641 results about "Linear fitting" patented technology

The invention provides an adaptive welding seam detection and three-dimensional reconstruction method based on deep learning and binocular vision. The adaptive welding seam detection and three-dimensional reconstruction method comprises the steps of S1, collecting samples and making a training data set; s2, the picture of the sample to be welded is processed, a feature region is recognized, the image quality of the region to be welded is analyzed and evaluated through wavelet transform and local variance, and the noise level and the contrast ratio are calculated; s3, dynamically generating edge detection parameters and model fitting parameters according to the image quality; s4, using an edge detection algorithm to extract edge point cloud of the welding seam area; s5, performing RANSAC linear fitting, weighted least square fitting and polynomial curve fitting on the edge point cloud in parallel; s6, selecting an optimal fitting result based on an image quality adaptive dynamic scoring model; and S7, carrying out three-dimensional coordinate conversion in combination with the three-dimensional matching model IGEV-Stereo, and outputting a final welding seam three-dimensional coordinate. According to the invention, automatic detection of the position and size of the welding seam can be efficiently and accurately realized.

The invention relates to the technical field of 3D printing error compensation and precision control, and particularly discloses a printing comprehensive precision measurement method for 3D printing equipment, which comprises the following steps: designing a standard test piece containing various typical geometrical characteristics, and combining non-contact three-dimensional optical scanning and contact probe measurement means to measure the printing comprehensive precision of the 3D printing equipment. Spatial distribution data of printing errors are obtained in a controlled environment, a three-dimensional error field model with direction and amplitude information is constructed through point cloud registration and feature comparison, and based on error distribution features, the local resolution is dynamically adjusted by adopting a multi-level self-adaptive grid division strategy, so that a three-dimensional error field model with direction and amplitude information is constructed. A local error compensation model is established by combining tightly-supported radial basis function interpolation and a linear fitting method, a boundary consistency matching algorithm is introduced to realize multi-level model fusion, a unified global error compensation field is formed, an error offset vector field is embedded into a slicing process, point-by-point dynamic correction of a nozzle path is realized by using a trilinear interpolation algorithm, and the nozzle path is dynamically corrected. And the forming precision and the surface quality of the key structure are improved.

The invention provides a production line equipment fault prediction method and system, and relates to the technical field of fault prediction.The method comprises the steps that multi-source time sequence signals are synchronously collected, a multi-dimensional health feature sequence is extracted, the comprehensive sensing capacity covering multiple states of equipment is established, and further, the fault prediction efficiency is improved. By selecting a key health feature sequence for each device and performing linear fitting to quantify the performance degradation severity, accurate description and early recognition of the performance degradation trend of the device are realized, and in addition, the causal influence intensity between the devices is quantified by adopting a transfer entropy algorithm, so that the accuracy of the performance degradation trend is improved. Scientific modeling and visual analysis of a fault propagation path and a linkage effect in a production line are realized, and a comprehensive risk index is generated through weighted fusion of performance degradation severity and a global impact factor, so that a comprehensive risk assessment index is formed. Based on the cumulative failure probability function, the comprehensive risk index is converted into a fault probability curve changing along with time, a visual mathematical expression of the risk trend is formed, and a scientific basis is provided for decision making.

The invention discloses a pulse eddy current signal late period slope feature extraction method and system based on segmentation fitting, and belongs to the technical field of nondestructive testing. The method comprises the following steps: performing logarithmic transformation and filtering processing on a to-be-detected eddy current signal; intercepting a characteristic signal segment based on the filtering signal; dividing the feature signal segment into a plurality of sub-segments, performing piecewise linear fitting, determining a linear segment, and extracting a slope as a feature value; repeating the steps to obtain characteristic values of the test pieces with different thicknesses, performing curve fitting according to the characteristic values of the test pieces with different thicknesses and the corresponding thicknesses, and calculating a fitting error; and finally, iteratively optimizing the division number of the sub-segments by taking the minimum fitting error as a target. Through adaptive filtering, segmentation fitting and an intelligent optimization algorithm, the anti-interference performance, the linearity and the measurement precision of pulse eddy current signal feature extraction are remarkably improved, and efficient and reliable thickness detection of the high-temperature and high-pressure industrial pipeline under the condition that a heat preservation layer does not need to be disassembled is achieved.

The invention relates to the technical field of remote sensing science and technology, in particular to a frozen soil region freeze-thaw disaster identification method and system and a storage medium, and the method comprises the steps: generating a target differential interference image set; carrying out homogeneous point filtering, phase unwrapping optimization and unwrapping error correction on the basis of the target differential interference image set to obtain an unwrapped phase image set; for each unwrapped phase diagram, performing uniform sub-block division, and based on an elevation-phase empirical relationship, estimating and removing an atmospheric delay phase existing in each sub-block through a linear fitting method to obtain a phase optimization diagram set after atmospheric influence is removed; performing sequential superposition on unwrapping phases corresponding to the interferograms in the phase optimization diagram set, and performing analysis by using the relationship between the average phase change rate after superposition and the radar wavelength to obtain earth surface deformation data; and unsupervised classification and screening are carried out based on the earth surface deformation data, and freeze-thaw change areas are optimized, so that a high-precision freeze-thaw recognition result is obtained.

The invention discloses a high-energy-efficiency logarithm floating point multiplier based on truncation error compensation. The system comprises a compensation parameter modeling unit and a floating point multiplier module. The compensation parameter modeling unit obtains an optimal compensation value k through linear fitting and parameter optimization, and realizes regional self-adaptive compensation in combination with block modeling so as to reduce truncation errors; the floating point multiplier module comprises a special value detection module, a symbol exclusive or module, an exponential addition module and a mantissa truncation compensation module. In a logarithm domain, multiplication is replaced by addition, and compensation logic is introduced to correct errors. The multiplier supports various floating point precisions and configurable truncation bit widths, provides three representative structures to give consideration to both the precision and the energy efficiency, and is suitable for high-energy-efficiency calculation scenes such as neural networks and image processing.

The present invention discloses a supply chain logistics information management system based on artificial intelligence, specifically related to the field of artificial intelligence technology, including a linear fitting module, a non-linear fitting module, a dual-mode construction module, a distribution verification module, and a management intervention module. The linear fitting module obtains linear conventional coefficients according to linear scenario information, the non-linear fitting module obtains non-linear special coefficients according to non-linear scenario information, the dual-mode construction module constructs a dual-mode management model based on the linear conventional coefficients and non-linear special coefficients and outputs a time cost delay index, the distribution verification module classifies the delay status in the supply chain logistics information management process, and the management intervention module conducts intervention management according to the classification results. This application predicts the time cost of the most time-consuming and laborious customs declaration and tax payment links in the supply chain logistics information management process, improving the management effectiveness and real-time performance of the entire logistics information management chain.

The invention discloses a method and system for detecting abnormal welding spots in the spot welding process. The method comprises the steps that a time sequence data sequence collected in real time in the welding process is obtained; calculating a first-order difference sequence of the time sequence data sequence; screening a target data segment representing a stable trend based on the first-order difference sequence; performing linear fitting on the first K data points of the target data segment to obtain a first trend direction; performing linear fitting on the last K data points of the target data segment to obtain a second trend direction; calculating the variable quantity of the first trend direction and the second trend direction; and when the variable quantity exceeds a preset threshold value, it is judged that welding is abnormal, and an alarm is triggered. The stable trend section is screened through the differential sequence, noise interference is effectively eliminated, and it is ensured that the quality criterion is based on reliable data. And whether the welding spot is abnormal or not is quickly and accurately judged, so that real-time and non-destructive spot welding quality on-line monitoring is realized.

The invention relates to the field of financial stock price markets, in particular to a stock price prediction method based on a Transform-KAN model, which comprises the following steps: S1, selecting a proper data set on a Kaggle platform, processing missing values and abnormal values of the stock price data set, dividing the data set into a training set and a test set, carrying out maximum-minimum normalization processing on the training set and the test set; s2, generating a time window feature by using a time sequence feature extraction method, and converting the time window feature into an input format suitable for a Transform structure; s3, a Transform-KAN model is constructed for stock price prediction, a long-term dependency relationship of a time sequence is extracted by using a Transform, and an MLP layer of the Transform is replaced by using KAN (Kolmogorov-Arnold Networks), so that the nonlinear fitting capability of the model is enhanced; s4, defining a loss function and an optimizer, and setting hyper-parameters such as a learning rate and a batch size; s5, inputting the data into the network, training the model by using the training set, and storing the trained model; and S6, loading the stored model, predicting the test set, and evaluating by using various evaluation indexes.

The invention relates to an ocean internal temperature inversion method fusing PIES and sea surface multi-source parameters, and belongs to the technical field of ocean environment parameter inversion and ocean dynamics observation. Comprising the following steps: constructing a nonlinear empirical model (Multi-RF) of seawater temperature inversion by using a random forest algorithm and fusing data such as a historical observation temperature-salinity profile observed by a temperature-salinity-depth meter (CTD) and sea surface multi-source parameters; and inverting the seawater temperature through a Multi-RF model by using the sound propagation time and multi-source sea surface data calculated by the PIES. Compared with a traditional empirical mode mapping (GEM) model, the Mull-RF model provided by the invention has the advantages that on one hand, the Mull-RF model utilizes fewer samples to establish a mapping relation between the seawater temperature and multiple parameters, and the problem of sample shortage in construction of a seawater temperature inversion model fusing sea surface multiple parameters and PIES observation sound propagation time is solved; on the other hand, compared with polynomial fitting in a GEM model, the random forest adopted by the Multi-RF model can achieve nonlinear fitting from multiple parameters to the seawater temperature, and therefore the temperature inversion precision is remarkably improved.

The invention discloses a welding seam self-adaption B spline curve fitting method for a 3D dense out-of-order point set, and aims at solving the problem of complex curved surface welding seam track generation in robot welding and breaking through the limitation of the prior art through a three-stage self-adaption fitting mechanism. The method comprises the following steps: firstly, self-adaptive screening and projection sorting are carried out by utilizing a straight line fitting error, so that the disordering problem of discrete points of a quadratic curved surface intersection line is effectively solved, and the strong dependence of a traditional method on a CAD model is overcome; secondly, in combination with a region growing method guided by an end tangent vector, robust conversion from an out-of-order point set to an ordered control point is realized through dynamic neighborhood search and projection value sorting, and path continuity of an open / closed curve is guaranteed; and finally, mean sampling quadratic fitting is adopted, control point distribution is optimized while noise is restrained, and a smooth B-spline track meeting the movement requirement of the welding gun is generated. According to the method, direct fitting of the 3D dense out-of-order point set under the model-free condition is achieved for the first time, and the generation efficiency and adaptability of the irregular welding seam track are remarkably improved.

According to the hybrid fitting FPGA technology chaotic signal generator implementation method, a memristive chaotic system with complex nonlinear characteristics such as a hyperbolic function and a natural logarithm can be efficiently generated on a resource-limited FPGA. According to the scheme, target function slope distribution is analyzed, binomial fitting is adopted for slope steep intervals, hardware architecture is optimized to reduce resource occupation, and linear fitting is adopted for slope gentle intervals to simplify implementation logic. According to the memristor chaotic system model constructed based on the hybrid strategy, an iterative operation module is integrated on a chip. Experimental verification shows that the method obviously reduces the consumption of storage resources while keeping high precision, and is particularly suitable for chaotic signal generation scenes with low power consumption and high real-time requirements.

A straight line fitting method for image detection in a complex interference environment comprises the following steps: firstly, carrying out interference point deletion on a feature point set of an environment image, then carrying out multiple times of random sampling, forming a total point set by using associated points through distance operation, obtaining a plurality of straight line models through initial fitting, and carrying out linear fitting on the linear models; and then calculating an interior point proportion in the total point set according to the linear model, finally selecting the total point set with the maximum number of interior points, and continuously reducing a residual threshold in multiple iterative calculations to improve an interior point proportion threshold by dynamically adjusting a feedback mechanism of the residual threshold when the interior points are calculated. In the initial stage of iteration, more points can enter the model to ensure the diversity and robustness of the model, and the threshold value is gradually tightened along with the convergence of the model, so that the interior points conforming to the model are screened out more accurately. And finally, performing secondary sampling on the inner points and performing straight line fitting on an inner point set subjected to secondary sampling again to eliminate potential error inner points and further improve the fitting precision.

The invention provides a trapezoidal thread parameter measurement method and system based on machine vision. The method comprises the following steps: acquiring a thread image and extracting thread contour features; based on the extracted thread contour features, carrying out tooth crest, tooth bottom and two-side thread profile contour segmentation; performing straight line fitting on the tooth crest contour and the tooth bottom contour of each tooth form to obtain a straight line point set of each tooth crest, then fitting the tooth crest straight line point set in the visible range to obtain a tooth crest straight line, and obtaining a tooth bottom straight line by adopting the same method as the tooth crest; and finally, based on the straight lines of the tooth crests and the tooth bottoms of the threads on the two sides of the to-be-measured rod piece, calculating the central axis of the threads, the tooth crest distance and the tooth bottom distance of the threads on the two sides of the to-be-measured rod piece, the tooth height and the tooth width. The trapezoidal thread tooth crest and the trapezoidal thread tooth bottom are segmented by fusing a graphics method, thread parameter measurement is divided into small-size parameter measurement and large-size parameter measurement, the thread measurement precision is improved while the thread measurement difficulty is simplified, and the adaptability of a visual scheme in trapezoidal thread measurement is enhanced.

The invention provides an artificial intelligence driving cascade direct-hanging SVG carrier phase shift optimization method and system, and the method comprises the steps: constructing a sensor input layer, a slow decision layer and a fast execution layer; multidimensional power grid state information collected by the sensor input layer is transmitted to the slow decision-making layer and the fast execution layer; the slow decision-making layer outputs modulation control parameters based on the reinforcement learning neural network; and the fast execution layer outputs a modulation signal based on the modulation control parameter and the multi-dimensional power grid state information, and is used for driving the cascaded H-bridge module. The system is simple in structure and low in control and construction cost, a fast and slow double-layer coordination framework is constructed, and a reinforcement learning neural network is combined in a method level, so that the nonlinear fitting capability is realized, dynamic parameter adjustment and collaborative optimization can be carried out for multiple dimensions and multiple targets, the stability of a cascade structure is enhanced, and the stability of the cascade structure is improved. And the comprehensive performance of the SVG is improved.

The invention discloses a patch clamp electrophysiological data processing method and a patch clamp electrophysiological data processing system. The method comprises the following steps: acquiring ion channel current data through a patch clamp microelectrode; baseline drift removal processing is carried out on the ion channel current data through an adaptive quadratic linear fitting function to obtain baseline correction current data; performing discretization processing and high-frequency filtering processing on the baseline correction current data based on a preset sampling frequency to obtain a filtering current signal sequence; performing calculation through a false nearest neighbor method to obtain an optimal embedding dimension, performing calculation based on an average displacement method to obtain optimal delay time, and performing second phase space reconstruction through the filtering current signal sequence, the optimal embedding dimension and the optimal delay time to obtain an optimal vector set and a phase space reconstruction matrix; calculating through a singular value decomposition algorithm and a Shannon formula to obtain a principal component vector set and an information entropy corresponding to each vector, judging through a preset information entropy threshold to obtain a principal vector sequence, and reconstructing through a singular matrix to obtain electrophysiological feature data.

The invention relates to a low ionized layer electron density inversion method based on occultation data. The method comprises the following steps: obtaining a secondary data file provided by an occultation item consisting of a plurality of low-orbit satellites, then carrying out TEC data preprocessing, extracting an F-layer component of an ionized layer in a TEC profile according to two-section linear fitting within a certain height interval range based on the preprocessed data, and obtaining an F-layer TEC component of the ionized layer after interpolation; calculating the TEC component of a low ionized layer according to the occultation observation value after interpolation and the TEC component of the F layer of the ionized layer; constructing an electron density inversion linear equation set based on an Abel weighting function according to the height distribution of the TEC component of the low ionized layer, and solving by adopting non-negative least squares to obtain an inverted electron density profile; and performing smooth fitting on the inverted electron density profile by using a Gaussian function to obtain an electron density profile product. By adopting the method, the inversion precision of the electron density of the low ionosphere can be improved.

The invention relates to a hitch angle estimation device, a hitch angle estimation method, and a program product. A hitch angle estimation device according to the present disclosure performs conversion from an observation coordinate system to a world coordinate system on a point sequence representing a tow bar and a lower end portion of a trailer in an image captured by a camera mounted on a vehicle that tows the trailer via the tow bar. In the present invention, linear fitting of a point row representing a tow bar and a lower end of a trailer after the execution of the transformation is performed is performed, and a hitch angle of the trailer in a world coordinate system is calculated on the basis of a straight line representing the tow bar in the world coordinate system and a straight line representing the lower end of the trailer in the world coordinate system obtained by the linear fitting. And executing Kalman filtering processing on the hitching angle of the trailer in the world coordinate system.

The invention discloses a disastrous big wave occurrence probability and wave height combined prediction method, device and equipment, a medium and a product, and relates to the technical field of meteorological ocean forecasting. The method comprises the following steps: acquiring an effective wave height measured value and environmental factor data of a to-be-predicted region in a historical time period; key environment factors are screened through spatial correlation analysis and linear fitting verification, a first training data set and a second training data set are constructed, and a multiple linear regression model and a logic regression model are trained respectively to obtain a wave height prediction model and a big wave occurrence probability prediction model; and inputting the key environmental factors of the current time period of the to-be-predicted region into the model to obtain the predicted occurrence probability of the disastrous waves and the predicted value of the significant wave height in the predicted time period. According to the method, the key environmental factors are screened and the two models are used for respective prediction, the influence of the various environmental factors on the big waves is considered, the occurrence probability and the wave height of the disastrous big waves can be predicted more accurately, and the ocean safety is effectively guaranteed.

The invention provides an IGBT (Insulated Gate Bipolar Translator) turn-off time measuring method, system, equipment and medium, and belongs to the field of power semiconductor device testing technology and state monitoring, and the method comprises the following steps: firstly, selecting 20% Vdc and 80% Vdc as turn-off initial measuring points, secondly, carrying out first error correction by adopting a linear estimation method, and finally, carrying out second error correction by adopting a linear estimation method; performing linear fitting on the voltage difference Vce data through a least square method to obtain initial turn-off time; and finally, aiming at systematic deviation caused by inherent concavity and convexity characteristics of the voltage curve, performing secondary error correction by adopting a convex hull algorithm, acquiring a lower envelope line of the ic-tOFF relation curve through an Andriw algorithm, and identifying and eliminating the problem of Overestimation of the turn-off time based on a convex hull accurate point set. According to the invention, accurate measurement of the turn-off time of the IGBT under the condition of low sampling rate is realized, the hardware cost is significantly reduced, and a reliable technical basis is provided for IGBT state monitoring and life prediction.

The invention relates to the field of heat transfer, in particular to a deep learning-numerical simulation-based rapid and accurate prediction method for heat transfer of a data-driven fluid. Comprising the following steps: S1, constructing physical and mathematical descriptions of a semicircular micro-channel, and establishing a control equation of the semicircular micro-channel; s2, acquiring input conditions of specific prediction; s3, based on a three-dimensional finite element analysis result, constructing a corresponding three-dimensional model and carrying out finite element analysis; s4, designing a full-connection feedforward neural network with one input layer, four hidden layers and one output layer; s5, learning the neural network by using a mean square loss function as a criterion; s6, performing network parameter learning by using stochastic gradient descent based on a back propagation algorithm; and S7, carrying out gradient solution by adopting an Adm algorithm so as to improve the generalization ability of the model. The activation function is selected from a ramp function, namely a Received Line Unit function; finally, the deep learning model is driven through data to predict the heat transfer behavior, and the linear goodness of fit R2 serves as an evaluation index.

The invention provides a method and a system for identifying and processing abnormal startup and shutdown of measurement and control equipment of a satellite service system. The method comprises the following steps of: S1, de-noising telemetry information received by a preprocessed measurement and control responder by using a filtering model; s2, dynamically adjusting the weight ratio of the signal strength, the error rate and the response time to evaluate the on-orbit performance of the measurement and control responder; and S3, if the measurement and control responder is in a normal working state and receives a host or standby shutdown instruction of the responder, firstly judging the state of the whole satellite, then judging the code block telemetering information, and if no problem exists, judging the instruction as an abnormal behavior, and shielding the instruction by the security software. According to the method, the new measurement value can be responded in real time through continuously updating the state estimation by adopting the Erman filtering, and the adaptability of the system to the dynamic change is ensured; the system characteristics are judged through linear fitting, and the Kalman gain is calculated, so that the weighting between the predicted value and the measured value is more accurate.

An intelligent monitoring and early warning method for damage failure of a filling body beside a gob-side entry retaining roadway comprises the steps that a high-water-content material standard sample is prepared, uniaxial compression testing and acoustic emission monitoring are carried out, and mechanical property data and acoustic emission waveform signals are synchronously obtained; carrying out failure stage division based on the maximum stress value and a sliding linear fitting method, and carrying out automatic classification and labeling operation; solving variational mode decomposition key parameters through an intelligent optimization algorithm to obtain decomposed intrinsic mode components; constructing a sample data set through a data enhancement process; training to obtain a waveform-based destruction stage classification model; elastic wave signals are collected in real time, variational mode decomposition and noise reduction processing are carried out, intrinsic mode components highly related to the damage degree are obtained and serve as input data, and a classification result is output through a waveform-based damage stage classification model; and performing graded early warning. According to the method, the real-time dynamic monitoring of the damage failure of the roadside filling body can be realized, and the early warning of the damage failure of the high-water material roadside filling body can be realized.

The invention discloses a power transformer detection and energy efficiency evaluation method and system based on model analysis, and the method comprises the steps: collecting a performance parameter sequence and an environment parameter sequence in a transformer through a preset frequency, constructing an LSTM prediction model, and carrying out the window training of historical performance data; during prediction, generating a performance change curve and marking an over-threshold boundary segment, synchronously performing piecewise linear fitting on environmental parameters, calculating a fluctuation coefficient and screening an abnormal time period; and defining the intersection of the boundary segment and the abnormal time period as an optimization interval, dynamically adjusting the boundary parameter according to the fluctuation coefficient, generating an optimization parameter sequence, and carrying out precise energy efficiency evaluation through the optimization parameter sequence. Through multi-parameter coupling analysis, the correlation influence of the environmental parameters and the performance parameters is effectively analyzed, the abnormal data state identification capability and the data optimization level of the transformer are improved, the effectiveness and the accuracy of energy efficiency evaluation are improved, and data support is provided for intelligent operation and maintenance of the transformer.

The invention provides a DEM-based reservoir return water inundation range analysis method, device and equipment and a medium, and relates to the technical field of water conservancy projects. Historical water level data of a plurality of test points in a reservoir are obtained, linear fitting is carried out according to a preset direction, a corresponding water level distribution curve is obtained, and a reservoir model corresponding to the reservoir is divided into a plurality of reservoir model sections with water level equal difference distribution according to a water level equal difference principle and a preset fixed value. Calculating a water level elevation value of a central point of each reservoir model section, constructing a corresponding digital elevation model (DEM), determining a boundary contour of a submerged area, and performing smooth connection processing to obtain a reservoir backwater submerged model, namely a reservoir backwater submerged range; the water level change can be quickly responded, the inundation prediction result can be updated in real time, the abnormal data recognition rate is increased, the inundation analysis accuracy is improved, the high-resolution DEM data is combined, meter-scale-precision inundation range delimitation is achieved, and the method is suitable for the reservoir area with the complex terrain.

The present invention relates to the technical field of chip detection and image processing, and discloses a method for detecting dense gold wires on the surface of a chip, comprising the following steps: 1. Read in the collected image of the dense gold wires on the surface of the chip, the detection area and the number of gold wires; 2. Divide the gold wires within the detection area into several segments, and fit each segment of the gold wire into a straight line segment; 3. Select a number of radial cross-sections at equal intervals in the area of each straight line segment, and use Gaussian curve fitting to obtain the position of the center point of each cross-section; 4. If the number of center points of the cross-sections is equal to the number of gold wires, assign the center points of the cross-sections to the point sequences corresponding to the straight line segments. If they are not equal, use the center points as spare points; 5. Perform linear fitting on all the point sequences on the same straight line segment; 6. Repeat steps 3 to 5 in other segmented areas of the detection area; 7. Connect all the fitted straight line segments into complete gold wires. The detection performance of the present invention is excellent, with strong robustness and good compatibility.

The invention discloses a lightweight time sequence information fusion perception control method and system, and relates to the technical field of Internet of Things weak terminal data acquisition, and the method comprises the steps: obtaining key perception data, and tracking the local task load and communication load of a node in real time; based on a feasible space window of various non-linear fitting functions, carrying out adaptive segmentation on input sensing data and channel states; on the basis of curve features of the time series data, preprocessing the time series data, then performing feature extraction on a curve, and selecting important features; analyzing and predicting the compressed curve characteristic data, classifying the time sequence data into curves with different characteristics, selecting a corresponding empirical function model for fitting according to a classification result, and finally dynamically changing the running state of the node by a self-adaptive adjustment algorithm; and performing dynamic control based on the future trend of the data. According to the method, the future trend of the task and the communication load is coupled with the running state of the task and the communication load, and the change rate of node tracking data is combined with the historical condition of the node tracking data, so that the self-adaptive adjustment of the running state of the node is realized.

The method comprises the following steps: calculating diffusion coefficients according to an impedance spectrum of a low-frequency region of a lithium ion battery to be diagnosed, carrying out linear fitting on a plurality of diffusion coefficients to obtain a linear slope, and determining whether the lithium ion battery to be diagnosed has a short-circuit fault or not according to the positive and negative linear slopes. According to the method, the problem that the fault is difficult to recognize due to weak changes of electrical and temperature signals in the early stage of the battery short-circuit fault can be effectively solved, and the requirements for accuracy, high efficiency and real-time performance in actual application scenes are met.

The invention discloses a differential processing algorithm for laser pulse ranging echoes, which is characterized in that the echoes of laser pulse signals are differentiated through a mathematical method, so that an extreme point, namely the peak moment of the echoes, is solved. According to the differential processing algorithm of the laser pulse ranging echo, the extreme point is obtained by differentiating the Gaussian signal of the time t. The change of the intensity of the echo does not cause the change of the extreme point moment, i.e., the t0 moment is not changed, so that the method fundamentally eliminates the moment discrimination error delta t of the leading edge discrimination method, naturally does not need to be optimized to obtain the dual-threshold leading edge discrimination method, and also avoids the problems of other methods in use. When the echo size changes, the peak moment can still be accurately identified, the drift error is greatly eliminated, in the pulse laser ranging echo moment identification method, the accuracy is far higher than that of other methods, linear fitting and other secondary error compensation are not needed, the engineering complexity is reduced, the workload is reduced, and the adaptability is improved.

The invention belongs to the technical field of hydraulics, and particularly relates to an earth and rockfill dam overtopping dam break peak flow prediction method, which comprises the following steps: collecting a water level-area-reservoir capacity curve, a dam crest elevation and a dam site section reservoir bottom elevation data of a prediction target reservoir; according to the dam crest elevation and the water level-reservoir capacity curve, the reservoir capacity is obtained through linear interpolation, and the maximum dam break height is obtained by subtracting the dam site section reservoir bottom elevation from the dam crest elevation; according to the water level-area curve, converting the water level into false height by taking the reservoir bottom elevation as a reference surface, and fitting the false height to obtain a power coefficient; acquiring a flow coefficient by adopting a linear fitting mode according to the database data of the broken dam case; and predicting the reservoir dam break peak flow according to the breach peak flow calculation formula. The dam break peak flow prediction index provided by the invention is clear in physical significance, has a remarkable linear correlation characteristic with the dam break peak flow, is easy to obtain, and can be used as a trend analysis index to test the rationality of a calculation result.