650 results about "Linear relationship" patented technology

The invention discloses a landslide disaster early warning system and method, and belongs to the technical field of geological disaster monitoring, and the method comprises the steps: carrying out the omnibearing monitoring of a target region, and collecting monitoring data; the method comprises the following steps: preprocessing collected monitoring data to obtain monitoring processing data; analyzing the monitoring processing data, and performing feature extraction to obtain a target feature vector; constructing a landslide disaster risk assessment model by adopting logistic regression and a decision tree; acquiring historical monitoring data according to a large amount of historical data and landslide cases, and training the model by using the historical monitoring data; real-time monitoring data are collected and input into the landslide disaster risk assessment model, the landslide risk level of the target area at the current moment is judged, and a corresponding early warning prompt is given; the method comprehensively considers the linear and non-linear relationship of the data, can accurately evaluate the landslide risk grade of the target area, and gives out graded early warning prompts. Compared with an existing scheme, the method has the advantage that the monitoring data source, the data processing precision and the model prediction capability are remarkably improved.

The invention discloses an SAR imaging method and system based on sparse and low-rank decomposition, and a readable storage medium. The imaging method comprises the following steps: collecting an SAR image and two-dimensional echo data, and preprocessing the two-dimensional echo data to obtain a target reflectivity; and based on the linear relationship between the two-dimensional echo data and the target reflectivity, decomposing the SAR image by using a preset method to obtain a sparse component and a low-rank component. A decomposition result is iteratively updated through a preset optimization algorithm, the precision is improved until errors converge, and a sparse and low-rank decomposition algorithm expression is obtained; and solving a preset SAR imaging model by using the sparse and low-rank decomposition algorithm expression, outputting an SAR imaging result, and finally outputting the SAR imaging result. According to the invention, the complexity of SAR imaging can be effectively reduced, and the imaging efficiency is improved. Even if the data sampling rate is reduced, the accuracy of image reconstruction can be ensured.

The invention discloses a motor electric control brake system clamping force self-adaptive correction method based on deep learning, and belongs to the technical field of chassis control, the method fully considers the wear condition of a brake pad in the use process, introduces a deep learning neural network optimization control strategy for the nonlinear change problem caused by the wear of the brake pad, and corrects the clamping force of the motor electric control brake system. The complex nonlinear relation between the clamping force estimated by the rotation angle of the motor and the actual clamping force is modeled as predictable probability distribution, and self-adaptive correction is performed, so that the clamping force control is optimized under the brake pad abrasion condition, the dynamic change caused by the brake pad abrasion is effectively dealt with, the real-time performance of system control is enhanced, and the system reliability is improved. The prediction precision and the response speed of the system are greatly improved, the stability, the reliability and the safety of the brake system are comprehensively enhanced, and meanwhile, the situation that a large number of sensors are adopted for real-time clamping force monitoring is avoided.

The invention discloses a diesel generator turn-to-turn short circuit fault protection method and system based on multi-parameter characteristics, and relates to the technical field of intelligent fault diagnosis of power equipment, and the method comprises the steps: carrying out the feature extraction of operation state monitoring data, carrying out the integration according to a time sequence, and generating a real-time feature sequence set; performing spatio-temporal feature coding and fusion on the real-time feature sequence set to generate a multi-dimensional spatio-temporal feature map; mapping the comprehensive matching score set into fault confidence according to a linear relation, obtaining a fault confidence sequence, judging a fault level in combination with a characteristic amplitude overrun condition, and generating a fault diagnosis result set; and executing hierarchical response based on the fault diagnosis result set, generating a hierarchical protection action execution record, and generating a short-circuit fault protection report in combination with the fault diagnosis result set. According to the method, quantitative similarity analysis of real-time features and standard modes is realized, the fault probability is accurately evaluated through a multi-parameter weighted fusion mechanism, and the reliability and interpretability of a diagnosis result are improved.

The invention relates to the technical field of machine learning optimization, in particular to a machine learning optimization method based on a QUBO model and quantum annealing, and the method comprises the following steps: firstly, discretizing continuous parameters of the machine learning model into binary variables, and constructing a QUBO objective function; then optimizing the QUBO model by using a quantum annealing algorithm; and finally, solving a globally optimal solution by adjusting annealing parameters, wherein the annealing parameters comprise the initial temperature, the cooling coefficient and the number of iterations. The QUBO model can discretize continuous variables (including parameters such as weights and offsets) in models such as AR, SVM and CNN in machine learning into binary variables, so that a nonlinear relation is better processed, and the calculation complexity in the training process is reduced. Through the QUBO model, the regularization item can be better controlled, and the problem of overfitting is avoided. Meanwhile, the QUBO model can be solved in parallel through quantum calculation or a simulated annealing algorithm, and the calculation efficiency under large-scale data is remarkably improved.

The invention discloses a navigation control method of an articulated vehicle and a related device, and relates to the field of automatic driving control, and the method comprises the following steps: obtaining navigation information of the articulated vehicle in real time, and calculating a steering curvature error based on the navigation information of the articulated vehicle and a preset discrete error tracking model. An expected value of the steering curvature is calculated based on the steering curvature error and a reference value of the steering curvature. And calculating the expected value of the hinge angle according to the expected value of the steering curvature. And generating a steering driving instruction to control a steering motor to drive the hinge angle to change into an expected value of the hinge angle. The discrete error tracking model is used for describing a linear relation between a pose tracking error vector in a discrete form and a steering curvature error, and the pose tracking error is composed of an X-axis tracking error, a Y-axis tracking error and a course angle tracking error. The motion state model of the articulated vehicle is linearized, and the control precision of curve tracking of the articulated vehicle is improved.

The invention discloses a fan unit blade root ultrasonic pretightening force monitoring method, and relates to the technical field of fan units, and the method comprises the following specific steps: carrying out ultrasonic fastening stress coefficient calibration on a fan unit blade root bolt material, applying axial tension to a plurality of sample bolts made of the same material, and determining the blade root ultrasonic pretightening force of the fan unit under each tension value; according to the method for monitoring the ultrasonic pretightening force of the blade root of the fan unit, the transmission speeds of ultrasonic waves in the sample bolt in different stress states are measured by utilizing ultrasonic detection equipment, and the accuracy of monitoring data can be remarkably improved; according to the method, the ultrasonic fastening stress coefficient of a bolt material is calibrated, high-precision real-time monitoring of the pretightening force of the bolt is realized by utilizing the linear relation between the ultrasonic wave spreading in the bolt and the stress, the influence of friction force on torque measurement is eliminated, and the misjudgment of pretightening force attenuation caused by temperature and vibration factors is also avoided.

The invention relates to the technical field of electric power fault detection, and discloses a fault monitoring system and method based on an electric power acquisition terminal, and the method comprises the steps: constructing a non-linear relation between the power consumption and the temperature through analyzing the historical power consumption data of a user and the weather temperature; the method comprises the following steps: dividing a user electricity consumption day into a plurality of time periods, and setting a time interval threshold value; and in each time period, data are acquired through the electric power and temperature acquisition devices, an electric quantity and temperature scatter diagram is constructed, and nonlinear data fitting is carried out. Thirdly, the similarity between the temperature nonlinear relational expressions of different time periods is calculated, the time periods are divided into sets, and a temperature retrieval set is established; and searching a corresponding electric quantity relation set by matching the temperature nonlinear relational expression of the user in the current time period, and judging whether electric power fluctuation and fault exist or not according to the priority ranking and the relation similarity.

An optimization method and system based on low-rank regression of historical iteration data are provided. The method includes: obtaining historical iteration data including change data of physical parameters to be optimized and result deviation data obtained through simulation by using a forward model; and iteratively updating matrix coefficients and the physical parameters to be optimized continuously through low-rank linear regression based on the change data of the physical parameters to be optimized and the result deviation data, and evaluating updated physical parameters to be optimized, until an iteration stop condition is met. A low-rank matrix approximation that represents a small signal linear relationship between the change data of the physical parameters and the result deviation data in the historical iteration is construct and used to solve the optimization problem, which effectively reduces the computational complexity of the optimization process and improves the computational efficiency of the optimization process.

The invention provides an intelligent interpolation method for laser radar data missing, and the method comprises the steps: firstly obtaining long-time-sequence laser radar data, carrying out the preliminary screening and processing of the data through a quality control program, and then carrying out the classification of possible missing data. According to the method, linear function interpolation is executed on the condition of partial distance gate data missing caused by weather influence, values of missing data points are quickly and effectively estimated by utilizing a linear relation, and the continuity of the data can be well recovered in a scene that the data change is relatively stable. And for the condition of full-distance layer data missing in the time sequence caused by blade shielding and the like, a bidirectional recursion time sequence interpolation model based on LSTM is adopted, the advantages of a deep learning model in the aspect of processing time sequence data are fully played, data in a missing time period are accurately filled through historical data learning and bidirectional recursion prediction, and the accuracy of time sequence interpolation is improved. Therefore, the integrity and availability of the laser radar data are effectively improved.

The invention discloses a landslide risk and risk evaluation method suitable for a tectonic active area, and belongs to the field of geological disaster risk evaluation, and the risk evaluation method is characterized in that risk evaluation is superposed with vulnerability evaluation. The risk evaluation method comprises the following steps: calculating a deterministic coefficient value of each evaluation factor, and weighting to a grid layer of each evaluation factor to generate a deterministic coefficient grid layer of the evaluation factor; importing the deterministic coefficient layer into a logistic regression model, and generating a landslide risk evaluation result based on a logistic regression-deterministic coefficient coupling model; the vulnerability evaluation method is an analytic hierarchy process. By coupling a deterministic coefficient model and a logistic regression model, a linear relationship of multiple evaluation factors is effectively processed, and nonlinear contribution of a single evaluation factor to landslide development is captured, so that the landslide risk evaluation precision of a geological environment complex region and a structure active region is improved; and the evaluation result can more objectively and truly reflect the landslide risk of the active fault zone and the nearby area.

The invention discloses a building carbon emission habit analysis method, device and equipment and a storage medium, and relates to the technical field of electric digital data processing. According to the method, the total energy consumption, such as the power consumption and the fuel combustion amount, of the building is obtained from resource purchasing which is easy to inquire, the total energy consumption, such as the power consumption and the fuel combustion amount, of the building can be obtained simply, and then each energy consumption device is separated to a thermal power station and a nuclear power station and to household appliances, cooking benches and the like of a residential building. The linear relation between the starting-up duration and the total carbon emission of each device is analyzed through linear regression, meanwhile, the size of a linear regression coefficient reflects the carbon emission of the device in unit time, and the larger the linear regression coefficient is, the larger the carbon emission of the device in unit time is. The carbon emission per unit time of each device can also be obtained from linear regression, and necessary data support is provided for the fields of subsequent large-scale carbon emission research and the like.

The invention discloses a carbon emission prediction method and device based on the relation between capture variables and a medium, and relates to the technical field of traffic carbon emission prediction. The method comprises the following steps: based on a preset traffic carbon emission influence factor set, calculating the correlation between the traffic carbon emission influence factor set and the traffic carbon emission, and carrying out preliminary screening to obtain a preliminary variable candidate pool; performing multi-collinearity detection on the preliminary variable candidate pool and updating the preliminary variable candidate pool to generate a screened variable candidate pool; performing nonlinear relation analysis on the screened variable candidate pool through an XGBoost algorithm, and screening to obtain a key variable sequence; inputting the key variable sequence and the traffic carbon emission into a Transform model, carrying out feature embedding and position coding, and generating a time sequence vector; and respectively predicting traffic carbon emission levels under different levels for the time sequence vectors. According to the method, the scientificity of the variable screening process and the reliability of model construction are ensured, and then the prediction capability of the model is enhanced.

The invention discloses an inductance material electromagnetic performance prediction method and system based on machine learning, and the method comprises the steps: collecting inductance performance index data, carrying out the data preprocessing of an inductance material performance index optimization data set, and carrying out the visualization of the data set, and obtaining the linear relation between the variables of the quantized inductance material performance index optimization data set. Through inductance material magnetic conductivity prediction, saturated magnetic field intensity prediction and inductance material electromagnetic performance prediction, inductance material electromagnetic performance characterization reference data are finally obtained. According to the method, the potential nonlinear relationship among the inductance raw materials is mined by utilizing the powerful calculation learning capability of the machine learning model, so that the raw material selection and formula design of inductance products with different specifications are accurately guided.

The present application relates to the technical field of storage, and provides a method and apparatus for determining an optimal read voltage parameter of a flash memory, and a flash memory device. The method comprises: on the basis of a linear relationship model between a read retry voltage offset value and a bit flip count, acquiring a new read retry voltage offset value corresponding to a minimized predicted bit flip count of the flash memory; determining whether data can be successfully read under the new read retry voltage offset value; if yes, adding the new read retry voltage offset value to the header of a read retry offset table to form a new read retry offset table; and on the basis of the new read retry offset table, updating the linear relationship model by means of an adaptive learning model. By means of the efficient and adaptive voltage parameter determination method, the present application can improve the success rate of data recovery and reduce performance loss.

The invention discloses a climate mode pre-estimation downscaling correction method based on generative deep learning, and the method comprises the steps: recognizing the global climate mode hydro meteorological variable pre-estimation deviation through an unsupervised generative adversarial network, continuously reducing the adversarial loss through the adversarial training of a generator and a discriminator, and improving the accuracy of the adversarial training. Therefore, the trained generative deep learning network has the capability of processing a complex non-linear relationship so as to achieve the comprehensive optimum of downscaling and deviation correction. The method can be used for correcting the estimated average deviation of a multi-mode variable (rainfall, temperature and the like) set and improving the resolution ratio of the multi-mode variable set. The embodiment of the invention shows that the method can effectively estimate and downscale a multi-model set, and has a better downscaling effect than common quantile mapping and a convolutional neural network.

The invention discloses a vegetation index time sequence prediction method and device based on deep learning, and the method comprises the following steps: obtaining and processing historical period vegetation indexes and environmental factor data of a preset time period, and obtaining NDVI, EVI and kNDVI data as vegetation indexes, environment factor data including temperature, rainfall, evapotranspiration, wind speed, relative humidity and soil humidity; by determining the intensity and direction of the linear relation between the environmental factors and the vegetation indexes, the environmental factors with high correlation with the vegetation indexes are selected; a CNN-BiLSTM-AM model comprising an input layer, a CNN layer, a BiLSTM layer, an AM layer and an output layer is constructed to predict a vegetation index time sequence, environmental factors with high correlation serve as input data, and NDVI, EVI and kNDVI vegetation indexes serve as output prediction targets. The technical process is simple and clear, and the accuracy of vegetation index time sequence prediction is greatly improved.

The invention discloses an industrial park load analysis method based on an optimized K-means clustering algorithm, and the method comprises the steps: collecting the power load data of an industrial park, including the internal data of the total power consumption of equipment, the time-sharing power consumption, the power consumption peak value, the energy consumption of the equipment and the like, and the external data of weather data, electricity price market information, policies and regulations and the like; the method comprises the following steps of: performing unified standardization processing on data, including cleaning and normalizing the data, and filling by adopting a KNN method aiming at the problems of missing values and errors in the data, so as to avoid the problem of low baseline load analysis accuracy caused by errors in data collection; afterwards, a KPCA nonlinear dimension reduction technology is adopted to process a nonlinear relation between the power demand of the production line and external data, the complexity of a model is reduced by simplifying a data set, more nonlinear features in the external data are extracted, and the rule of the power demand of the industrial park can be better mastered; the problem that a power demand side management method is short in processing a non-linear relation is solved.

The invention relates to the technical field of data reasoning, and discloses an AI-based power load prediction method, which comprises the following steps: compensating a missing value in target power data with a unified timestamp to obtain a standardized data set of a target power grid, and adaptively extracting a multi-dimensional feature vector; predicting a historical power load based on a training set in the historical power data set, and when an error value between the historical power load and a verification set in the historical power data set is lower than a load threshold value, obtaining a load prediction weight; fitting a linear relationship between the temperature change in the historical power data set and the error value, and representing the slope of the linear relationship as a compensation coefficient; predicting the power load condition of the target power grid, and correcting the load fluctuation of the power load condition based on the compensation coefficient; encoding the corrected power load condition into a power data packet, and transmitting the power data packet to the historical power data set in S2 to obtain a target load prediction report; according to the invention, the accuracy of power load prediction can be improved.

The invention provides a channel knowledge map construction method and system based on nerve ray tracing, and the method comprises the steps: collecting the actual measurement data of electromagnetic wave propagation through deploying channel detection equipment; the actually measured data are preprocessed; training a deep learning model by using the preprocessed data and a material library, so that the deep learning model can learn a complex nonlinear relationship of interaction between electromagnetic waves and the environment; through a trained ray tracing frame based on a neural network, after a three-dimensional map and resolution are given, nerve ray tracing is carried out on each position, according to obtained multipath channel information, related channel parameters are predicted, a channel knowledge map in the current environment is generated, and proper values are set to visualize the channel knowledge map. According to the method, the fitting capability of the neural network to the nonlinear relationship is used for calculating the interaction effect of the ray and the environment, the electromagnetic property of the material in an actual scene can be better predicted, and the method has excellent migration capability.

The invention relates to the technical field of coupling alignment of single-mode optical fibers, in particular to an optical fiber coupling alignment method and system based on wavefront aberration and a sensitivity matrix. The system comprises an optical measurement module, a processing and control module and a multi-axis precision adjustment platform, and the alignment method comprises the steps that firstly, an interference fringe image is obtained and processed through an interference light path, continuous wavefront is recovered through Fourier transform and phase unwrapping, and a Zernike coefficient for quantizing wavefront distortion is obtained through Zernike polynomial fitting. A sensitivity matrix representing the linear relation between the degree-of-freedom adjustment amount and Zernike coefficient changes is established and serves as a physical guide item to introduce a speed updating formula of a particle swarm optimization algorithm, an SG-PSO algorithm is formed, a particle swarm is guided to conduct directional intelligent search and rapidly output the optimal adjustment amount, and finally a multi-axis precision adjustment platform is driven to complete alignment. According to the method, optimization of wavefront sensing and physical model guiding is effectively fused, and the convergence speed, the alignment precision and the global search capability are remarkably improved.

The invention provides an overcurrent prediction method, medium and system for a liquid-cooling over-charging pile, and belongs to the technical field of electrical variable measurement, and the method comprises the steps: designing a time-sensitive HyperLogLog (TS-HLL) structure through collecting the charging parameters and heat exchange parameters of the liquid-cooling over-charging pile, and coding the parameters; statistical characteristics of charging parameters and heat exchange parameters are extracted by using a time sliding window technology, and a correlation matrix between the charging parameters and the heat exchange parameters is calculated. Based on the correlation matrix, a relation graph of the charging current and other parameters is constructed, a graph neural network is used for learning the relation graph, and a complex nonlinear relation between the charging current and other parameters is extracted. And finally, combining the learned features with historical charging data, inputting the combined features and historical charging data into a recurrent neural network, and predicting the charging current in a period of time in the future, thereby solving the problem of rapid over-current prediction of the liquid cooling over-charging pile, which is difficult to solve in the prior art.

The invention discloses a scheduling optimization method of a hydrogen-electric coupling system for source load prediction, and relates to the technical field of electric power, and the method comprises the steps: S1, building a state space model based on the operation data of the hydrogen-electric coupling system, building a prediction control model through calculating the state variable observation value of the system, and building the operation constraint of the prediction control model; s2, establishing a first objective function based on the model variable of the predictive control model and the intra-day real-time scheduling expectation of the hydrogen-electricity coupling system; s3, carrying out quadratic programming conversion on the first target function, calculating a real-time source load prediction value through a prediction control model, updating the converted first target function, and obtaining a second target function containing a linear relation; s4, iteratively solving the second objective function to obtain an optimal system scheduling scheme; the calculation efficiency is remarkably improved, the scheduling efficiency is further improved, the real-time dynamic response of the system is reflected by establishing the state space model, and reliable scheduling of the system is achieved.

The invention discloses a low-voltage power distribution network line loss prediction method, a model training method and a related device, and the model training method comprises the steps: (1) employing a neural network structure combining a time sequence feature extraction module and a multi-scale feature extraction module, and capturing the long-term trend of photovoltaic output and load fluctuation, line loss change modes under different time scales are effectively modeled; (2) a deep feature extraction module introduces a deep feature extraction mechanism with mutually different architectures, and the expression ability and generalization performance of the model to a complex nonlinear relationship are improved; (3) a crown porcupine optimization algorithm is adopted to perform joint optimization on model parameters and structures, efficient search can be realized in a high-dimensional non-convex space, local optimum is avoided, and prediction precision and stability are improved; based on the line loss prediction model, the line loss prediction method provided by the invention can be directly used for power distribution network operation situation evaluation and energy efficiency analysis, and has good engineering application value under the background of wide distributed photovoltaic access.

The invention provides an insect pest prediction method based on PSO-LSTM, belongs to the technical field of agricultural information, and aims to solve the problems that a single machine learning model is adopted in a traditional insect pest prediction method, the long-term modeling capability of time sequence data is insufficient, the complex nonlinear relation between the number of insect pests and meteorological factors is difficult to capture, and the prediction precision is low. Comprising the following steps: S1, collecting insect pest data and establishing a multivariable time sequence data set; s2, data preprocessing; s3, feature selection; and S4, establishing an insect pest prediction model, optimizing the insect pest prediction model by using a PSO module, and obtaining a prediction value of the number of insect pests at the next moment based on the optimized insect pest prediction model.

The invention relates to the technical field of electric power, in particular to an electricity price fluctuation quantification method, device, equipment and medium based on regression analysis, and the method comprises the steps: taking electric power price fluctuation in peak load of electric power as a dependent variable, and taking new energy power generation, thermal power generation, electric power demand, generating capacity and capacity electricity price as independent variables; establishing a multiple linear regression model; verifying whether the independent variable and the dependent variable have a linear relationship or not, preliminarily carrying out multiple linear regression analysis, finding an optimal regression coefficient by minimizing a residual sum of squares, and carrying out multiple linear problem judgment; a ridge regression analysis method is adopted, a regularization item is added into a loss function to limit the size of a regression coefficient, overfitting of the model is reduced, and a ridge function is confirmed through cross validation; performing overall significance test and regression coefficient significance test on the multiple linear regression model, and analyzing the fitting degree; and performing quantitative analysis on electricity price fluctuation by using the fitted multiple linear regression model.

The invention discloses a design method of a pressure control throttle valve with the pressure drop linearly changing along with the stroke, and belongs to the technical field of automation of oil and gas exploitation equipment.The design method comprises the following steps that the linear relation between the pressure drop and the stroke of the throttle valve serves as a target; a mathematical model of a valve element contour is established by establishing a minimum throttling area equation of a cylindrical valve and a wedge-shaped valve, correcting a flow pressure drop characteristic equation and deducing a differential pressure linear adjustment equation, a valve element contour curve meeting the linear requirement is deduced through variable-diameter contour optimization design, and therefore the valve element structure of the throttling valve is obtained. By means of the design method, the valve element contour curve with the wider linearity range of pressure control adjustment and the better precision can be obtained, and the design method is more efficient and universal.

The invention discloses a crop yield prediction method based on remote sensing inversion, and the method comprises the steps: obtaining historical remote sensing image data and historical ground actual measurement data, and building a standard value library based on the historical crop yield data, the vegetation features of different crops, and the linear relation between the vegetation features in the same year and the crop yield data; constructing a crop yield estimation model, and training the crop yield estimation model to obtain a trained crop yield estimation model; obtaining current-year remote sensing image data, and inputting vegetation features obtained by the current-year remote sensing image data into the trained crop yield estimation model to obtain a preliminary yield estimation value; and obtaining agricultural meteorological data of the current year, correcting the preliminary yield estimation value according to the agricultural meteorological data, and obtaining a target yield estimation value, thereby being beneficial to improving the precision of crop yield estimation.

The invention discloses a multi-channel NDIR infrared gas sensor detection data analysis system and method, and relates to the technical field of gas detection.The method comprises the steps that infrared light intensity of each gas channel is collected to serve as original data, and a fluctuation value of the original data is calculated to serve as an original fluctuation value; acquiring historical temperature and humidity data, fitting a linear relationship between the historical temperature and humidity data and the historical infrared light intensity data, and calibrating the original data based on a correction coefficient obtained by fitting to obtain correction data and a corresponding calibration fluctuation value; calculating a variable coefficient of the correction data, and setting a detection threshold value for each gas channel in combination with the calibration fluctuation value so as to screen out effective original data; distributing an initial weight for each gas channel, dynamically adjusting the weight based on the variable coefficient of the effective data, and processing the adjusted weight through a smoothing algorithm to obtain a smooth weight; and converting the effective original data into gas concentration data, and carrying out weighted fusion on the gas concentration data and the smoothed weight so as to output final gas concentration data.

The invention relates to the field of electromagnetic calculation, in particular to a petal-shaped Bessel light-based distance measurement method in hypersonic plasma turbulence, which comprises the following steps of: simulating plasma sheath turbulence by establishing a multi-layer random phase screen, calculating diffraction transmission of petal-shaped Bessel light after the petal-shaped Bessel light passes through the turbulence, and capturing intensity distribution of reflected light beams; the rotation angle is determined based on the correlation with the ideal intensity distribution, and high-precision distance measurement is realized by using the linear relation between the rotation angle and the propagation distance. The petal-shaped Bessel light is formed by superposing two Bessel mode light beams with different orbital angular momentum orders, has a spatial structure strongly related to the propagation distance, and can keep higher stability and anti-interference capability in a turbulent environment. By combining the self-focusing and approximately non-diffraction characteristics of the Bessel beam, the target detection and distance measurement precision in hypersonic plasma turbulence is remarkably improved, and reliable technical support is provided for communication and target detection of hypersonic aircrafts.