34 results about "Fixed-point iteration" patented technology

The invention provides a digital twinning application-oriented electrical equipment electromagnetic heat flow coupling rapid calculation method, and belongs to the technical field of electrical digital data processing.The method comprises the steps that firstly, a three-dimensional model of electrical equipment is acquired and preprocessed, and a full-order electromagnetic heat flow coupling calculation model is established and verified through a temperature rise test; generating an experimental point matrix by using a Latin hypercube sampling method, and constructing a current temperature power density relational data set; performing regional division on the power density field by applying a K-means clustering algorithm, and constructing an electromagnetic response surface model through a radial basis function; establishing a heat flow field order reduction model based on an intrinsic orthogonal decomposition technology, and extracting a dominant mode primary function; bidirectional coupling of an electromagnetic field and a heat flow field reduced-order model is achieved, an improved Lagrange multiplier method and a fixed point iteration method are adopted for processing the nonlinear coupling problem, finally, a software development kit supporting an open platform communication unified architecture protocol is packaged, and the electromagnetic heat flow coupling rapid calculation capacity needed by digital twinning application is achieved.

The invention discloses a generator state estimation method and system considering noise and parameter uncertainty constraints. The method comprises the following steps: acquiring model parameters and dynamic state vectors of a generator, and establishing augmented state vectors; performing unscented transformation on the augmented state vector to obtain a particle set; improving to obtain robust mixed Kalman particle filtering, and in the process of performing unscented Kalman filtering on an augmented state vector, taking correlation entropy maximization of a measurement information sequence as a target function, and solving by adopting a fixed point iteration method to obtain a filtering gain; determining a filtering gain according to the updated state of the measurement information; robust mixed Kalman particle filtering is executed, physical constraints of model parameters serve as a feasible region, after resampling, projections of the model parameters in new-generation particles exceed the feasible region, the model parameters are set to be closest boundary points, and then resampling is conducted again; a weighted average value of the particle set is an optimal joint estimation value, a dynamic state estimation value and a model parameter identification result are separated, and reliable uncertainty quantization is provided for state and parameter estimation.

The invention discloses an unmanned aerial vehicle state estimation method, a readable storage medium and a navigation device, and belongs to the technical field of unmanned aerial vehicle navigation. The method comprises the following steps: establishing a linear state space model with multi-cluster measurement noise, modeling the measurement noise as multivariate Gaussian distribution, and introducing a measurement noise covariance matrix coefficient; joint prior updating is carried out based on posterior information of a previous moment, and prior estimation of a state, a state covariance, a measurement noise covariance matrix coefficient and a generalized inverse Gaussian (GIG) distribution parameter is obtained; performing pre-clustering on the measured values; online adaptive clustering is realized by using an EM algorithm, and unknown outdoor scene noise can be dynamically identified and divided; joint posterior updating is carried out, fixed point iteration is not needed, and therefore an updated measurement noise covariance matrix is obtained; updating the state and outputting the unmanned aerial vehicle state. According to the invention, through precise noise modeling, online clustering and adaptive adjustment of the measurement noise covariance matrix, the positioning precision and system robustness of the unmanned aerial vehicle are significantly improved.

The invention discloses a time-frequency analysis method and system for non-stationary signals of periodic pulses, and the method comprises the following steps: S1, carrying out the preprocessing of a vibration signal through a rapid CMSP method, and obtaining a periodic pulse component in the vibration signal; s2, carrying out iterative estimation on group delay by utilizing fixed point iteration; s3, utilizing iteration group delay estimation, and redistributing time-frequency coefficients of the STFT through time rearrangement multiple times of synchronous extrusion transformation; s4, performing integration on a time rearrangement multiple-time synchronous extrusion transformation result along a time direction so as to reconstruct the signal; according to the method, the problem that STFT time-frequency representation is fuzzy is solved through a fixed point iterative algorithm, meanwhile, signal reconstruction can be achieved, synchronous extrusion operation is carried out through iterative group delay estimation, an estimated value is closer to real group delay, divergent time-frequency coefficients in STFT after FC processing are redistributed, and the estimation accuracy is improved. Therefore, the energy concentration degree of time-frequency representation is improved.

The invention provides a hydraulic fracturing fracture multi-scale numerical simulation method based on an implicit level set, which comprises the following steps: modeling a hydraulic fracturing fracture, and constructing a linear elasticity hydraulic fracturing model based on a three-dimensional plane fracture; solving an elastic fluid mechanics equation based on the linear elastic hydraulic fracturing model; solving a nonlinear elastic fluid mechanics equation through collection of elastic fluid mechanics equations to obtain a near-end asymptotic solution of the stable movement hydraulic fracturing crack; and establishing a hydraulic fracturing fracture multi-scale numerical simulation system based on the implicit level set to verify the asymptotic solution. On the basis of constructing a linear elastic hydraulic fracturing model of a three-dimensional plane fracture, the hydraulic fracturing fracture multi-scale numerical simulation method based on an implicit level set is provided, and a set of numerical simulation system is established by adopting an elastic fluid mechanics equation fixed-point iteration mode; the method has practical significance for improving the final recovery ratio in an oil and gas well development domain, and can provide support for subsequent efficient development of oil and gas wells.

The invention discloses a state estimation method and system for a distributed multi-region power system, and relates to the technical field of power management. The method comprises the following steps: acquiring local measurement data, a neighbor measurement signal and a neighbor state estimation value signal of a to-be-measured power system in a target region in the distributed multi-region power system so as to calculate a state priori estimation value, a state error covariance matrix upper bound and a neighbor gain matrix of the to-be-measured power system at a next moment; the method comprises the following steps of: obtaining an upper bound and a neighbor gain parameter for calculating an error covariance, obtaining a weighted error covariance through arbor decomposition, calculating a state error covariance weight matrix and a measurement error covariance weight matrix through a preset Cauchy kernel function, and calculating a gain matrix of a local estimator through a fixed point iteration method, and state estimation of the to-be-tested power system is completed. A reasonable Cauchy kernel maximum relative entropy condition is designed to replace a minimum mean square error condition, so that state estimation in a non-Gaussian noise environment is improved.

The invention discloses a serialization model parallel training method based on immobile point iteration, which is characterized in that the immobile point iteration and a Prefix Sums algorithm are adopted to realize the parallel training of a linear or nonlinear sequence model, and the method specifically comprises the steps of immobile point recursion formula, parallel algorithm forward propagation, gradient back propagation and the like. The fixed point iteration adopts a fixed point recursion formula for obtaining model iteration based on fixed points, and a linear recursion model is obtained according to a general form of a sequence model; according to the parallel prefix sum algorithm, an adaptive parallel Prefix Sums algorithm is adopted, optimal hyper-parameter configuration is automatically selected, and model parameters are updated through back propagation. Compared with the prior art, the method has the advantages that the serialization model training efficiency is improved, the model is not required to be linear, the system can adapt to more models, the problem that the training efficiency of sequence models such as RNN and Neural ODE is limited by sequence dependence between time steps is effectively solved, and a new method is provided for efficient training and application of the sequence models.

The application particularly relates to a radar main lobe interference suppression method based on direction constraint, which realizes suppression of interference and separation of target echo in a main lobe suppression type interference scene by embedding array manifold vectors into FastICA fixed point iteration and applying direction constraint to the separation vectors after each update so that the separation vectors are always focused on a target angle subspace.

The invention relates to a distributed power distribution network fusion estimation method. The method comprises the following steps: S1, establishing a power distribution network system model; s2, obtaining predicted values and a predicted information matrix of the node i and a neighbor node j thereof; s3, setting an iteration initial step and a filtering estimation value when t times of fixed point iteration is carried out on the ith node; s4, Cholesky decomposition is carried out; s5, calculating a consensus weighted item of the node i, and obtaining an optimal estimation value of a current state by using a fixed point iteration method of a maximum correlation entropy extension information filter; and S6, comparing the current state estimation value with the estimation value of the previous iteration step, if the current state estimation value is smaller than a threshold value, taking the current state estimation value as the state estimation value of the next iteration, and calculating the state estimation error covariance, otherwise, updating the number of iterations, and returning to S5. Compared with the prior art, the method has the advantages of improving the precision of the filter adopted by the power distribution network state estimation method and the like.

The invention belongs to the technical field of line resistance network analysis, and discloses a fast line resistance network analysis method for a nonvolatile memory array, which comprises the following steps of: establishing electrode KCL equations of all rows and columns in a line resistance network of the nonvolatile memory array; a voltage line and a coefficient matrix in the KCL equation are transformed and decomposed, a fixed point iterative algorithm is introduced, and voltage distribution of nodes at the top and the bottom of the array is iteratively calculated. An original complex large-scale linear equation set problem is converted into two small-scale sub-problems. And solving is carried out through mutual iteration of the two sub-problems, so that the calculation complexity and the calculation memory are reduced. According to the method, the influence of IR-drop on neural network calculation acceleration can be predicted and evaluated in the design stage. The method has the advantages that the accuracy is high, and the influence of IR-drop can be simulated with the accuracy exceeding 95%; the calculation complexity is low, and the calculation complexity is reduced from O ((2mn) 2) to O (mn); the memory load is small, and the use of the memory is minimized while high accuracy is kept.

The application provides a non-Gaussian noise suppression method and device with adaptive kernel width, and belongs to the field of inertial base combined navigation algorithm and state estimation. The method solves the problems that the robust filtering method based on fixed kernel width is difficult to adapt to time-varying noise characteristics, and the scheme depending on an optimization algorithm has the problem of insufficient real-time performance. The method comprises the following steps: initializing filter parameters according to a SINS / DVL combined navigation system; performing time updating to obtain a predicted state vector and a predicted state covariance matrix at the current moment; updating a measurement noise covariance matrix through a variational Bayesian method, wherein the measurement noise covariance matrix is modeled as an inverse Wishart distribution; adaptively updating a kernel width parameter according to a filter innovation at the current moment and the measurement noise covariance matrix; and updating a state quantity estimation value and a state covariance matrix at the current moment through a fixed-point iteration algorithm by using the updated kernel width. The method is used in the field of underwater resource exploration.

The present invention belongs to the technical field of line resistance network analysis, and discloses a fast line resistance network analysis method for a non-volatile memory array, comprising: establishing KCL equations for electrodes in all rows and columns of the line resistance network of the non-volatile memory array; transforming and decomposing the voltage lines and coefficient matrices in the KCL equations, introducing a fixed-point iteration algorithm, and iteratively calculating the voltage distribution of the top and bottom nodes of the array. The originally complex large-scale linear equations problem is converted into two smaller-scale sub-problems. The two sub-problems are solved by mutual iteration, thereby reducing the computational complexity and computational memory. The method of the present invention can predict and evaluate the impact of IR-drop on the acceleration of neural network calculations during the design phase. Advantages include: high accuracy, capable of simulating the impact of IR-drop with an accuracy of more than 95%; low computational complexity, reducing the computational complexity from O((2mn) 2 ) is reduced to O(mn); the memory load is small, minimizing memory usage while maintaining high accuracy.

The present invention proposes a multi-scale numerical simulation method for hydraulic fracturing cracks based on implicit level sets, comprising: modeling hydraulic fracturing cracks and constructing a linear elastic hydraulic fracturing model based on three-dimensional plane fractures; solving elastic fluid mechanics equations based on the linear elastic hydraulic fracturing model; solving nonlinear elastic fluid mechanics equations by converging elastic fluid mechanics equations to obtain the proximal asymptotic solution of the stable motion hydraulic fracturing cracks; and establishing a multi-scale numerical simulation system for hydraulic fracturing cracks based on implicit level sets to verify the asymptotic solution. Based on the construction of a linear elastic hydraulic fracturing model for three-dimensional plane fractures, the present invention proposes a multi-scale numerical simulation method for hydraulic fracturing cracks based on implicit level sets, adopts a fixed-point iteration method of the elastic fluid mechanics equations, and establishes a numerical simulation system. This system has practical significance for improving the ultimate recovery rate in the oil and gas well development domain, and can provide support for the subsequent efficient development of oil and gas wells.

The invention discloses a power flow calculation method and system based on power distribution network root node voltage sensitivity, and the method comprises the steps: building a power flow calculation model through representing a transmission and distribution integrated power flow calculation problem as a voltage fixed point iteration problem; iteratively solving the voltage of the transformer substation in the load flow calculation model, and obtaining an iteration result and real-time operation data after each iteration is completed; calculating a power transmission network sensitivity matrix and a power distribution network root node sensitivity matrix by adopting a least square method according to the real-time operation data; according to the power transmission network sensitivity matrix and the power distribution network root node sensitivity matrix, determining the convergence of an iteration result; and when the iteration result converges, stopping iteration solution, and obtaining a transmission and distribution integrated power flow value according to the iteration result. By adopting the embodiment of the invention, the dependency on complete network topology and parameter information in the load flow calculation process can be eliminated, and the convergence of load flow calculation can be quickly and accurately judged, so that the convergence of transmission and distribution integrated load flow calculation is further improved.

The invention provides an inertia and acoustics integrated navigation method based on enhanced hybrid minimum error entropy and unscented Kalman filtering. The method comprises the following steps: establishing a state equation model of an inertia and acoustics integrated navigation system; establishing an enhanced mixed minimum error entropy measurement model considering a sound ray bending effect and a multi-modal noise characteristic; based on a state equation model and an enhanced hybrid minimum error entropy measurement model, an EnMMEE-UKF framework is constructed, nonlinear mapping is processed through unscented transformation, an attenuation factor in a strong tracking filtering theory is introduced to correct an error covariance matrix, and an expectation maximization algorithm is adopted to adaptively adjust a hybrid coefficient of a hybrid kernel function. And state estimation and a covariance matrix are recursively updated in combination with a fixed point iteration method, so that real-time updating and feedback of errors are realized.

An embodiment of the present invention provides a program detection method, system and electronic device. The method includes: parsing the source code of a target program based on a package manager to determine an intermediate intermediate representation corresponding to the source code; analyzing the intermediate intermediate representation to construct a control flow graph; performing fixed-point iteration analysis on the target program based on the control flow graph to determine state data corresponding to each function in the target program, and integrating the state data into global data; performing at least one of memory safety detection, concurrent safety detection and numerical safety detection on the global data, and generating a detection result. The embodiments of the present invention can improve the detection efficiency of Rust programs, reduce the false positive rate, and improve the detection accuracy.

The invention discloses a fast and stable fixed point iteration method for solving a hysteresis magnetic field under non-sine excitation, and belongs to the technical field of numerical simulation of motors and power electronics. Aiming at core loss increase and calculation convergence challenge caused by non-sinusoidal excitation, a time domain finite element method is combined with a fixed point iteration method to process hysteresis characteristics, and the hysteresis characteristics are analyzed through an inverse Preisach model; at a non-rotation point, a fixed point coefficient is set by using the differential magnetic resistance rate to accelerate iteration; at the rotation point, a global coefficient is set, and an Anderson acceleration method is introduced to ensure stable and rapid convergence of iteration; according to the method, on the basis of a traditional local coefficient method, a global coefficient and an Anderson acceleration method are combined, stable convergence at a rotation point and rapid iteration at a non-rotation point are achieved, the calculation efficiency is remarkably improved, meanwhile, the stability of overall calculation is guaranteed, and the method is suitable for large-scale popularization and application. The method has a wide application prospect in the aspects of electromagnet design, electrical equipment operation state prediction, loss evaluation and the like.

The embodiment of the invention provides a noise filtering method and system for a sensor, and the method comprises the steps: carrying out the modeling of the noise statistical characteristics of a signal of the sensor through employing Gaussian-Gaussian-Gaussian inverse index mixed distribution for a sensor used in an automatic driving scene, obtaining a noise model, and carrying out the calculation of the noise model based on a system state equation and a measurement equation, constructing a joint probability density function of a system state, and forming the joint probability density function into a joint posterior probability density function by adopting a variational Bayesian method; solving an optimal approximate probability density function through a KL divergence between a minimum approximate probability density function and a joint posterior probability density function, and alternately updating q (xk), q (yk), q (pi k) and q (lambda k) by using a fixed point iteration method until iteration convergence; and obtaining the optimal estimation value of the system state xk according to the converged approximate probability density function. The fitting degree of noise description is improved from the source, and precise adaptation and efficient suppression of multi-mode noise of the sensor are achieved.

The invention discloses an electromagnetic transient packaging model interface calling method and a related device thereof, and the method comprises the steps: reading a to-be-simulated power system example input file, and reading the input and output port information of an electromagnetic transient packaging model interface from the file; the initialization stage function is operated, and initialization operation of the electromagnetic transient packaging model is called; an electromagnetic transient simulation program is started, and a control system equation is obtained according to the input and output relation of all elements of the control system; performing assignment according to the state matrix and the simulation initial value of each variable to obtain a control system overall solution equation standard formula, and calling a calculation stage function to construct a packaging model solution equation standard formula; and solving a packaging model solving equation standard formula by adopting a fixed point iteration method, updating an output port signal value of the electromagnetic transient packaging model according to a solving result, calling a termination stage function to release a memory and update a log, and ending operation of an interface of the electromagnetic transient packaging model. According to the method, the calculation speed is increased, and the simulation time consumption is reduced.

The invention discloses a maximum correlation entropy Kalman filtering method based on a rational kernel function, and belongs to the technical field of signal processing. The method specifically comprises the following steps: 1) constructing a linear system equation and a measurement equation; step 2) selecting a kernel width of a rational quadratic kernel function, and initializing a system state and a covariance; 3) according to a system equation, updating one-step prediction of a state and a covariance; (4) the state value is initialized again at the fixed point iteration starting moment; 5) performing system model deformation according to the initial system and the measurement equation to obtain an error vector after deformation; 6) according to a concept based on a weighting criterion and entropy, defining a cost function by using an error vector; 7) for the cost function, solving an optimal solution of a state estimation value according to a maximum correlation entropy principle; and step 8) estimating the variance of a posterior estimation value. Compared with the existing GSKF, HF and MCKF algorithms, the method provided by the invention has the advantage that the accuracy of state estimation and the robustness of estimation are greatly improved.

The invention provides a robust Kalman filtering method based on STIW distribution, and relates to the technical field of state estimation and filtering. The method comprises the following steps: S1, constructing a state space model, and initializing parameters; step S2, on the basis of the initialized parameters, modeling the process noise as Gaussian-inverse Wishart distribution; s3, modeling the measurement noise as STIW distribution based on the initialized parameters, and generating a three-layer layering model; s4, based on the state space model, executing time updating, predicting state and covariance, and updating noise parameters; step S5, based on the output of the step S4, using a VB method to update posterior distribution through fixed-point iteration; and step S6, repeating the step S5 until a convergence condition or a maximum number of iterations is reached, and outputting a final estimation result. According to the robust Kalman filtering method based on STIW distribution, the precision and robustness of an estimation result in a complex noise environment are improved.

The present invention discloses a method for characterizing complex superimposed tight sandstone reservoirs in a statistical synchronous extrusion transform domain, comprising the following steps: S1, inputting a seismic signal x(t) to be analyzed, where t is time; S2, presetting a window function g(t) and an order K, and calculating the window function g(t) with different window functions t k g(t) and its derivative g′(t) under the short-time Fourier transform #imgabs0# and #imgabs1##imgabs2# where ω is the frequency; S3. Select an appropriate frequency filter h(ω) and calculate the inner products #imgabs3# and #imgabs4# between the above transformations. S4. Calculate the instantaneous frequency estimate of the signal in the short-time Fourier transform domain based on the above inner products #imgabs5# S5. According to the squeezing principle, use the fixed point iteration algorithm to obtain the statistical synchronous squeezing transform T x,K (t,ω); S6, analyze the frequency change in the synchronous compression transform domain to characterize the thickness variation characteristics of the complex superimposed tight sandstone reservoir. In addition, use T x,K (t,ω) can also reconstruct the original signal. The present invention can provide a highly robust and focused time-frequency distribution to finely characterize the characteristics of complex superimposed tight sandstone reservoirs.

The invention relates to a power distribution network producer and consumer hierarchical collaborative optimization scheduling method and device and a medium, and the method comprises the steps: building a double-layer optimization model facing producers and consumer accessed to a power distribution network; according to the required power reported by the consumer and the load prediction information of each node of the power distribution network, clearing by using an upper-layer power distribution market clearing model, and outputting a clearing electricity price; on the basis of a Lagrange relaxation method, relaxation is carried out on coupling constraints in the lower-layer disappearance producer cluster model, a relaxed Lagrange function is decomposed into a single disappearance producer problem, and a subgradient method is adopted to carry out repeated iteration of demand power and clearing electricity price so as to realize power demand balance of high-yield disappearance producers; and introducing a fixed point theorem to describe a hierarchical decision behavior between the power demanded by the consumer and the clearing electricity price of the power distribution network, and when a fixed point iteration termination condition is satisfied, obtaining a transaction price and transaction power in an equilibrium state. According to the invention, excessive information interaction can be avoided, and the risk of privacy disclosure is reduced.

The present disclosure provides a time-frequency analysis method and system for bearing fault signals, and the scheme includes: obtaining a strong frequency-varying modulation signal of a bearing to be analyzed; processing the strong frequency-varying modulation signal based on wavelet transform to obtain an initial time-frequency distribution result; obtaining an initial group delay estimation operator of the signal based on the obtained initial time-frequency distribution result; rearranging the time-frequency energy in the obtained initial time-frequency distribution result along the time direction based on the initial group delay estimation operator and time-rearranged synchronous compression transformation to obtain an initial redistribution result; obtaining an iterative group delay estimation operator based on a fixed point iteration method, and performing secondary optimization rearrangement of the time-frequency energy in the obtained initial redistribution result along the time direction based on the iterative group delay estimation operator to obtain an optimized time-frequency distribution result; and realizing analysis of the strong frequency-varying modulation signal based on the optimized time-frequency distribution result.

The invention discloses a transformer area safety boundary division method considering voltage constraint conditions. The transformer area safety boundary division method comprises the following steps: constructing a Zbus linearization power flow model based on sequential iteration of single fixed points; calculating the partial derivative of each node voltage relative to the injection power based on a power flow model to obtain the constraint expression of each node voltage, calculating the balance node voltage based on the PQ node, and determining the influence of the balance node voltage on each PQ node voltage; calculating lower network point power of a coupling point of a distribution network and a main network and a station area power balance safety boundary by using distributed load and distributed photovoltaic prediction data under the same time scale; and with the transformer area node voltage qualification as the target, correcting the transformer area power balance safety boundary based on the obtained node voltage qualification safety boundary, and obtaining the safety operation boundary considering the node voltage qualification. According to the method, the safe operation boundary of the distributed new energy element transformer area and the node voltage safety constraint are considered, and the situation that the node voltage in the transformer area is unqualified due to power balance allocation of the transformer area is avoided.

The invention discloses an equipment parameter estimation method, and particularly relates to a capacitance estimation method and device based on improved variational Bayesian Kalman filtering. The method comprises the following steps: estimating a prior distribution parameter at a moment k based on a posterior distribution parameter at a moment k-1; n times of fixed point iteration operation are executed based on the measurement information of the moment k and the prior distribution parameters of the moment k, and posterior distribution parameters of the moment k are obtained; determining an estimated value of the state vector at the moment k based on the posterior distribution parameter at the moment k; calculating the capacitance of the capacitor based on the estimated value by using a pre-built pre-charging model to obtain an estimated capacitance; wherein the measurement information comprises capacitor voltage, the state vector comprises model parameters of the pre-charging model, and the measurement noise covariance matrix is a covariance matrix of random noise in the measurement process of the measurement information. The method can improve the accuracy of capacitance estimation of the capacitor.

A Mixed Precision controller configured to operate in a High-Performance Computing, HPC, system, where the HPC system includes a high-precision processor array and a low-precision processor array. The Mixed Precision controller is configured to receive a system of partial differential equations and utilize the high-precision processor array and the low-precision processor array to solve the system of partial differential equations utilizing a fixed-point iterative scheme, g. The Mixed Precision controller is characterized in that the Mixed Precision controller is configured to utilize a defect correction term which is defined as the difference between a high-precision residual, RH, and a low-precision residual, RL, where the defect correction term is added to an independent constants vector of the system of partial differential equations. The use of the MP controller in the HPC system enables high-precision accuracy of final solution while reducing computational cost and overall execution time of non-linear partial differential equations.

The application discloses an elastic body simulation acceleration method based on projection dynamics, and belongs to the technical field of elastic body simulation acceleration. In the application, a local-global solver of projection dynamics is regarded as a fixed point iteration process, and an improved Anderson acceleration method is introduced to improve the convergence speed. Meanwhile, the influence of abnormal values is reduced through the robustness of L1 norm, and under the joint action of energy monotonicity checking and adaptive restart, a higher efficient PD solver acceleration effect is realized.

The invention belongs to the field of integrated circuit design and optimization, and discloses a chip performance optimization technology based on IrDrop prediction. According to the technology, a simulation tool and a deep learning model are combined, and efficient design and optimization of chip dynamic voltage frequency regulation (DVFS) are achieved. The method comprises the following steps: processing randomly generated and sampled power and frequency pairs, constructing a circuit model, acquiring data in combination with simulation calculation, obtaining voltage drop distribution of a multi-core chip, and improving calculation precision by adopting a fixed point iteration method. Data sets of power budget, frequency and voltage drop are further collected, and a convolutional neural network model is trained, so that nanosecond-level voltage drop rapid calculation of the complex multi-core chip is realized, and an efficient and accurate solution is provided for chip performance optimization.

The present invention specifically discloses a method for quantifying a cutting heat distribution model that takes into account convection cooling and contact thermal resistance, and relates to the technical field of metal cutting processing. The method comprises: S1, constructing a coordinate system for the cutting heat source and dividing it into a first deformation zone heat source, a second deformation zone heat source and a convection heat transfer heat sink, and spatially discretizing the second deformation zone heat source; S2, calculating the heat flux of the first deformation zone and the second deformation zone based on a mirror heat source model; S3, constructing a steady-state chip temperature model, calculating the temperature rise generated in the first deformation zone and the second deformation zone in the chip; S4, constructing a steady-state tool temperature model, calculating the total tool temperature rise; S5, establishing a temperature continuity equation based on the tool-chip contact thermal resistance, and solving the potential-variable heat distribution coefficient through fixed point iteration and trust region reflection algorithm. This method couples convection heat transfer with contact state to achieve accurate calculation of the spatial distribution of the heat distribution coefficient, improve cutting thermal management accuracy, and is suitable for metal cutting process optimization.