spectrum fusion feature vector, carrying out analysis based on a machine learning model to obtain mineral combination analysis information of the first mine sample, and finally, obtaining a second spectrum fusion feature vector of the first mine sample. At least one mine sample with the similarity meeting a preset requirement is found out from historical mine sample data according to the mineral combination analysis information, the determined collection parameters of the target mine sample serve as an optimal photon energy-time parameter set, analysis processing of the first mine sample is completed, and an analysis report is formed. According to the technical scheme, the optimal analysis parameter is determined through two steps of artificial intelligence and big data screening, so that the mine analysis result is more accurate.

The invention relates to the technical field of image correction, and particularly discloses an X-ray image correction method and device based on deep learning and terminal equipment. The method comprises the following steps: acquiring a pulse full-time original image and an energy spectrum time sequence signal, and extracting to obtain a distortion core feature; a distortion transmission path is extracted based on the distortion core features, a reverse mapping model is constructed to output distortion magnification times, and a calibration coefficient matrix is established; screening a core energy interval and a corresponding core trace element peak; the calibration direction is bidirectionally constrained through a bimodal linkage calibration mechanism, and a corrected signal and image are output; the device comprises a feature extraction module, a distortion calibration module, a core extraction module and a linkage calibration module. The terminal device executes the steps of the program implementation method through the processor, and the storage medium stores the corresponding computer program. According to the invention, the trace element peak detection precision and calibration stability are improved, and the reliability of X-ray detection is enhanced.

The invention relates to the technical field of measurement, in particular to a float-type water level gauge dynamic compensation detection method which comprises the following steps: collecting float angle sequence data; synchronously acquiring environment disturbance parameters; calculating the floater angle sequence data to obtain a preliminary water level value; constructing a dynamic compensation model, and outputting an inertia force correction term, a fluid damping correction term and a mechanical hysteresis correction term; the inertia force correction term, the fluid damping correction term and the mechanical hysteresis correction term are multiplied by corresponding weight factors respectively, then algebraic sum is solved, a total compensation amount is obtained, and the weight factors are dynamically adjusted and normalized according to the current wave high energy spectral density, the wind speed gradient change rate and the water temperature viscosity correction coefficient; and deducting the total compensation amount from the initial water level value, and outputting a final compensated water level value. According to the method, the problems of poor multi-physical field sensing capability and poor online parameter identification capability of a compensation method in the prior art are solved.

The invention relates to the technical field of line loss assessment, in particular to a power system line loss assessment method and system based on big data processing, and the method comprises the following steps: collecting voltage, current and phase angle signals of a power supply section, carrying out the frequency domain transformation, extracting the amplitude and phase of a frequency component, and generating a frequency spectrum feature set; energy spectrum characteristics and a migration sequence are calculated to analyze spectrum form stability, a risk section is identified by combining a power flow direction and an energy migration included angle difference, and a line loss assessment result is generated by combining a gravity center drift trend and an energy change sequence. Energy spectrum features and migration sequences are extracted to describe power grid energy distribution changes, an included angle difference between a power flow direction vector and an energy migration direction is calculated to screen an abnormal section, an energy drift trend is analyzed in combination with a frequency spectrum gravity center track, line loss real-time quantification and area positioning are achieved, and abnormal loss identification precision and operation evaluation stability and reliability are improved.

The invention discloses a nuclear radiation energy spectrum double-domain collaborative optimization method and a related device, and belongs to the technical field of nuclear radiation measurement, and the method comprises the steps: obtaining an original distortion pulse sequence generated by nuclear radiation measurement; a double-domain collaborative energy spectrum optimization model is constructed, the double-domain collaborative energy spectrum optimization model comprises a pulse domain repair model and an energy spectrum domain reconstruction model which are arranged in parallel, and the pulse domain repair model is used for performing pulse-by-pulse repair on the amplitude and the arrival time of the original distorted pulse sequence and outputting a repaired pulse sequence; the energy spectrum domain reconstruction model is used for converting the repaired pulse sequence into an energy spectrum diagram; in the training process of the double-domain collaborative energy spectrum optimization model, the pulse domain repair model and the energy spectrum domain reconstruction model share the back propagation gradient of the joint loss function, so that bidirectional closed-loop collaborative optimization of the pulse repair model and the energy spectrum reconstruction model can be realized, the problem of step-by-step error amplification caused by a traditional serial processing mode is avoided, and the performance of the system is improved. And the authenticity and the accuracy of the output energy spectrum are obviously improved.

The invention relates to the technical field of pre-diagnosis and health management, in particular to a low-voltage switch cabinet insulation safety diagnosis method based on discharge characteristic analysis, and the method comprises the steps: synchronously collecting original optical signals through a multispectral sensor array; performing energy compensation by using a multipath reflection equivalent attenuation model to generate reconstructed spectral line intensity data; determining a virtual time zero point by using an autocorrelation function and generating virtual phase analysis atlas data; and constructing an energy spectrum gravity center space coordinate in the energy spectrum feature space to extract a gravity center red shift feature quantity. According to the method, through multi-band relative attenuation rate compensation, power frequency period statistical peak value extraction and energy spectrum space gravity center mapping, the diagnosis problem that the insulation state is difficult to quantitatively evaluate due to signal distortion, reference phase loss and fuzzy characteristic evolution in a non-line-of-sight environment is solved.

The invention discloses a spin-coating acceleration self-adaptive control method based on liquid level disturbance feedback. The method comprises the following steps: collecting a liquid level disturbance signal; extracting liquid level parameters and obtaining a disturbance energy spectrum; establishing a disturbance-film thickness correlation function; designing an adaptive control algorithm; and performing closed-loop execution and optimization. According to the method, the liquid level morphology change data is obtained by using a high-speed camera and a laser reflection method, and the disturbance amplitude, frequency and phase characteristics can be extracted in real time, so that the liquid level stability is quantitatively represented. According to the method, a correlation model between liquid level disturbance parameters and film thickness evolution is established. By coupling the disturbance amplitude and hydrodynamic characteristic parameters (such as Reynolds number, Marangoni number, viscosity and volatilization rate), the corresponding relation between the disturbance intensity and the film thickness deviation is constructed, and the film forming trend of different spin coating stages can be dynamically predicted. According to the invention, a self-adaptive acceleration control algorithm based on disturbance feedback is adopted, and closed-loop optimization control of the spin-coating process is realized.

The invention belongs to the technical field of nuclear logging data processing, and particularly relates to a neutron detector pulse signal intelligent discrimination method and system based on deep learning, and the method comprises the following steps: collecting a pulse signal outputted by a detector, and when the rising edge of the pulse signal is detected to exceed a preset ground noise threshold value, determining the pulse signal; intercepting a time sequence containing the complete pulse signal, and carrying out normalization processing on the time sequence to obtain a normalized dimensionless voltage sequence; and calculating a first-order difference absolute value of adjacent sampling points in the dimensionless voltage sequence, and carrying out weighted calculation in combination with the time distance from the current sampling point to the peak moment to obtain a waveform oscillation entropy index representing the waveform microcosmic oscillation degree. According to the method, more than 98% of neutron tube ignition interference can be effectively eliminated, the high-energy part of the energy spectrum is remarkably purified, and the problem that data of the aged neutron tube are unavailable is solved.

The invention relates to the field of organic matter-rich shale mineral type and cause research, and discloses a shale clay mineral composition quantitative recovery method, which comprises: S1, cutting and polishing a shale sample, and carrying out argon ion polishing; s2, based on SEM-EDS combined analysis, collecting morphology and energy spectrum characteristics of clay minerals with different causes, and constructing a clay mineral cause standard library; s3, AMICS analysis parameters are optimized, and 2-4 vision fields are selected for scanning to generate original data; s4, processing the scanning data, calculating the mineral content, and performing cross validation with an XRD-Rietveld result; and S5, in combination with a diagenetic evolution sequence and a mass conservation principle, inverting the contents of original montmorillonite and clastic clay minerals. According to the method, clay minerals of different causes are distinguished through the SEM-EDS double-constraint model, AMICS parameters are optimized, the recognition precision is improved, and quantitative recovery of original components is achieved in combination with diagenetic inversion.