82 results about "Coefficient matrix" patented technology

The invention discloses a deep and far sea wind power time-varying impedance model grid connection analysis method based on a time domain residual error method, relates to the technical field of power system modeling simulation, and can efficiently and accurately analyze deep and far sea wind power grid connection. The method comprises the following steps: establishing a differential algebraic equation set containing high-capacity submarine cable impedance and a plurality of nonlinear links, and constructing a recursive coefficient matrix; dividing a preset global time domain into a plurality of time domain subintervals, and solving the recursion coefficient matrix in each time domain subinterval by referring to a universal analytic solution represented by a power series explicit operator to generate a global time domain explicit solution corresponding to the preset global time domain; performing discrete Fourier transform on the global time domain explicit solution to obtain a time-frequency domain spectrum result; and constructing a time-varying impedance model according to the differential algebraic equation, setting the time-varying impedance model by adopting a time-frequency domain spectrum result, and analyzing the deep and far sea wind power grid-connected control system by utilizing the set time-varying impedance model.

The invention relates to the technical field of ion beam physical calculation, in particular to an ion beam optical property evaluation method based on a CPU-GPU hybrid parallel stable double-gradient conjugate algorithm. According to the technical scheme, the method comprises the following steps that a CPU-GPU hybrid parallel architecture is initialized, and CPU end and GPU end memory allocation, CUDA related object creation and GPU equipment initialization and parameter setting are completed; constructing a linear equation set, a sparse coefficient matrix A and a right-end vector b, and setting an initial solution vector, convergence precision epsilon and related parameters of the number of iterations; and analyzing the sparseness of the coefficient matrix A. Through combination of three core mechanisms of CPU-GPU cooperative calculation, dynamic load balancing and intelligent preprocessor selection, efficient, stable and universal evaluation of the ion beam optical characteristics is successfully realized, and an excellent solution is provided for solving the solving problem of a large-scale sparse linear equation set in the field of high-performance calculation.

The invention relates to the technical field of geometric error evaluation of hole and shaft parts in the field of machinery, and discloses a hole and shaft part action boundary discrimination method, which comprises the following steps of: deriving a motion coefficient vector for describing radius change of each measuring point based on a tiny rigid body motion model of a surface measuring point set of a measured part; and the motion coefficients are combined into a motion coefficient matrix. Furthermore, the method establishes a discriminant inequality group based on a mutual exclusion proposition. The judgment process is realized by analyzing the rank of the motion coefficient matrix and iteratively solving an equation set formed by the maximum linear independent matrix of the motion coefficient matrix, and finally, whether the fitting cylinder is the action boundary or not is determined by judging whether the inequality set has a feasible solution or not. According to the method, the problems of complicated geometric discrimination conditions, lack of universality and the like in the existing evaluation method can be solved, and a reliable basis which is easy to realize in a stylization manner is provided for determining the action boundary of the hole-shaft part.

The invention provides a balance calibration modeling method and system based on sample errors and space optimization. The method comprises the steps of obtaining a calibration data set and dividing the calibration data set into a sample set and a verification set; calculating a load space weight and a load complexity weight of the sample set, and obtaining a comprehensive weight based on the load space weight and the load complexity weight; and constructing a polynomial model of the sample set, and solving a coefficient matrix in the polynomial model based on the comprehensive weight to obtain a final balance calibration model. The method further comprises the following steps: verifying and evaluating the balance calibration model by using the sample set and the verification set, and optimizing the balance calibration model according to an evaluation result. According to the balance calibration modeling method cooperating with the sample space distance and the load complexity weight provided by the invention, nonlinear system errors introduced by calibration load complexity are fully considered, so that the influence of error distribution of calibration samples under various load combinations on modeling is more uniform and reasonable; and meanwhile, the problem of sample space occupation ratio is also solved.

The invention relates to a POD secondary manifold lower chamber flow field nonlinear model order reduction method based on anchor point optimization, and the method comprises the steps: obtaining a two-dimensional lower chamber computational fluid mechanics model as a full-order model, and building a snapshot matrix according to the full-order model; carrying out POD order reduction on the snapshot matrix, and calculating a POD reconstruction error matrix and a secondary manifold coefficient matrix of the snapshot matrix; determining an anchor point, and calculating a quadratic manifold modal matrix at the anchor point according to the POD reconstruction error matrix and the quadratic manifold coefficient matrix; according to the secondary manifold modal matrix at each anchor point, obtaining a secondary manifold modal matrix at the test point; and according to the quadratic manifold modal matrix at the test point, calculating an approximate snapshot of the POD quadratic manifold nonlinear reduced-order model at the test point. According to the method, the representative anchor points are introduced into the parameter sampling space, so that self-adaptive calculation of the secondary manifold modal matrix is realized, and the approximation precision of the reduced-order model is improved.

In order to overcome the problem that the current technology is difficult to estimate and continuously correct the bridge twin model by obtaining a series of test and test data, the application provides a bridge twin dynamic model Fourier modeling method based on test data, which is only related to the parameters of the bridge according to the system matrix related to all nodes of the bridge and the observation coefficient matrix related to all nodes, and the first continuous test process of the bridge does not change greatly, and the first comprehensive detection of the bridge twin dynamic mathematical model and the subsequent each time of the bridge twin dynamic mathematical model comprehensive correction process is relatively short, according to the characteristics of the fast and slow change matrix, the slow change matrix is set as a constant matrix in the first continuous test, and the system matrix related to all nodes of the bridge and the observation coefficient matrix related to all nodes in the bridge twin model are established through a series of effective tests and optimal indexes, and the bridge twin model is continuously corrected in the whole life cycle of the bridge, so that the technical problem of estimating and improving the bridge vibration model by obtaining a series of test and test data is solved.

The application discloses a neutron transport constitutive relation and low-dimensional control equation modeling method based on sparse regression, which comprises the following steps: a neutron flow matrix under multiple state parameters is formed by state parameters of each state and physical quantity parameters calculated through a neutron transport program; fitting physical quantity parameters are filled into a sparse regression physical quantity matrix; a coefficient matrix is obtained by using a least square method to calculate an inverse matrix; and a neutron transport constitutive relation under multiple state parameters is obtained by assembling. The application utilizes the accurate characteristics of the calculation result of the neutron transport equation, grasps the cause of the larger deviation of the calculation result of the neutron diffusion equation in a small scale range, combines sparse regression fitting to obtain the neutron transport constitutive relation, and further obtains a low-dimensional macroscopic neutron transport control equation. The theoretical derivation is less difficult, the research progress is easier, the effect of improving the calculation precision is more obvious, the applicability is better, the gap with the situation under the actual reactor core working condition is not large, and the application has certain practicality.

The application discloses a multi-unmanned aerial vehicle control method based on reinforcement learning and belongs to the technical field of unmanned aerial vehicle cooperative control. The application designs a control algorithm for a double-time-scale multi-agent, effectively avoids possible calculation errors in the process of solving a matrix equation by introducing a novel coefficient matrix, and proposes a hybrid iteration algorithm which is independent of initial stable controller gain and has a fast convergence speed and is used for solving an approximate solution of a game algebraic Riccati equation based on a communication topology between agents. The application can realize consistency between unmanned aerial vehicles, enables the unmanned aerial vehicles to work cooperatively, and completes a task.

Embodiments of the invention disclose a constraint equation determination method and apparatus, a medium, a device and a product. The method comprises the steps of obtaining a constraint equation of an augmented matrix group; the constraint equation is represented as follows: the sum of the first product and the second product is zero, a subordinate degree-of-freedom vector in the constraint equation is determined by an independent degree-of-freedom vector, a weight coefficient matrix, a displacement matrix and a distribution matrix, and the distribution matrix is determined by the weight coefficient matrix and the displacement matrix; and determining a target weight coefficient matrix and a target displacement matrix, and substituting the target weight coefficient matrix and the target displacement matrix into the constraint equation to obtain a target constraint equation of the augmented matrix group. According to the method, the weight coefficient matrix capable of accurately reflecting the physical coupling strength between the nodes and the displacement matrix capable of representing the real deformation state can be automatically calculated, the constraint equation is generated, the defect of insufficient calculation precision caused by simple hypothesis of the constraint relation is effectively overcome, and therefore the construction effectiveness of the constraint equation can be improved.

The invention discloses a parallel solving method and system for a system of linear equations of an electric power system based on a Woodbury theory, and belongs to the technical field of large-scale electric power system computation.The method comprises the steps that a coefficient matrix of a network equation of the electric power system is obtained and subjected to block representation; performing truncated singular value decomposition on the non-diagonal block coupling matrix, and realizing low-rank decomposition through an Arnoldi iterative algorithm; carrying out inversion on the coefficient matrix based on a Woodbury matrix identical equation, and carrying out parallel calculation on each block sub-matrix and an intermediate variable; and solving the system of linear equations by using the inversed matrix. According to the method, the low-rank characteristic and parallel computing advantages of the inter-region coupling matrix of the power system are fully utilized, the solving efficiency of the large-scale linear equation set is remarkably improved, and the method is suitable for transient analysis, real-time simulation and other high-performance computing scenes of the power system.

The invention discloses an efficient numerical solution method for a steady-state water head field of a dam containing a complex foundation, and relates to the technical field of geotechnical engineering. The method comprises the following steps: establishing a seepage mathematical model in a Cartesian coordinate system by acquiring foundation parameters and boundary conditions, introducing similar lines parallel to a far-field boundary to construct an improved proportional boundary finite element coordinate system mapping relation, and discretizing and deducing a variable coefficient control equation by adopting a one-dimensional high-order spectrum unit; an equation is simplified into a Riccati equation by analyzing the characteristics of a coefficient matrix, an infinite seepage matrix is solved in combination with Schur decomposition, a seepage matrix at a boundary is obtained through iteration of a fourth-order Runge-Kutta method with the infinite seepage matrix as an initial value, and finally steady-state water head field distribution is obtained. According to the method, the continuity of the interface water head and the flowing boundary condition is guaranteed, the precision and efficiency of complex foundation seepage solving are greatly improved, and reliable technical support is provided for engineering seepage-proofing design and stability evaluation.

The invention belongs to the technical field of underwater acoustic detection. The invention provides a distributed sound barrier layout optimization method based on a minimum estimation error. According to the embodiment of the invention, constraint conditions such as the number of sound sources and receivers, the laying range and the like are input, and a linear equation set of an observation model is established in combination with a target motion model; taking the equation set coefficient matrix condition number as an optimization index, taking a sonar node position vector as an optimization independent variable, and taking matrix condition number minimization as a principle to establish a system layout objective function; a detection area is gridded to reduce the calculation amount, and a global optimal solution of the position of the sonar node is given in a traversal search mode. According to the method, specific transmitting and receiving configuration is not needed, the target detection precision is practically improved through layout optimization, and higher practicability and flexibility and universality are achieved in application.

The application belongs to the technical field of robot dynamics parameter identification, and discloses a double sparse efficient identification method and system for robot dynamics parameters. The method comprises the following steps: S1, collecting joint positions, joint speeds, joint torques and joint accelerations of a robot to be processed at each time; S2, constructing an implicit dynamics equation of the joint torque by taking the joint torque as a target; S3, constructing a base function database, evaluating the importance of each base function in the base function database, thereby completing the first sparse of the base function; and performing sparsification on the coefficient matrix to make some elements in the coefficient matrix 0, thereby completing the second sparse of the base function, determining the base function and the coefficient matrix, and realizing the acquisition of an explicit dynamics equation of the joint torque. Through the application, the technical problems of complex theoretical modeling or low interpretability of a black box model in the prior art are solved.

The present application relates to the technical field of data processing, and especially relates to a sparse direct solution method based on data kernel external storage, which comprises the following steps: obtaining a coefficient matrix of a high-dimension sparse linear equation group, analyzing a non-zero element distribution mode and a matrix topological structure to establish a task dependency relationship; detecting a system memory capacity and a disk performance parameter to set a slice size parameter, dividing a wavefront matrix into data slices with balanced floating-point operation amounts to execute a left-looking numerical decomposition; creating a message queue and a monitoring mechanism to realize asynchronous disk storage; reading data from the disk to execute a numerical update cycle; and establishing a back substitution solution memory work area to generate a numerical solution by using a previous generation back substitution algorithm. The present application utilizes disk storage to relieve memory pressure and supports large-scale finite element simulation application.

The application discloses a kind of based on parameter self-adapting time-invariant track information acquisition method, comprising: for the orbit equation of spacecraft motion, construct linear random time-invariant system, determine initial state quantity and external disturbance term;Based on initial state quantity and external disturbance term, obtain constant coefficient matrix and constant disturbance input matrix;Input the parameter self-adaptive Runge-Kutta solver established, obtain the list of to-be-solved orbit parameters;According to the adaptive parameter constraint formula, the dependent relationship of each parameter in parameter list is solved, free parameters therein are optimized according to genetic algorithm, iteratively updated until the optimal parameters of determination standard are obtained, these parameters are used as the parameters of Runge-Kutta solver, and the orbit information is iteratively solved by Runge-Kutta solver to realize the acquisition of orbit information.The application can be widely applied to space science and technology satellite orbit solving aspect, and is not only suitable for single scientific satellite orbit determination, but also suitable for scientific satellite constellation orbit formulation.

The invention provides a quantum classical hybrid calculation method and device. The method comprises the following steps: performing linearization operation on an objective function and constraint conditions in a QCBO model; setting a variable matrix and a linear mapping operator according to the objective function after linearization operation, the constraint condition and the corresponding co-orthogonality optimization problem; according to the current variable matrix and the linear mapping operator, calculating to obtain a residual vector, and calculating to obtain a secondary unconstrained binary optimization coefficient matrix; solving the current secondary unconstrained binary optimization problem to obtain a current solution vector; determining an iteration direction according to the current solution vector, updating the variable matrix by using the first step length, and taking the updated variable matrix as a current variable matrix; and when the number of iterations is greater than a preset iteration threshold value, performing rounding operation on the current variable matrix, and extracting a vector from the variable matrix after the rounding operation as a final solution. By applying the method, the problem of 0-1 binary optimization with nonlinear constraints can be effectively solved.

The invention discloses a load flow calculation method, system and device based on quantum singular value transformation and a medium, and belongs to the technical field of load flow calculation, and the method comprises the steps: obtaining the initial power parameter of each node of a power system and the given power of each node, and determining the power unbalance amount; constructing a node admittance matrix according to the initial power parameters, constructing a correction equation of Newton method load flow calculation, and converting the correction equation into a linear equation set for expression; coding the coefficient matrix into a unitary matrix by using block coding, and executing polynomial transformation based on quantum singular value transformation to construct a quantum circuit representing an inverse matrix of the unitary matrix; encoding the power unbalance amount into a quantum initial state, and enabling a quantum circuit to act on the quantum initial state and performing measurement; and determining the correction of the node voltage based on the measurement result, judging whether to iterate based on the convergence condition, and outputting a load flow calculation result. By utilizing the characteristics of quantum parallelism and quantum singular value transformation, the time consumption of large-scale power system load flow calculation is remarkably reduced.

This invention discloses an advanced positioning algorithm that, by constructing a statically indeterminate linear equation system, can efficiently integrate data from various stations in a large-scale distributed positioning system and accurately determine the target location in a single calculation. Its innovation lies in utilizing the transformation relationship between polar and rectangular coordinate systems to directly construct a boundary value equation containing mixed variables based on the path difference equation. Through appropriate equation transformation and variable substitution, the algorithm achieves linearization of the boundary value equation, significantly simplifying the calculation process. Furthermore, addressing the decisive influence of the path difference construction method or matrix construction strategy on positioning accuracy, this invention proposes unique path difference construction rules or coefficient matrix construction methods. These rules and methods ensure highly accurate positioning results in practical applications. The new algorithm is not only simple but also significantly improves positioning efficiency, bringing a revolutionary change to large-scale distributed positioning.

The invention relates to the technical field of automatic driving, and discloses an automatic driving decision planning cooperation method and system based on fault prediction, and the method comprises the steps: collecting gateway operation data, carrying out the abnormal trend detection and analysis of time sequence features, and generating a fault probability prediction curve; acquiring a load trigger signal and dynamic response data, and estimating a quality parameter on line by a recursive least square algorithm; the mass center position is calculated by combining suspension displacement data with a statics equation, and a kinetic parameter updating package is generated through fusion; comparing the gateway fault probability with a threshold judgment risk, sending an update packet and generating a check code at a low risk, and pre-synchronizing a storage snapshot at a high risk; updating the kinetic equation coefficient matrix and outputting to a decision domain and a chassis domain; when the gateway fails, each domain operates independently based on a snapshot, and versions are compared to verify consistency and output a collaborative identifier; according to the invention, each function domain can still cooperatively operate based on the same kinetic parameter reference during the gateway fault period.

The invention discloses a coding and decoding method and device, a solving method and device, an encryption method, a learning method and a chip, and relates to the technical field of data processing. The encoding and decoding method comprises the following steps: constructing a linear equation set based on a preset key value table, and obtaining a coefficient matrix of the linear equation set; solving a maximum linear independence group of the coefficient matrix, and adding preset key value pairs corresponding to rows except linear independence rows in the maximum linear independence group into a linear correlation set; updating the system of linear equations based on the linear correlation set, and obtaining a coding vector based on the updated system of linear equations; for the query key, the query key is decoded based on the encoding vector and the linear correlation set. According to the method, the coding success can be ensured by setting the linear correlation set, and the coefficient of the storage overhead can be reduced, so that the storage overhead can be saved, and the query efficiency is improved.

The application discloses a reactor sub-channel comprehensive acceleration calculation method and device and electronic equipment, and relates to the technical field of reactor thermal hydraulic numerical simulation, wherein the method comprises the following steps: abstracting a sub-channel gap topology into an undirected graph and performing edge coloring grouping, and constructing and caching a CSR sparse structure and a write position index of each gap based on the topology; then, serial scheduling is performed according to the coloring grouping, a plurality of threads in the group accumulate the coupling coefficients to a global numerical array according to the pre-calculated position, and the coefficient matrix assembly is completed; finally, the asymmetric sparse linear equation set is solved by AMG pretreatment combined with FGMRES iteration, the pressure gradient solution is obtained, and the current step update is completed. Through edge coloring and sparse storage pre-calculation coupling, the application realizes lock-free parallel assembly and solves the problem of data race in multi-core concurrent writing.

The embodiment of the invention discloses a linear system acceleration solving method, a weather prediction method and a related device, and the method comprises the steps: carrying out the iterative calculation on a quantum computer according to a first type of target vector and a second type of target vector of a to-be-solved linear system, and obtaining a three-diagonal matrix, the plurality of first-class target vectors form a first-class target matrix, the plurality of second-class target vectors form a second-class target matrix, and the product of the transpose of the first first-class target vector in the first-class target matrix and the first second-class target vector in the second-class target matrix is 1; according to the unknown vector of the subspace linear system obtained by solving the subspace linear system of the linear system to be solved, the unknown vector of the linear system to be solved is determined, and the coefficient matrix of the subspace linear system is a tridiagonal matrix. By adopting the embodiment of the invention, the solving speed of the linear system can be improved when a large-scale sparse matrix is processed, and memory resources occupied by solving the linear system are saved.

The application discloses a gravity data downward continuation method based on a discrete model of a reverse band-limit Poisson integral, equipment, a medium and a product, relates to the technical field of gravity data processing, and performs gridization on discrete gravity point data, determines a grid limit frequency, a truncation order of grid gravity anomaly data and a type of a discrete model of a reverse band-limit Poisson integral, and when a direct discrete model in a removal recovery mode is removed, determines a coefficient matrix, a parameter vector and a constant vector based on the grid gravity anomaly data, a continuation height and an earth gravity field model; determines a target calculation formula based on the coefficient matrix, the parameter vector and the constant vector; when an indirect discrete model in a near-zone integral mode is removed, determines a target calculation formula of the discrete model based on the grid gravity anomaly data and the continuation height; and obtains a downward continuation result based on the target calculation formula of the discrete model. The application can stably and highly accurately perform downward continuation on gravity data.

The application discloses a nuclear reactor core sub-channel analysis method based on a two-fluid model, comprising the following steps: 1, establishing a fluid domain calculation grid according to the structure of a nuclear reactor core or an assembly; 2, establishing a control equation set and a fuel rod heat conduction model for continuous gas-liquid phases and discrete droplet phases respectively; 3, considering flow heat exchange, resistance pressure drop and turbulent flow mixing and other physical phenomena in the core, implanting corresponding constitutive models; 4, discretizing the control equation based on a second-order discrete format to obtain a discrete equation set; 5, dividing parallel tasks and parallel regions through an MPI parallel strategy to form a large coefficient matrix of a pressure equation; and 6, solving the discrete equation set and the large coefficient matrix through a pressure coupling semi-implicit algorithm and a stable double-conjugate gradient method. Through the method, key physical parameter distribution of a pressurized water reactor can be more accurately and reasonably calculated, thereby providing technical guidance for reactor safety analysis and engineering design.

The embodiment of the invention relates to the technical field of communication signal processing, in particular to an arbitrary rate conversion method and device. A specific embodiment of the method comprises the following steps: carrying out transformation processing on a pre-acquired interpolation filter impulse response function to obtain a filter transformation function; determining a target function based on the filter transformation function and a preset ideal frequency domain filter function; performing transformation processing on the ideal frequency domain filter function based on the to-be-solved coefficient matrix and the product matrix to obtain an ideal linear equation; adjusting a target filter based on the target function and the ideal linear equation; and inputting a pre-acquired original signal into the adjusted target filter to obtain a target rate conversion signal. According to the implementation mode, real-time arbitrary rate conversion of the FPGA can be realized.

The application discloses an ionospheric scintillation process GNSS positioning model optimization based on weight superposition and singular decomposition method, and comprises the following steps: determining GNSS satellite observation; and designing a GNSS observation weight matrix by superposition of satellite elevation angles and ionospheric scintillation indexes. The application innovatively proposes to design a GNSS observation weight matrix by superposition of satellite elevation angles and ionospheric scintillation indexes, and then solve GNSS observation equations by using a method combining Tikhonov and singular decomposition, so as to solve the problem of the increase of the condition number of a normal equation coefficient matrix caused by ionospheric scintillation, change the ill-conditioned nature of the normal equation, and improve GNSS positioning precision and reliability.

This application discloses a two-dimensional DC electric field forward modeling method, apparatus, electronic device, dielectric, and product. The method includes: taking the variational problem of a two-dimensional wavenumber domain point source DC electric field as the research object; dividing the model region of the dielectric model into several triangular elements and expanding the model region with a sparse mesh; reading the conductivity and potential of the principal nodes in each triangular element, and representing the conductivity and potential with quadratic interpolation shape functions to obtain a discrete model; constructing a system of linear equations based on the discrete model and the coefficient matrix of each triangular element; solving the system of linear equations to obtain the wavenumber domain forward modeling result; and performing an inverse Fourier transform on the forward modeling result to obtain the spatial domain electric field potential. The method increases the number of nodes in the mesh during model region meshing, reduces computation time by expanding the model, and utilizes more node property information through biquadratic interpolation of conductivity and potential within the mesh, thereby improving the accuracy of the two-dimensional DC electric field forward modeling fitting.

The invention relates to a power grid load flow calculation method and device based on a unified framework and a medium, and belongs to the technical field of power systems, and the method comprises the following steps: constructing an admittance matrix of power grid nodes, and obtaining a voltage real part and a voltage imaginary part of the admittance matrix; acquiring an initial power vector of a power grid node; setting an embedding factor, and constructing a power flow recursion formula of PV nodes and PQ nodes in the power grid nodes based on the embedding factor, the voltage real part, the voltage imaginary part and the initial power vector; constructing the power flow recursion of the power grid based on the power flow recursion of the PV nodes and the PQ nodes; and solving the power flow recursion formula of the power grid to obtain the power flow of the power grid. According to the method, a complex all-pure embedded recursive relation is converted into a matrix equation form with a fixed structure, the excellent constant numerical characteristic of a coefficient matrix is fully reserved, and the load flow calculation implementation difficulty is fundamentally simplified.

The application particularly relates to a linear equation set solving method for semiconductor device simulation, which comprises the following steps: determining a target linear equation set, and performing nested partitioning and reordering on a coefficient matrix of the target linear equation set to obtain a transformed coefficient matrix and a corresponding right end vector; based on a preset decomposition precision, the transformed coefficient matrix is decomposed through a selected main element LU decomposition, a forward substitution algorithm and a back substitution algorithm to obtain a current solution vector, and based on a preset working precision, when an initial residual vector does not satisfy a preset solving condition, a modified vector is obtained through a preconditioned GMRES algorithm; based on the preset working precision, the current solution vector and the modified vector, an updated solution vector is obtained as a current solution vector, and a step of calculating a residual vector is re-executed until a latest residual vector after iteration satisfies the preset solving condition, and a final solution is obtained based on the latest current solution vector. Thus, the problems of low solving precision and slow speed in semiconductor device simulation are solved.

Embodiments of the present application disclose a data processing method and device, computer equipment and program product, and belong to the technical field of big data. In the embodiments of the present application, after obtaining a target coefficient matrix and a target constant matrix corresponding to a target triangular matrix equation set to be solved, the inverse matrix of each sub-triangular matrix included in the target coefficient matrix is determined through a matrix inversion algorithm. Subsequently, the solution of the target triangular matrix equation set can be directly determined through a matrix multiplication algorithm according to the inverse matrix of each sub-triangular matrix and the target constant matrix. The scheme is not only suitable for the scenario where the scale of the coefficient matrix is large, but also the entire solving process can be realized through matrix operations, so that the efficiency of solving the target triangular matrix equation set is improved, and the computing power of the processor is improved.