99 results about "Mixed spectrum" patented technology

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 water quality detection, and discloses a multi-source spectrum composite pollutant identification method and system based on graph deep learning, and the method comprises the following steps: S1, obtaining a plurality of single-pollution samples, a plurality of multi-pollution samples, and a to-be-predicted unknown sample; s2, acquiring spectral topological characteristics of a plurality of single-pollution samples and a plurality of multi-pollution samples; s3, estimating a single pollution source prototype vector according to the spectral topological characteristics of the single pollution sample; and estimating a multi-pollution source prototype vector according to the spectral topological characteristics of the multi-pollution sample. The method has the beneficial effects that image deep learning is introduced into the field of spectrum pollutant identification for the first time, and compared with a traditional identification model, the method can better process a complex relation between nodes. In order to estimate a prototype vector of an unknown mixed spectrum by using a prototype vector of a known single pollution spectrum in a mixed pollution label missing scene, prototype contrast learning is introduced, and mixed pollution spectrum characterization is effectively extracted.

According to the invention, the multi-mode time-frequency domain conversion and the deep learning technology are combined, and a multi-component mixed Raman spectrum unmixing method is developed, so that clinical in-vivo and in-situ detection and disease diagnosis of novel Raman probes, instruments and the like are facilitated. The method comprises the following steps: (1) converting a mixed Raman spectrum from a time domain to a frequency domain by using fast Fourier transform (FFT), discrete cosine transform (DCT) and discrete sine transform (DST), and extracting frequency domain features; (2) extracting multi-scale local time-frequency domain characteristics of the mixed spectrum by using short-time Fourier transform (STFT) and discrete wavelet transform (DWT); (3) carrying out spectral unmixing calculation in each mode in combination with a one-dimensional attention mechanism U-shaped neural network model; and (4) fusing various modal unmixing results by using a meta-learning method, and analyzing the weight of each modal to obtain an accurate unmixing spectrum. Compared with a traditional Raman spectrum analysis method, the Raman spectrum multi-component signal unmixing method based on multi-modal time-frequency domain transformation and deep learning can accurately separate independent Raman signals of different tissue structures and biochemical components in a complex environment in a living body, so that the unmixing accuracy of the Raman spectrum multi-component signal is improved. Therefore, convenience is provided for subsequent disease mechanism analysis and diagnosis. The method provides an innovative and potential solution for in-vivo and in-situ detection analysis and disease diagnosis of medical clinical Raman spectroscopy.

The invention relates to the technical field of livestock breeding environment monitoring, regulation and control, and discloses a livestock breeding environment gas dynamic monitoring system. The system comprises a data acquisition module for acquiring data by using a multi-source gas sensor; the gas component analysis module is used for analyzing the data based on a mixed spectrum decomposition algorithm to generate a gas component characteristic matrix; the environment regulation and control decision module inputs the matrix into a multi-mode collaborative decision model to generate a regulation and control instruction; the optimization model construction module is used for constructing a multi-constraint dynamic optimization model to optimize ventilation equipment parameters; and the hierarchical control execution module is used for executing the regulation and control instruction through a hierarchical architecture. The system can comprehensively monitor breeding environment gas, accurately regulate and control ventilation equipment, achieve gas concentration equalization and energy consumption minimization, improve the breeding environment quality, reduce the breeding cost and push intelligent development of the livestock breeding industry.

The invention relates to the technical field of dyeing formula data processing, in particular to a dye formula optimization method and system for a disperse dark color one-bath dyeing process, and the method comprises the steps: constructing a process formula data set, calculating dye mixed spectrum data through a Beer-Lambert law, extracting features through a CNN and Transform prediction method, and fusing the features, thereby calculating chromatic aberration. And if the color difference exceeds the threshold value, optimizing the dye proportioning concentration by using a genetic algorithm. In the dyeing process, a dye bath fuzzy set is divided based on a fuzzy control model, a fuzzy rule is set, and the real-time process parameter adjustment amount is calculated and optimized in combination with the membership degree. After dyeing is completed, process image data are collected, and process chromatic aberration, maximum local chromatic aberration and color fastness are evaluated by means of a computer vision model. If the evaluation result does not reach the standard, the dye proportioning concentration and the real-time process parameter adjustment amount are adjusted, the chromatic aberration in the dyeing process of the dark-color sportswear is reduced, and the dye formula accuracy is improved.

The invention belongs to the technical field of optical fiber sensing, and particularly relates to a probe type humidity sensing multi-point measurement method and device based on optical fiber microspheres. According to the core scheme, a single-mode optical fiber and a multi-mode optical fiber are welded to form a Fabry-Perot (FP) microsphere sensing head, and a high-sensitivity FP interference cavity is constructed by controlling the curvature of microspheres on the end face of the multi-mode optical fiber and the thickness of an agarose gel film; temperature cross interference is eliminated by welding fiber bragg gratings (FBG) with different wavelengths; a 1 * N beam splitter is used for realizing multi-point sensing, and a mixed spectrum is separated through a Fourier transform (FFT) frequency domain filtering technology, so that independent demodulation of N sensing heads is realized. The probe-type humidity sensor has the advantages of compact probe-type structure, high sensitivity, accurate temperature compensation and multi-point synchronous measurement, and is suitable for accurate humidity detection in the fields of environmental monitoring, industrial control and the like.

The invention relates to a Mars mineral spectrum identification method and device based on deep learning. According to the Mars mineral spectrum recognition method based on deep learning, through four-dimensional constraints of standard deviation, spectral length, absorption depth and overall reflectivity, a background spectrum in an image is adaptively recognized, a ratio spectrum is calculated by taking a dynamic background spectrum as a reference, and interference of non-target factors such as atmospheric scattering and instrument noise can be effectively eliminated. The end member spectrum is directly extracted from the hyperspectral image, and the second ratio spectrum is combined with the preset standard spectrum library to construct the mixed spectrum library containing the Mars specific minerals, so that the data insufficiency of the preset standard spectrum library is made up, and the problem of poor regional adaptability caused by directly adopting the preset standard spectrum library is also avoided; that is, direct use of the earth spectrum library may not accurately reflect the Mars surface features, so that the adaptability of the model to the Mars surface is improved, and it is ensured that the model has higher recognition accuracy in a specific environment of Mars.

The invention discloses a full-color LED screen optical adjustment and intelligent energy-saving detection method, and relates to the field of LED screen adjustment and energy saving. Ambient light data is collected through a bionic distributed sensor network, and features such as illumination intensity and color temperature are extracted through PCA dimension reduction; constructing a human eye visual perception model based on the CIE chromaticity diagram, and introducing a visual adaptation factor to dynamically adjust color gamut mapping; dividing a screen dimming area, and calculating an optimal brightness value by adopting a local contrast enhancement algorithm; the RGB ratio is optimized through a genetic algorithm, and the color difference between the mixed spectrum and the target color gamut is minimized; power consumption is monitored in real time, an energy consumption prediction model is established, and an optimization strategy is triggered when a predicted value exceeds a threshold value. The color gamut coverage rate is high, the local contrast ratio is excellent, and the comprehensive energy-saving rate is greatly improved; blue light harm is reduced, screen flicker is eliminated, the visual comfort is improved, the functions of peep prevention and block chain energy consumption evidence storage are achieved, and the display quality, the energy efficiency performance and the use safety of the LED screen are comprehensively improved.

The invention provides an anti-interference detection method and system for a methane gas sensor in a complex environment, a storage medium and a program product, and relates to the technical field of gas detection. A methane gas sensor is used for acquiring an original value of methane concentration, and a multi-wavelength spectrum analyzer is used for acquiring a mixed spectrum containing methane and interference gas information. An interference coefficient library is utilized, the interference coefficient library comprises the interference coefficient of each interference gas to the methane sensor and a standard multi-wavelength spectrum, and the interference gas with the maximum contribution can be matched and recognized according to spectral characteristics. After the concentration is determined, the interference value is obtained by multiplying the interference coefficient, the interference value is accumulated, the corresponding scaled standard multi-wavelength spectrum is subtracted from the residual spectrum to update the residual spectrum, and the interference gas influence is stripped circularly. The method does not need to depend on easily-lost filtering or adsorbing materials, the problems of cost and workload increase caused by regular replacement due to material performance reduction are avoided, meanwhile, complex environment interference gas can be effectively dealt with, and high-precision and reliable methane concentration detection is achieved.

The invention relates to the technical field of optical detection, and particularly discloses a mobile phone glass cover plate flatness detection method and system, and the method comprises the steps: projecting a multiband composite light beam to to-be-detected glass through a confocal optical system, and synchronously receiving a mixed spectrum signal comprising multi-layer interface reflection; generating a key characteristic signal through compressed sampling and spectrum channel optimization; analyzing and separating reflection components of each layer in real time through matching with a pre-established template library; and reconstructing a surface three-dimensional shape based on a surface layer reflection signal, further extracting macroscopic warping degree and microdefect features, constructing a normalized and weighted comprehensive feature vector, and inputting the comprehensive feature vector into a deep neural network model to realize flatness intelligent evaluation.

Disclosed in the present invention is an MCR-ALS-based method for removing a matrix spectrum of a mixture system, comprising: S1) acquiring an original mixed spectrum containing a target substance and a matrix and an original matrix spectrum only comprising a matrix; S2) performing baseline correction and normalization processing to obtain a preprocessed mixed spectrum D and a preprocessed matrix spectrum B; S3) using an MCR-ALS algorithm to iteratively calculate a pure substance-based spectral matrix S and a weight matrix C; S4) reducing a target substance spectrum and a matrix spectrum on the basis of the matrix S and the matrix C; S5) matching the decomposed matrix spectrum with a known standard matrix spectrum and performing qualitative identification; and S6) calculating explained variance between the generated spectrum and the original spectrum. According to the present invention, a matrix substance spectrum in a mixture spectrum can be relatively completely removed, and the impact on a characteristic peak of a target substance spectrum is small, so that the impact of the matrix spectrum on the characteristic peak of the target substance spectrum is reduced, thereby facilitating subsequent quantitative and qualitative analysis.

The present invention relates to a method and device for identifying Martian mineral spectra based on deep learning. The Martian mineral spectra identification method based on deep learning uses four-dimensional constraints, namely, standard deviation, spectrum length, absorption depth, and overall reflectivity, to adaptively identify background spectra in images, and calculate ratio spectra based on dynamic background spectra, effectively eliminating interference from non-target factors such as atmospheric scattering and instrument noise. By directly extracting endmember spectra from hyperspectral images and combining the second ratio spectrum with a preset standard spectrum library, a mixed spectrum library containing Martian-specific minerals is constructed, thereby making up for the lack of data in the preset standard spectrum library and avoiding the problem of poor regional adaptability of directly using the preset standard spectrum library, that is, directly using the Earth spectrum library may not accurately reflect the surface characteristics of Mars, thereby improving the adaptability of the model to the Martian surface and ensuring that the model has higher recognition accuracy in the specific environment of Mars.

In a method for training a neural network for detecting an object, geometric dimensions of a test object from an object class are captured, and during a time period, recordings of the test object are generated by a plurality of cameras. From the captured geometric dimensions and the generated recordings, occupancy maps are generated. By a radar device, a radar signal is transmitted, and a radar signal reflected by the test object is received. The transmitted radar signal and the received radar signal are mixed into a complex baseband to form a mixed signal. A complex four-dimensional mixed spectrum of the mixed signal is calculated. From the complex four-dimensional mixed spectrum, a first complex two-dimensional partial spectrum and a second complex two-dimensional partial spectrum are calculated. The occupancy maps and the partial spectra are fusioned to form training data. The training data are fed to the neural network.

The present application proposes a method for human voice separation, which is applied to an execution device, and includes: receiving the voice of a user in a designated space; the designated space is a limited space with multiple members; determining a main speaker using the user's voice based on the registered voiceprints of the multiple members; determining that multiple members other than the main speaker are noise speakers; obtaining a mixed spectrum corresponding to mixed audio in the designated space, where the mixed audio is voice information aliased by the audio of the main speaker and the audio of at least one noise speaker; processing the mixed spectrum and the registered voiceprints of the multiple members through a voiceprint filtering network to obtain the spectrum of the main speaker in the mixed audio; wherein the voiceprint filtering network is used to use the voiceprint of the main speaker in the registered voiceprints as positive excitation and the voiceprints of the multiple noise speakers in the registered voiceprints as negative excitation to separate the spectrum of the main speaker from the mixed spectrum; and obtaining the audio of the main speaker based on the spectrum of the main speaker.

The invention relates to a water environment pollution source reasoning and tracing method, system and equipment and a medium. The method comprises the following steps: constructing a pollution source characteristic database containing spectrum fingerprints and spatio-temporal information; performing time sequence enhancement on the mixed spectral signal of the monitoring point to extract stable features; a hydrological model is coupled to dynamically simulate pollutant transport paths and probabilities, and a potential contribution source set with weights is generated; analyzing the mixed signal by adopting a deep unmixing network embedded with space-time constraint, and separating and quantifying known source contribution and unknown pollution components; and finally, generating a visual pollution contribution degree spatial distribution diagram and a quantitative traceability report through geographic information mapping. The method solves the problems that complex mixed signals are difficult to accurately analyze, multi-source contribution is difficult to dynamically quantify and unknown pollution components are difficult to effectively recognize in the prior art, and intelligent and accurate traceability of municipal water environment non-point source pollution is achieved.

The application provides a fault identification method and system based on multi-element spectral mineral anomaly analysis, relates to the fields of geology, spectroscopy, petrology and engineering geology, and a specific scheme comprises the following steps: in-situ testing of tunnel surrounding rock is carried out by using a visible light-near infrared, mid-infrared and laser Raman spectrometer, and mixed spectral variation characteristics are extracted; based on the mixed spectral variation characteristics, the mineral composition of the tunnel surrounding rock is identified according to the spectral identification standard of the abnormal mineral combination of the fault zone, quantitative inversion of the minerals is carried out, and the mineral content is obtained; according to the mineral anomaly mode of the fault zone, mineral anomaly analysis is carried out on the mineral composition, and the mineral anomaly type is obtained; based on the differential change of the mineral content and the mineral anomaly type, mineralogical anomaly characteristic analysis is carried out, and the fault identification result is output; the application replaces the experience knowledge of traditional geologists with quantitative information of multi-element spectral mineral anomaly, and further improves the accuracy and efficiency of fault identification in tunnels and underground engineering.

Embodiments may provide photobioreactor system such as a vertical growth enhancing mixing spectrum photobioreactor (1) including but not limited to at least one downcomer tower (22) having spiral impressions (211), an upcomer tower (21), an algae-fluid input (10), an algae downward fluid flow (25) in a downcomer tower; a gas input (18) to drive a system flow (250), a gas upward flow (20) in a upcomer tower; a carbon dioxide gas input (27) having a lower mass flow of carbon dioxide than a mass flow in an upcomer tower, a carbon dioxide gas upward flow (26) in a downcomer tower; a counterflow (230) created with the algae downward fluid flow and carbon dioxide gas upward flow; and perhaps even a collection tank (64) for dewatered algae. Each photobioreactor system may be connected to provide a plurality of photobioreactor systems of which can be automatically controlled.

The application discloses a kind of based on near infrared multiband detection abdominal fat distribution analysis device and method, it is related to fat analysis field, the spectrum of the information of subcutaneous adipose tissue (SAT) mainly reflected by close-range probe acquisition, accurate estimation is combined with pre-training model individualized subcutaneous fat profile parameter. Then, these parameters are used to drive an optical forward propagation model, dynamically predict and generate the SAT contribution spectrum of the individual at the remote probe. Finally, subtract this accurate prediction SAT contribution signal from the mixed spectrum collected by the remote probe, thereby effectively stripping the strong background noise. The signal-to-noise ratio of the residual spectrum obtained is significantly improved, and the visceral fat index can be accurately analyzed through a special correction model, solving the core technical problem that VAT cannot be measured due to SAT signal interference.

This invention relates to the field of computational optical imaging and provides a hyperspectral computational imaging method, system, and related equipment for multispectral acquisition. The method includes: acquiring filter parameters; constructing a spectral simulation reconstruction network; setting a minimum reconstruction error and using the filter parameters to set variables for the annealing algorithm, constructing a simulated transmittance curve to train the spectral simulation reconstruction network, calculating the error until the annealing algorithm calculates the optimal solution and obtains the optimal parameters; training the spectral reconstruction network based on the optimal parameters, simultaneously selecting corresponding filters to form spectral sampling units, and sampling the target spectrum to obtain a mixed spectral image; registering the mixed spectral image to obtain a registered spectral image; and inputting the registered spectral image into the spectral reconstruction network to obtain a reconstructed spectral image. This invention improves the spectral acquisition speed, solves the registration problem of snapshot-type spectral acquisition, and further improves the accuracy of hyperspectral computational imaging.

The application relates to the field of LED health lighting, and discloses a method for LED mixed generation of sunlight-like white light and an LED lamp set. The method comprises the following steps: determining the color points of each LED lamp bead around a blackbody radiation line, independently packaging each LED lamp bead, and comprising LED chips with specific peak wavelengths and various fluorescent powders; determining l target sunlight-like white light color points on the blackbody radiation line, and calculating l groups of luminous flux parameters; selecting a group of luminous flux parameters from the l groups of luminous flux parameters according to a preset target, and using the group of luminous flux parameters to control the m LED lamp beads to form mixed light, i.e. sunlight-like white light. According to the combination of different intensities of various light sources, the application changes the single waveband excitation of fluorescent powder for emitting white light by the existing full-spectrum white light LED into multi-waveband LED chip mixed dimming, fills the gap of the spectrum of about 480 nm, has a higher color rendering index, and has a more continuous mixed spectrum, and can realize sunlight-like dimming along the blackbody radiation line.

The invention discloses a hyperspectral blind unmixing method, which comprises the following steps of: constructing a hyperspectral blind unmixing model, inputting hyperspectral image data into the hyperspectral blind unmixing model, encoding spatial features and spectral features of a mixed spectrum of each pixel in the hyperspectral image data through a shallow encoder, and obtaining a hyperspectral blind unmixing model; synchronously transmitting the combined features to an end member extraction branch and an abundance estimation branch through a feature sharing mechanism; the end member extraction branch receives hidden end member features extracted from the joint features through a feature extraction module, and then pure end members are extracted from the joint features by using a Colmogolov-Arnold network; according to the abundance estimation branch, an abundance graph corresponding to a pure end member is extracted from the joint features by using a windowed multi-head self-attention mechanism, and an unmixing result of a mixed spectrum is obtained. The method does not need to depend on prior end member information, the dependence of a traditional method on a known end member library is reduced, and the de-mixing efficiency, stability and accuracy of the system are improved.

The invention discloses a garbage room comprehensive management method and system. The method comprises the following steps: establishing a recyclable molecular spectrum knowledge base; collecting image information of the garbage; calling interface identification; classifying for the first time; packaging the non-resourceful wastes; collecting near infrared spectrum data of the recyclable wastes; screening key characteristic wavelength points; similarity calculation is conducted, recyclable molecules of the recyclable waste are obtained, and mixed spectrum similarity measurement is adopted for similarity calculation; and performing secondary classification. The system comprises an information acquisition module, an intelligent robot classification and packaging module, a data module and a central control module. According to the method, the garbage is automatically identified, and the recyclable wastes in the garbage are divided out for independent treatment, so that the utilization rate of the wastes is increased; and meanwhile, a recyclable molecular spectrum knowledge base is established, the characteristic spectrum of the recyclable waste and the recyclable molecular spectrum are subjected to mixing similarity calculation, the classification of the recyclable waste is judged, and the subsequent recycling efficiency is improved.

The invention discloses an abdominal fat distribution analysis device and method based on near-infrared multi-band detection, relates to the field of fat analysis, and accurately estimates individualized subcutaneous fat profile parameters by combining a pre-training model through a spectrum which is acquired by a near-distance detector and mainly reflects subcutaneous fat (SAT) information. And then, driving an optical forward propagation model by using the parameters, and dynamically predicting and generating a due SAT contribution spectrum of the individual at the long-distance detector. And finally, subtracting the accurately predicted SAT contribution signal from a mixed spectrum collected by a long-distance detector, thereby realizing effective stripping of strong background noise. According to the residual spectrum obtained in this way, the signal-to-noise ratio of visceral fat (VAT) signals is remarkably increased, visceral fat indexes can be accurately analyzed through a special correction model, and the core technical problem that the VAT cannot be measured due to SAT signal interference is solved.

The present invention discloses a method for reconstructing relevant components and extracting important information in the mixed spectrum of seawater, which relates to the technical field of marine detection. The method includes performing sparse representation on the total spectral information; calculating the optimal over-complete matrix; based on the optimal over-complete matrix, calculating the sparse coefficient matrix function and the overall function expression corresponding to different spectral component numbers, and iterating each variable until it converges to the global minimum value to obtain the optimal sparse coefficient matrix; multiplying the optimal over-complete matrix and the optimal sparse coefficient matrix to obtain the estimated values of each spectral component, and re-establishing a two-dimensional image to collect highly correlated data for analysis. The present invention removes the interference of irrelevant spectral information, highlights and excavates the spectral information related to the target substance, and while improving the robustness and accuracy of the detection ability of marine sensors, satisfies the requirement that the sensor can realize intelligent and real-time data analysis on site.

The application discloses a pesticide residue detection method and system based on surface-enhanced Raman spectroscopy, and relates to the technical field of spectral analysis and machine learning, and specifically comprises the following steps: preparing a to-be-detected solution based on a to-be-detected agricultural product sample; collecting a Raman spectrum of the to-be-detected solution to obtain a to-be-detected spectrum; inputting the to-be-detected spectrum into an identification model to synchronously output a pesticide type identification result and a residual level semi-quantitative analysis result; wherein, the construction steps of a training data set are as follows: preparing a test data sample; collecting a Raman spectrum of the test data sample to construct a multi-dimensional spectrum library; inputting the Raman spectrum into a spectrum decomposition model to output a real pesticide spectrum; calculating the residual error of the measured Raman spectrum of the mixed sample and the real pesticide spectrum, and constructing a simulated residual error; superimposing the simulated residual error on the real pesticide spectrum to construct an enhanced mixed spectrum; and combining the multi-dimensional spectrum library to construct the training data set. The application can realize high-precision detection of trace pesticides under a complex background.

The embodiment of the application provides a kind of miniature infrared spectrometer and electronic equipment, for environmental monitoring, biological medical treatment, food safety etc..The spectrometer additionally increases narrow-band light source, for emitting second light beam, by micro stepping motor control plane grating, the first background spectrum and second background spectrum are generated in linear array detector by the second light beam spectrophotometry;In addition, the first light beam is emitted to the surface of the object to be measured by wide spectrum light source and occurs diffuse reflection, by micro stepping motor control plane grating, the first mixed spectrum and second mixed spectrum are generated in linear array detector by the mixed light beam spectrophotometry of first light beam and second light beam.Finally, the spectrum of the object to be measured is determined by the spectrum of narrow-band light source, first mixed spectrum and second mixed spectrum, and first background spectrum and second background spectrum by linear array detector.The technical scheme of the application can further compress the volume of spectrometer, reduce cost, and also can realize wide spectral range detection.

The present invention belongs to the field of nuclear radiation measurement technology and relates to an artificial intelligence-based liquid scintillator (TDCR) multi-nuclide β spectrum analysis method and system. The method comprises: using Monte Carlo technology to construct a numerical model of a liquid scintillator detector spectrometer to simulate the energy spectrum response of a single nuclide under different quenching conditions; using a parameterized data synthesis algorithm to construct a training database; constructing a multi-task mixed spectrum analysis neural network model; using the constructed database to train the model; inputting a measured spectrum, and using the trained neural network model to output the multi-nuclide absolute activity, detector efficiency, and decomposed energy spectrum contribution curve in real time. The present invention innovatively integrates the differential distribution characteristics of the β continuum energy spectrum with the characteristics of the quenching response curve to construct a neural network architecture with physical mechanism constraints, achieving decoupling of the liquid scintillator TDCR multi-nuclide energy spectrum characteristics and accurate activity solution under unknown quenching conditions.

The application discloses a kind of based on regularized non-negative least square method's production method carbon-oxygen ratio calculation method.There is following step: S1, obtains stratum common element and carbon-oxygen ratio logging instrument's inelastic gamma standard spectrum;S2, by carbon-oxygen ratio logging instrument to the stratum of place is measured, and the inelastic gamma mixed energy spectrum obtained by measurement is analyzed;S3, establish by each standard energy interval's inelastic gamma standard spectrum and inelastic gamma mixed spectrum count constitutes the contradictory linear equation group;S4, obtains the production of stratum carbon, oxygen element;S5, using the production of stratum carbon, oxygen element obtained in step S4 does ratio, obtains the production method carbon-oxygen ratio not by energy spectrum count statistics influence.The beneficial effect of the application is: improve the accuracy of production method carbon-oxygen ratio and the sensitivity of carbon-oxygen ratio to oil layer identification.

The invention provides an engine identification method based on aero-engine thermal jet mixed spectrum characteristics, and the method comprises the steps: firstly carrying out the precise measurement of the thermal jets of different models of aero-engines through employing a Fourier infrared spectrometer, and enabling collected data to comprise the thermal jet spectrums independently generated by the engines of different models; spectral information of mutual mixing of thermal jets of various types of engines is obtained; then, the mixed spectrum of the thermal jets of various types of aero-engines is subjected to unmixing treatment operation through a VCA-LS algorithm, independent pure spectrums of the thermal jets of various types of engines are successfully obtained through VCA, the proportion of each pure spectrum in the mixed spectrum is deduced, and a solid foundation is laid for subsequent in-depth analysis; and finally, through a one-dimensional convolution deep learning MHSA-CNN algorithm, feature extraction is performed on different types of aero-engine thermal jets subjected to unmixing processing, so that internal characteristics and differences of the aero-engine thermal jets can be grasped more accurately, and powerful data support and technical support are provided for fault diagnosis of the aero-engine.

The invention discloses a polymerization reaction intelligent monitoring regulation and control method and device based on spectral analysis, and relates to the technical field of intelligent monitoring, and the method comprises the steps: constructing a particle size-track comparison feature library based on a prior polymerization reaction knowledge library, and combining track feature vector prior thresholds of different particle size intervals to obtain a particle size-track comparison feature library; decoupling the mixed spectral signal of the current polymerization reaction, and outputting spectral signals and track feature vectors of different particle size intervals; and comparing the track feature vectors with a prior threshold value, performing abnormal deviation analysis, and triggering a corresponding directional regulation and control strategy according to an analysis result. The technical problems that an existing polymerization reaction monitoring method is poor in real-time performance and low in precision, and abnormal fluctuation in the reaction process cannot be effectively handled are solved, and the technical effects that intelligent monitoring and signal decoupling based on spectral analysis are achieved, real-time and accurate regulation and control of the polymerization reaction process are achieved, and therefore the product quality and the production efficiency are improved are achieved.