63 results about "Curve reconstruction" patented technology

The invention discloses a geological map-oriented breakpoint repairing and closed curve reconstruction method and system and a medium. The method comprises the following steps of: preprocessing an original geological map to obtain a grayscale image; carrying out binarization and morphological closed operation processing to obtain a grayscale image of the edge of the smooth contour curve; performing pixel skeletonization to obtain a pixel-level skeleton diagram; performing secondary edge extraction, connected domain marking and endpoint statistics on the pixel-level skeleton diagram, and screening out a non-closed curve and an endpoint set; carrying out nearest neighbor retrieval by adopting KD-Tree to obtain a candidate pairing set of each end point; performing priority pairing in combination with the spatial distance and the direction smoothness; and generating a transition line segment according to a pairing result to supplement the fracture part of the non-closed curve. According to the method, non-closed breakpoints generated by scanning or drawing errors in the geological map can be automatically restored, and a closed curve with topological integrity and a smooth boundary is generated.

The invention relates to the technical field of photovoltaic cell testing, and discloses a volt-ampere characteristic curve testing system for a high-power photovoltaic cell. The system comprises a multi-source data acquisition module, a cross-modal feature fusion module, a volt-ampere curve reconstruction module and a dynamic parameter adjustment module. The multi-source data acquisition module synchronously acquires real-time irradiance time sequence data, assembly temperature distribution data and a current and voltage sampling data set of a target photovoltaic array; the cross-modal feature fusion module performs feature interaction processing on the multi-source data to generate a fusion electrical feature matrix; a volt-ampere curve reconstruction module performs dynamic curve fitting based on the matrix to generate a volt-ampere characteristic curve under a standard test condition; and the dynamic parameter adjustment module executes parameter calibration according to the curve and outputs a test parameter dynamic adjustment strategy to the acquisition module. The system can eliminate environmental interference, optimize test parameters, guarantee long-term stable operation, and meet accurate test requirements of high-power photovoltaic cells.

The present application belongs to the technical field of geophysical well logging data processing, and particularly relates to a multi-scale dual-branch neural network well logging curve reconstruction method based on seismic constraint, which comprises reading in well-seismic and interpreted horizon data, target interval sublayering and facies belt division based on three-dimensional seismic interpretation, pyramid multi-scale training set sample construction by fusing multi-curve and well flanking seismic trace information, residual-Transformer dual-branch neural network training model establishment taking a composite loss function as the core, three-stage progressive training strategy and reconstruction of target well missing section and result evaluation. The method still maintains high stability and robustness under the condition of complex heterogeneous reservoirs; on the basis of seismic data constraint, geological sublayering and facies belt division and other interpretation information are fused, so that the reconstruction result is more in line with geological laws; and the obtained reconstructed curve provides reliable support for re-interpretation of old data and full use of incomplete well logging data.

The invention discloses a three-dimensional Gaussian splashing method and system for sparse view curve reconstruction, and the method reconstructs a three-dimensional curve scene through a sparse image: firstly, obtaining a dense point cloud and a camera pose, extracting a two-dimensional line segment graph, employing a line segment guide Gaussian initialization strategy, and obtaining a three-dimensional curve scene; generating an initial Gaussian set on the basis of the dense point cloud and the two-dimensional line segment graph; structure perception Gaussian pruning is executed in the optimization process of the initial Gaussian set, and spatial outliers and visibility redundant gauss are removed; and finally, calculating the total loss containing sparse regular terms through the difference between the rendered image and the sparse image, and iteratively optimizing Gaussian parameters until the total loss converges, thereby obtaining a final three-dimensional curve reconstruction result. According to the method, the problem of geometric prior deficiency under a sparse view is solved through line segment guide initialization, overfitting and artifacts are effectively inhibited by using structure pruning and sparse constraint, and high-quality three-dimensional curve reconstruction is realized.

The invention discloses an underwater intelligent cable spatial form reconstruction system based on fusion of fiber bragg grating sensing and underwater acoustic positioning, and belongs to the technical field of underwater measurement and sensing. A plurality of fiber bragg grating sensors are inscribed on a three-core fiber in the intelligent cable of the system along the length direction to measure multi-point strain data along the length direction. According to the strain distribution, the bending curvature and the torsion of each measuring point are calculated, a continuous curvature and torsion function is constructed, the tangent vector, the normal vector and the sub-normal vector of each point of the cable are solved in combination with a Freen-Serret algorithm, and the tangent vector is integrated to obtain the space curve form of the cable. And determining the absolute position of the cable terminal in the three-dimensional space according to the acoustic ranging result. And realizing spatial form and attitude recognition of the whole underwater cable through constraint fusion of a curve reconstruction result and an end point position. According to the system, an inertial measurement unit is arranged on a cable to obtain local attitude information, so that the accuracy and the stability of curve reconstruction are improved. The method can be widely applied to space state monitoring of underwater long cable operation.

The invention provides an environmental protection equipment energy consumption optimization and energy-saving regulation and control method based on multi-source data fusion, and relates to the technical field of environmental protection equipment energy-saving control, and the method comprises the steps: collecting the operation data of environmental protection equipment, carrying out the multi-scale feature extraction, recognizing the energy consumption abnormity through combining the energy efficiency curve reconstruction, and carrying out the energy-saving control of the environmental protection equipment; energy consumption sensitive parameters are extracted, energy-saving space is calculated based on load fluctuation, and an optimized parameter adjusting scheme and an optimized time sequence control sequence are generated by integrating the environment load change rate and the equipment thermal inertia coefficient. The energy consumption abnormity can be accurately identified, the energy-saving space is scientifically quantified, the intelligent optimization and dynamic regulation and control of the energy consumption of the environmental protection equipment are realized, and the operation efficiency of the equipment is obviously improved.

The invention belongs to the technical field of geophysical logging data processing, and particularly relates to a multi-scale double-branch neural network logging curve reconstruction method based on seismic constraints. Comprising the steps of reading in well seismic and interpreting horizon data, performing target layer section layering and phase belt division based on three-dimensional seismic interpretation, fusing multiple curves and well-side seismic channel information to construct a pyramid multi-scale training set sample, establishing a residual-Transformer double-branch neural network training model by taking a composite loss function as a core, and constructing a multi-scale training set sample based on the residual-Transformer double-branch neural network training model. And a three-stage progressive training strategy is adopted, and target well missing section reconstruction and result evaluation are carried out. According to the method, high stability and robustness are still kept under the condition of a complex heterogeneous reservoir; interpretation information such as geological layering and phase belt division is fused on the basis of seismic data constraints, so that a reconstruction result better conforms to geological laws; and the obtained reconstruction curve provides reliable support for re-explanation of old data and full utilization of incomplete logging data.

The invention discloses a surface blasting full-time-history vibration synthesis prediction method which comprises the following steps: S1, acquiring geological parameters of a surface blasting area, blast hole arrangement parameters and explosive quantity parameters, and arranging vibration monitoring points in a protection area of the blasting area; and S2, on the basis of the Anderson theory, a single-hole vibration curve reconstruction model is established when the free surface condition is not changed in combination with an on-site limited single-hole blasting experiment, and the model takes the quantitative mapping relation between the vibration velocity peak value and the explosive quantity, the distance from the blast hole to the monitoring point and the site factor as the core. According to the method, accurate prediction of large-scale open blasting full-time-history vibration is realized, and a reliable technical means is provided for collaborative optimization of blasting vibration control and a crushing effect.

The application relates to a lithium battery state of charge evaluation method based on charging curve reconstruction, which comprises the following steps: presetting charging cutoff conditions, discharging cutoff conditions and cycle cutoff conditions, continuously charging and discharging the lithium battery according to the preset cutoff conditions, and recording data information in the experiment process; collecting charging curve data of the lithium battery, performing data cleaning, and removing error data; pre-processing the cleaned data, filling in missing values by adopting a linear interpolation method, and obtaining voltage-time sequence data of the lithium battery; forming a data set by sliding a window to the voltage-time sequence data; training the data set, and constructing a charging curve reconstruction model; verifying the performance of the charging curve reconstruction model by using a test set and a verification set, and calculating the battery power based on the charging curve reconstruction model; and the lithium battery state of charge evaluation method can strengthen short-term time sequence feature extraction and provide data support for lithium battery health management.

The application discloses a screw motor stator-rotor linear optimization method for directional drilling, relates to the technical field of directional drilling of oil and natural gas, and comprises the following steps: according to the design requirements of a screw motor for directional drilling, design parameters of an eccentricity, a rotor head number, a stator head number, a base circle diameter and a theoretical equidistance radius are determined, and an initial linear data set containing discrete data points is generated based on an internal or external cycloid equation; a non-uniform rational B-spline method is used to perform curve reconstruction on the initial linear data set, local fairing treatment is performed on regions with curvature mutations by adjusting control point weight factors and node vectors of the non-uniform rational B-spline curve, and a smooth linear parameter model is generated; and based on the smooth linear parameter model, a thermodynamic coupling simulation model based on a rubber viscoelastic constitutive relation is established to simulate periodic plunger movement of the screw motor under a rated working condition.

The present application relates to the technical field of reservoir capacity curve reconstruction, in particular to a reservoir capacity curve reconstruction method and system based on multi-source data fusion, comprising the following steps: obtaining multi-source data fusion elevation information, reconstructing the water level-storage capacity relationship curve, and outputting the reservoir capacity curve reconstruction graphical expression result. In the present application, the water body boundary surface information is obtained by fusing the unmanned aerial vehicle remote sensing image to identify the boundary pixel area, the abnormal water level line number is identified by combining the difference index comparison, the complete three-dimensional fusion data set is constructed by matching the water point cloud and the underwater elevation data, the grid network is generated according to the unified projection and the abnormal elevation points are corrected, the continuous trajectory line sequence is established and integrated to form the two-dimensional boundary expression, which can quickly identify the reservoir capacity change in the dynamic environment, effectively avoid the incomplete boundary extraction and the data precision decline caused by the complex terrain and the tedious operation in the traditional measurement method, and improve the comprehensive performance of the reservoir capacity curve reconstruction in the spatial coverage, data updating and expression precision.

The invention belongs to the technical field of power system relay protection, and particularly relates to a distribution network load characteristic identification method and system, electronic equipment and a storage medium. The method comprises the steps that an original power distribution network load curve is processed, a load curve reconstruction model based on cubic spline interpolation is constructed, and time sequence synchronization of multi-source load data is achieved; constructing a power distribution network load identification model based on a deep neural network; an automatic framework search mechanism based on Bayesian optimization is provided, the number of network layers and neuron parameters are dynamically optimized, and an optimal network framework is obtained; based on the monitoring data and the optimal network architecture, decoupling and fine analysis of the equipment-level electrical load are carried out; and carrying out multi-dimensional analysis on the load characteristics of the power distribution network based on decoupling and refined analysis results. According to the method, the engineering applicability of load characteristic identification is remarkably improved, the problem of data distortion under the complex working condition of the distribution network is effectively solved, and the matching precision of the low-frequency load shedding control strategy and the actual working condition of the system is improved.

This invention discloses a LiDAR human motion capture method based on Bézier curve degradation modeling, comprising: acquiring a continuous multi-frame LiDAR point cloud sequence; constructing a trajectory-aware Bézier motion degradation module to fit the original joint trajectory and gradually reduce control points through a trajectory preservation strategy to generate a multi-level motion representation from coarse to fine; designing a progressive motion reconstruction module, using a multi-timescale motion transformer (TMT) to predict Bézier motion curves at multiple time scales based on point cloud features, and using a multi-level motion aggregator (MMA) to adaptively fuse the multi-scale curves to reconstruct a detailed and temporally coherent 3D human posture sequence. This invention effectively alleviates the posture jitter or failure problem caused by occlusion, noise, and sparse point clouds, significantly improving the accuracy and temporal continuity of motion capture, and is suitable for complex open scenarios such as autonomous driving and robotics.

The invention discloses an optimal arrangement and curve estimation method based on IMU nodes, and belongs to the technical field of three-dimensional curve measurement and restoration. According to the method, initial nodes are generated through uniform sampling on a curve, and the sensitivity value of each node to a curve recovery error is calculated by using local attitude information collected by an IMU sensor, so that a local error distribution function is constructed. And aiming at the error sensitivities of different nodes, a density adaptive sampling strategy is adopted to realize the optimization of the overall node distribution. According to the method, the validity of the optimization point distribution strategy is quantitatively verified by combining an error evaluation index. Through the combination of density adaptive point distribution and spherical interpolation restoration, the method can significantly reduce curve reconstruction errors, improves the space efficiency of IMU arrangement, and is suitable for flexible cables and other three-dimensional curve accurate measurement scenes. The method has the advantages of being simple in algorithm, capable of achieving automatic point distribution, high in error control precision and the like, and an efficient and reliable technical scheme is provided for three-dimensional curve measurement and restoration.

This application relates to the fields of mechanical engineering and automated inspection technology, and discloses a method and system for detecting the edge drop of strip steel based on the collaborative operation of multiple measuring heads of edge drop gauges. It aims to solve the technical problems in existing technologies, such as spatial misalignment of data from multiple thickness gauges, distortion of the edge drop curve reconstruction, and inability to accurately reflect the thickness distribution of the same cross-section caused by the high-speed operation and speed fluctuations of the strip steel. The method includes: synchronously acquiring thickness data using multiple non-contact thickness gauges arranged in a linear array; obtaining the actual displacement of the strip steel using a high-resolution rotary encoder to establish a spatiotemporal mapping relationship between the thickness data and the physical cross-section; reconstructing a distortion-free cross-sectional thickness distribution curve using a dynamic interpolation algorithm based on a preset sampling interval, and calculating the edge drop value. This application significantly improves the accuracy and dynamic response capability of edge drop detection through a synchronization mechanism and interpolation reconstruction based on the actual displacement of the strip steel, providing high-fidelity data support for strip shape control.

The invention relates to the technical field of geophysical well logging, in particular to a well logging curve reconstruction-oriented AI large model construction method, reconstruction method and device, electronic equipment and a storage medium, comprising an LB-DALR model, a specific curve trend semantic embedding preprocessing unit obtains a numerical matrix and a semantic embedding vector, the feature extraction and fusion unit extracts numerical features through a numerical path and extracts trend semantic features through a semantic path, and multi-scale fusion features are obtained after fusion; and the decoding reconstruction unit reconstructs the logging curve. According to the method, the problem that in the geological logging curve reconstruction process of a single-mode model, the curve reconstruction deviation is large when the single-mode model faces complex geological logging data and data interfered by a severe underground environment is solved, and numerical features and trend semantic features are extracted from a numerical path and a semantic path which are operated in parallel; and the high-precision reconstruction is realized through the synergistic effect of the CrossModal multi-head cross attention mechanism and the FusionGate autocorrelation dynamic gating.

The present application belongs to the technical field of rotating machinery state monitoring and industrial deployment, and particularly relates to a reducer input shaft end-to-end rotating speed adaptive inference method based on variable-length sequence. In view of the fact that actual monitoring data is a continuous sequence of any length, and a deep learning model usually relies on fixed-length input, and traditional sliding window inference is prone to introducing phase discontinuity and splicing artifacts at the boundary, the present application establishes a deployment paradigm of "fixed-length training and variable-length inference". The continuous variable-length sequence is divided by a sliding window with an overlap rate, and a backtracking interception strategy is designed for the case of insufficient end sequence. After batch inference using a pre-trained model, the local prediction results are integrated and smoothed by using an overlap addition mechanism and a weighted average algorithm. The present application effectively eliminates the Gibbs effect at the window boundary, ensures the phase integrity of the terminal, and realizes seamless and smooth rotating speed curve reconstruction of continuous monitoring data of any length.

The invention relates to the technical field of electricity market electricity price prediction, and discloses a spot clearing electricity price prediction system fusing new energy output fluctuation characteristics, and the system comprises a fluctuation characteristic extraction module which carries out the variational mode decomposition of a new energy output prediction sequence, and generates fluctuation impact momentum and duration; the climbing scarcity calculation module is used for calculating a reference equivalent climbing scarcity in combination with the polymerization residual climbing capability and the fluctuation impact momentum; the hysteresis state correction module is used for correcting and generating a scarcity degree state index by using a state attenuation parameter based on fluctuation duration; the supply curve reconstruction module is used for generating a dynamic effective supply curve by carrying out linear deformation on the reference residual supply curve by utilizing the physical capacity cut-off quantity mapped by the index and a quotation slope correction coefficient; and the clearing electricity price solving module is used for outputting a predicted value based on the curve. The new energy fluctuation impact and hysteresis effect are effectively quantified, and the prediction precision is improved.

The invention provides a multi-modal surface data processing optimization system based on a spherical Transform. The multi-modal surface data processing optimization system comprises an image acquisition and preprocessing module, a spherical uniform symmetric grid surface generation module, a spherical convolution and pooling module, a spherical Transform module, a spherical upper sampling and lower sampling module and an application analysis module. The image acquisition and preprocessing module is used for segmenting a region of interest, reconstructing a surface curved surface, completing rigid registration and extracting features at the vertex of the curved surface; the application analysis module integrates a spherical convolution and pooling module, a spherical Transform module and a spherical upper sampling and lower sampling module, establishes a spherical auto-encoder model, a segmentation model and a classification model, and is used for target identification, risk prediction or anomaly detection tasks. According to the method, a complex mode which is difficult to extract by a traditional CNN can be captured, multi-source feature complementarity is improved based on a cross-modal attention mechanism, and efficient multi-source data fusion is realized.

The invention discloses a bedrock lithology identification method based on a normalized curve reconstruction amplitude difference. The method comprises the following identification steps: performing normalization processing on four logging curves of sound wave time difference AC, neutron porosity CNL, stratum density DEN and natural gamma GR; the amplitude difference between any two normalized well logging curves is calculated, and amplitude difference curves between any two well logging curves are defined as DIFFRCD, DIFFCD, DIFFCG and DIFFDG; according to different lithologic characteristic parameters and logging curves, analyzing each lithologic response characteristic, and selecting a discriminant logging curve as a main discriminant curve and a plurality of secondary discriminant curves; and on the basis of any two discriminant logging curve cross plots, discriminant logging curve cutoff values are optimized, and lithology identification is carried out. According to the method, various lithology of the bedrock can be successfully identified, the accuracy of identifying the lithology of the bedrock is improved, the logging cost is low, and the applicability is wider.

The invention provides a method for calculating a gas-liquid phase root of a PC-SAFT equation. The method comprises the following steps: acquiring the temperature, actual pressure and composition of a calculation system; iteratively solving a point of which the derivative of the difference between the calculated pressure and the actual pressure to the contrast density is 0 by using a secant method; solving a point of which the difference value between the calculated pressure and the actual pressure is 0 by using a Newton iteration method; in response to the fact that the solving results of the secant method are a gas phase critical value and a liquid phase critical value, whether the gas phase and the liquid phase of the equation root are accurate or not is judged based on the gas phase critical value and the liquid phase critical value; when the gas-liquid phase of the equation root is accurate, outputting the equation root; when the gas-liquid phase of the equation root is inaccurate or the solving result of the Newton iteration method is no solution, solving and outputting a new virtual equation root by adopting a curve reconstruction method; and responding to the condition that the solving result of the secant method is no solution, solving an interpolation minimum value point by adopting a quadratic interpolation method, and judging the gas-liquid phase of the equation root obtained by the Newton iteration method according to the interpolation minimum value point so as to determine an output value.

This invention provides a method for construction replacement and parameter verification of AC / DC converter devices, relating to the technical field of smart grids. The method includes: acquiring the first pantograph-catenary state sequence, geometric parameter set, and tension parameter set corresponding to the phase-splitting device; constructing a multi-pantograph coupling disturbance function based on the pantograph-catenary state sequence and generating a disturbance distribution set; performing dynamic negative sag parameter reconstruction processing on the phase-splitting device according to the disturbance distribution set; performing spatial curve reconstruction processing based on the dynamic negative sag parameters and geometric parameter set to obtain a target geometric parameter set; performing construction replacement and iterative adjustment control on the phase-splitting device according to the target geometric parameter set and tension parameter set; performing disturbance consistency verification based on the updated second pantograph-catenary state sequence, and performing reverse correction of the dynamic negative sag parameters according to the verification result, outputting the verification result when preset conditions are met. This invention can improve the efficiency of construction adjustment in multi-pantograph coupling operation scenarios.

The invention discloses an echocardiography parameter curve reconstruction smoothing method based on ECG data. The echocardiography parameter curve reconstruction smoothing method comprises the following steps that S1, the ECG data and ultrasonic data which are synchronous in clock are collected; s2, constructing physiological priori; s3, performing time phase segmentation on the ECG data, and performing time phase constraint reconstruction on the ultrasonic curve; and S4, reconstructing a smooth curve, extracting features, and establishing an output interface. The method has the beneficial effects that the feature points of the ECG curve are extracted, and the ultrasonic curve is positioned in an auxiliary manner, so that the ultrasonic continuous parameters are smoothly reconstructed, and the parameter curve of the real continuous motion trend of the heart can be accurately reflected.

The application provides a curve reconstruction method and device based on deep learning, equipment and medium, the method comprises the following steps: obtaining the logging curve data of the non-expanded section, and establishing a training set and a test set; a density curve reconstruction model is established and the training set is used to train the density curve reconstruction model, the density curve reconstruction model is a deep learning model; the test set is used to test the density curve reconstruction model, and a reconstructed density curve is generated, the density curve reconstruction model is adjusted until the correlation of the reconstructed density curve and the original density curve of the non-expanded section and the relative error of the density curve reconstruction model reach a preset condition; the density curve of the target well is reconstructed by using the adjusted density curve reconstruction model. The application simplifies the operation process, especially in complex formation conditions, can accurately reflect the actual situation of the formation, and improves the calculation accuracy of the reservoir parameters.

The invention relates to the technical field of oil and gas logging evaluation, and discloses an oil well reservoir fluid property identification method and system, a storage medium and electronic equipment. The method comprises the steps of constructing a multiple regression model based on a standard series curve of an existing oil well; performing curve reconstruction on the oil well to be interpreted based on the multiple regression model to obtain a 0.5 m resistivity reconstruction curve; and determining the fluid properties of different reservoirs of the oil well to be explained according to the 0.5 m resistivity reconstruction curve and a pre-constructed fluid property identification chart. According to the method, the multiple regression model is obtained on the basis of the standard series curve of the existing oil well, the 0.5-meter resistivity of the oil well to be explained is obtained by applying the multiple regression model, the problem that the fluid property cannot be identified due to curve deficiency is solved, and a reference basis is provided for formulating a remaining oil development scheme in an old district.

The application discloses a kind of nuclear fusion star simulator magnet coil curvature deformation precision scanning measurement device and method, belong to nuclear fusion device precision measurement technical field.Device includes pedestal, support column, outer ring frame, rotary motor sliding seat, radial adjusting rod, main measurement scanning ring probe, center shaft and auxiliary measurement arm;The star simulator magnet coil to be measured is installed in the inside of outer ring frame, rotary motor sliding seat is slid along the circumference of outer ring frame 360 °, radial adjusting rod drives measurement probe to realize radial precision feed, and the supplementary measurement of complex area of coil is completed.Method is realized by establishing unified measurement coordinate system, and three-dimensional point cloud data are collected by full circumferential continuous scanning, and curvature deformation deviation report is generated by curve reconstruction, curvature calculation and comparison with theoretical model.The application solves the problems of low efficiency, insufficient accuracy and inability to continuously scan in full circumferential direction of traditional measurement methods, and the measurement accuracy reaches sub-micron level, providing data support for coil manufacturing, assembly and operation and maintenance.

The invention discloses a method for predicting a glutenite high-quality reservoir stratum, and the method specifically comprises the following steps: S1, seismic data interpretation processing: carrying out the frequency extension of seismic data based on a self-adaptive amplitude-preserving frequency extension technology, and obtaining amplitude-preserved high-resolution seismic data; s2, establishing a lithology identification plate: performing pairwise intersection on conventional logging curves by using a cross plot, performing sensitive factor intersection analysis, establishing the lithology identification plate, searching a sensitive curve capable of identifying a high-quality reservoir stratum, and in order to ensure high correlation between the curve and an earthquake, performing curve reconstruction to obtain a reconstructed characteristic curve; s3, model solving: establishing a low-frequency impedance model and a high-frequency reconstruction characteristic curve model D by using a model difference method; s4, initial reservoir stratum inversion: carrying out glutenite reservoir stratum prediction based on the high-resolution seismic data and the low-frequency impedance model to obtain an initial reservoir stratum prediction result; and S5, iterative inversion: based on the initial reservoir stratum prediction result and the high-frequency reconstruction characteristic curve model, carrying out nonlinear data combination on the initial reservoir stratum prediction result and the high-frequency reconstruction characteristic curve model, and finally obtaining an iterative inversion result. On the basis of the self-adaptive amplitude-preserving frequency-expanding technology, the self-adaptive molecular decomposition time-frequency analysis algorithm is introduced, the resolution of seismic data is improved, meanwhile, the predication method of the glutenite reservoir stratum is optimized, and the predication precision of the glutenite high-quality reservoir stratum is improved. The method is suitable for predicting the glutenite high-quality reservoir stratum.

The application discloses a multi-measurement mode side logging curve reconstruction method, which is composed of 9 metal electrodes which are symmetrical in upper and lower parts, including a main electrode A0, inner and outer supervision electrodes M1, M1', M2 and M2', inner and outer shielding electrodes A1, A1', A2 and A2' in deep measurement mode, inner shielding electrodes A1, A1', loop electrodes A2 and A2' in shallow measurement mode, and the main electrode A0, the inner and outer shielding electrodes A1, A1', A2 and A2' emitting current in deep measurement operation, and the current flowing back to the far side of the instrument. The multi-measurement mode side logging curve reconstruction method, without changing the hardware circuit and mechanical structure of the existing deep and shallow measurement mode double side logging instrument, only through the deep and shallow measurement resistivity curve data collected by the instrument in the well, adopts a curve reconstruction algorithm to reconstruct the shallow measurement resistivity curve, obtains the reconstructed shallow resistivity curve reflecting the real resistivity information of the formation, and improves the qualified rate of the deep and shallow resistivity curves.

The application discloses a lithium battery SOH estimation method based on a polynomial reconstruction charge-discharge curve, and the method comprises the following steps: obtaining a lithium battery aging data set; data preprocessing; training and obtaining an SOH estimation model; training and obtaining a charge-discharge curve reconstruction model; reconstructing a missing charge-discharge curve; and online estimating the SOH of the lithium battery. The constant-current charging voltage curve, the constant-voltage charging current curve and the constant-current discharging voltage curve are characterized by a polynomial fitting function, and a corresponding charge-discharge curve reconstruction model is constructed based on the coefficients of the polynomial fitting function. When a kind of curve appears a serious data missing condition, the polynomial coefficient of the polynomial fitting function of the missing curve can be estimated through the charge-discharge curve reconstruction model, and the reconstructed curve is further characterized by the function curve corresponding to the polynomial fitting function, so as to complete the reconstruction of the missing curve.