382 results about "Ft ir spectra" patented technology

The invention relates to an infrared spectrum baseline drift dynamic compensation method and system based on multi-parameter fusion, and the method comprises the steps: continuously obtaining spectrum data, current environment parameters and light source state data of a target oil full wave band, carrying out the feature extraction, and obtaining a baseline drift sensitive feature set and a coupling feature set; constructing a prediction model taking the baseline drift amount as output, and taking the baseline drift sensitive feature set and the coupling feature set as input; the reverse baseline drift amount based on the baseline offset of each wave band is superposed in the spectral data as compensation; according to the invention, through multi-source data acquisition and analysis, monitoring of spectrum baseline drift influence factors is realized; accurate correction of different spectral characteristics is realized through a dynamic compensation strategy special for a wave band; and through a continuous self-adaptive updating mechanism, stable tracking of the long-term drift trend is realized, and the effect of prolonging the equipment maintenance period is achieved.

A rock mass mechanical parameter prediction method based on an underground powerhouse of a pumped storage power station relates to the technical field of mechanical parameter prediction, and comprises the following steps: identifying an embedding position and distribution characteristics of a sulfur-containing shale interlayer by constructing a three-dimensional geologic structure model and lithologic information of a construction area, and establishing a structural domain partition parameter prediction area system; constructing a nonlinear regression function model taking infrared spectrum characteristic factors and environment control parameters as input and taking elasticity modulus and the like as output by combining infrared spectrum characteristic data and a historical evolution track; the model is embedded into a regional system for dynamic assignment, and a parameter space-time change atlas is constructed; the laser point cloud data and the micro-seismic monitoring data are fused to invert the actual response of the surrounding rock; when the monitoring value deviates from the prediction map, triggering parameter degradation function re-calibration to complete parameter dynamic updating and prediction closed loop; according to the method, the accuracy of rock mass mechanical parameter prediction of the pumped storage power station underground powerhouse can be improved.

The present invention belongs to the technical field of coal gangue recognition and sorting, and in particular, relates to a coal gangue recognition method based on visible-near-infrared spectrum and image multi-modal information fusion. The method includes: S1 collecting spectral information and image information about a sample to be recognized; S2 preprocessing the spectral information and the image information respectively; S3 extracting spectral features from a spectral data set by using a spectral feature extraction neural network model; and extracting image features from an image data set by using an image feature extraction neural network model; S4 inputting the spectral features and the image features obtained from feature extraction into a two-stream fusion network; S5 inputting extracted spectral features, extracted image features and a comprehensive feature into a spectral branch classifier, an image branch classifier and a fusion branch classifier, respectively; S6 calculating importance weights of a spectral branch, an image branch and an image-spectrum fusion branch; and S7 subjecting the importance weights and corresponding confidence to multiply-accumulate operation to obtain a score matrix of coal gangue, and using the score matrix to achieve coal gangue recognition.

The invention relates to the field of gas concentration detection, and particularly discloses a real-time feedback and adaptive learning method for natural gas infrared spectrum measurement, which comprises the following steps: S1, acquiring infrared spectrum information of a natural gas body by using an infrared spectrometer, and constructing a historical sample set; s2, preprocessing the spectral data of the historical sample set; s3, selecting an optimal algorithm and a hyper-parameter by adopting XGBoost and Bayesian optimization; s4, constructing a qualitative model to identify gas types and match data; s5, calculating the similarity between a field sample and a historical sample through a Siamese network, and setting a threshold value to screen local data; s6, improving the KNN to construct a local dynamic quantitative model to predict the concentration; s7, processing low-similarity abnormal data by the global dynamic model, and improving the reliability by combining moving average and abnormal calibration; and S8, introducing reinforcement learning and online gradient descent to adjust parameters in real time to optimize the precision. According to the technical scheme, high-accuracy natural gas detection can be carried out in a complex environment.

The invention relates to an automatic magnesium oxide coating analysis system and method, and belongs to the technical field of coating quality analysis. The method comprises the following steps: acquiring attributes of a magnesium oxide coating, an infrared spectrum and environment temperature and humidity data, and constructing a multi-source feature set; analyzing the distribution dispersion and local enrichment risk of free water based on a dynamic migration model, and analyzing the bonding weakening degree and dissociation risk of bound water based on a chemical stable state model; respectively generating free water treatment process parameters for inhibiting structural damage and combined water treatment process parameters for preventing performance degradation according to the abnormal analysis result and the coating attribute; by performing coupling influence analysis on the two types of process parameters, the balance requirements of the structural integrity and chemical stability of the coating are coordinated, an optimized process scheme is generated, and cooperative regulation is realized. According to the method, accurate identification, independent analysis and cooperative control of the free water and the bound water are realized, and the accuracy, reliability and quality consistency of coating water treatment are improved.

The invention relates to the technical field of data processing, and discloses an automatic control method and system for wastewater desalination treatment based on the Internet of Things. The method comprises the following steps: acquiring a membrane surface image, an interface potential, an infrared spectrum, a transmembrane pressure difference, a feeding flow rate and a flocculation image; calculating a salt gradient attention feature vector according to the salt concentration difference value and the Beckey number; inputting the multi-modal data into a membrane pollution time sequence memory network to obtain a pollution development rate and a flocculant adding proportion; extracting a floc particle size parameter to obtain a flocculation effect score; and constructing a multi-objective function optimization solution to obtain a control instruction. The salt gradient attention mechanism and the membrane pollution time sequence memory network are introduced, and visual image recognition and multi-modal data fusion are combined, so that the problems of inaccurate membrane pollution state evaluation, low intelligent level of a control algorithm and insufficient multi-target collaborative optimization capability in the prior art are solved; the automation level and the treatment efficiency of wastewater desalination treatment are improved.

The invention relates to the technical field of molecular simulation, and provides a method for constructing a heavy component average molecular structure of thickened oil, which comprises the following steps: inputting parameters, generating a core aromatic ring unit, constructing an inner-layer aliphatic ring unit, constructing an outer-layer aliphatic ring unit, generating a side chain unit, and storing and outputting structural information. A plurality of molecular structures meeting experimental data are generated at a time through a traversal algorithm thought, composition characteristics of a heavy oil complex mixture are met, more available molecular structures are provided for molecular simulation, the accuracy and efficiency of molecular modeling are improved, the molecular structures completely meeting the experimental data can be rapidly generated, and the method is suitable for large-scale popularization and application. And only the mass spectrometer, the elemental analyzer, the nuclear magnetic resonance hydrogen spectrum and the infrared spectrum data need to be input, so that the experiment cost is reduced.

The invention mainly relates to the technical field of spectral analysis, and provides a white spirit brewing spectral analysis method and system based on a multi-modal large language model in order to efficiently and accurately obtain white spirit brewing information according to multi-modal spectral data of all links of white spirit brewing. The method is characterized by comprising the following steps of: collecting multi-mode spectrum data including gas chromatography data, infrared spectrum data and Raman spectrum data in each link of white spirit brewing; converting the multi-modal spectral data and the natural language instruction into a feature embedding vector which can be recognized by a large language model to train the large language model which is finely adjusted based on professional knowledge in the field of white spirit, and receiving a question of a user in a natural language instruction form by the trained large language model; wine making analysis tasks such as flavor compound identification, key microorganism detection, fermentation state evaluation and base wine quality grading are carried out, and finally analysis results and wine making process optimization suggestions are output in a natural language form, so that the accuracy and efficiency of spectrum data interpretation in the white wine making process are improved.

The embodiment of the invention provides a fire source trend prediction method and device, equipment, a medium and a product. The method comprises the following steps: acquiring multi-source data such as a visible light image, an infrared image and environmental data of a target area, respectively carrying out fire source feature extraction on the visible light image and the infrared image to obtain a visible spectrum feature and an infrared spectrum feature, then fusing the visible spectrum feature and the infrared spectrum feature to obtain a fire source fusion feature, and finally obtaining a fire source fusion feature; space-time coding fusion processing is carried out on the continuous geographic coordinates corresponding to the target area, the fire source fusion features and meteorological features in the environmental data to generate fire source space-time coding features, space-time correlation focusing processing is carried out on the fire source space-time coding features to obtain fire source space-time deepening features, and finally, the fire source space-time deepening features are obtained. And performing multi-scale space-time decoding on the fire source space-time deepening features to obtain a fire source prediction probability graph of the target area. The method is used for achieving the effect of improving the fire source trend prediction precision.

The invention relates to the technical field of composite material performance prediction, and discloses a composite material flame retardant property and thermal stability prediction method based on thermogravimetric analysis and infrared spectrum data fusion. The invention aims to solve the problem that it is difficult to effectively fuse multi-modal data to realize quantitative prediction in the prior art. The method comprises the following steps: firstly, collecting thermogravimetric and infrared spectrum combined data, calculating gas transmission lag time, and carrying out forward correction on infrared spectrum data to realize time sequence alignment; then solid-phase thermogravimetric feature vectors and gas-phase infrared feature vectors are extracted, and a fusion feature matrix is generated; extracting solid-phase and gas-phase characteristics respectively by using a dual-channel LSTM network, and performing weighted fusion based on a self-attention mechanism; and finally, synchronously outputting a flame retardant index predicted value and a flame retardant grade classification result through numerical mapping and classification judgment. According to the method, multi-dimensional feature mining of the whole pyrolysis process of the material is realized, and a rapid and accurate digital evaluation means is provided for screening of the flame-retardant material.

The invention relates to the technical field of image recognition, in particular to an intelligent solid waste recognition system based on multi-modal fusion, which comprises a data acquisition module, a visual guidance module, a recognition module and a cooperative processing module, and is characterized in that the system quickly analyzes surface information such as visible light images, three-dimensional point clouds and infrared spectrums of materials through the visual guidance module; and coding to generate a dynamic vector for guiding a subsequent refined analysis task. The identification module constructs a two-dimensional pixel map on the internal density map according to the guidance of the dynamic vectors, and utilizes a map neural network to deeply analyze local density textures so as to identify high-value components such as an internal circuit board; and the co-processing module performs template matching of the internal components and external surface anomaly detection according to the instruction so as to assess the security risk. According to the method, the external form and internal structure information of the material are deeply fused through the strategy of coarse first, fine second and dynamic guidance, and intelligent recognition of the complex electronic waste is achieved.

The invention discloses a coal mine gas cloud contour infrared spectrum inversion method. The method comprises the following steps: 1, simulating an underground gas cloud formation environment; 2, acquiring infrared image data containing gas cloud by using an infrared spectrum detection device; step 3, carrying out de-noising processing on the infrared image data; 4, performing temperature drift correction and radiation calibration on the denoised infrared image, and converting a DN value into radiation brightness; 5, based on the multi-frame de-noised image, performing target detection and contour extraction on the gas cloud by using a Methane-YOLO model, performing multi-view geometric matching of feature points, performing adjustment optimization of three-dimensional coordinates in combination with a bundle method, generating a point cloud model, and reconstructing a curved surface; 6, based on the three-layer radiation transmission model and the Lambert-Beer law, inverting the gas concentration; and step 7, based on the CEEMDAN-VMD secondary decomposition and CNN-LSTM hybrid network, carrying out joint training on the space contour and time sequence data, and carrying out three-dimensional space concentration distribution prediction.

The application provides a kind of semiconductor crystal bar's roll grinding machine equipment, it is related to the processing equipment field of semiconductor crystal bar, it includes clamping device, mobile platform, FT-IR infrared spectrometer, roll grinding machine, water spraying device, drying device and X-ray crystal direction detection device;Clamping device includes first clamping part, second clamping part, first drive assembly and first rotating assembly;Roll grinding machine is used to roll grinding workpiece;Water spraying device is used for workpiece water spraying;Drying device is used for workpiece drying;X-ray crystal direction detection device is used for workpiece crystal direction search;FT-IR infrared spectrometer is used for workpiece gap oxygen content measurement.By FT-IR infrared spectrometer, roll grinding machine, water spraying device, drying device and X-ray crystal direction detection device together are set on mobile platform, can be after roll grinding, automatically measure the gap oxygen content of crystal bar, to reduce the handling of crystal bar after roll grinding, reduce handling risk, simplify work flow, improve production efficiency.

The invention discloses a regeneration method of building solid waste high-quality recycled aggregate. The method comprises the following steps: raw material pretreatment: removing impurities by adopting an AI vision-near infrared spectrum collaborative sorting method, pre-crushing by adopting a crushing parameter dynamic closed-loop control method, and pre-screening and removing mud by adopting a gradient spraying-negative pressure mud removal method; performing multi-stage crushing, namely performing intermediate crushing by adopting a lamination energy grading distribution method and performing fine crushing by adopting a dual-mode switching shaping method; a magnetic field-rotating speed linkage optimization method is adopted for magnetic separation, a density grading winnowing method is adopted for removing light impurities, an ultrasonic wave-water flow collaborative cleaning method is adopted for deep cleaning, and a negative pressure-high frequency vibration collaborative powder removal method is adopted for powder removal; performing particle optimization: performing shaping by adopting a real-time morphology feedback-energy regulation and control method, and performing secondary screening by adopting an intelligent grading-backflow regulation and control method; dehydrating and drying; managing and controlling finished products; storing and transporting; the finally produced recycled aggregate satisfies the following conditions: the crushing value is less than or equal to 23%, the mud content is less than or equal to 0.3%, the needle-sheet content is less than or equal to 12%, the roundness coefficient is greater than or equal to 0.75, the water content is 3-5%, and the recycled aggregate is adaptive to C30 and above recycled concrete.

The invention relates to the technical field of intelligent detection, and discloses a T-SVAE feature extraction strategy and a method for improving near infrared spectrum soil rapidly available potassium measurement precision by using the T-SVAE feature extraction strategy, and the method comprises the following steps: step 1, collecting soil surface samples of different plots, obtaining near infrared spectrum data of the soil samples by using a Fourier transform near infrared spectrometer, and calculating the near infrared spectrum data of the soil samples; determining the actual content of quick-acting potassium in the soil sample by adopting a national standard method; the method comprises the following steps: 1, acquiring near infrared spectrum data, 2, preprocessing the acquired near infrared spectrum data, and removing impurity signals caused by instrument fluctuation, environmental interference and sample physical form difference, and 3, constructing a Transform and supervision constraint fused variational self-encoding model (T-SVAE). By constructing a variational self-encoding model fusing Transform and supervision constraint, the problem of feature blindness caused by high-dimensional data difficulty, nonlinear modeling limitation and unsupervised learning in near infrared spectrum data processing of a traditional feature extraction method is effectively solved.

The invention relates to a smoke identification and classification early warning method, which comprises the following steps: carrying an infrared camera, an infrared spectrum sensor and a temperature and humidity sensor on an unmanned aerial vehicle, and carrying out a smoke inspection activity on a set target area; the method comprises the following steps: acquiring an infrared visual image, infrared spectrum data and environmental parameters of a target area by using an unmanned aerial vehicle, and preprocessing the acquired data; the infrared spectrum data comprises the concentration of CO2 and the concentration of CO, and the environmental parameters comprise the temperature, the humidity and the wind speed; multi-modal data fusion is carried out on the preprocessed data to construct a fusion feature vector, and a trained deep learning classification model is used to identify the smoke type according to the fusion feature vector; and determining an early warning level according to the identified smoke type, and dynamically realizing early warning triggering based on the early warning level and a set grading response rule. According to the method, accurate distinguishing and early warning decision making can be carried out on smoke in the urban comprehensive treatment complex environment.

The invention provides a nondestructive identification method for a licorice producing area and a cultivation type based on a near infrared spectrum and deep learning, and the method comprises the steps: collecting the reflection spectrum data of a licorice sample, and carrying out the parallel enhancement processing through a plurality of enhancement methods; constructing a lossless identification model of the producing area and the cultivation type of the liquorice, wherein the lossless identification model performs lossless identification of the producing area and the cultivation type of the liquorice by adopting a multi-branch parallel convolution structure in combination with a self-adaptive weight adjustment mechanism based on feature response; training the licorice origin and cultivation type nondestructive identification model; and acquiring near infrared spectrum data of the to-be-classified liquorice, performing organization and standardization treatment, and performing nondestructive identification on the origin and the cultivation type of the to-be-classified liquorice subjected to standardization treatment by utilizing the trained liquorice origin and cultivation type nondestructive identification model. According to the method, multi-dimensional features can be fully mined, interference of redundant features and invalid information is not likely to happen, and therefore the liquorice classification precision is improved.

The invention provides a target detection method and device based on multi-modal data fusion, and relates to the technical field of image processing, and the method comprises the steps: carrying out the ternary weighting calculation of visible light data and infrared spectrum data, so as to determine an optimal matching relation, and generating a video frame sequence; constructing a Laplacian decomposition structure according to the video frame sequence, executing weight fusion layer by layer to form cross-scale fusion data and generate a dark channel, estimating an atmospheric light value and transmissivity to recover a smog-free image, and executing local contrast limited histogram equalization processing and dynamic brightness adjustment to obtain an enhanced image; applying a dynamic convolution structure, a deformable convolution structure and a spatial pyramid structure to the enhanced image to extract multi-scale key features; and taking the multi-scale key features as attention calculation input, and processing the enhanced image based on a double-layer routing attention structure to output a target detection result. According to the invention, the accuracy of target detection under the smog shielding condition can be improved.

The invention relates to the technical field of mine water disaster prevention and control, in particular to a mine water disaster treatment slurry stone body recognition system and method based on an infrared spectrum. According to the technical scheme, the mine water disaster treatment slurry stone body recognition system and method based on the infrared spectrum comprises a rock debris preprocessing and conveying control module, a spectral data acquisition and control module and an intelligent recognition and output control module; by arranging the rock debris conveying and preprocessing module, continuous receiving and rapid preparation of rock debris are achieved, meanwhile, the near infrared spectrum detection module conducts spectrum scanning on flowing rock debris above a conveying belt and captures molecular fingerprint information, and generated spectrum data are transmitted to the data processing and intelligent recognition module; the recognition result is synchronously fused with parameters such as drilling depth and pumping pressure, a dynamic slurry diffusion map is generated, the recognition process and the drilling process are kept synchronous, the distribution condition of slurry stone bodies at different depths can be visually seen, and therefore the real-time performance of slurry stone body recognition is effectively improved.

The invention provides a method for rapidly detecting the ABTS < + > free radical scavenging rate of a traditional Chinese medicine extract based on Fourier infrared spectroscopy, which comprises the following steps: (1) collecting the Fourier infrared spectroscopy of a dandelion ethanol extract, and measuring the true value of the ABTS < + > free radical scavenging rate corresponding to the Fourier infrared spectroscopy by using spectrophotometry; (2) preprocessing the collected Fourier infrared spectrum to obtain a preprocessed spectrum; (3) carrying out characteristic wavelength variable extraction on the preprocessed spectrum by adopting a VIP algorithm, and combining spectrum segments corresponding to extracted characteristic wavelengths into a characteristic spectrum; (4) performing PCA analysis and step length segmentation processing on the characteristic spectrum to obtain an angle characteristic vector alpha y, and forming an angle characteristic matrix alpha by the angle characteristic vectors of all the traditional Chinese medicine extracts; and (5) constructing a regression model by using the angle feature vector matrix alpha. Compared with a traditional spectrophotometric detection method, the method has the advantage that the accuracy and the stability of a detection result can be better improved.

The invention discloses a toughening forming method of a high-performance PEI plate for a high-heat-resistance structural part, and particularly relates to the technical field of high-performance composite material forming. Pre-embedding a PEI toughening layer between thermosetting prepreg layers, and controlling the temperature, the pressure, the pressure maintaining time and the heating rate in the hot pressing process; carrying out infrared spectrum or solid nuclear magnetic resonance testing on the interface area sample subjected to hot press molding, and calculating the chain segment residual rate Rp; determining the interface penetration depth Di through a transmission electron microscope in combination with energy spectrum analysis; inputting the Rp and the Di into an interface coupling model, and outputting an interface combination quality factor Q by the model in a form of combining weighted linearity and logarithmic functions; the interface bonding grade is judged according to the Q value, and when the Q value is lower than a threshold value, hot-pressing process parameters are backtracked for optimization; according to the method, quantitative evaluation of the interface performance and process closed-loop optimization are realized, and the interface bonding strength and service reliability of the composite material can be effectively improved.

The invention provides an asphalt material softening point prediction modeling method based on an infrared spectrum, and belongs to the technical field of road engineering material performance prediction. The method comprises the following steps: collecting infrared spectrum data of a multi-source asphalt sample and carrying out standardization pretreatment; fusing and extracting functional group quantitative features, wavelet multi-scale features and principal component dimension reduction features; constructing a high-stability feature subset by adopting a progressive feature optimization strategy guided by a physical mechanism; a self-adaptive Stacking fusion model is constructed and trained, the model integrates four base learners including PLSR, SVR, random forest and gradient boosting regression, and Bayesian ridge regression is used as a meta-learner to realize uncertainty quantification of a prediction result. The method can quickly output the predicted value of the softening point without a physical test, solves the technical problems of low efficiency and poor consistency of a traditional method, and is suitable for production quality control, on-site quick detection and formula optimization of asphalt materials.

The invention relates to a near infrared spectrum sorting device and method for corn kernels. According to the technical scheme, a corn kernel picking and sampling module is used for separating and transferring single corn seeds from a kernel group; the corn kernel rotary conveying module is used for bearing and conveying a single corn seed to the near infrared spectrum identification module for identification; the near-infrared spectrum identification module is arranged on a conveying path of the corn kernel rotary conveying module and is used for carrying out near-infrared spectrum acquisition and identification on the single seed conveyed to a detection station of the near-infrared spectrum identification module; and the corn kernel blowing and sorting module is arranged on a conveying path of the corn kernel rotary conveying module, is positioned at the next station of the near infrared spectrum identification module, and is used for sorting the single seeds into different sorting hoppers according to an identification result. The device has the beneficial effects that the structure is compact, the rhythm is stable, the design of the trilobal turntable is ingenious, single-particle sample injection, accurate positioning and conveying, spectrum identification and physical separation can be seamlessly integrated, and the separation efficiency is greatly improved.

The present application belongs to the technical field of forest fire research, and specifically discloses a forest fire simulation monitoring method and system. The method divides multiple forest fire simulation areas, determines the area where the ignition point is located as a target area; uses a UAV to carry a Fourier infrared spectrometer to collect spectral images in the target area; compares the spectral images with the spectral bands generated by the combustion of existing combustion products to determine the types of combustion products in the target area; based on the spectral images in the target area and the corresponding combustion product types, a flame spectral radiation model is constructed; multiple forest fire danger levels and the corresponding flame spectral radiation ranges of each forest fire danger level are set in advance, and the flame spectral radiation parameters output by the flame spectral radiation model are compared with all flame spectral radiation ranges to determine the forest fire danger level of the target area. By using the flame spectral radiation model, the present application realizes accurate detection and identification of forest fires.

The invention discloses a long-wave infrared spectrum abnormal data detection method and a storage medium, according to the method, abnormal judgment is carried out on spectrum data from a data spatial domain and a transformed frequency domain, and spectrum data abnormal detection is carried out through spatial domain and frequency domain joint analysis by utilizing physical characteristics of long-wave infrared light radiation. According to the technical scheme, the method comprises the following steps: performing Fourier transform on infrared spectrum data, and calculating an average energy value of a specific frequency band; meanwhile, peak value characteristic information of the data is extracted, and a peak value detection statistical array is constructed; and setting a multi-level threshold value judgment system which comprises a frequency domain average energy threshold value, an energy layering threshold value and a peak value detection threshold value, and realizing abnormal data identification through double-feature comprehensive judgment. Compared with a traditional z-score posterior detection method, the method has the advantage of being high in real-time performance, abnormity can be recognized in real time in the data collection process, post-processing analysis does not need to be conducted depending on a large amount of historical data, and the detection efficiency and practicability are effectively improved.

The application relates to an automatic magnesium oxide coating analysis system and method, and belongs to the technical field of coating quality analysis. The method comprises the following steps: acquiring the properties, infrared spectrum and environmental temperature and humidity data of the magnesium oxide coating, and constructing a multi-source feature set; analyzing the distribution dispersion and local enrichment risk of free water based on a dynamic migration model, and analyzing the bond weakening degree and dissociation risk of combined water based on a chemical stability model; generating free water treatment process parameters for inhibiting structural damage and combined water treatment process parameters for preventing performance attenuation according to abnormal analysis results and coating properties; and through coupling influence analysis on the two types of process parameters, the balance requirements of coating structural integrity and chemical stability are coordinated, an optimized process scheme is generated, and synergistic regulation and control are realized. The application realizes accurate identification, independent analysis and synergistic control of free water and combined water, and improves the accuracy, reliability and quality consistency of coating moisture treatment.

The application relates to the technical field of data processing, and discloses a wastewater desalination treatment automatic control method and system based on an Internet of Things. The method comprises the following steps: collecting a membrane surface image, an interfacial potential, an infrared spectrum, a transmembrane pressure difference, a feed flow and a flocculation image; calculating a salt gradient attention feature vector according to a salt concentration difference value and a Berkeley number; inputting the membrane pollution time sequence memory network combined with multi-modal data to obtain a pollution development rate and a flocculant dosage ratio; extracting a floc particle size parameter to obtain a flocculation effect score; and constructing a multi-objective function optimization solution to obtain a control instruction. By introducing a salt gradient attention mechanism and a membrane pollution time sequence memory network, combining visual image recognition and multi-modal data fusion, the problems of inaccurate membrane pollution state evaluation, low intelligent level of a control algorithm and insufficient multi-target collaborative optimization capability in the prior art are solved, and the automation level and processing efficiency of wastewater desalination treatment are improved.

The application discloses a formation fluid identification method, comprising the following steps: acquiring first infrared spectrum data and second infrared spectrum data of a formation section to be interpreted; performing curve fitting on the first infrared spectrum data to obtain a fitting curve, determining a first factor according to an extreme value of a first wavelength section of the fitting curve, determining a second factor according to an opening and area integral of the fitting curve, and determining a third factor according to a center of a second wavelength section of the fitting curve; calculating a standard deviation curve of a standard deviation of longitudinal absorption at all wavelengths of a same depth point according to the second infrared spectrum data, and calculating a peak tailing factor of the standard deviation curve; and determining a type of the formation section according to the first factor, the second factor, the third factor and the peak tailing factor. The application further discloses a system, a computer device and a readable storage medium.

A method for producing a MEMS component. The MEMS component includes a layer stack of layers stacked in a Z-direction, wherein layers of the layer stack are successively deposited on a substrate and, lithographically structured in a defined alignment with respect to a reference plane to form layer structures. A lower layer of the layer stack exhibits at least one lower alignment mark in a defined alignment with respect to the reference plane. The lower alignment mark is detected for determining the alignment of the layer stack with respect to the reference plane by detecting electromagnetic radiation in the infrared spectral range, by detecting an infrared contrast. An upper layer is provided with a surface structuring in a defined alignment with respect to the reference plane. The surface structuring exhibits at least one upper alignment mark in a defined alignment with respect to the reference plane.

The present application relates to the field of electrochemical energy storage. Disclosed are a negative electrode material, a negative electrode sheet, and a secondary battery. The negative electrode material comprises an inner core and a coating layer provided on at least part of the surface of the inner core. The coating layer comprises a polymer. In the infrared spectrum of the negative electrode material, there are a first characteristic peak, a second characteristic peak and a third characteristic peak within a wave number range of 1320±10 cm-1 to 1880±10 cm-1, wherein the first characteristic peak is a bending vibration peak of the -CH2 bond; the second characteristic peak is a stretching vibration peak of the C-N bond; and the third characteristic peak is a stretching vibration peak of the C=C bond. The ratio of the peak area of the first characteristic peak to the third characteristic peak is Z, and Z is 0.35 to 0.8. The negative electrode sheet and the secondary battery comprise the negative electrode material, and the secondary battery has a relatively high initial coulombic efficiency.