1948 results about "Least squares" patented technology

The invention relates to the technical field of power supply detection, and discloses a fire-fighting equipment power supply detection method and system.The detection method comprises the following steps that voltage fluctuation, current phase and ripple coefficients are collected through a multi-channel sensor group, a feature sequence is generated through sliding window noise reduction, the Euclidean distance is calculated through Z-score standardization, and a calibration instruction is generated when a threshold value is exceeded; kalman filtering adjusts frequency output detection parameters, fast Fourier transform is executed to extract harmonic amplitude to generate a suppression vector, and a least square reconstruction model outputs a state identifier; according to the method, voltage, current and ripples are collected through multiple channels, anti-interference performance and data integrity are enhanced through sliding window noise reduction, a threshold value is adjusted through Z-score and Euclidean distance self-matching, sampling frequency and detection parameters are optimized through Kalman filtering, harmonic characteristics are extracted through FFT, and the state is evaluated through a least square reconstruction model. And a multi-dimensional classification system is constructed by fusing the distortion rate and the phase deviation, so that the detection precision, the sensitivity and the response real-time performance are improved.

The invention relates to the technical field of reaction kettle temperature control, and discloses a fine chemical engineering reaction kettle temperature intelligent control method based on self-adaptive search, which comprises the following steps: S1, data acquisition: acquiring operation data of a reaction kettle, including temperature in the kettle, jacket inlet and outlet temperature, fluid flow and valve opening signals; s2, heat balance calculation: establishing a heat balance equation based on the collected data, and calculating reaction heat release and an equivalent heat transfer coefficient; s3, updating the model; s4, predictive control solution is carried out; s5, performing adaptive search; s6, carrying out security constraint; s7, stage identification; and S8, performing control execution. The reaction heat release amount and the equivalent heat transfer coefficient are calculated in real time through linkage of a heat balance calculation module and a model updating module, online parameter identification is achieved through a recursive least square algorithm, a heat model can be dynamically corrected along with working condition changes, it is ensured that a prediction model is consistent with the actual reaction process, and the prediction efficiency is improved. And the temperature prediction precision and the reliability of control response are obviously improved.

The invention discloses a micro-grid intelligent scheduling method and system based on an AI large model, and the method comprises the steps: collecting and processing the real-time output data of a photovoltaic power station and a wind power station, and obtaining a standardized micro-grid operation data set; a discrete time micro-grid dynamic model is established and a recursive least square method is adopted to carry out system parameter online estimation so as to obtain a robust scheduling scheme oriented to uncertainty interference; and in combination with real-time operation state monitoring, real-time micro-grid intelligent scheduling is carried out by deploying edge computing nodes. According to the method, the Lyapunov stability theory and the control barrier function are combined, a safety reinforcement learning framework oriented to micro-grid dispatching is constructed, and the absolute safety of system operation in the dispatching process is ensured.

The invention discloses a cold station multi-equipment combination energy efficiency optimization method and system, relates to the field of cold station energy efficiency optimization, and aims to solve the problems of low energy efficiency and response lag caused by manual adjustment. The method comprises the following steps: S1, acquiring key operation data (power, state, temperature, flow and frequency) of a water chilling unit, a cooling tower and a water pump and external environment data (temperature and humidity, wind speed and load prediction) in real time; s2, constructing an equipment energy efficiency model based on historical data, and quantifying the change of the equipment efficiency along with the load rate, the temperature difference and the environment temperature; s3, according to the current load and environment prediction, simulating and traversing the energy consumption of the equipment start-stop combination and the water pump frequency combination by applying a Monte Carlo algorithm, and screening a combination strategy with the lowest total energy consumption of the system; s4, converting the optimization strategy into a control instruction, and issuing the control instruction to equipment for execution; and S5, comparing the actual energy consumption with the predicted value, and if the deviation exceeds 5%, correcting model parameters by adopting a least square regression algorithm to realize closed-loop optimization. And the energy efficiency and the response speed of the cold station are obviously improved.

A GNSS positioning slowly-varying fault detection method based on residual-SVR regression comprises the steps that an observation information sequence is acquired based on a Kalman filter, and a covariance matrix of the observation information sequence is calculated; accumulating multi-step information through a sliding window, and constructing chi-square statistics; based on the fault-free data, constructing a training set by taking an innovation sequence as input and chi-square statistics as output, and generating an innovation-statistics mapping function; and fitting a normal slope threshold value based on an SVR predicted value, carrying out least square fitting on an observation statistic curve by sliding a window in real time, and judging whether to start a slow change fault alarm or not. According to the method, the residual error sequence is directly used as model input, and the dynamic chi-square statistical magnitude is used for replacing a traditional dichotomy label, so that the detection delay is reduced; an SVR detection model based on grid search and cross validation collaborative optimization is utilized, and an optimal parameter combination of a minimum mean square error (MSE) is screened through logarithm uniform sampling, interval linear sampling and five-fold cross validation, so that the average absolute error of slowly varying fault detection is reduced.

The invention provides an intelligent control method and system based on electro-hydraulic linkage. The method comprises the steps that multi-mode environment parameters are collected, a digital twin environment model is constructed, and data are synchronized with an electro-hydraulic servo system in real time. And establishing a nonlinear model, identifying parameters by using a least square method and a recursive least square method, verifying the precision through a step response test, and updating. And constructing an adaptive LSTM neural network inverse model to output a control voltage signal. And the signal is subjected to weighted fusion with the output of a PID controller, and the PID controller dynamically adjusts a gain parameter according to an error integral term to generate a control instruction to drive the system. According to the invention, the LSTM inverse model provides feed-forward compensation, the PID controller realizes feedback correction, and the LSTM inverse model and the PID controller are complementary. Meanwhile, based on a related nonlinear model and a parameter self-adaptive updating mechanism, the influence of working condition change is accurately described, and high-precision control of the electro-hydraulic servo system under the complex working condition is achieved.

The invention discloses a self-adaptive battery life calibration method for cloud-side cooperation, and belongs to the crossing field of an energy storage system and cloud-side cooperation calculation. According to the invention, a cloud-edge double-layer collaborative framework is provided; an edge end estimates the health state and the residual life of a battery in real time through a recursive least square extended Kalman filtering model; the error observer calculates a prediction error based on a sliding window, and a dynamic threshold triggers an uploading mechanism; the edge end adopts an auto-encoder to compress original time sequence features into abstract vectors, and the abstract vectors and error statistics are uploaded together; the cloud performs incremental learning by using a deep sequential network, and only finely adjusts tail level parameters of which the gradient sensitivity exceeds a threshold value to generate a correction value; and the correction value is compressed and issued to an edge end, local model parameters are updated through weighted fusion, and a covariance matrix is adjusted. The method realizes high-precision life prediction and dynamic calibration, remarkably reduces the communication load, and is suitable for electric vehicles, power grid energy storage and other scenes.

The invention provides a wind power plant AGC optimization control method and system based on model prediction control, and relates to the technical field of model control, and the method comprises the steps: building an adaptive prediction model, carrying out the online identification of parameters through a recursive least square method, constructing an active power optimization objective function, solving a control instruction sequence, designing a feedback compensator, and correcting a control instruction. And finally, issuing to each fan to execute and realize closed-loop control. According to the method, the AGC control precision of the wind power plant can be improved, the power fluctuation is reduced, and the power grid friendliness and the adaptability of an AGC control system are enhanced.

The invention discloses a high-temperature surface contact thermal resistance elimination method based on double thermocouple compensation, and belongs to the technical field of material thermal property characterization and testing. The method comprises the following steps: synchronously acquiring double-thermocouple temperature, and obtaining a temperature difference signal through filtering calibration and environment correction; calculating the real-time heat flux density according to the corrected temperature difference, the Seebeck coefficient and the dynamic correction factor; establishing a nonlinear dynamic coupling model containing contact thermal resistance parameters, and performing temperature interval division; identifying a contact thermal resistance parameter on line by adopting a self-adaptive recursive least square algorithm, and generating a dynamic thermal resistance compensation value; and applying the compensation value to a double-thermocouple signal according to the weight, and outputting accurate high-temperature surface temperature. According to the method, data acquisition and preprocessing, dynamic heat flow correction, nonlinear segmented modeling, self-adaptive online identification and refined compensation output strategies are adopted, the influence of high-temperature surface contact thermal resistance can be effectively eliminated, and the accuracy and reliability of temperature measurement in the high-temperature environment are remarkably improved.

The invention relates to the technical field of numerical control programming, in particular to a five-axis machining path planning method and system based on data driving, and the method comprises the following steps: obtaining real-time coordinates of each axis of a machine tool, calculating linear velocity and angular velocity components to construct a Jacobian matrix, executing singular value decomposition, and calculating a conditional number ratio by using maximum and minimum singular values; and inputting a nonlinear mapping function to calculate a dynamic penalty factor, generating a rotating shaft weighted item in combination with a rotating shaft identifier, constructing a weighted damping least square objective function, calculating a five-axis motion increment, and accumulating the five-axis motion increment with a real-time coordinate to generate a target absolute position coordinate. According to the method, the pose singularity degree is quantified by monitoring the machine tool pose condition number ratio and converted into the dynamic penalty factor to apply the self-adaptive constraint to the rotating shaft, the severe sudden change of the rotating shaft in the singularity area is inhibited, the tool nose track following error is minimized, and meanwhile smooth distribution of the motion increment is achieved; and the dynamic stability and the surface quality of five-axis linkage machining are improved.

The invention relates to the technical field of three-dimensional modeling, in particular to a three-dimensional modeling system and method based on a laser radar, and the system comprises a point cloud boundary extraction module, a boundary error analysis module, a self-adaptive cutting repair module and a stitching structure optimization module. According to the method, three-dimensional coordinates, normal vectors, reflection intensity, distances and indexes of boundary points are called layer by layer, a Sobel operator and a Canny algorithm are combined to cooperatively recognize a boundary and an error region, then local region repair is completed based on least square plane fitting and spatial residual analysis, a tension vector is analyzed through dynamic planning, and stitching path adjustment is executed. On the cooperative basis of multi-type point cloud features, a spatial topological structure and boundary continuity dynamic modeling, the problems of local damage, connection errors and instable stitching paths of point cloud boundaries in complex scenes are solved, and the integrity of boundary structures, the coherence of stitching transition and the rationality of spatial geometric expression are remarkably improved.

The invention discloses a rotating body ancient building part model reconstruction method based on a least square method, and relates to the technical field of model reconstruction analysis, and the method comprises the steps: obtaining ancient building part point cloud through employing a three-dimensional laser scanning technology; the historic building part point cloud is sliced and projected to a horizontal plane, a circle is fitted by using a least square method, circle center coordinates are extracted, and the position of a rotating shaft is determined. According to the method, the precision of model reconstruction is effectively improved by accurately extracting the rotating shaft and fitting the rotating generatrix, high-precision point cloud data is obtained by using a three-dimensional laser scanning technology, and accurate extraction of the rotating shaft and the rotating generatrix is ensured through the steps of slicing, projection, least square fitting and the like; a non-uniform rational B-spline curve is adopted to fit a rotating bus, the rotating bus is rotated around a rotating shaft to reconstruct a three-dimensional model, the three-dimensional model of the ancient building part model is parameterized, the geometric accuracy of the model is further improved, and the reconstructed model can more accurately reflect the actual shape and size of the ancient building part.

The invention provides a dam anomaly detection method and system based on EMD and an isolated forest, and relates to the field of dam monitoring, and the method comprises the steps: obtaining an original monitoring signal of a dam, and decomposing the original monitoring signal into a plurality of IMF components and long-term trend components; performing linear fitting on the long-term trend component by adopting a least square method, calculating a trend slope, and standardizing the trend slope into a trend anomaly score; extracting a plurality of statistical features and entropy features of each IMF component and each long-term trend component, and combining the statistical features and the entropy features into a comprehensive feature vector; analyzing each comprehensive feature vector by using an isolated forest model, and outputting a random forest anomaly score; performing correlation coefficient analysis on the dam monitoring data and the water level signal data, and calculating a correlation anomaly score; calculating a comprehensive anomaly score according to the trend anomaly score, the random forest anomaly score and the correlation anomaly score; if the comprehensive abnormal score is greater than the preset abnormal score, the original monitoring signal is abnormal; otherwise, the original monitoring signal is not abnormal.

The invention provides a method for automatically centering and assembling a bearing on a long-axis part, and belongs to the technical field of bearing assembly.The method comprises the steps that position information of the long-axis part and the bearing is collected in real time through a binocular camera and a laser distance measuring sensor, a central line equation is calculated through the least square method, and position deviation is determined; a centering adjustment vector is generated to control a multi-degree-of-freedom precise attitude adjustment platform to perform preliminary adjustment, a mechanical arm is matched with high-precision laser ranging to perform precise centering, the centering process is modeled into a weighted undirected graph, an optimal adjustment path is solved, optimal press fitting parameters are calculated based on a bearing press fitting physical mechanics equation, and press fitting is performed. Meanwhile, the assembling process is monitored through a force sensor and a displacement sensor, an assembling quality evaluation matrix is constructed, finally, the assembling result is detected and evaluated, and high-precision automatic assembling of the long-axis part and the bearing is achieved.

The invention relates to the technical field of motor control, in particular to a control and parameter intelligent optimization method for an anti-impact high-precision PMSM (permanent magnet synchronous motor) feeding servo system, which comprises the following steps of: S1, establishing a physical structure and a dynamic model of a PMSM driving feeding system; s2, designing a variable gain fractional order super-spiral sliding mode controller VGFSTSMC, realizing finite time convergence of position tracking errors by dynamically adjusting and controlling gain coefficients, and inhibiting system jitter; s3, designing an adaptive sliding mode disturbance observer ASMDO, and estimating and compensating unknown disturbance in real time through adaptive gain adjustment and an integral sliding mode surface; s4, identifying a system Stribeck friction model based on a least square method, and inputting the system Stribeck friction model as a friction feedforward compensation FFC to a current loop to reduce the disturbance uncertainty of the system; s5, dynamically adjusting and optimizing control parameters of the VGFSTSMC and the ASMDO by adopting an optimization algorithm so as to minimize a root-mean-square value RMSE of a position tracking error and a maximum instantaneous error; and S6, VGFSTSMC, ASMDO and FFC are combined with an optimization algorithm, and the PMSM is driven to realize high-precision position control.

The invention relates to a temperature drift compensation method for a pressure instrument, and belongs to the technical field of pressure instruments, and the method comprises the following steps: obtaining output signal values of a pressure sensor at different temperature points, and building a temperature-output signal data set; constructing a polynomial fitting model as a temperature drift compensation model, fitting a temperature coefficient through a least square method, and storing the coefficient into an instrument memory; original values of temperature and pressure are collected in real time and substituted into the model to calculate a compensated pressure value. The method also supports various strategies such as temperature sensor calibration, nonlinear error compensation, Kalman filtering noise reduction, dynamic temperature change rate compensation and neural network compensation, and has self-calibration and remote updating functions. According to the invention, the measurement precision and stability of the pressure instrument in a variable-temperature environment are significantly improved, and the method is suitable for high-precision industrial measurement and intelligent monitoring scenes of the Internet of Things.

The invention provides a battery pack temperature equalization control method. According to the method, multi-zone temperature and heat dissipation distribution data acquisition is carried out, a temperature distribution model is established in combination with K-means clustering and a heat conduction equation, heat dissipation parameters are optimized by using a least square method, differential temperature control instructions are generated by using a PID control algorithm, and Q-learning reinforcement learning is introduced to dynamically adjust control parameters. According to the method, accurate monitoring and intelligent regulation and control of the internal temperature of the battery pack are achieved, the problems that a traditional temperature control system is lagged in response and poor in equalization effect are effectively solved, the temperature equalization performance of the battery pack is remarkably improved through multi-level data analysis and a self-adaptive control strategy, the service life of the battery is prolonged, and the service life of the battery pack is prolonged. And reliable guarantee is provided for safe and efficient operation of the new energy automobile power battery.

A reference point is arranged on the ground to calibrate a ground world coordinate system, a robot base coordinate system is established based on robot base features, and a rigid transformation matrix between the two is calculated to achieve spatial alignment. A multi-sensor network is constructed, a visual sensor and a laser radar are associated to a double-reference coordinate system, and data are synchronously collected and unified to the same coordinate system. And according to the spatial consistency error of the data, dynamically evaluating the credibility weight of the sensor, constructing a joint error equation, and based on least square method optimization, weighting fusion data and outputting calibration parameters. According to the invention, the problems of dependence on single sensor precision, lack of a system-level calibration scheme and complex calibration process are solved, and the calibration precision and efficiency of the robot system are improved.

The invention belongs to the technical field of geophysics. According to the truncation model space three-dimensional magnetotelluric inversion method and system, a three-dimensional inversion objective function is constructed according to an inversion initial model, the inversion objective function is optimized by adopting a Gaussian-Newton method, and a Gaussian-Newton increment equation is obtained, a sensitivity matrix in the Gaussian-Newton increment equation is a sparse matrix which is obtained after truncation is carried out by adopting a distance truncation threshold value and a sensitivity amplitude truncation threshold value, the Gaussian-Newton increment equation is converted into an unconstrained least square form, and an inversion updating direction is determined according to the least square form; and performing iterative optimization according to the initial inversion model, the inversion updating direction and the model updating step length to obtain an updated inversion model. According to the invention, units with small contribution to inversion updating are effectively cut, and the model space corresponding to each piece of data is reduced.

The invention relates to a combined attitude determination technology, in particular to a high-precision combined attitude determination method based on a multi-antenna GNSS (Global Navigation Satellite System) and an INS (Inertial Navigation Satellite System), which comprises the following steps of: S1, fixing a GNSS receiver and the INS on a carrier; s2, calculating carrier position information and carrier speed information, and establishing a double-difference carrier phase observation model; s3, calculating a baseline vector predicted value between the antennas; s4, an LAMBDA algorithm model based on INS attitude information constraint is constructed; resolving the IC-LAMBDA algorithm model by using a least square method to obtain an optimal integer solution of the integer ambiguity; s5, calculating a baseline vector between the antennas; carrier attitude information is solved; and S6, fusing the GNSS navigation information and the INS navigation information by using an extended Kalman filter. The method solves the problems of low attitude measurement precision and poor scene adaptability of the existing integrated navigation technology, and is suitable for the fields of unmanned aerial vehicle control, unmanned vehicle driving, unmanned agricultural machinery seeding, marine navigation surveying and mapping and the like.

The invention provides a visual semantic information-assisted point cloud feature extraction and pose estimation method and device, and belongs to the technical field of robot autonomous navigation and computer vision crossing, and the method comprises the steps: obtaining a laser point cloud and a visual image, inputting the visual image into a semantic segmentation network, outputting a pixel-level semantic tag map, and carrying out the segmentation of the pixel-level semantic tag map; projecting the laser point cloud to a pixel-level semantic tag map to obtain a semantic point cloud; the semantic point cloud is utilized to preliminarily distinguish edge and plane features, the local curvature of the semantic point cloud is calculated, the plane feature points and the edge feature points are screened again, an error function of the pose estimation process is constructed, and a Hessian matrix is calculated; calculating a degradation factor based on a Hessian matrix, respectively carrying out degradation judgment on the plane features and the edge features, constructing an orthogonal projection matrix, combining the orthogonal projection matrixes of the plane features and the edge features, and constructing a pose estimation least square problem until the motion of the carrier is finished, and obtaining the optimal pose estimation.

A predictive maintenance method based on an elevator operation and maintenance time sequence knowledge graph comprises the steps that firstly, the elevator operation and maintenance time sequence knowledge graph is constructed, predictive maintenance of electromechanical equipment parts is achieved based on elevator part maintenance period optimization, a theoretical distribution model based on Weibull distribution is constructed, and parameters of Weibull distribution are fitted through a least square method. Therefore, the model can accurately reflect the failure rule of the elevator parts; calculating an inference result of the elevator operation and maintenance time sequence knowledge graph through a graph convolution model and a Horkes process model, and carrying out recursive relation calculation of component fault rate functions in N maintenance cycles; an elevator component maintenance comprehensive cost simulation model is constructed, and the optimal maintenance cycle of elevator components is calculated through a continuous time Markov chain method; the fault prediction accuracy is improved.

The invention provides a motor parameter online identification method based on an improved golden section method, and belongs to the technical field of motor parameter identification, and the method comprises the steps: S1, obtaining a residual function of a recursive least square method with a forgetting factor when parameter identification is not carried out on a motor model for the first time; s2, detecting whether the residual function exceeds a preset threshold value or not, and executing a step S3 when the residual function exceeds the preset threshold value; s3, taking the residual function as a target function, and adjusting the forgetting factor by using a golden section method to obtain an optimized forgetting factor; and S4, carrying out motor parameter online identification by using a recursive least square method with the optimized forgetting factor. The method has the advantages that the forgetting factor is dynamically adjusted through the golden section method, the residual function serves as the target function, the optimal solution of the forgetting factor under the corresponding working condition can be found when the system changes rapidly, the tracking speed is increased, the steady-state precision is further improved, and therefore the overall performance of parameter identification is improved.

The invention discloses a machine tool spindle thermal error real-time compensation method and system, and relates to the technical field of machine tool machining. At least three temperature sensors are arranged on the surface of a bearing machine body based on a main shaft motor, a three-dimensional laser displacement sensor is coaxially installed at the output end of a main shaft, and multi-point temperature data (T1, T2,..., Tn) and real-time displacements (Px, Py, Pz) of the main shaft in the X, Y and Z directions are collected in real time. Thermal error model parameters can be updated in real time through the set recursive least squares algorithm with the forgetting factor, thermal characteristic changes of the main shaft under different working conditions are automatically adapted, frequent manual intervention is not needed, an error compensation priority judgment unit of the motion compensation module can automatically switch compensation modes according to the thermal error change rate, and the accuracy of thermal error compensation is improved. When the error change rate exceeds 0.1 mm / min, the influence time can be shortened to 50 ms through the rapid compensation mode, and the automation level and the production efficiency of machine tool machining are effectively improved.

The invention discloses an environment modeling method, system and equipment based on laser radar and vision fusion, and belongs to the technical field of mobile robot environment modeling. Through synchronous acquisition of three-dimensional point cloud data and image data, feature extraction and distortion correction are completed on the data acquired through the two different ways; accurate modeling features can be obtained, a geometric pose state quantity and a luminosity pose state quantity are calculated through an implicit moving least square iterative nearest point algorithm, the calculation precision of a curved surface in an environment is improved, and through point cloud splicing, a two-dimensional grid map and a three-dimensional point cloud map are generated. The two-dimensional grid map and the three-dimensional point cloud map are fused to complete environment modeling, the modeling speed is increased, the environment object information is aligned, information dislocation is avoided, and the environment modeling efficiency and accuracy are improved.

The invention discloses a high-precision electrical calibration method and system for a wedge flowmeter. The method comprises the following steps: step 1, data acquisition and preprocessing; step 2, dynamic working condition identification; 3, updating the self-adaptive model; 4, multi-source data fusion calibration is carried out; and 5, performing closed-loop verification and compensation. According to the invention, multiple types of signals are collected in real time through the multi-dimensional sensor array and de-noised to construct a data set, so that comprehensive and accurate data collection is realized, and a good basis is provided for calibration; carrying out dynamic working condition identification by utilizing a support vector machine, triggering model updating, and endowing calibration with dynamic self-adaptive capability; model parameters are updated by adopting a recursive least square method, compensation items are established, and the characterization capability of the model is enhanced; a calibration coefficient is generated by means of a data fusion algorithm, and the calibration precision is improved; through verification and compensation of a digital twinborn model, traditional defects are effectively overcome, model transplantation errors are reduced, the measurement precision of the wedge flowmeter is improved under extreme working conditions, and production stability and product quality are guaranteed.

The invention provides an RFID trajectory tracking system based on least square speed adaptation, and the method comprises the steps: firstly building a half-wavelength observation distance expansion model, and effectively solving the problem of error divergence caused by a conventional phase unwrapping scheme; then, generating an initial position candidate set by using a triangular centroid weighting algorithm, performing instantaneous speed estimation by using a least square algorithm, and introducing a dynamic threshold-weight constraint mechanism to obtain a high-quality initial state set; thirdly, speed estimation based on least square is fused in the EKF, speed data are updated through the least square method before each round of antenna scheduling is started, target tracking trajectories estimated based on different initial positions are obtained, and the problem that in the target tracking process, the speed suddenly changes or the tracking error is large during steering is solved; and finally, dynamically deleting the tracking trajectory with the zero weight, and selecting the trajectory with the maximum weight value as the final tracking trajectory of the target, thereby effectively reducing the calculation complexity of the algorithm. The invention provides an RFID trajectory tracking system which is convenient and simple to deploy, can realize trajectory tracking of a target tag, and is suitable for most indoor positioning scenes.

The invention relates to the field of computer heat dissipation control, and discloses a high-performance computer system temperature regulation and control method which comprises the following steps: dividing a computer system into a plurality of hot areas and initializing thermodynamic parameters; collecting temperature, power consumption and process data in real time, identifying a burst process and constructing a dynamic heat capacity model; the heat capacity parameter and the heat dissipation coefficient are updated online through a recursive least square method; the multi-target weight is dynamically adjusted based on the temperature deviation degree and the heat capacity change rate, a distributed optimization model containing heat diffusion coupling constraints is established, and an optimal heat dissipation strategy is solved by adopting an improved ADMM algorithm; model prediction deviation is corrected through a double-loop control mechanism, and data updating circulation is triggered in combination with burst process marks and temperature out-of-limit events. According to the method, through a dynamic modeling, weight self-adaption and closed-loop control fusion mechanism, the temperature control precision and the system safety in a complex thermal coupling scene are remarkably improved, and meanwhile heat dissipation efficiency, noise suppression and energy efficiency balance optimization are achieved.

The invention discloses a voltage sensitivity fitting method and system driven by measurement data of a power distribution network, and the method comprises the steps: constructing a node input feature matrix based on the injection power, measurement voltage and neighborhood features of a power distribution network node in a certain operation state, inputting the node input feature matrix to a variational auto-encoder model, and estimating the node voltage of the whole network; constructing a plurality of groups of micro-power reference disturbance samples around the operation state, and generating corresponding voltage response by using the model again; and finally, calculating a power difference and a voltage difference between each disturbance sample and the reference state, constructing an overdetermined equation set, fitting a voltage sensitivity matrix by adopting a least square method, and averaging fitting results to improve the estimation precision. According to the method, high-precision voltage estimation and voltage sensitivity fitting can still be realized under the conditions of lack of topological structures and line parameters and sparse voltage measurement, and the method is suitable for state estimation, voltage treatment and operation optimization tasks in typical power distribution network scenes of sparse voltage measurement, incomplete topological information and the like.

The invention provides a front and rear axle load estimation method and system and a steer-by-wire vehicle, and the method comprises the steps: (1) when the vehicle is in a longitudinal stable and straight driving state, updating the mass of the whole vehicle; (2) establishing a least square recursion equation with a forgetting factor based on the dynamic motion equation of the vehicle on the ramp, and calculating the gradient of the current ramp; and (3) calculating a front axle load and a rear axle load based on the whole vehicle mass and the current gradient. The whole vehicle mass estimation is started under the working conditions of longitudinal stability and straight driving, so that the reliability and the stability of the whole vehicle mass calculation process are ensured; besides, vehicle dynamics and kinematics models are fused, recursive calculation is performed by applying a least square method with a forgetting factor, the road gradient can be estimated in real time at high frequency and high precision under various driving working conditions of the vehicle, and the limitation of a traditional fixed parameter model is broken through.