2132 results about "Energy spectrum" patented technology

The invention discloses a mixed tower structure full-life damage intelligent sensing array early warning system, which relates to the field of mixed tower structure detection and comprises a multi-modal data collection module, an array topology optimization module, a self-adaptive signal processing module, a digital twin life prediction module, a grading early warning module and a visualization system. The multi-modal data collection module comprises a multi-modal sensor array, a self-powered module and a wireless transmission module. According to the invention, a full-scale sensing network is constructed, full-dimension damage perception from distributed monitoring to sudden damage capture and structural modal analysis is realized, wavelet transform and blind source separation are combined to eliminate environmental noise interference, a damage characteristic ultrasonic attenuation coefficient, an acoustic emission energy spectrum peak value, optical fiber strain gradient anomaly and vibration modal frequency deviation are extracted, and the detection accuracy is improved. And classification and positioning of damage types and intelligent diagnosis of severity levels are realized through a convolutional neural network and long and short memory neural network hybrid model, and a closed-loop processing flow from data acquisition to feature analysis is formed.

The invention discloses a loess tunnel surrounding rock deformation monitoring method, and relates to the technical field of civil engineering and geological engineering, and the method comprises the steps: integrating a spiral winding type optical fiber sensor, a double-cavity humidity compensation vibrating wire sensor and a microseismic array, and capturing surrounding rock strain, vibration and geological activities; the vibrating wire sensor suppresses humidity interference through a silicone oil damping medium and self-adaptive excitation frequency, and the optical fiber sensor is fixed through a pre-embedded silica gel sleeve to adapt to surrounding rock deformation; edge computing nodes are deployed on the inner wall of the tunnel, an FPGA chip and a lightweight GRU model are integrated, optical fiber strain, a micro-seismic energy spectrum and laser point cloud displacement field data are fused in real time, the strain gradient is analyzed, and a crack propagation probability cloud picture is generated; redundant optical fiber link switching is combined with a six-degree-of-freedom mechanical arm to realize breakpoint self-repairing, and a self-cleaning air curtain is integrated to inhibit dust adhesion; and constructing a geological parameter library based on a BIM-GIS fusion platform, driving a finite element-discrete element coupling model to dynamically update boundary conditions, and predicting surrounding rock deformation and collapse risks.

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 network information security adaptive threat intelligence analysis and response method and system. The method comprises the following steps: acquiring an encrypted traffic load byte stream, and segmenting the encrypted traffic load byte stream into a time sequence traffic matrix according to a time window; and analyzing the communication metadata, matching the features of the threat intelligence library, and generating a dynamic threat fingerprint based on protocol compliance and behavior abnormality. And performing multi-stage wavelet packet decomposition on the matrix, extracting the energy spectrum density, the information entropy and the time-frequency variable coefficient of the high-frequency component, and performing weighted fusion to generate a frequency-domain composite abnormal index representing heartbeat signal characteristics. And identifying hidden heartbeat periodicity and protocol violation modes through a multi-mode fusion mechanism in combination with the frequency domain index and the threat fingerprint, and outputting a threat score. And if the attack exceeds the threshold value, intercepting the flow, verifying a new attack feature, and then reversely updating the threat intelligence library to form a defense self-evolution closed loop. According to the method, automatic interception and closed-loop updating of the threat intelligence library are realized, and the defense adaptability is remarkably improved.

The invention relates to the technical field of intelligent sensor application, and discloses a water meter flow real-time correction method and system based on an intelligent sensor, and the system comprises a flow signal collection module, a signal feature decomposition module, a frequency domain feature fusion module, an error compensation decision module and a correction instruction execution module. The method comprises the following steps: acquiring multi-dimensional vibration waveform data of a water meter pipeline through a multi-band sensor, performing time-frequency domain decomposition and energy spectrum feature extraction to obtain a local frequency domain feature tensor set, and performing dynamic noise suppression and band correlation optimization to obtain a local optimization feature tensor set; and performing global feature reconstruction based on frequency domain energy distribution to generate an error compensation decision feature map, and finally generating a real-time correction control signal. Through multi-dimensional signal acquisition and intelligent feature processing, the precision and dynamic adaptability of flow metering in a complex pipe network environment are improved, and the method is suitable for high-precision flow correction of an intelligent water meter and has the advantages of being high in anti-interference capacity, accurate in compensation and the like.

The invention belongs to the technical field of nondestructive testing, and particularly relates to a detection method for quantitatively evaluating fatigue damage in a pipeline through a nonlinear ultrasonic technology. 316 stainless steel and SA335GRP-11 ferrite steel samples are adopted, a welding seam area is formed through V-shaped groove welding, fatigue loading is conducted, and actual pipeline fatigue damage is simulated; for the samples with different fatigue cycles, nonlinear response signals of the samples are obtained; fourier transform is carried out on the obtained signal to obtain an energy spectrum of the signal, and energy changes of the signal on a fundamental frequency band and a second harmonic frequency band are analyzed; and calculating a nonlinear coefficient, and analyzing a change rule of the nonlinear coefficient along with fatigue cycles, so that the evolution process of fatigue damage in the pipeline is evaluated. According to the method, energy is used for replacing the amplitude value, on the basis of the formula principle, integration is used for replacing the amplitude value to improve the intensity of the characterization nonlinear ultrasonic signal, the early stage of damage and the later stage of crack initiation are detected, and reliable guarantee is provided for safe operation of an industrial pipeline system.

The invention relates to the technical field of data processing, in particular to a fault detection method for a wire for a new energy automobile, and the method comprises the steps: obtaining a data sequence of detection data; determining the fluctuation time difference weight of the acquisition period based on the change of the autocorrelation strength between the data sequences when the data sequences fluctuate in the acquisition period; setting an amplitude threshold based on the amplitudes of the two kinds of detection data on all frequency components of the frequency domain, determining a fitting straight line based on the screened peak points by using a data fitting mode so as to determine a difference trend factor of the detection data, and determining a fault anomaly weight by combining a fluctuation time difference weight and an energy spectrum density difference so as to realize fault detection. Determining a connection influence coefficient of the detection data by combining the variable quantity of the elements in the data sequence; and obtaining an abnormal data point based on the connection influence coefficient by using a data clustering algorithm, and determining a fault detection result. According to the method, the clustering algorithm is optimized, so that the distinction degree of different fluctuation characteristic data is improved, and a more accurate fault detection result is obtained.

The invention discloses a power battery management method and system based on data driving, particularly relates to the technical field of battery degradation monitoring, and is used for solving the problem that hidden degradation of a battery in a microcirculation state is difficult to monitor in real time in the prior art. According to the invention, microcirculation energy spectrum is analyzed through a transient spectrum clustering algorithm, and risks of spectrum concentration abnormity and structure degradation are identified; the internal resistance deviation trend of the battery is analyzed, and the influence of internal resistance fluctuation on active material granulation is evaluated; judging whether microcirculation abnormity risks exist or not based on spectrum abnormity and internal resistance fluctuation conditions; when an abnormal risk exists, analyzing a stress accumulation condition through tensor decomposition and singular value decomposition, and evaluating an attenuation gradient of an electrode polarization signal in combination with variational mode decomposition and a recurrent neural network so as to determine an overall degradation trend of the battery; management of the power battery can be dynamically optimized, the service life is prolonged, and safety and reliability are improved.

The invention provides a soil nutrient detection method based on a gamma-ray energy spectrum and an XGboost algorithm, and the method comprises the steps: constructing a training sample for soil nutrient detection, the training sample comprises radioactive element data, and the label of the training sample is soil nutrient data with the same soil point location as the radioactive element data; the training sample is input into an XGboost model, the XGboost model extracts features of the training sample and predicts soil nutrients of the training sample, the soil nutrients serve as nutrient predicted values, a loss function is constructed according to the nutrient predicted values and labels of the training sample, the XGboost model is trained, and a soil nutrient detection model is obtained; acquiring soil radioactive elements of to-be-detected soil through a gamma-ray energy spectrometer, and inputting the soil radioactive elements into the soil nutrient detection model to obtain a nutrient detection result of the to-be-detected soil.

The invention discloses a motor rotating speed measuring method, system and equipment based on electromagnetic induction, and belongs to the field of motors. According to the embodiment of the invention, firstly, a magnetostriction resonance signal and an electromagnetic induction signal of a motor rotating shaft are obtained; performing frequency domain conversion on the magnetostrictive resonance signal to obtain magnetostrictive resonance frequency domain characteristics; performing frequency domain conversion on the electromagnetic induction signal to obtain electromagnetic induction frequency domain characteristics; frequency domain energy spectrum fusion reconstruction is carried out on the magnetostriction frequency domain characteristics and the electromagnetic induction frequency domain characteristics based on the frequency band matching relation between the magnetostriction frequency domain characteristics and the electromagnetic induction frequency domain characteristics, and a dual-frequency coupling energy spectrum is generated; and finally, determining the rotating speed of the motor rotating shaft according to the frequency position and amplitude distribution of the energy peak in the dual-frequency coupling energy spectrum, thereby improving the measurement precision of the rotating speed of the motor in a complex environment.

The invention relates to a deep learning technology, and provides a dual-energy cone beam CT metal artifact suppression reconstruction method, device and equipment and a storage medium, and the method comprises the steps: carrying out the attenuation processing of high-energy original data and low-energy original data which are obtained in advance, and obtaining a dual-energy initial projection image of a scanned object; sequentially performing three-dimensional reconstruction, threshold segmentation and forward projection on the dual-energy initial projection image to obtain a metal mask projection image; carrying out metal artifact suppression processing on the high-energy initial reconstruction image and then carrying out forward projection to obtain a metal prior projection image; performing information correction on the dual-energy initial projection image according to the metal mask projection image and the metal prior projection image to obtain a dual-energy correction projection image; and generating a dual-energy correction reconstructed image of the dual-energy correction projection image, and performing energy spectrum synthesis on the dual-energy correction reconstructed image to obtain a virtual single-energy image with metal artifacts eliminated. According to the invention, the quality of reconstructed images can be improved.

The invention relates to the technical field of power system fault positioning, in particular to a primary and secondary fusion ring main unit with an intelligent fault positioning function, and the primary and secondary fusion ring main unit comprises a ring main unit traveling wave signal collection module which collects a traveling wave signal when a power transmission line has a fault; the ring main unit traveling wave signal analysis module is used for equally dividing the traveling wave signal; according to the fluctuation degree of the signal amplitude in the signal segment, combining the energy spectrum of the signal segment to obtain a mutation coefficient; obtaining an energy low-frequency concentration ratio according to the frequency domain characteristics and the frequency domain shape characteristics of the energy spectrum; obtaining the size of a filtering window of each sampling point in the signal segment; and the ring main unit traveling wave fault positioning module filters the traveling wave signal, carries out fault positioning, and sends an instruction to the primary and secondary fusion switch. The invention aims to effectively remove the impulse noise in the traveling wave signal, reserve the detail information of the fault traveling wave signal and enhance the accuracy of fault positioning.

The invention discloses a rock crack evolution numerical simulation method based on data analysis, and relates to the technical field of rock mechanics engineering numerical simulation, and the method comprises the following steps: S1, obtaining microstructure data of a target rock, including a CT scanning image, an SEM image and mineral energy spectrum data; and S2, constructing a spatial distribution dynamic mechanical parameter field based on the microstructure data. According to the rock crack evolution numerical simulation method based on data analysis, a dynamic mechanical parameter field driven by a microstructure is constructed, so that the inherent defect that rock is simplified into a homogeneous material in a traditional method is overcome; microscopic characteristics such as mineral hardness gradient, grain boundary strength distribution and pore morphology are effectively quantified into an elastic modulus field, a tensile strength field and a fracture energy field which are spatially distributed, so that the numerical prediction of crack initiation and expansion truly reflects the inherent heterogeneity of the rock for the first time.

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 discloses an adaptive modulation mode method for a terminal radio station, which comprises the following steps: acquiring original signal data of a receiving antenna, extracting feature points through time-frequency transformation and energy spectrum analysis, and carrying out noise reduction processing to obtain preprocessed signal data; extracting frequency, amplitude and phase features from the preprocessed signal data to generate feature matrix data; fusing the feature matrix data with historical channel feature data to obtain channel feature data; interference feature analysis is carried out based on the channel feature data, and interference feature data and interference confidence data are obtained through probability statistical modeling; performing multi-objective optimization in combination with the modulation strategy data to generate modulation parameter data and reliability index data; and performing resource optimization allocation according to the network state data and the service priority data, and outputting resource allocation data and time slot allocation data. The adaptive capacity and the resource utilization efficiency of the communication system in a complex electromagnetic environment are improved.

The invention discloses an ocean in-situ radioactivity monitoring method, which is characterized in that feature extraction is carried out based on a maximum likelihood expected value maximization (MLEM) algorithm, and spectrum unfolding operation is carried out by adopting a multi-layer perceptron. The method comprises the following steps: firstly, measuring FWHM related parameters of a CeBr3 detector by adopting a standard source, simulating the response of the detector to gamma rays in a marine environment based on a Monte Carlo method, and establishing a full-spectrum response matrix H in a range of 0-2048keV; based on the response matrix H, reconstructing a gamma energy spectrum y obtained by the detector by using an MLEM iterative algorithm; and the reconstructed spectrum is used as the input of a multi-layer perceptron (MLP), and qualitative and quantitative analysis of radionuclides in the energy spectrum is realized. According to the method, the discrete characteristic peak signal is convolved into the single path address corresponding to the characteristic energy, so that the accuracy of the MLP model in the spectrum unfolding process is effectively improved.

Systems and methods for determining a target multi-source model of a radiation source corresponding to an energy spectrum is provided. The systems may obtain an initial multi-source model of the radiation source, which includes an initial phase space file that includes information of a plurality of simulated particles of a plurality of energy levels. The systems may estimate, based on the initial phase space file, a plurality of component PDD curves corresponding to the plurality of energy levels. The systems may obtain a measured PDD curve corresponding to radiation of the energy spectrum. For each energy level, the systems may determine, based on the plurality of component PDD curves and the measured PDD curve, a weight for the each energy level. The systems may further determine the target multi-source model of the radiation source based at least in part on the initial multi-source model and the weights.

The invention provides a vertical axis wind turbine power prediction method based on multi-dimensional environment data, and relates to the technical field of vertical axis wind turbine power based on the multi-dimensional environment data. Multi-dimensional data are collected in real time based on a sensor network, and the multi-dimensional data comprise environment parameters such as wind speed, turbulence intensity and temperature and humidity, and operation states such as rotor rotating speed and pitch angle; secondly, performing empirical mode decomposition on a vibration acceleration signal, constructing a physical information neural network, taking environmental data and an aerodynamic load frequency domain energy spectrum as input, outputting a real-time wind energy utilization coefficient and an aerodynamic coefficient, and finally, determining the aerodynamic load based on an aerodynamic load time domain signal. A load cyclic spectrum is extracted by adopting online rain flow counting of dynamic window length, an accumulated damage value is calculated in combination with a fatigue damage model of humidity correction, and when the residual life is lower than a threshold value, load peak suppression and power generation efficiency balance are realized through cooperative control of magnetic resistance torque linear attenuation and flap fuzzy PID (Proportion Integration Differentiation).

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 embodiment of the invention discloses a method for determining abnormal disturbance of a volcano ionized layer. The method comprises the following steps: acquiring ground-based GNSS (Global Navigation Satellite System) original observation data; preprocessing the ground-based GNSS original observation data to obtain an ionized layer TEC data set; according to the variance of the wavelet energy spectrum measurement time sequence of the ionized layer TEC data set in the time domain and the frequency domain, ionized layer TEC data highly related to the space weather environment is removed, and an optimal ionized layer TEC data set not affected by the space weather environment is obtained; an LSTM algorithm is improved through a Prophet algorithm, and an upper boundary line and a lower boundary line of volcano ionized layer TEC abnormal disturbance are determined based on the optimal ionized layer TEC data set; and determining whether the volcano ionosphere has abnormal disturbance or not according to a comparison relationship between the TEC observation data of the ionosphere and the upper boundary line and the lower boundary line of the abnormal disturbance of the TEC of the volcano ionosphere. According to the method, data processing is carried out, space weather interference is removed, an LSTM algorithm is improved by using a Prophet algorithm to realize abnormal disturbance identification, and volcanic eruption can be efficiently monitored.

The invention discloses a blast furnace cooling wall damage probability prediction method, which relates to the field of industrial equipment maintenance, and comprises the following steps: arranging a temperature sensor array, a vibration sensor and an acoustic emission sensor, and collecting temperature gradient data, mechanical vibration frequency and high-frequency stress wave signals in real time; calculating the slag crust thickness of the hot surface of the cooling wall based on an unsteady state heat transfer equation, introducing an in-furnace gas flow parameter correction model, and generating a dynamic slag crust thickness distribution cloud picture; establishing temperature and vibration reference threshold values under different working conditions according to historical operation data of the cooling wall, and detecting temperature over-limit accumulated duration and a vibration energy spectrum abnormal frequency band in real time; taking the temperature gradient range, the slag skin thickness variation coefficient and the vibration dominant frequency offset as input characteristics, training a multi-dimensional coupled damage probability prediction model through a random forest algorithm, and outputting a recent dynamic damage probability value of each section of cooling wall; and dividing risk grades according to the damage probability value, dynamically adjusting a threshold interval in combination with the real-time smelting strength of the blast furnace, and generating a differentiated maintenance suggestion set.

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 relates to the technical field of production line monitoring control, in particular to a monitoring control method and system for an automatic production line. According to the technical scheme, the pulse laser is transmitted to the monitoring optical cable of the battery production line, and the feedback scattering signal is received; performing time-frequency analysis, space-time array analysis and frequency-domain space array analysis on the scattered signals to obtain multi-dimensional vibration information of each operation device of the battery production line; the multi-dimensional vibration information comprises the vibration main frequency of the operation equipment and the vibration source position and the energy spectrum density of the bearing area to which the operation equipment belongs, so that the actual operation state of each operation equipment during execution of the current production task can be determined based on the multi-dimensional vibration information of each operation equipment and the progress information of the corresponding production task; and performing interlocking operation control on the production takt of the battery production line according to the actual operation state of each operation device. According to the technical scheme, the stability of production control of a battery production line and the consistency of battery product quality are improved.

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 provides a model training method, a substance element density distribution determination method and device, electronic equipment and a storage medium, and relates to the field of physics and computers, in particular to application of physics and computer technologies in the field of radiation medical treatment. The model training method comprises the following steps: acquiring respective characteristic parameters of M scanning energy spectrums of computed tomography (CT) equipment; based on the M characteristic parameters and the element density distribution of the N sample materials, M sample CT images of the N sample materials under the M scanning energy spectrums are determined, and M * N sample CT images are obtained; constructing a sample data set based on respective M sample CT images and respective element density distribution of the N sample materials; the initial model is trained through the sample data set, a target model corresponding to the CT equipment is obtained, and the target model is used for determining element density distribution based on a CT image obtained through scanning of the CT equipment.

The invention provides a near-earth orbit neutron and gamma ray detector group, detection equipment and a detection method, and the detection equipment is a neutron / gamma spectrometer which is small in size, small in mass, low in power consumption and high in speed and is designed based on a multilayer single / double sensitive area Si detector and a cadmium zinc telluride (CZT) detector array. The neutron / gamma spectrometer can serve as an independent load to be carried on a small satellite, and measurement of thermal neutrons, fast neutrons below 20 MeV and gamma rays is achieved. Different layers of the detector group in the detection equipment are matched to form a plurality of anticoincidence groups, so that the accuracy of energy spectrum inversion is improved. According to the detection method, a stacking and discarding algorithm is provided, so that interference caused by signal overlapping is avoided, and the system resolution of the whole neutron / gamma spectrometer is improved.

The invention belongs to the technical field of nuclear radiation measurement, and relates to a liquid flash TDCR multi-nuclide beta spectrum analysis method and system based on artificial intelligence. The method comprises the following steps: constructing a numerical model of a liquid flash detector spectrometer by using a Monte Carlo technology to simulate the energy spectrum response of a single nuclide under different quenching conditions; constructing a training database by adopting a parameterized data synthesis algorithm; constructing a multi-task mixed spectrum analysis neural network model; carrying out model training by utilizing the constructed database; and inputting an actual measurement spectrogram, and outputting the multi-nuclide absolute activity, the detector efficiency and the decomposition energy spectrum contribution curve in real time by using the trained neural network model. According to the method, differential distribution characteristics and quenching response curve characteristics of the beta continuous energy spectrum are creatively fused, a neural network architecture with physical mechanism constraints is constructed, and liquid flash TDCR multi-nuclide energy spectrum characteristic decoupling and accurate activity solving are achieved under the unknown quenching condition.

The invention relates to the technical field of material detection, and discloses a material multi-modal X-ray foreign matter detection method, which comprises the following steps of: acquiring a transmission gray-scale time sequence array and a scattered photon energy spectrum distribution matrix output by a multi-modal X-ray acquisition system, generating a composite characteristic body through cross-modal characteristic interactive coding, constructing a material-contour associated nodal topology network, and obtaining a material-contour associated nodal topology network; simulating a foreign matter feature conduction path and outputting a correlation confidence value set, performing space-time dimension mapping transformation to generate a foreign matter-detection coupling prediction curved surface, calculating a sensitivity adjustment factor and generating a standardized coupling coefficient thermodynamic cloud chart, extracting a detection quantitative index set and dynamically matching the detection quantitative index set with a preset reference range; and when the indexes exceed the boundaries, generating a regulation and control scheme. According to the method, the accuracy, reliability and real-time performance of foreign matter detection are improved, and the detection requirements of complex scenes can be met.