1141 results about "Energy spectrum" patented technology

The invention discloses a geological disaster monitoring system based on multi-modal data, and relates to the technical field of geological disaster monitoring, and the system comprises a crack analysis module which carries out the time-space correlation mining of the crack propagation rate of a monitoring region, and analyzes the nonlinear evolution characteristics and spatial differentiation rules of rock mass fracture; and the critical identification module is used for performing wavelet packet energy spectrum analysis on the inclination angle change rate of the geologic body, identifying a critical turning point of rigidity attenuation of the geologic structure in combination with a preset algorithm, judging whether the overall stability enters an instability acceleration stage or not, and performing multi-parameter collaborative detection, crack evolution cross validation and dynamic trend stability verification to obtain the stability of the geologic structure. The accuracy and reliability of geological structure rigidity attenuation critical turning point recognition are remarkably improved, a more accurate rigidity attenuation stage judgment basis is provided for an early warning module, and the capturing capacity of a multi-modal data geological disaster monitoring system for structure instability precursor is enhanced.

The invention relates to the technical field of medical image processing, in particular to an artificial intelligence assisted intraoperative imaging method, which comprises the following steps: S1, preprocessing a multi-modal medical image, segmenting and recognizing an anatomical structure by a deep learning model according to the preprocessed image, measuring anatomical parameters based on a segmentation and recognition result, and generating an operation planning path by artificial intelligence according to the anatomical parameters; s2, collecting a C-shaped arm perspective image stream in real time, dynamically tracking space coordinates of a surgical instrument, comparing the position of the instrument with a surgical planned path, calculating offset, and when the offset is greater than an offset threshold, outputting correction guidance through an AR superposition layer; and S3, monitoring an image quality index in real time, dynamically adjusting exposure parameters through a reinforcement learning model, and when a metal implant is detected, switching a dual-energy-spectrum mode and executing an artifact suppression algorithm. According to the method, preoperative precise planning and intraoperative assistance are realized through artificial intelligence, the problems of poor image quality and high radiation risk are solved through technical optimization, and the method has important clinical application value.

The invention provides a medical RFQ accelerator low-level signal phase stability control method and system. The method comprises the following steps: constructing a time-frequency energy spectrum feature vector based on wavelet packet transformation; extracting a second disturbance feature based on a lightweight convolutional neural network and an attention mechanism; constructing a phase dynamic trend prediction module based on a long short-term memory network, and obtaining a first prediction phase error; constructing a phase compensation module based on a residual control network to obtain a second phase compensation amount; and outputting a real-time driving control signal based on the extended Kalman filter. According to the method, the time-frequency energy spectrum feature vector based on wavelet packet transformation is constructed, accurate characterization of the multi-scale disturbance features of the low-level signals is achieved, a medical RFQ accelerator phase dynamic compensation system is established in combination with a deep learning network and an extended Kalman filtering algorithm, the control precision and the anti-interference capability of signal phase stability are remarkably improved, and the method is suitable for popularization and application. The method is suitable for a high-precision medical particle accelerator control system.

The invention relates to the technical field of bridge structure health monitoring, and particularly provides a bridge earthquake damage rapid assessment method and system, and the method comprises the steps: carrying out the cross-modal coupling calibration of a time-history sudden change feature and a phase of a space energy spectrum, and generating a high-confidence three-dimensional dynamic response field; inputting the spatial gradient distribution characteristics into a nonlinear material state inversion program, performing path integrity verification in combination with an energy dissipation path, and outputting a potential damage topology boundary of the bridge; dividing an output bridge potential damage topological boundary into discretized damage units, and mapping strain constraints of the discretized damage units into triple decision-making indexes including a traffic capacity attenuation coefficient, an emergency disposal aging threshold value and an aftershock risk level through a bridge structure function attenuation rule. The system comprises a mixed field dynamic response reconstruction module, a damage sensitive domain reverse locking module and a function level damage quantitative mapping module. According to the method, deterministic mapping of the bridge structure damage state and the engineering function index is realized.

The invention belongs to the technical field of nondestructive testing, and discloses a gold ore and iron ore metal content detection method. The method comprises the following steps: acquiring an XRF energy spectrum by using an X-ray tube, acquiring an LIBS spectrum by using double-pulse Nd: YAG laser, and acquiring a Raman spectrum by using a semiconductor laser; performing baseline correction on the LIBS spectrum, performing Savitzky-Golay smoothing pretreatment on the Raman spectrum, and respectively extracting three types of spectrum characteristics; constructing a physical constraint network model, inputting XRF, LIBS and Raman features, and then outputting the correction content, fusion weight and LIBS dominant factors of Fe / Au elements; and finally, through a dynamic weight fusion strategy, combining XRF, LIBS fusion weights and network output parameters, calculating to obtain the content of the gold ore and the content of the iron ore. The technical breakthrough of multi-element cooperative detection and complex matrix interference suppression is realized, and the detection precision of Fe and Au is effectively improved.

The invention provides a multi-frequency adaptive unmanned aerial vehicle spectrum detection method and system, and the method comprises the steps: extracting the spatial and temporal distribution characteristics of carbon particle concentration and temperature gradient intensity based on a real-time environment data set, determining the attenuation coefficient distribution of different frequency band signals in a current environment, and obtaining a frequency band sensitivity analysis result; on the basis of a frequency band sensitivity analysis result, identifying a frequency band range which is most seriously influenced, and generating a signal data set which is optimized according to frequency bands; according to the signal data set optimized in different frequency bands, in combination with turbulence energy spectrum distribution and refractive index disturbance intensity, determining a random interference mode of a fire scene turbulence field on unmanned aerial vehicle detection signals and frequency dependence of the random interference mode; and adjusting the working frequency range of the spectrum detection equipment according to the frequency self-adaptive correction propagation path data set, and generating a multi-band collaborative optimization detection parameter set.

The invention discloses an image feature extraction method based on multi-dimensional decomposition, which relates to the technical field of image processing, and comprises the following steps: constructing a full-dimensional sampling cube, carrying out synchronous sampling on an image in a spatial dimension, a color dimension, a frequency dimension and a time dimension, and generating an original multi-dimensional tensor substrate which is used as a data basis for subsequent processing; an original multi-dimensional tensor base serves as input, energy layered projection is carried out based on a self-adaptive reference anchor, a multi-scale energy spectrum map is obtained, a dominant frequency band is calibrated in the energy spectrum map, and an energy base line is established. According to the method, stable extraction of multi-dimensional image features is realized through construction of a full-dimensional sampling cube, energy layered projection, phase alignment, sparse low-rank decomposition and morphological spectrum consistency verification; and a combined energy spectrum diagram and a dynamic energy level inhibition mechanism are introduced, so that feature drift caused by illumination abrupt change and rapid motion is effectively dealt with, subspace structure collapse is prevented, feature robustness and discrimination capability are improved, and the method is suitable for high-reliability visual scenes.

The invention relates to the technical field of engine testing, and discloses a reliability test method and system for a small general engine, and the method comprises the steps: collecting an engine vibration signal through a superconducting quantum interferometer, and collecting a temperature distribution parameter through a quantum dot temperature sensor; performing combined testing of cold and hot shock, high-load operation and random working conditions, and synchronously monitoring dynamic response characteristic data; constructing a Weibull probability model, and determining key influence factors through a global sensitivity analysis method; acquiring a surface roughness parameter by adopting a field emission scanning electron microscope, obtaining a material component parameter through energy spectrum analysis, and establishing a parameter mapping relation; establishing a vibration and temperature early warning system, executing a graded sweep frequency excitation and abrasive particle acceleration test, and recording a parameter degradation process; and generating a three-dimensional parameter cloud picture, and outputting a reliability test report. According to the method, the reliability of the small general engine can be comprehensively and efficiently evaluated, and a scientific basis is provided for design, maintenance and improvement of the engine.

The invention belongs to the technical field of power system risk assessment, and particularly relates to a new energy station broadband oscillation risk redisk assessment method based on multivariate data, which comprises the following steps: performing data acquisition and data preprocessing on a new energy station-power grid coupling system to obtain a historical data set; extracting oscillation characteristic indexes to form index vectors; carrying out oscillation frequency source positioning analysis, calculating a time-frequency spectrum energy function to obtain an oscillation energy spectrum, carrying out main frequency energy track extraction, and introducing a frequency-power cross analysis mechanism to realize accurate identification of an oscillation source device; constructing a source-network coupled transfer function, performing stability margin calculation, and judging the stable state of the coupled system; and based on the index vector and the stability margin, carrying out oscillation risk grade division, and combining a source positioning result to generate an oscillation redisk map. According to the invention, panoramic recovery and risk quantitative evaluation of the broadband oscillation problem of the new energy field station can be realized.

The invention discloses a multi-modal load prediction computing power center dynamic energy-saving scheduling method and system, and relates to the technical field of energy-saving scheduling, and the method comprises the following steps: executing long-tail fractal filtering based on service load data in a steady state for a long time in a computing power center, extracting a steady-state energy spectrum, and constructing a zero-drift contrast base plane; applying micro-amplitude random phase disturbance on the zero-drift contrast base plane, and enhancing weak disturbance characteristics in the steady-state energy spectrum in combination with an entropy gradient amplification strategy; and converting the enhanced precursor micro-fluctuation characteristics into a time sequence difference energy cloud picture. According to the method, zero-drift contrast base surface enhancement perturbation identification is constructed, and multi-scale energy analysis and Bayesian confidence correction are combined, so that accurate prediction and quantification of the burst load are realized; during surge triggering, a fractal slicing and low-power-consumption parallel scheduling strategy is adopted, high response is ensured, meanwhile, energy consumption is reduced, and the method is suitable for a computing power center scene with high performance and energy efficiency requirements.

The invention provides a material characterization image analysis system and an analysis method thereof. The invention discloses a material characterization image analysis system and an analysis method thereof, and the method comprises the following steps: S1, obtaining a back scattering electron image, a secondary electron image and energy spectrum data of a material through a scanning electron microscope, and dynamically adjusting the acceleration voltage, beam current and detector gain of the scanning electron microscope through a parameter optimization algorithm based on a preset material type and an observation target; s2, carrying out multi-scale feature enhancement preprocessing on the back scattering electron image and the secondary electron image, wherein the preprocessing comprises noise suppression, non-uniform illumination correction and micro-area texture enhancement; s3, pixel-level space registration is carried out on the back scattering electron image and the energy spectrum element distribution diagram, and a multi-modal fusion feature matrix is constructed. The material characterization image analysis system and the analysis method thereof provided by the invention have the advantages that the distortion problem can be well solved, the micro-structure identification accuracy can be improved, and the life prediction error can be reduced.

The invention provides a pulse passing rate improving method based on accumulation signal demodulation, and belongs to the technical field of nuclear electronics digital pulse processing. Comprising the following steps: performing baseline deduction and monopulse screening on a radiation field pulse signal; constructing a nuclear pulse signal model, fitting to obtain model parameters, and constructing a system response matrix; modeling a nuclear detection physical process into a mathematical model; solving a constraint optimization problem by using a non-negative least square method; and demodulating and calculating all the pulse signals including the stacked pulses which are discarded by stacking, so as to obtain delta pulses containing energy information. According to the invention, the accumulation signal which is abandoned by a traditional accumulation abandoning algorithm is demodulated, so that the technical problem of increasing the pulse passing rate is solved, and meanwhile, heavy peaks and high-energy trailing in an energy spectrum are inhibited. Compared with an existing accumulation discarding method, the pulse passing rate effect is remarkably improved on the premise that the energy spectrum accuracy is guaranteed.

The invention relates to an intelligent control method and system for extrusion forming of an aluminum profile, and the method comprises the following steps: carrying out acoustic emission sensor array measurement and preprocessing on a mold inlet region of the aluminum profile in an extrusion process, and obtaining a preprocessed acoustic emission signal sequence; performing time-frequency domain energy distribution analysis on the acoustic emission spectrum to generate an acoustic emission energy spectrogram; carrying out material flow stability analysis based on the spectrogram to obtain a flow analysis result; if the flow instability degree exceeds the target stability range, multi-constraint optimization calculation is conducted on the main cylinder pressure and the extrusion speed based on the analysis result, and optimization control parameters are obtained; and finally, closed-loop feedback control is performed on the extruder through a distributed PID controller, so that the material flow stability is kept in a target range, intelligent high-precision forming control is realized, and the problem that in the traditional extrusion process, the material flow stability is often influenced by factors such as the extrusion speed, the main cylinder pressure and the mold design is solved. And a flow instability phenomenon is easy to occur.

The invention discloses a multi-light-spot anti-interference deformation measuring device and method based on stroboscopic energy spectrum.The multi-light-spot anti-interference deformation measuring device comprises a point light source, a first cylindrical mirror is arranged below the point light source, a first linear array sensor is arranged below the first cylindrical mirror, and the first linear array sensor is electrically connected with a data acquisition device; light emitted by the point light source passes through the first cylindrical mirror and then is focused into a line segment to be imaged on the first linear array sensor, and the first linear array sensor converts optical signals of the focused line segment into digital signals and outputs the digital signals to the data acquisition device. According to the multi-light-spot anti-interference deformation measuring device, a multi-dimensional displacement sensor measuring system is established by applying the cylindrical mirror, the linear array sensor and the point light source, a displacement measuring system with high precision, high resolution and high sampling rate can be realized, and the multi-light-spot anti-interference deformation measuring device is few in related components, small in size, simple to maintain, low in cost, high in system stability and easy to popularize. The method is more suitable for outdoor multi-point monitoring of engineering structures.

The invention relates to the field of electrical measurement and testing, in particular to a fault monitoring method and early warning system for a bottom loading and unloading crane pipe, and the method comprises the steps: obtaining and preprocessing the current, voltage and vibration sequence of a servo motor, and obtaining a corresponding IMF component and an energy spectrum; and in combination with historical normal operation data and to-be-monitored data, the importance degree of the IMF component is evaluated, and the time domain anomaly degree is obtained based on the standard deviation of the sliding window variance and the DTW distance. Odd harmonic information is extracted from the energy spectrum, and the time domain anomaly degree is combined to obtain the comprehensive anomaly degree. And obtaining a fault degree through weighted integration, and inputting the fault degree into a control system to judge a fault. According to the method, the energy spectrum difference of the IMF components, the first-order difference sequence difference and the time-frequency domain abnormal degree are comprehensively analyzed, the operation state of the servo motor is comprehensively evaluated, key fault information loss and single time domain feature monitoring limitation caused by elimination of high noise components in a traditional method are avoided, and the accuracy and sensitivity of fault monitoring are remarkably improved.

The invention provides an automatic equipment anomaly monitoring method and system based on image processing, and relates to the technical field of automatic equipment anomaly monitoring, and the method comprises the steps: converting an equipment operation video into a multi-dimensional physical field feature: in a motion field dimension, based on optical flow field analysis, extracting a full-period displacement statistical histogram and a space thermodynamic diagram, the motion instability characteristic of the mechanical transmission system is accurately quantified; in a vibration field dimension, an energy spectrum and a vibration thermodynamic diagram are generated innovatively through time-frequency transformation of a displacement signal of a frequency spectrum monitoring point, and frequency domain feature visualization of a hidden vibration fault is achieved; in the dimension of a structure field, edge gradient analysis and texture feature extraction technologies are fused, a time sequence structure thermodynamic diagram sequence is constructed to capture a progressive damage evolution rule, a three-field abnormal index dynamic weighting fusion mechanism overcomes the limitation of traditional single-point monitoring, connected domain analysis of a fused thermodynamic diagram is combined with an LBP texture and morphological feature decision tree, and the defect of the prior art is overcome. Automatic equipment abnormity monitoring based on image processing is realized.

The invention discloses an adaptive energy calibration method for a Timepix3 detector, and belongs to the field of plasma diagnosis, and the method comprises the steps: collecting a photon event through a detector, the photon event comprising arrival time, coordinates and energy information; sorting the photon events according to the arrival time of the photon events to obtain a sequence; mapping the sequence to an empty matrix in a time window sliding mode, searching and recording the area of a pixel cluster and the sum of count values, and compensating the count values according to a set threshold value; classifying according to the area of the pixel cluster, summing the counting values of the pixels in the pixel cluster to obtain the volume of the pixel cluster, and drawing a statistical histogram of the volume of the pixel cluster to form an energy spectrum; identifying an energy peak of each energy spectrum and performing Gaussian fitting, calculating a peak abscissa, and establishing a relationship between the volume of a pixel cluster and actual energy; and completing energy calibration by utilizing linear fitting of volume energy data of multiple groups of pixel clusters. According to the invention, the requirements for setting parameters such as radioactive source intensity, detector threshold, exposure time and the like are reduced.

The invention relates to the field of crystal detection, and discloses a multifunctional comprehensive scintillation crystal and semiconductor material energy spectrum test system, which comprises a crystal adaptation module for judging the type and size of a placed crystal through an optical identification and parameter matching mechanism, and a signal acquisition module for amplifying, filtering and digitally processing an acquired signal, the signal correction module is used for correcting waveform distortion and baseline drift through a dynamic gain adjustment algorithm of a low-noise pre-amplifier and a multi-channel pulse analyzer; the data analysis module is used for generating an energy spectrum curve according to input signals and calculating energy resolution, counting rate, light attenuation time or carrier mobility, and the environment calibration module is used for dynamically evaluating shading degree, environment light, temperature and electromagnetic interference through an optical sensor and a temperature and humidity detector in the camera obscura and outputting updated calibration parameters. The method has the advantages of improving the detection efficiency, precision and consistency.

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 provides a motion compensation method and device for passive sonar detection. The compensation method comprises the following steps: acquiring underwater acoustic signals acquired by a passive sonar in real time within a preset period; performing segmentation processing and Fourier transform on the underwater acoustic signal to obtain an underwater acoustic frequency domain signal segment; constructing a parameterized search matrix of each water audio frequency domain signal segment, and determining a motion compensation parameter of each water audio frequency domain signal segment through similar energy spectrum search; based on the motion compensation parameter, performing phase compensation on the water audio frequency domain signal segment to obtain a compensation frequency domain signal segment; and performing inverse discrete Fourier transform on the compensation frequency domain signal segment to obtain a compensation time domain signal segment so as to determine a long-time coherent integration result corresponding to the underwater acoustic signal. Through the method, the gain of passive sonar detection processing is improved under the condition of low signal-to-noise ratio, and the accuracy and stability of passive sonar detection are improved.

The invention discloses a photon counting CT image noise reduction method based on multi-channel 3D U-net, relates to the technical field of medical image processing, and aims to solve the problems of insufficient spatial information utilization of a traditional 2D CNN, complex calculation of a 3DCNN and poor noise reduction effect of a traditional method. According to the method, high-energy, low-energy and all-energy images obtained by photon counting CT are used as three-dimensional multi-channel input, and a matrix containing space and energy spectrum dimensions is formed after preprocessing. Multi-scale three-dimensional features are extracted through a 3D U-net encoder, deep semantic information is aggregated through a bottleneck layer, a decoder carries out deconvolution up-sampling and is fused with an encoder feature map through jump connection, and meanwhile, an attention module is embedded in the jump connection to generate an attention mask so as to strengthen key area features; a mixed loss function training network including mean square error loss and structural similarity loss is adopted, and pixel-level precision and structural retention are balanced.

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 provides a water pump fault diagnosis method and device based on voiceprint recognition and a medium, and the method comprises the steps: obtaining a water pump voiceprint signal, and employing a sparrow search algorithm optimization-based VMD decomposition method to decompose the voiceprint signal to obtain a preliminary decomposition signal; processing the preliminary decomposition signal to obtain an FBANK energy spectrum and a GTFB optimization frequency band energy distribution feature, and then obtaining a multi-dimensional optimization feature vector through a feature weighted fusion strategy; and inputting the multi-dimensional optimization feature vector into a preset fusion model to realize fault classification. According to the technical scheme provided by the invention, accurate identification of various faults of the water pump bearing in a complex industrial scene is realized, and a new technical path is provided for intelligent operation and maintenance of the water pump bearing.

The invention relates to the technical field of oil and gas drilling, and discloses a rock density data processing method and system based on machine learning, a terminal and a medium. The photoelectric absorption index, the compensation density and an energy window parameter output through rock density logging are used as input parameters, the photoelectric absorption index and the compensation density of a nominal value are used as labels, a sample library is established, a learning model is established according to the sample library, and an artificial intelligence algorithm is used for processing energy spectrum data measured by the lithologic density logging instrument. The density and photoelectric absorption index evaluation accuracy and the lithologic density logging measurement precision are improved under the condition of complex well holes and stratums, and the exploration requirements of complex reservoirs such as unconventional reservoirs are effectively met.

The invention belongs to the technical field of semiconductor detectors, and particularly relates to a whole process simulation method and system for response of a semiconductor nuclear radiation detector. According to the method, through cooperative operation of a detector working parameter setting module, a carrier transport simulation module and a detection system electronics response module, whole-process response prediction of the semiconductor detector from radiation interaction to electronics output can be realized under different carrier transport characteristics and device structure conditions; according to the method, the carrier drift process is simulated in a small-step iteration mode, accurate reconstruction of a carrier drift track can be synchronously achieved, CSA signals and main amplifier forming signals can be continuously generated in real time, simulation output of an energy spectrum is finally completed, and therefore core performance indexes of a detector are comprehensively reflected. The method is wide in applicable semiconductor material range and device structure, the main amplifier module type can be flexibly configured, the overall simulation process is simple, convenient and efficient, and the method has good universality and engineering application value.

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 power equipment monitoring, and discloses an abnormal discharge electric field feature extraction method. The method comprises the following steps: standardizing and normalizing data acquired from a multi-channel electric field sensor array of a transformer substation to generate a standardized data stream; performing wavelet decomposition, adaptive threshold noise reduction and reconstruction on the basis of the stream to obtain a denoised signal; time domain statistics and frequency domain energy spectrum analysis are carried out on the signals, features are fused, and a joint feature vector is constructed; training a convolutional neural network classifier based on the vector; and updating a dynamic threshold rule according to the classifier output probability, and finally realizing real-time early warning and model feedback through GPU parallel computing. According to the method, the signal-to-noise ratio and the feature expression capability of the abnormal discharge signal can be effectively improved, and the detection accuracy, the real-time performance and the self-adaptive capability of the system are enhanced.

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 discloses a dynamic paper feeding control method for double-probe aerosol monitoring, and relates to the field of environmental monitoring instrument manufacturing. The method comprises the following steps: acquiring a currently set paper feeding mode of the aerosol detection device, and performing paper feeding control on filter paper of the aerosol detection device according to a filter paper target displacement corresponding to the paper feeding mode; acquiring a corresponding real-time accumulated pulse number after the aerosol detection device starts paper feeding control, and stopping paper feeding when a paper feeding stopping condition is met; acquiring the perimeter of the positioning shaft and the number of pulses per circle; real-time displacement is determined according to the perimeter of the positioning shaft, the number of pulses in each circle and the number of real-time accumulated pulses; and correcting the real-time displacement according to the target displacement of the filter paper, and dynamically controlling the filter paper of the aerosol detection device again according to the corrected real-time displacement. According to the invention, actual transportation positioning of the filter paper in different paper feeding modes is considered and correction is carried out, so that huge deviation of the detection efficiency of the monitor and serious distortion of a measurement energy spectrum can be avoided.

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.