29 results about "Spectral inversion" patented technology

The invention discloses a crop spectrum inversion model adaptive construction method. The method comprises the following steps: analyzing field actually-measured water nitrogen dry matter data and a crop physiological coupling rule, constructing a water nitrogen dry matter physiological constraint feasible region in a three-dimensional state space, generating a virtual sample, and fusing the virtual sample with the actually-measured water nitrogen dry matter data to construct a physical consistent training sample set; based on the combination of the moisture level and the nitrogen level, constructing a water-nitrogen cooperation state category, independently determining a spectral feature subspace, and training to generate a water-nitrogen cooperation state submodel with independent parameters; and judging the category of the target water-nitrogen coordination state, routing to the corresponding water-nitrogen coordination state sub-model, and resolving to obtain a pixel water-nitrogen instant inversion result. According to the method, the problems of model generalization and physical consistency under small samples are solved through physiological mechanism constraints, the complex influence of water-nitrogen interaction stress on the spectrum is decoupled through a state typing mechanism, and the accuracy and robustness of crop water-nitrogen inversion are improved.

The invention discloses a multi-spectral radiation temperature measurement method and system for improving a quantum particle swarm, relates to the field of radiation temperature measurement and photoelectric signal processing, and solves the problems that when an existing standard PSO algorithm is applied to high-dimensional and nonlinear multi-spectral inversion, local extreme values are likely to be trapped, and the self-adaptive capacity is poor. Setting parameters, generating an initial particle position by using Tent chaotic mapping, calculating the particle fitness of an initialized population, updating initial individual optimum and global optimum, and calculating an average optimal particle position; updating the optimal particle position through a Levy flight mechanism; and repeating the steps, calculating the multispectral radiation temperature measurement target function value of the updated position, updating the initial individual optimum and the global optimum, and if an error threshold value or the maximum number of iterations is met, outputting the global optimum solution. The method is also suitable for the application field of inverting the real temperature and the spectral emissivity of the target from the multi-channel spectral radiation signals and the like.

This invention provides a curvewave domain-based method for removing strong axes based on reflection coefficient attribute volumes, relating to the field of seismic data processing technology. The method includes: performing spectral inversion processing on raw seismic data to generate a reflection coefficient volume; based on the reflection coefficient volume, performing multi-scale, multi-directional layer-by-layer analysis in the curvewave domain, i.e., extracting reflection coefficient curves for each analysis window, determining inflection point locations by calculating the zero point of the second derivative of the curves to identify abrupt changes in the reflection coefficient; simultaneously, constructing local discrete surfaces based on the reflection coefficient volume, identifying local geometric anomaly regions of the reflection coefficient volume by fitting surface patches and calculating Gaussian curvature; and extracting strong reflection coefficient components by combining the determined inflection point locations and the identified local geometric anomaly regions. This invention can achieve precise stripping of strong reflection energy and faithful protection of effective weak signals, improving the reliability of seismic data in identifying and analyzing subsurface lithology and geological structures.

The invention belongs to the technical field of agricultural remote sensing monitoring, and discloses a saline-alkali soil wheat grain total phenol content remote sensing monitoring method and system. Based on multi-source remote sensing data, meteorological and vegetation characteristics are extracted through phenological matching, and a protein content estimation model containing a salt stress nonlinear inhibition item is constructed; and establishing a quantitative relationship between the protein and the total phenol content, and finally, performing inversion by using an optimization algorithm to obtain the high-precision total phenol content, thereby realizing effective monitoring on the quality of the wheat grains in the wide-range saline-alkali soil. A'protein content-total phenol content 'secondary inversion framework is created for the first time, total phenol content monitoring is indirectly achieved through a protein remote sensing inversion technology, and the technical problem that direct spectrum inversion is low in precision is effectively solved; the saline-alkali soil specificity stress response model is creatively established, the non-linear inhibition effect of salinity on the crop physiological process is considered in the inversion process, the monitoring precision and the model adaptability are remarkably improved, and the method has the advantages of being high in monitoring precision, high in practicability, wide in application range and the like.

The application provides a micro-transient temperature field detection system and method. The detection system comprises: a light source module adapted to emit laser light; a sensing module comprising a reference grating and an atomic force probe; a feedback control module connected to the atomic force probe in the sensing module, the feedback control module being adapted to obtain a surface topography scanning result of a sample to be measured after the atomic force probe scans the sample to be measured; and a data processing terminal configured to process temperature sensing information by a spectral inversion algorithm and combine the surface topography scanning result to obtain a temperature field distribution of the surface of the sample to be measured. The micro-transient temperature field detection system and method can improve the accuracy and speed of micro-transient temperature field detection, and the detection result has high spatial and temporal resolution.

This invention provides a method and system for spectral inversion calculation of chirped gratings in lithium niobate thin films. The method includes: sequentially performing time-frequency domain transformation, window truncation, and time-shifting on a preset ideal target response of the grating to obtain a physically realizable target response; using the physically realizable target response as the initial complex reflection response, performing layer-by-layer recursive calculation on the grating using a layer-by-layer peeling algorithm to obtain the complex coupling coefficients corresponding to each layer of the grating, and extracting the coupling coefficient distribution and phase distribution of the grating; obtaining the structural parameters of the chirped Bragg grating based on the coupling coefficient distribution and phase distribution; constructing a physical model of the grating based on the structural parameters, and performing simulation calculations on the physical model to obtain the spectral response. This invention, based on the traditional grating design process, introduces a layer-by-layer peeling algorithm for inversion calculation of the grating structure, inversely obtaining the coupling coefficients and phase distribution of the grating, as well as the corresponding grating structural parameters, and constructing a corresponding physical model, exhibiting strong target-oriented characteristics.

This invention discloses a combustion state inversion method, system, and apparatus based on neural networks and absorption spectra. The inversion method includes the following steps: data acquisition and preprocessing, and spectral inversion. In the data acquisition and preprocessing step, the original absorption spectrum signal of the combustion region to be tested is acquired. Background subtraction is performed on the original absorption spectrum signal to obtain a preprocessed spectral signal, and the apparent absorbance is calculated from the preprocessed spectral signal. In the spectral inversion step, the apparent absorbance is input into a pre-trained spectral inversion neural network model. The spectral inversion neural network model directly outputs the inverted combustion state parameters, which include at least temperature, pressure, and concentration. This invention utilizes an envelope correction network built based on U-Net and a temperature inversion network and a pressure and concentration inversion network built based on Inception-ResNet. These technologies can directly extract effective absorption features from the original absorption spectrum, which contains unknown and complex baselines, avoiding dependence on independent baseline measurements or estimates.

The application discloses a LIBS inversion method based on EMA-CNN and a weighted loss function, and relates to deep learning and spectral analysis.A LIBS spectral inversion model is constructed based on a deep convolutional neural network (CNN), an efficient multi-scale attention mechanism (EMA) is introduced, a weighted mean square error loss function is designed, and multi-component content collaborative prediction is realized.Conventional deep learning methods adopt equal attention allocation for all spectral features, the EMA module of the application can effectively guide the model to focus more reasonable attention, and the extraction and learning efficiency of key features are significantly improved.The conventional loss function is prone to the problem of unbalanced loss components caused by the order of magnitude difference of each component content, and the loss function of the application can weight the corresponding loss components of each component to collaboratively improve the prediction accuracy of all components.The method has the advantages of accuracy, stability and high efficiency, and is suitable for LIBS multi-component synchronous inversion tasks in a large data volume scene.

The present application relates to the technical field of magnetic resonance inversion, and discloses a kind of magnetic resonance time domain spectrum inversion method, device and program product, the method includes: obtaining the target inversion kernel matrix of magnetic resonance experiment and magnetic resonance original sampling data set;According to the preset peak type, construct complete base matrix, and establish the initial inversion function without explicit regularization term;Using physical priori field knowledge, the initial inversion function is simplified to obtain the target inversion function, and the physical priori field knowledge includes one or more of relaxation time interval, diffusion coefficient interval, peak ridge interval and peak edge interval;Using target inversion kernel matrix and magnetic resonance original sampling data set, the target inversion function is solved, and magnetic resonance time domain inversion spectrum is generated, which not only guarantees the physical rationality and interpretability of inversion result, but also greatly reduces the operation scale.

The invention discloses a field scale soil organic matter spectrum inversion modeling-oriented sample transfer learning method and program product, and the method comprises the steps: carrying out the first-order differential processing of spectrum data in a source domain data set and a target domain data set, environment variables with Spearman correlation coefficients and variance expansion factors higher than threshold values in the source domain data set are eliminated, and target domain samples are divided into a training set and a test set; calculating environment similarity and spectrum similarity between the source domain and the training set samples, further obtaining comprehensive similarity between each source domain sample and each training set sample, and obtaining an intersection of the source domain samples with the comprehensive similarity larger than a threshold value and the source domain samples with the organic matter content within a preset range to serve as a source domain migration sample set; and obtaining a union set of the source domain migration sample set and the training set as an amplification training data set of soil organic matter spectrum inversion learning. The test precision obtained by performing field scale organic matter spectrum inversion based on the amplification training data set formed by the method is higher.

The invention provides an image color calibration method and device, electronic equipment and a storage medium. The image color calibration method comprises the following steps: acquiring a multispectral image, and preprocessing the multispectral image; dividing the preprocessed multispectral image into at least one spectral correlation area; performing spectrum inversion on the spectrum correlation area to obtain a reflection spectrum curve; determining a target color temperature value of the spectrum correlation area according to the reflection spectrum curve; and calibrating the color temperature of the spectrum correlation area according to the target color temperature value. According to the image color calibration method, the color temperature of the spectrum correlation area can be calibrated, global or local accurate color rendition of the multispectral image is achieved, and high-accuracy color rendition under the complex illumination condition is achieved.

The invention is suitable for the technical field of agriculture, and provides a fruit quality grading method and system based on a multi-modal model, which improves the grading precision and universality by constructing a multi-modal and multi-scale feature fusion system, directly obtains accurate physicochemical index parameters such as sweetness, pH, vitamin C concentration and the like, avoids spectrum inversion errors, and improves the quality of fruits. And an appearance and internal quality combined judgment basis is formed in combination with a double-view-angle image. Meanwhile, the cross-modal interaction relation is dynamically modeled, the modal weight is adaptively distributed, and the redundancy problem of an existing fusion means is solved. In addition, global attributes and local details are synchronously captured through a pyramid structure, fruits with large size differences are compatible, and the grading performance and compatibility of the system are remarkably improved. A hierarchical model is deployed in built-in edge computing hardware of fast detection equipment, and a full-link localization closed loop of data acquisition, feature extraction, hierarchical discrimination and result display is completed. Network dependence is eliminated, off-line real-time detection is supported, and the operation and maintenance cost is greatly reduced.

The application belongs to the technical field of agricultural remote sensing monitoring, and discloses a kind of saline-alkali soil wheat grain total phenol content remote sensing monitoring method and system, based on multi-source remote sensing data, extract weather and vegetation characteristics by phenology matching, construct protein content estimation model containing salt stress nonlinear inhibition term, and accordingly establish the quantitative relationship between protein and total phenol content, finally, high-precision total phenol content is obtained by using optimization algorithm inversion, and effective monitoring of large-scale saline-alkali soil wheat grain quality is realized.The application initiates the "protein content-total phenol content" secondary inversion architecture, indirectly realizes the total phenol content monitoring through the protein remote sensing inversion technology, effectively solves the technical problem of low direct spectral inversion precision;Innovatively establishes the specific stress response model of saline-alkali soil, the nonlinear inhibition effect of salt on crop physiological process is considered in the inversion process, which significantly improves the monitoring precision and model adaptability, and has the characteristics of high monitoring precision, strong practicability and wide application range.

The application discloses a SIF spectrum inversion method based on singular value decomposition and machine learning regression. The method comprises the following steps: step 1, acquiring sample radiance spectrum data, solar-induced chlorophyll fluorescence spectrum data corresponding to the radiance spectrum data and the like; step 2, performing singular value decomposition on a feature matrix, selecting a plurality of main singular vectors to construct a reconstruction base matrix, and calculating low-dimensional expansion coefficients of the fluorescence spectrum; step 3, performing singular value decomposition on the feature matrix; step 4, taking the low-dimensional feature coefficients of the radiance spectrum, preset key band radiance features, band ratio features and observation geometry features as input variables of a machine learning regression model; and step 5, inputting a sample to be inverted into the trained machine learning regression model. The application converts the high-dimensional solar-induced chlorophyll fluorescence spectrum inversion problem into a low-dimensional expansion coefficient prediction problem, and has the advantages of high inversion efficiency, strong model stability and suitability for popularization and application.

The invention provides a non-linear constrained time domain high-resolution spectrum inversion method, which relates to the field of seismic data broadband processing, and comprises the following steps: constructing a wedge-shaped model library composed of odd-even reflection coefficient pairs; in a time domain, carrying out convolution on the wedge-shaped model library and the wavelets to obtain a wedge-shaped model library responded by a simple layer; forming expected inversion seismic data through the combination of different layer responses; subtraction is carried out on the seismic data and an expected result, and an objective function is designed by applying L2-Cauchy constraint; solving a non-linear problem of regularization constraint by using a fast iterative shrinkage threshold algorithm to obtain a high-resolution reflection coefficient profile; and carrying out profile convolution shaping on the high-resolution reflection coefficient to obtain high-resolution wide-spectrum inversion seismic data. The method has the beneficial effects that the high-resolution reflection coefficient profile convolution shaping broadband wavelet is performed to obtain the high-resolution wide-spectrum inversion seismic data, the low frequency and the high frequency of the seismic data are compensated, and thin-layer information is effectively identified.

The invention discloses an all-sky background radiometer full-channel calibration method based on spectral inversion, and the method comprises the steps: obtaining a discrete response value of a non-absorption wave band of an all-sky background radiometer based on solar direct radiation and a Langley calibration method in combination with the solar irradiance of an atmospheric boundary layer, the atmospheric relative quality and a sun-ground distance correction factor; a solar direct radiation and gas absorption calibration method is utilized, a gas absorption effect is considered, and a discrete response value of an absorption wave band is calculated through inversion of the total amount of water vapor; determining a full-band discrete irradiance calibration coefficient based on the discrete response values of the non-absorption band and the absorption band; fitting by using the full-band discrete irradiance calibration coefficient and adopting an interpolation algorithm to obtain a full-band instrument irradiance calibration function; and transmitting an instrument irradiance calibration function to the direct incidence channel and the scattering channel, and obtaining radiance calibration coefficients of the direct incidence channel and the scattering channel in combination with the field angle and the attenuation coefficient of the optical filter, thereby completing full-channel calibration.

The invention provides a spectrum inversion high-resolution processing method based on strong axis stripping, and relates to the field of seismic exploration, and the technical scheme is that the spectrum inversion high-resolution processing method based on strong axis stripping is characterized by comprising the following steps: inputting original seismic data, and extracting strong reflection wave event seismic data; subtracting the extracted strong reflection wave event seismic data from the original seismic data to obtain seismic data after strong reflection suppression; replacing the original seismic data with the seismic data subjected to strong reflection suppression, and establishing a spectrum inversion objective function; and solving the spectral inversion objective function to obtain a high-resolution seismic section which suppresses strong reflected waves and highlights weak signals. The method has the advantages that a spectral inversion high-resolution processing mechanism based on strong axis stripping is clear, and time domain iteration deconvolution stripping of the strong reflection wave event, spectral inversion high-resolution processing stripping of the strong reflection wave event, strong reflection suppression and spectral inversion high-resolution thin layer recognition are involved.

The application provides a kind of optical fiber pressure sensor demodulation method and device for sensing ship-bridge impact force, and relates to the field of optical fiber sensing technology.The method comprises: constructing an optical fiber pressure sensor demodulation system, an initial multi-modal model based on a double-branch radial basis function neural network, and using a hybrid optimization algorithm including an improved adaptive particle swarm global exploration and an enhanced sparrow search local optimization.In the global stage, a nonlinear adaptive PSO is used to obtain the global optimal solution, and in the local stage, the solution is fine-tuned by SSA and dynamic refocusing is achieved using multi-modal monitoring to obtain the optimal solution of the model.The characteristic vector is input into the model, the analytic signal is obtained by Hilbert transform, the envelope is extracted by extracting the modulus, and the real-time pressure prediction value is output by the weighted fusion layer.The application can significantly improve the convergence speed and accuracy of nonlinear spectral inversion, and meet the demand for real-time and high robustness monitoring of local high pressure in ship-bridge impact scenarios.

The invention provides an optical fiber pressure sensor demodulation method and device for sensing impact force of ship colliding with a bridge, and relates to the technical field of optical fiber sensing. The method comprises the following steps: constructing an optical fiber pressure sensor demodulation system, initializing a multi-modal model based on a double-branch radial basis function neural network, and adopting a hybrid optimization algorithm including improved adaptive particle swarm global exploration and enhanced sparrow search local optimization. In the global stage, nonlinear adaptive PSO is adopted to obtain a global optimal solution, and in the local stage, dynamic refocusing is achieved through SSA fine tuning and multi-modal monitoring, and a model optimal solution is obtained. And inputting the feature vector into a model, performing Hilbert transform to obtain an analysis signal, extracting a module value to obtain an envelope, and outputting a real-time pressure prediction value through a weighted fusion layer. According to the method, the convergence speed and precision of nonlinear spectral inversion can be remarkably improved, and the requirements for real-time and high-robustness monitoring of local high pressure in a ship and bridge collision scene are met.

The application discloses a kind of fusion multispectral inversion's semi-autonomous remote sensing water area generalization segmentation method, it is related to remote sensing image processing technical field, specific implementation scheme is as follows: acquisition remote sensing multispectral data;Remote sensing multispectral data and coordinate point prompt information are used as the input of end-to-end model, after being processed through multispectral inversion branch, generalization segmentation branch, cross-modal feature fusion, generate optimal water area segmentation mask;After morphological filtering and vector conversion, the optimal water area segmentation mask is converted into high-precision remote sensing water area segmentation vector result.The application embeds multispectral inversion and generalization segmentation depth into the same model structure, realizes the cooperation of spectral physical priori and semantic feature by three-stage progressive fusion, and utilizes GPU parallel computing optimization framework, significantly improves the labeling accuracy and calculation efficiency.

The application discloses a spectral inversion frequency expansion processing method based on stratum structure dimension reduction, obtains post-stack seismic data after preprocessing such as denoising and zero-phase; designs different simple thin-layer stratum structure forward models, establishes the relationship between the seismic response characteristics of different models and the corresponding waveform phase information; obtains odd and even component data reflecting different simple stratum structure thin-layer information and obtains seismic data after phase decomposition; based on the phase decomposition method, the dimension of the complex stratum structure is reduced by decomposing the seismic data, and the optimal solution is searched in the data space after dimension reduction, and accurate high-resolution seismic data is obtained. Compared with the prior art, the data space size of spectral inversion optimization is reduced, the reliability and stability of the spectral inversion frequency expansion result are improved, and the fine seismic reservoir characterization ability of the spectral inversion result, such as thin-layer identification, is enhanced.

The invention relates to a fluid refractive index measuring device and a fluid refractive index measuring method, in particular to a compact fluid refractive index measuring system and a fluid refractive index measuring method, and solves the technical problems that the sensing sensitivity and the comprehensive performance evaluation index of an existing optical refractive index sensor are low, and the high-precision measurement requirement of the fluid refractive index is difficult to meet. According to the compact fluid refractive index measuring system provided by the invention, the refractive index is converted into spectral information by utilizing the high-Q-value metasurface based on the double-layer metasurface, and the interaction strength between the measuring light beam and the fluid to be measured is improved by virtue of the narrow linewidth and near-field enhancement characteristics, so that the measuring precision can be improved; by combining a miniaturized spectrometer, spectral information is converted into visual light intensity information, and the refractive index of the fluid to be measured is quickly obtained by analyzing and processing the light intensity information, so that the measurement process can be simplified, and the measurement efficiency can be improved. According to the fluid refractive index measurement method provided by the invention, an error term is introduced into spectral inversion, and accurate inversion of spectral information can be realized.

The utility model discloses a high-sensitivity temperature and humidity sensing device based on a side-polished SNMNS cascade conical structure. The device comprises a broadband light source, a one-way isolator, a sensing unit, an attenuator, a signal processing unit and an upper computer which are sequentially connected through single-mode optical fibers. The sensing unit is composed of a tapering optical fiber cascaded side-polished SNMNS structure with the taper waist diameter of 4 microns, the taper area length of 20 mm and the waist area length of 1 mm. The non-adiabatic tapered optical fiber is prepared through a fused biconical taper method, and the free spectral range is expanded by using the vernier effect of a cascade structure, so that sensitivity amplification is realized. The refractive index of the optical fiber changes due to changes of external temperature and humidity, and the signal processing unit demodulates a spectrum inversion parameter value by modulating wavelength drift of an interference spectrum. The problems of low sensitivity and cross sensitivity of a traditional sensor are solved, and the sensor is suitable for multi-parameter high-precision sensing of environments such as industrial monitoring and biomedical treatment.

This invention proposes an inverse Monte Carlo method based on upper confidence intervals (UCB) for structure optimization in spectral inversion. This method combines the upper confidence interval strategy from reinforcement learning, intelligently selecting the optimal atom movement strategy during the optimization process to achieve efficient structure inversion modeling. Users can specify experimental spectra as needed, and the program will efficiently infer structural information without relying on an initial structure.

The invention discloses a source-sink rapid analysis method and system based on a Wheeler domain. The method comprises the following steps: acquiring an original seismic data volume; determining a target sequence interface, and based on the target sequence interface, performing Wheeler domain conversion on the original seismic data volume to obtain a Wheeler domain data volume; and performing Wheeler domain conversion on the target sequence interface to obtain the converted target sequence interface, namely the ancient micro-landform. Performing spectrum shaping on the Wheeler domain data volume by using a spectrum shaping method, and performing seismic spectrum inversion on the Wheeler domain data volume after the spectrum shaping to obtain a spectrum inversion data volume; based on the Wheeler domain data body, taking the top surface of the target sequence interface as a reference, and utilizing the bottom surface of the target sequence interface to carry out stratum horizon slice extraction on the spectrum inversion data body to obtain a spectrum inversion stratum slice; and source-sink analysis is carried out based on the ancient micro-landform and the spectral inversion stratigraphic slice. According to the invention, source-sink analysis can be rapidly and accurately carried out.

The application provides a method for removing strong axis based on a reflection coefficient attribute body in a curved wave domain, and relates to the technical field of seismic data processing. The method comprises the following steps: performing spectral inversion processing on original seismic data to generate a reflection coefficient body; based on the reflection coefficient body, performing multi-scale and multi-directional layer analysis in the curved wave domain, that is, for each analysis time window, extracting a reflection coefficient curve, determining the inflection point position by calculating the zero value point of the second derivative of the curve, and using the inflection point position to identify the mutation characteristics of the reflection coefficient; at the same time, constructing a local discrete curved surface based on the reflection coefficient body, fitting the curved surface sheet, and calculating the Gaussian curvature to identify the local geometric abnormal area of the reflection coefficient body; and comprehensively determining the inflection point position and the identified local geometric abnormal area to extract a strong reflection coefficient component. The application can realize accurate stripping of strong reflection energy and faithful protection of effective weak signals, and improves the reliability of identification and analysis of underground stratum lithology and geological structure of seismic data.

This invention discloses an adaptive construction method for crop spectral inversion models, comprising: analyzing the coupling law between measured field water, nitrogen, and dry matter data and crop physiology; constructing a feasible region of water, nitrogen, and dry matter physiological constraints in a three-dimensional state space; generating virtual samples; fusing virtual samples with measured water, nitrogen, and dry matter data to construct a physically consistent training sample set; constructing water-nitrogen co-state categories based on the combination of water and nitrogen levels, and independently determining the spectral feature subspace; training and generating parameter-independent water-nitrogen co-state sub-models; determining the target water-nitrogen co-state category, routing to the corresponding water-nitrogen co-state sub-model, and calculating the real-time inversion results of pixel water and nitrogen. This invention solves the model generalization and physical consistency problems under small sample conditions through physiological mechanism constraints, and decouples the complex influence of water-nitrogen interactive stress on the spectrum through a state classification mechanism, thereby improving the accuracy and robustness of crop water and nitrogen inversion.

This invention relates to a fluid refractive index measurement device and method, specifically a compact fluid refractive index measurement system and method. It addresses the technical problem that existing optical refractive index sensors have low sensing sensitivity and overall performance evaluation indicators, making it difficult to meet the high-precision measurement requirements of fluid refractive index. The compact fluid refractive index measurement system provided by this invention is based on a double-layer metasurface. It utilizes a high-Q metasurface to convert the refractive index into spectral information. By leveraging its narrow linewidth and near-field enhancement characteristics, it improves the interaction intensity between the measurement beam and the fluid under test, thereby increasing measurement accuracy. Combined with a miniaturized spectrometer, the spectral information is converted into intuitive light intensity information. Through analysis and processing of this light intensity information, the refractive index of the fluid under test can be quickly obtained, simplifying the measurement process and improving measurement efficiency. The fluid refractive index measurement method provided by this invention introduces an error term in the spectral inversion, enabling accurate inversion of spectral information.

1. The name of the design product: soil spectrum inversion graphical user interface of electronic equipment. 2. The use of the design product: the design product is used for electronic equipment. 3. The design points of the design product: the combination, content and interaction mode of the graphical interface. 4. The picture or photo that best indicates the design points: front view. 5. The front view, left view, right view, top view and bottom view are omitted. 6. The use of the graphical user interface: for the collection, processing and display of soil spectrum inversion data. 7. The change state description of the graphical user interface: in the front view, after the user inputs the mailbox and password and clicks the "login" button, the interface enters the change state view. 8. Other circumstances that need to be explained: "XX" in the interface is a number, a letter or a word.