210 results about "Linear relationship" patented technology

The invention relates to a wind noise modeling method and system based on machine learning and application, and belongs to the field of marine acoustics and environmental noise modeling, and the method comprises the steps of data preprocessing and feature extraction, hybrid network model building, hybrid network model training and verification. According to the method, a hybrid network model of multiple linear regression and a multi-layer perceptron is constructed based on a known physical mechanism of wind noise and two main noise generation mechanisms of surface turbulence and bubble oscillation, a linear relation and a non-linear relation are modeled respectively, outputs of the two models are integrated through a weighted fusion strategy, and a multi-layer perceptron model is constructed. Smooth transition modeling from a low-wind-speed linear relation to a high-wind-speed nonlinear relation is achieved.

The invention discloses a humidity self-adaptive calibration method of a resistance type gas sensor. The method comprises the following steps: firstly, measuring basic resistance values of a gas sensor under different humidities and response resistance values of target test gases with different humidities and different concentrations; fitting a linear relation between a gas response resistance change value and a basic resistance value under a fixed target test gas concentration to obtain a proportionality coefficient and a bias coefficient; fitting a function relationship among the target test gas concentration, the proportionality coefficient and the bias coefficient through a linear regression method, and calculating the actual concentration value of the target test gas under different humidity conditions by using the function relationship, so as to adaptively calibrate the humidity drift of the gas sensor. By revealing the linear relation between the basic resistance value and the gas response resistance change value when the humidity changes, the self-calibration method which does not need external hardware and is low in calculation complexity is achieved, and a reliable low-cost solution is provided for actual deployment of the sensor.

The invention provides a deep water gravity flow gas field reservoir water saturation earthquake prediction method based on TDCN-TransNet. The method comprises the following specific steps: a TDCN-TransNet network architecture comprises an input processing layer, a dual TWT position code, a TDCN module, a Transformer encoder layer and an output layer; mapping the features to a hidden dimension in an input processing layer through linear full connection; a dynamic Gaussian convolution kernel and a CBAM attention mechanism are embedded in the TDCN, and modeling of a nonlinear relation between an elastic parameter and saturation is enhanced through multi-scale time sequence feature extraction; tCBlock is integrated on a Transform encoder layer, and long-distance sequence dependence is captured by using attention pooling and an SE channel excitation mechanism; a TWT position code is introduced between the TDCN and the Transform, and sequence position information is enhanced; constructing a combined loss function of a mean square error and L2 regularization; finally, training and reasoning are conducted based on the model, and high-precision water saturation prediction is achieved. The method provided by the invention has high generalization and accuracy, and shows excellent prediction performance in a complex geological environment of a deepwater gravity flow reservoir.

The invention relates to the technical field of industrial equipment monitoring, in particular to an industrial equipment remote monitoring and early warning method based on data fusion, which comprises a multi-dimensional parameter dynamic correlation analysis module, an edge side distributed data processing unit, a self-adaptive threshold generator and an abnormal state early warning mechanism. Through capturing a multi-parameter nonlinear relationship, distributed data processing, dynamic threshold adjustment and a hierarchical early warning strategy, the monitoring real-time performance and accuracy are significantly improved, and the false alarm rate and the missing report rate are reduced. The method can effectively solve the problems of long monitoring period, high abnormity identification delay and the like, is suitable for large-scale industrial scenes, and has a wide application prospect.

The invention discloses a tea garden soil moisture physical simulation and data driving fusion prediction method and device, and relates to the field of soil moisture prediction.The method comprises the steps that based on soil basic attributes and multi-depth soil moisture time sequence observation data, a soil moisture physical baseline sequence is obtained in combination with boundary conditions and initial conditions; respectively extracting data of a target soil layer in a preset time window from the soil water content physical baseline sequence, the multi-source driving data and the multi-depth soil water content time sequence observation data so as to splice the data into an input feature set; obtaining a water content prediction value of the target soil layer on the prediction day based on the input feature set and a preset soil water content prediction model; according to the method, the HYDRUS-1D simulation result is taken as a baseline, the time sequence dependence of soil moisture, especially the lag response of different soil layers, is effectively learned, and the modeling capability of a complex nonlinear relationship is improved.

The invention discloses a linear variation stiffness matrix method for suspension bridge space cable forming form finding, relates to the technical field of bridge numerical simulation, and solves the defects of a stiffness matrix in the process of solving the suspension bridge forming form finding through a segmented catenary method. The method comprises the steps that a suspension bridge is simplified into a space cable system composed of a main span, a side span and an anchor span, and the main span, the side span and the anchor span are sequentially subjected to a bridge forming and shape finding process; the linear change stiffness matrix solved in the bridge forming and shape finding process of the main span is used for representing the linear relation between the displacement change of the control point and the initial force change of the main cable; according to the method, an analytical expression of a linear change stiffness matrix is deduced, and an efficient iterative algorithm is established. According to the algorithm, the initial force correction is directly calculated according to the displacement error of the control point in each iteration.

A system and method for neural network confidence regularization is disclosed. A classification system uses a neural network training model to generate prediction. The confidence of the neural network training model is adjusted based on feature prevalence. The system processes mixed data types (binary, categorical, continuous, and date) through type-specific transformations and tensor construction. A tempering factor is calculated from the unweighted sum of features and applied to intermediate neural network outputs. This tempering mechanism reduces model confidence when several low-weight features are present, enabling faster convergence, better generalization, and improved classification accuracy compared to standard neural networks, particularly for complex non-linear relationships in tabular data domains.

The invention discloses an ultra-sparse CBCT (cone beam computed tomography) reconstruction method, system and equipment based on an ordinary differential equation, belongs to CBCT reconstruction in the field of artificial intelligence, and aims to solve the technical problem of low quality of CBCT reconstructed images. The method comprises the following steps: acquiring sample data, preprocessing the data, constructing and training a CBCT-CT nonlinear relation reconstruction model, and performing real-time reconstruction; during preprocessing, converting the three-dimensional image volume data into simulated X-ray projection data, and reconstructing the simulated X-ray projection data by adopting an FDK reconstruction algorithm to obtain an FDK-CBCT image; the CBCT-CT nonlinear relation reconstruction model comprises an encoder, a NODE module and a decoder; in the training process, the CBCT-CT nonlinear relation reconstruction model is trained through the obtained CT sample image and the FDK-CBCT image. In the reconstruction model, through continuous evolution of NODE modeling image features, the model can model a continuous evolution mapping process from a sparse low-quality image to a high-quality CT image during training, so that stripe artifacts and structural distortion do not easily exist in the reconstructed image, and the reconstruction quality is high.

The invention relates to the technical field of dental image shooting, and discloses a label-based intelligent positioning method and device, a positioner and a ray machine, and the method comprises the steps: collecting response signals of a plurality of induction labels of an imaging sensor aiming at a signal transmitted by a label positioner arranged on the ray machine, and receiving signal intensity and carrier phase information; the method comprises the following steps: estimating an estimated distance for any induction tag by fusing a linear relationship between an attenuation model of received signal strength and a carrier phase, and eliminating a single tag error by utilizing multi-point sampling in combination with the estimated distance and position information of all the induction tags and a position offset generated by a positioning imaging sensor relative to a ray machine, so as to realize submillimeter-level accurate positioning. The alignment accuracy and efficiency of the two are improved, so that the position is accurate when the dental X image is shot, and the imaging sensor is ensured to completely fall into the range of the projection area; the device is flexible in installation, low in cost, small in angle dependence, and suitable for low-power-consumption scenes where the size of the sensor in the oral cavity is limited and no independent power supply exists.

The application discloses a random stress spectrum acquisition method of a flange bolt, and comprises the following steps: installing a strain module on a fan tower drum; the strain module collects information and feeds back to a dynamic strain collector to obtain a plurality of pairs of dynamic strain data; the dynamic strain data is converted into stress, and a plurality of surface stresses are obtained through difference calculation; a finite element model is established to obtain a fan tower drum model, and a grid unit is divided; a stress value and angle relationship equation is calculated through the surface stress, and is applied to the fan tower drum model, and then simulation analysis is carried out; according to one simulation analysis result, a flange bolt cross-section internal force is obtained in combination with a section method principle; there is a linear relationship between the simulation analysis result and the flange bolt cross-section internal force, and a cross-section stress linear relationship formula is obtained; the cross-section stress linear relationship formula and a pre-tightening stress of the fan tower drum are combined to obtain a random stress spectrum. The application solves the problems that a large number of sensors are needed and the flange bolt needs to be removed when the sensors are damaged.

The invention provides a fine adjustment device for the meshing depth of a transmission gear, which belongs to the technical field of attack angle sensors and comprises a flange bracket, a mounting bracket, a rotary transformer assembly, a damper assembly and a weather vane rotating shaft assembly, the rotary transformer assembly, the damper assembly and the weather vane rotating shaft assembly are fixed in the circular mounting groove through an eccentric mounting flange and are meshed with one another, and mounting bracket scale marks are arranged on the eccentric mounting flange and the circular mounting groove. According to the device, a linear relation is established between the meshing depth and the scale marks, and the gear meshing depth debugging process of all parts of the attack angle sensor, which is adjusted according to artificial experience, is converted into an indication value which can be directly visually observed. The requirement for experience of operators is lowered, unmeasurable artificial experience is solidified into measurable indication data, the debugging efficiency is improved, and the number of times of wind tunnel test verification caused by repeated debugging is reduced.

The invention provides a hot continuous rolling and finish rolling force prediction method fusing machine learning and a mechanism model, and relates to the technical field of metallurgical process control. By integrating a flow stress mechanism model based on a physical metallurgy rule and a data-driven XGBoost machine learning model, the interpretability of the mechanism model and the fitting ability of machine learning to a complex nonlinear relationship are fully exerted; based on a competitive mechanism between a work hardening effect and a recrystallization / phase change softening effect in a metal deformation process, flow stress mechanism model parameters are optimized by taking a flow stress measured value as a target under the guidance of a PSO optimization algorithm, physical mechanism constraints are embedded, and it is ensured that an optimization result conforms to physical reality; according to the method, key variables of chemical components and process parameters are used as input, a machine learning model for softening rate prediction is established based on an XGBoost algorithm, and a prediction result is used as key parameter input of a mechanism model, so that model precision attenuation caused by working condition change is effectively avoided.

This invention provides a method, apparatus, equipment, and medium for constructing an oil and gas field production growth curve model, belonging to the field of oil and gas field production evolution technology. The method collects multiple sets of geological reserves, first and second rate coefficients, and cumulative production data for oil and gas fields. Then, it applies the oil and gas field growth differential equation and obtains a growth model for cumulative production through mathematical solution. After the model is converted to a first-order linear form, iterative calculations are used to find at least two sets of data to ensure a linear relationship between the independent and dependent variables. These data are used to determine the slope and intercept of the linear equation, thereby estimating the final recoverable reserves and model constants of the oil and gas field. Finally, combining the rate coefficients and estimated parameters, a growth curve model for predicting the cumulative production of the oil and gas field is constructed. This invention improves the accuracy of oil production prediction by constructing an accurate oil and gas field production growth curve model.

The invention belongs to the technical field of fiber-optic gyroscopes, and particularly relates to an acceleration-based fiber-optic gyroscope vibration error compensation method and an inertial measurement unit. Based on normal strain, optical fiber dielectric impermeability tensor variable quantity, refractive index variable quantity of light in the fast axis direction and phase variable quantity corresponding to a sensitive axis of an optical fiber gyroscope, quantitative relations between the phase variable quantity and the fast axis direction of the optical fiber and between the phase variable quantity and the length of a micro-section optical fiber are established, and the equivalent value of the stress borne by the optical fiber and the phase difference of an optical fiber ring is obtained; the linear relation between the phase difference equivalent value and the angular velocity to be compensated of the fiber-optic gyroscope is determined, the relation of converting the vibration acceleration to the phase to be compensated is established, and the required compensation coefficient is calibrated by combining a sinusoidal vibration test to compensate the vibration error of the fiber-optic gyroscope. According to the invention, vibration error compensation of the fiber-optic gyroscope can be realized without adding extra devices, and the real-time performance and accuracy of error compensation are improved.

This invention discloses a method for improving the accuracy of inertial guidance based on polynomial recursive least squares, comprising: constructing a guidance tool error model based on the difference between inertial guidance telemetry observations and a flight environment function, wherein the guidance tool error model satisfies a linear relationship; constructing a recursive formula based on polynomial recursive least squares based on the guidance tool error model; determining the estimated value of the guidance tool error coefficient at the current recursive time based on the difference between guidance telemetry observations at the current recursive time and the recursive formula of least squares, and using the estimated value of the guidance tool error coefficient to compensate for the guidance telemetry observations at the current recursive time. This invention can significantly improve estimation accuracy and efficiency, and has a wider range of applications and higher engineering value.

The invention relates to an engine flame tube life prediction method, equipment and a program product. The method comprises the following steps: acquiring a crack propagation parameter of a target flame tube to be subjected to life prediction; inputting the crack propagation parameters into a pre-trained life prediction agent model, and outputting a residual life prediction value of the target flame tube through the life prediction agent model; wherein the life prediction agent model is obtained by training the echo state network by taking a flame tube crack propagation data set obtained by simulation as a training sample, and the flame tube crack propagation data set comprises a plurality of groups of crack propagation parameters and corresponding residual life prediction values. According to the method, the complex nonlinear relationship between the crack form and the life can be efficiently mapped, and the residual life prediction value can be quickly and accurately output; moreover, the method does not need to depend on complex real-time simulation calculation, can be directly connected with actual monitoring data, and effectively reduces the operation and maintenance cost.

The invention discloses a fault diagnosis analysis method and system for a chromatographic instrument, and relates to the technical field of chromatographic instrument fault diagnos.The fault diagnosis analysis method comprises the following steps that a gas thermal field dynamic mapping model is established based on real-time temperature distribution information in a chromatographic instrument detection pool; a temperature gradient distribution diagram in the detection pool is generated through continuous collection of the distributed multi-point temperature sensing unit; on the basis of a temperature gradient distribution diagram output by the gas thermal field dynamic mapping model, controllable micro-amplitude pulsating gas flow is applied to a gas inlet path of the detection pool, and a diffusible gas disturbance area is formed in the detection pool by adjusting the pulsating frequency and amplitude disturbance gas flow state. Through thermal field mapping and gas disturbance regulation and control, temperature and flow field balance of the detection cell is realized, thermal stratification and retention effects are eliminated, and a detection response linear relation is recovered; and the time reference is dynamically corrected through a signal drift self-suppression mechanism, and the complete output of the low-concentration signal is ensured, so that the sensitivity, the stability and the quantitative precision of the chromatograph are improved.

The invention discloses a BOTDR (Brillouin Optical Time Domain Reflectometer)-based OPGW (Optical Fiber Composite Overhead Ground Wire) light icing identification method, which comprises the following steps of: firstly, establishing a thermal conduction model of an OPGW in a non-icing state and an icing state, and constructing a theoretical expression of an OPGW icing thermal hysteresis effect by utilizing a linear relation between Brillouin frequency shift and temperature; then, the Brillouin frequency shift distribution along the line is obtained through a BOTDR system, actually measured data is matched with a model calculation result, and the icing thickness and the distribution interval are obtained through a regularization least square inversion method. According to the invention, clear identification of the light icing section can be realized, and good stability can be maintained under different illumination radiation intensity conditions. The method is clear in mechanism, high in model precision and suitable for early icing identification and anti-icing and deicing early warning application of the long-distance OPGW line.

The application provides a dynamic load determination method, comprising: obtaining test data, the test data being determined based on a dynamic test of a structure model corresponding to an application structure; obtaining simulation data, the simulation data being determined through simulation calculation based on a simulation model corresponding to the structure model; fitting the test data and the simulation data into quadratic expressions about vibration frequencies respectively, and determining a linear relationship between the test data and the simulation data; and determining a dynamic load of the application structure based on simulation strain data of the application structure and the linear relationship.

The invention relates to the technical field of vehicle-mounted radar signal processing, in particular to a millimeter-wave radar point cloud speed decoupling method suitable for a steering scene, and the method comprises the steps: firstly obtaining point cloud data with a radial distance, a radial relative speed and a horizontal / pitching angle in a vehicle steering scene, estimating the straight speed of a vehicle through employing a formula, estimating the vcar, and carrying out the calculation of the vcar after the vcar is estimated, the influence of the point cloud on the vehicle speed is filtered out, the abnormal speed at the position of the large horizontal observation angle caused by the filtered straight speed and residual steering speed of the vehicle is obtained, at the moment, the point cloud shows a linear relation on a sine value-relative speed relation graph, the linear speed value caused by steering of the vehicle is filtered out through a linear regression or RANSAC method, and the obtained slope k contains the angular speed; finally, the obtained road edge is a static point of which the speed is 0, the vehicle has the speed, and the point cloud becomes input data of a subsequent automatic driving perception algorithm module. Therefore, the problem of inaccurate point cloud absolute speed estimation during steering is solved.

The invention belongs to the technical field of ship electric power, and discloses a method and a system for quickly deducing a damaged state of a ship electric power system, and the method comprises the steps: carrying out the modeling of a tree-shaped Bayesian network; and quickly calculating the joint probability of the outage combination. According to the method, a rapid inference algorithm of linear complexity is provided by combining a tree operation structure of a power system, and the exponential complexity O (2n) of a traditional method is reduced to the linear polynomial complexity O (n). The method is realized by calculating the joint probability of the outage combination in a recursion mode: matrix multiplication operation is only required to be performed on each node once, and the total operation times and the node number n are in a linear relation. For an 86-node ship power system, the deduced time consumption is reduced from several hours of a traditional method to less than 0.75 second, and the efficiency is improved by more than 10000 times.

The present application relates to a kind of multi-sensor wideband sound source positioning method suitable for deep-sea reliable sound path environment, utilize the same sound source D-SR time delay measurement value observed by deep-sea large depth multi-sensor to carry out three-dimensional positioning to sound source.In the first stage, based on the position of each sensor, depth, observed D-SR time delay and water body sound velocity profile information, a matrix and a vector are constructed, there is a linear relationship between the two and the position of sound source, the least square method is used to solve this linear relationship, obtain the estimation result of the two-dimensional position of sound source, based on two-dimensional position estimation result, the depth estimation result of sound source is obtained using depth solution formula;In the second stage, based on the three-dimensional position of sound source estimated in the first stage, the D-SR time delay value of the position of each sensor is predicted, then an auxiliary variable matrix is constructed, and new sound source two-dimensional horizontal position estimation result is obtained according to the rule of auxiliary variable estimator (IVE), and finally the final sound source depth estimation result is obtained according to depth solution formula.

The invention provides a weak magnetic boundary determination method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining calibration test data, the calibration test data being test data of a permanent magnet synchronous motor under a weak magnetic condition, and the calibration test data comprising a motor rotating speed and a d-axis current; the calibration test data are input into a flux-weakening voltage model, target parameters are obtained, the target parameters are used for calculating the torque of the permanent magnet synchronous motor, and the flux-weakening voltage model is used for representing the linear relation between the d-axis current and the angular speed of the motor; and determining a first weak magnetic boundary curve of the permanent magnet synchronous motor based on the target parameter. According to the method provided by the invention, the weak magnetic boundary of the permanent magnet synchronous motor can be more clearly determined, and the performance of the permanent magnet synchronous motor is improved.

The application discloses a canopy coverage considering aggregation index inversion method and correction method, relates to the technical field of remote sensing image processing and aggregation index inversion, collects remote sensing images of a vegetation canopy, extracts hot spot reflectivity and dark spot reflectivity respectively, calculates a normalized hot spot and dark spot index NDHD, calculates an initial value of an aggregation index CI according to a linear relationship between the NDHD and the CI, and corrects the initial value of the CI by using a canopy coverage Ccan, wherein an error between the initial value of the CI and a real value of the CI has a linear relationship with the Ccan. The application first establishes a CI correction model based on the Ccan, and solves the deviation problem of a traditional algorithm in a sample field with high or low Ccan. By improving a mainstream CI inversion algorithm based on the NDHD, the application significantly improves the precision of CI inversion, and provides more reliable technical support for forest ecological monitoring, carbon cycle research and remote sensing application.

The invention belongs to the technical field of oil and gas field seismic data processing, and particularly discloses a first arrival prediction method, device and equipment under a complex mountain strong interference background and a medium. The method disclosed by the invention comprises the following steps: acquiring a surface structure interpretation result of a test point, the thickness of a low deceleration zone and the elevation of each detection point; thirdly, smoothing and calculating the elevation of each arranged detection point to obtain a static correction predicted value of each detection point; then, on the basis of single-shot data, loading static correction predicted values of all detection points, and carrying out preliminary first arrival pickup; and finally, selecting the first arrival data which can be picked up and the offset distance of each channel, performing multi-segment linear fitting on the left and right arrangements, and adding with the height-range static correction value of the reference surface of each channel to obtain a final predicted first arrival value. According to the method, the predicted value of the first arrival time of each channel is obtained by using the linear relation function, a basis is provided for accurately picking up the first arrival, and the first arrival picking efficiency and quality are improved. The method can be widely applied to the field of seismic exploration.

The embodiment of the invention provides a substrate temperature value detection method and a chip package, and relates to the technical field of chip testing. The method comprises the following steps: detecting the resistivity of a metal wire under the condition that the metal wire of a metal layer of a substrate is connected with current; and determining the temperature value of the metal layer based on the linear relation between the resistivity and the temperature value so as to obtain a temperature value distribution cloud picture in the substrate. According to the embodiment of the invention, the method at least solves a problem that the temperature value in the substrate obtained through a calculation mode is not accurate in the related technology, and improves the accuracy and real-time performance of the detection of the temperature value in the substrate.

The invention provides a pattern generation method of a spiral inductor manufactured by a semiconductor process, which comprises the following steps of: setting the number of turns of a coil of the spiral inductor to be M, and setting the shape of a single-turn coil to be an N-sided polygon; the center of an N-polygon where an inner ring is located serves as an X-axis and Y-axis coordinate center original point, N areas are correspondingly divided, the first area is provided with a starting point edge and an ending point edge which are parallel to an X axis or a Y axis, and the other N-1 areas correspond to one edge of the N-polygon respectively, the difference between the distance between the starting point edge and the ending point edge and the distance between the starting point edge and the original point is S, the distance between the N + 1 sides and the original point is the following expression: r1 is the inner diameter of the N-sided coil; i = 1, 2, 3... N + 1; step is equal to S / N, and S is the sum of the line width and the line distance of the N-polygon coil; and respectively listing linear relational expressions corresponding to the areas where the N edges are located, solving an intersection point coordinate of two adjacent edges as a vertex coordinate of the N-polygon, obtaining other vertex coordinates in this way, and drawing a single-turn coil of the spiral inductor.

This invention discloses a method for optimizing flue gas NO based on thermal parameters. x The emission control method includes the following steps: online measurement of thermal parameters for each stage of pellet roasting; analysis of the thermal parameters for each stage and their relationship with the NO inlet of the SCR denitrification system. x The correlation between concentrations was determined to establish the relationship between NO at the SCR denitrification inlet and each process stage. x Significantly correlated thermal parameters with concentration; constructing and solving for NO at the SCR denitrification inlet. x A multiple linear regression mathematical model for NO concentration was used to predict the NO concentration at the SCR denitrification inlet. x The trend of NO concentration change; identify the relationship with the SCR denitrification inlet NO. x The single factor most relevant to the concentration was established, and the relationship between the single factor and the NO inlet of the SCR denitrification system was established. x A linear relationship between concentrations was established to adjust NO x A univariate linear regression mathematical model for NO concentration is used to regulate NO. x Concentration. This invention can predict NO concentration at the SCR denitrification inlet. x The concentration trend can be observed, and the NO inlet of the SCR denitrification system can be precisely controlled. x Concentration, effectively achieving NO concentration in pellet roasting x Emissions are controlled throughout the process to achieve the goals of energy conservation and environmental protection.

The invention provides a method and a system for positioning an abnormal attenuation position of an optical fiber. The method comprises the following steps: acquiring a real value of receiving optical power and a real value of back Rayleigh scattering light power; determining a receiving optical power normal reference value and a back Rayleigh scattering power normal reference value according to the incident fiber power of the incident continuous service optical signal, and calculating a difference value between the receiving optical power normal reference value and a receiving optical power true value to obtain a receiving optical power drop amount; calculating the instantaneous transmittance by using the linear relationship between the drop amount of the received optical power and the instantaneous transmittance of the incident continuous service optical signal from the transmitting end to the receiving end and combining the normal reference value of the received optical power; and calculating the difference value between the normal reference value of the back Rayleigh scattering power and the true value of the back Rayleigh scattering power to obtain the drop amount of the back Rayleigh scattering power, and calculating the abnormal attenuation position of the optical fiber according to the linear relation between the drop amount of the back Rayleigh scattering power and the abnormal attenuation position of the optical fiber.

The application discloses a kind of electrical parameter calculation methods of para-metamorphic rock weathering crust, comprising: reading the conventional logging curve data of the depth corresponding to para-metamorphic rock weathering alteration index;Logging curve data and para-metamorphic rock weathering alteration index data are fitted into linear relationship chart;According to the correlation of the linear relationship chart of fitting, select the logging curve with high sensitivity of para-metamorphic rock weathering crust identification;The logging curve data with high sensitivity of para-metamorphic rock weathering crust identification is normalized;Fitting para-metamorphic rock weathering crust electrical parameter calculation formula;The logging curve data after normalization is brought into formula to calculate and identify para-metamorphic rock weathering crust.The present application is based on the analysis of a large amount of logging curve data, establishes a new electrical parameter calculation model of para-metamorphic rock weathering crust, and proposes a new method for identifying para-metamorphic rock weathering crust.