52 results about "Parallel algorithm" patented technology

The invention relates to the technical field of data processing, in particular to a new energy uncertainty multi-type energy storage staged capacity optimization configuration method, which comprises the following steps: acquiring energy storage parameters, new energy output and user load data through edge equipment, and verifying photovoltaic conversion efficiency and lithium battery electrochemical behaviors through a physical model to generate a high-confidence data set; the federal cooperative system fuses distributed node data to output a health degree distribution diagram, and the memory network analyzes the temperature and the charge-discharge coupling effect to generate a dynamic compensation coefficient alpha; an optimization engine receives the alpha value and wind and light prediction, generates a staged scheme through a parallel algorithm, and inputs the staged scheme into a simulation system for verification; and the edge node executes the scheme and collects response data, and twin compares a simulation value with an actual deviation to update a collection strategy. The method eliminates data distortion through physical rule verification, corrects aging unit state deviation through a dynamic compensation mechanism, optimizes decision binding real-time health degree, and effectively defends charging and discharging analysis errors of a virtual power plant dispatching desk.

The invention discloses a large power grid security check calculation method suitable for multiple time scales, and belongs to the technical field of data processing, and the method comprises the following steps: S1, obtaining the calculation content of a multi-user security check task, evaluating the complexity of the calculation content, and selecting a parallel calculation mode based on an evaluation result; s2, based on a parallel computing device pool set in a parallel computing mode and a job scheduling strategy, extracting computing sub-tasks of the multi-user security check task, and dynamically distributing the computing sub-tasks to corresponding computing nodes by adopting a load balancing mechanism; and S3, processing the calculation subtasks of the plurality of calculation nodes by adopting a parallel algorithm of a CPU + GPU hybrid architecture, merging calculation subtask results of the plurality of users, and generating a final security check report. According to the scheme, the computing resource utilization rate of the resource pool is improved, and the large-scale power grid safety checking efficiency is remarkably improved.

The invention discloses an efficient and parallel PIC / MCC rapid calculation method and system. According to the method, a PIC / MCC calculation process is divided into a charge distribution module, an electric field solution module, a particle propulsion module and a collision processing module, and parallel execution is carried out on a GPU through an independent kernel function. All simulation data are resident in a GPU video memory, and storage access is optimized by adopting a structured array and row main sequence layout. GPU atomic operation is introduced in charge distribution to ensure data consistency; selecting a parallel algorithm to accelerate Poisson equation solution according to dimensions in electric field solution; an electric field is obtained through interpolation in particle propulsion, and the particle motion state is updated; and adopting a Monte Carlo method to judge the collision type in parallel in the collision processing. Synchronous control among the modules is realized through a GPU event mechanism, and self-consistency of a physical process is ensured. According to the method, the bottleneck of traditional serial calculation is broken through, the speed-up ratio of more than 30 times is realized while the calculation precision is kept, and the efficiency and expandability of plasma numerical simulation are remarkably improved.

Disclosed in the present invention is a runoff forecasting method based on an EEMD-ANN and meteorological factors and using a multi-core parallel algorithm. The method comprises: S1, performing feature screening, involving: on the basis of historical data of previous inflows and meteorology, performing screening of lag terms of the previous inflows by means of a partial autocorrelation function, using an inverse distance weighting method to reduce the weight of meteorological data, using a cross-correlation function and an index of agreement to perform screening of lag terms of rainfall, then performing feature selection by means of a correlation coefficient γ, and finally, using a correlation coefficient τ to perform screening of input factors; S2, performing data pre-processing, involving: using an empirical mode decomposition technique to decompose the input factors, performing data normalization processing, and dividing data into a training set and a test set; and S3, performing model forecasting and assessment, involving: integrating the input factors and inputting same into a forecast model, then performing parameter optimization by means of a parallel algorithm, outputting a forecast result, and performing model assessment. The present invention effectively improves forecast accuracy, accelerates the efficiency of model parameter calibration, and reduces model training time.

The invention provides a self-adaptive collaborative parallel optimization aviation complex structural member production scheduling method and system and a program product. The method comprises the following steps: presetting parallel algorithm related parameters; initializing a population, wherein the initialized population comprises a first scheduling scheme generated by using a random greedy heuristic algorithm; performing evolutionary optimization on the initialized population or the derivative population of the initialized population in parallel by utilizing a plurality of configured sub-threads to generate a new scheduling scheme; according to a comparison result of the current CPU utilization rate and the memory utilization rate and the target CPU utilization rate and the target memory utilization rate, adjusting the number of sub-threads which are running; according to the method, the problems that in the prior art, a scheduling scheme is not high in quality and low in optimization efficiency, and the utilization rate of computing resources is difficult to consider are effectively solved, the high-quality and high-adaptability scheduling scheme can be generated, the solving time is shortened, the computing resources are fully utilized, and the scheduling efficiency is improved. The production efficiency and the resource utilization rate are improved.

The invention relates to the technical field of environmental science, and discloses an analogue simulation method and system for large-scale gas diffusion. The method comprises the following steps: determining a simulation purpose and collecting related data, constructing a geometric model of a simulation area, carrying out multi-scale grid division, estimating a gas diffusion path, speed and concentration through preliminary simulation, then correcting an estimation result and selecting a proper physical model, and using a multi-scale coupling strategy to simulate the gas diffusion path, speed and concentration. The method comprises the following steps: carrying out coupling modeling on physical models of different scales, designing a parallel algorithm to simulate a multi-scale coupling model, evaluating a simulation result and analyzing an error in combination with actually measured data, carrying out optimization, analyzing the simulation result and evaluating an environmental risk, and displaying a gas diffusion process through three-dimensional visualization. According to the method, the simulation precision, the environmental adaptability and the response flexibility are improved, and precise simulation and risk assessment of large-scale gas diffusion in a complex environment are realized.

The invention discloses a power transmission line lightning stroke risk accelerated assessment system and method and a storage medium, and the method comprises the steps: obtaining the potential of each point in a space electric field of a to-be-assessed region according to a parallel algorithm, carrying out the simulation calculation of thundercloud and a downlink pilot model of the to-be-assessed region, and obtaining the ground thunderbolt distribution of the to-be-assessed region; according to the ground-to-ground lightning density of the to-be-evaluated region and the back-strike trip-out rate sub-model, the shielding failure trip-out rate sub-model and the inductive lightning trip-out rate sub-model, calculating the back-strike trip-out rate, the shielding failure trip-out rate and the inductive lightning trip-out rate of the power transmission line of the to-be-evaluated region respectively; and combining and calculating the back strike trip-out rate, the shielding failure trip-out rate and the inductive lightning trip-out rate of the power transmission line in the to-be-evaluated region to obtain an average value of the lightning trip-out rates of the base towers of the power transmission line in the to-be-evaluated region, and evaluating the lightning risk of the to-be-evaluated region through the average value of the lightning trip-out rates. By adopting the method, a lightning stroke risk assessment result can be obtained in a shorter time, and the lightning protection capability and the operation safety of the power system are improved.

The invention provides a distributed unstructured grid topology construction method, and relates to the field of unstructured grids, and the method comprises the steps: judging the dimension of an unstructured grid; if the grid is a three-dimensional grid, sequentially constructing 16 grid topologies consisting of points, edges, surfaces and a body on the basis of the body-point topology; if the grid is a two-dimensional grid, nine grid topologies composed of points, edges and surfaces are constructed in sequence on the basis of the surface-point topology; if the grid is a one-dimensional grid, sequentially constructing four grid topologies consisting of points and edges on the basis of the edge-point topology; when the grid topology is constructed, cross-process adjacency information is supplemented and synchronized through communication. According to the method, the problems of large memory demand, unbalanced load, repeated calculation and high communication cost during use in the prior art can be solved, a complete distributed unstructured grid topology construction parallel algorithm process is provided, and it is ensured that the method can be efficiently applied to different numerical simulation scenes.

The application discloses a kind of super-resolution SIM-FRET image reconstruction methods based on GPU parallel acceleration, including steps: FRET original image is collected using acquisition card and is stored;Program initialization is carried out;FRET single-channel original image is obtained and transmitted asynchronously to GPU global memory;Parallel image preprocessing is carried out to FRET single-channel original image group by GPU;Normalizing separation image is iterated by the heterogeneous computing of CPU and GPU Correlation Parameter Estimation, and the modulation intensity and initial phase of illumination light vector are estimated;GPU parallel SR-SIM linear wiener reconstruction;Super-resolution FRET three-channel image is obtained;FRET three-channel super-resolution image is processed based on channel sensitization intensity measurement method E-FRET data by GPU.The application improves the parallel algorithm structure suitable for GPU acceleration, and the heterogeneous computing of CPU and GPU is used for the iterative correlation parameter estimation algorithm which cannot be completely parallelized, improves the GPU histogram summation algorithm of biological image, and improves the time resolution of super-resolution SIM-FRET image reconstruction.

A layered hybrid parallel cloud edge end heterogeneous resource collaborative training strategy, research on how to fully utilize the multi-layered hybrid parallel strategy of the search space of the search algorithm of the layered hybrid parallel algorithm, through the dynamic programming algorithm and the resource constraint mechanism to search the possible layered hybrid parallel algorithm, realize the efficient layered hybrid parallel of the cloud edge end collaborative cross-domain scene; research on the runtime cross-domain layered hybrid parallel optimization strategy, through the secondary balanced allocation of the data load and the calculation load in the calculation cluster, adjust the calculation speed between each node, avoid the emergence of slow nodes at runtime. Through the above mechanism, the efficient adaptation of cloud edge heterogeneous computing resources and hybrid parallel strategy is realized, the utilization efficiency of cloud edge heterogeneous computing resources is improved, and the model cross-domain distributed training speed is improved.

The application discloses a regional scale process model simulation precision optimization method, system and device, relates to the field of model optimization, and comprises the following steps: inputting a crop parameter combination to be optimized into a crop-water productivity model, determining result variables yield and canopy coverage; performing cross-validation on the result variables yield and canopy coverage to determine evaluation indexes; and constructing a multi-objective index evaluation optimization model for optimization of key parameters of a regional scale PCMs; wherein the multi-objective index evaluation optimization model takes the sum of Nash efficiency coefficients NSE of the canopy coverage and the result variable yield in the training set and the test set as an objective function, and takes a root mean square error RMSE as a constraint condition; the evaluation indexes are input into the multi-objective index evaluation optimization model, a parallel algorithm group composed of multiple meta-heuristic optimization methods is adopted to solve the multi-objective index evaluation optimization model, and the key parameters of the regional scale PCMs are optimized to obtain an optimal crop parameter combination.

The application belongs to the technical field of neural network simulation and analog, and particularly relates to a large-scale cortex pulse neural network asynchronous parallel simulation algorithm based on GPU. The application utilizes the advantages of multi-thread and texture memory of a computing graphics card, combines the general form of a biological brain receiving external stimulation and the general connection mode between neurons in the cortex, designs an asynchronous parallel algorithm framework of GPU and CPU, GPU is responsible for parallel evolution of neuron dynamics equations and block parallel calculation of isotropic connection in a local network, CPU is responsible for processing anisotropic long-range connection, and different neuron dynamics equations and plasticity learning rules can be compatible. Compared with the prior art, the application can effectively improve simulation speed, provides a tool for simulating a biological brain cortex in a single computing node, and is suitable for a single node multi-graphics card and a multi-node multi-graphics card distributed operation model.

The invention discloses an optimization method for a sparse dense matrix multiplication parallel algorithm based on CSR characteristics, including CSR sparse matrix data buffer design, load balancing of buffer parallel data preloading, load balancing of dense matrix calculation tasks on threads, and temporary storage design of partial cumulative sums of calculation results. The beneficial effect of the invention is that by analyzing some memory access problems existing in the current SpMM parallel algorithm, buffer design is used as an entry point, and load balancing is used as a means to achieve the purpose of improving computing performance and improving computer resource utilization.

The application discloses an intersatellite link satellite data transmission task scheduling method and system and a storage medium, and comprises the following steps: acquiring a satellite set, a task target set and a ground station set; determining a network connection plan and a data transmission plan in each time slot; acquiring a task constraint condition, establishing an intersatellite link satellite data transmission task scheduling model, and taking the maximum complete task data value transmitted to the ground station after T time slots as the scheduling model; constructing a parallel algorithm group, acquiring an optimal algorithm and corresponding parameter configuration, and matching the optimal algorithm with a scene instance. The method constructs an integer programming model of the intersatellite link satellite data transmission task model, constructs a parallel algorithm group, searches for an optimal algorithm configuration in a machine learning mode, and overcomes the contradiction between the cost and quality of huge computing resources required by parallelism.

This invention relates to the field of traffic safety technology, specifically to an efficient simulation method for quantum systems based on parallel order reduction. The method includes the following steps: receiving the physical parameters of the quantum system and simulation requirements, whereby the physical parameters include electron mass, potential well size, and initial wave packet parameters; and the simulation requirements include simulation physical time and accuracy requirements. Based on the physical parameters, a time-dependent Schrödinger equation describing the dynamic behavior of the quantum system is established, and the Schrödinger equation is rearranged into matrix form using the Kronecker product. This invention utilizes Arnoldi to reduce the order of the matrix-form Schrödinger equation. By reducing the order, the large matrix in the original space is projected into a relatively small subspace, improving the efficiency of the simulation solution. The reduced-order Schrödinger equation is solved using a time-parallel algorithm. This approach overcomes the time step limitation imposed by the CFL condition, ensuring that a stable solution can be obtained with fewer time steps.

The invention relates to the technical field of electromagnetic field simulation modeling, in particular to a magnetotelluric efficient parallel simulation method and device based on random path integration, and the method comprises the steps: rewriting a deterministic solution based on a solution matrix equation into a form based on random path integration; in the offline preparation stage, the number of times that random walk particles pass through each lattice point is recorded, a path matrix is generated, the number of times that all random walk particles pass through all lattice points of a Robin boundary is recorded, a boundary path matrix is generated, and preset model grids are interpolated to different random walk grids to generate an interpolation matrix; and in the online stage, obtaining a final calculation result according to the path matrix, the boundary path matrix and the interpolation matrix. Therefore, the problems of unit communication, load balancing and I / O bottleneck in parallel simulation of a large-scale electromagnetic positive problem caused by parallelization transformation of a non-parallel algorithm adopted by an existing deterministic simulation method based on equation set solution in the prior art are solved.

The present application relates to an electromagnetic anisotropic large-scale parallel forward modeling method based on a PDE solving base. The method includes: constructing an anisotropic electromagnetic model according to the constitutive relationship between the electric and magnetic fields when facing anisotropic media; the constitutive parameters of the constitutive relationship include the dielectric constant, magnetic permeability, and conductivity of the anisotropic media; inputting the configuration file information of the electromagnetic problem to be solved into the PDE solving base, and the PDE solving base loads the electromagnetic model plug-in according to the configuration file information, constructs an electromagnetic field solving module, divides the calculation area into multiple sub-domains, allocates processes from the process set to each sub-domain, and in the processes corresponding to each sub-domain, parallelly solves the anisotropic electromagnetic model of the sub-domain according to the multi-level multi-granularity parallel algorithm and the configuration information in the electromagnetic field solving module, and outputs the electromagnetic field results of the sub-domain; obtaining the forward modeling result according to the electromagnetic field results of each sub-domain. Using this method can improve the efficiency and reliability of large-scale electromagnetic forward modeling.

The application discloses an intelligent optimization method based on a multiphase flow LBM parallel algorithm, and comprises the following steps: 1) constructing a data dependency relationship model of the multiphase flow LBM parallel algorithm; 2) constructing a memory access performance analysis model of the multiphase flow LBM parallel algorithm; 3) according to the data dependency relationship model, the characteristics of GPU memory access and the data read-write relationship, proposing a kernel fusion rule of the multiphase flow LBM algorithm and a performance change estimation method after fusion; 4) combining the fusion rule and the performance change estimation method into a grouping genetic algorithm to search for an optimal fusion scheme; and 5) optimizing according to the optimal fusion scheme and comparing the performance before and after optimization. The method comprehensively considers the data transmission and memory access characteristics of the CUDA kernel, explores the performance gain under various CUDA kernel fusion strategies by using an intelligent search algorithm, and finally obtains the optimal CUDA kernel fusion strategy, and the acceleration effect of the method is significantly improved compared with a traditional multiphase flow LBM parallel method.

The application discloses a method for recognizing and judging the health condition of a dairy cow based on video skeleton, and relates to the technical field of video behavior recognition, and comprises the following steps: step one: a camera collects images and sends the images to a terminal for processing, manual labeling and detection of key points of a dairy cow skeleton; step two: a Bottom-up posture estimation method algorithm is used to obtain the posture of the dairy cow, the Bottom-up posture estimation method comprising feature extraction, landmark positioning, part grouping and tracking; feature extraction: a parallel algorithm of bilinear interpolation is used to process the images, BatchNormalization is used for batch data standardization, self-attention gating is used to highlight the significant features of the region of interest, and the U-net feature extraction network model is improved; the application applies the skeleton calibration technology to the dairy cow to recognize the behavior of the dairy cow, and the recognition accuracy is increased and the innovation is improved; compared with the skeleton data, other modalities will produce more calculation consumption, and the robustness is insufficient when facing complex backgrounds and human scale changes, view angle changes and motion speed changes.

The application discloses a kind of based on cloud computing's multi-source heterogeneous big data fast cleaning and fusion method, to solve the technical problems of poor adaptability of heterogeneous data in the prior art, low processing efficiency, insufficient fusion accuracy, flow batch processing fragmentation and lack of quality closed loop.The method is based on cloud native distributed architecture, steps include: building global semantic ontology model, realizing the unified access and standardization adaptation of multi-source heterogeneous data, combining consistent hash fragmentation and elastic computing power scheduling to complete data efficient distribution;Adaptive missing value repair, robust outlier detection and distributed bloom filter deduplication are performed using parallel algorithms, data cleaning is realized quickly;Through two-stage entity alignment and data source credibility weighted conflict resolution, multi-source data deep fusion is completed;After full-amount quality check, hierarchical storage is carried out, rule adaptive optimization is realized relying on reinforcement learning, and data consistency and low latency are guaranteed by supporting flow batch incremental processing mechanism.

A method and high-performance apparatus for creating an approximate maximum k-cut of a graph. In various embodiments, nodes and weighted edges in a graph are used to compute a partitioning of a graph such that the edge weight between partitions is maximized. In various embodiments, the method and apparatus use a greedy random construction of cuts for its first approximation, whose solutions are then subject to local search for better solutions, and local path-relinking for the final level of refinement. In various embodiments, one or more of the greedy random construction, local search, and path relinking are parallelized. A novel parallel algorithm is disclosed for perturbing the state of a cut to see if a better one is cheaply available.

The invention relates to an electromagnetic anisotropy large-scale parallel forward modeling method based on a PDE solving base. The method comprises the following steps: constructing an anisotropic electromagnetic model according to a constitutive relationship between an electric field and a magnetic field when facing an anisotropic medium; the constitutive parameters of the constitutive relationship comprise the dielectric constant, the magnetic conductivity and the conductivity of the anisotropic medium; configuration file information of an electromagnetic problem to be solved is input into a PDE solving base, the PDE solving base loads an electromagnetic model plug-in according to the configuration file information, an electromagnetic field solving module is constructed, a calculation area is divided into a plurality of sub-domains, processes are distributed to each sub-domain from a process set, and in the processes corresponding to the sub-domains, an electromagnetic field solving module is constructed; solving the anisotropic electromagnetic model of the sub-domain in parallel according to a multi-level multi-granularity parallel algorithm and configuration information in the electromagnetic field solving module, and outputting a sub-domain electromagnetic field result; and obtaining a forward modeling result according to the electromagnetic field result of each sub-domain. By adopting the method, the efficiency and reliability of large-scale electromagnetic forward modeling can be improved.

The invention discloses a regional scale process model simulation precision optimization method, system and device, and relates to the field of model optimization, and the method comprises the steps: inputting a to-be-optimized crop parameter combination into a crop-water productivity model, and determining the result variable yield and canopy coverage; performing cross validation on the result variable yield and the canopy coverage to determine an evaluation index; constructing a multi-target index evaluation optimization model for optimizing the key parameters of the regional scale PCMs; wherein the multi-objective index evaluation optimization model takes the sum of Nash efficiency coefficients NSE of minimizing canopy coverage and result variable yield in a training set and a test set as an objective function, and takes a root-mean-square error RMSE as a constraint condition; and inputting the evaluation indexes into a multi-target index evaluation optimization model, and solving the multi-target index evaluation optimization model by adopting a parallel algorithm group formed by a plurality of meta-heuristic optimization methods so as to optimize the key parameters of the regional scale PCMs and obtain an optimal crop parameter combination.

The invention relates to a multi-core architecture satellite navigation SoC based on an RISC-V vector processor, a chip and a receiver. Belongs to the field of satellite navigation and chip design. The SoC comprises an instruction memory for storing a navigation receiver program after rewriting a parallelism algorithm; the processor is used for acquiring and running a navigation receiver program from the instruction memory; the main processor is used for performing receiver control and processing and navigation control and processing functions and outputting a positioning result, and is used for assisting the main processor to execute a vector instruction when the main processor runs a navigation receiver program; and for the vector instruction needing to return the scalar value, the vector coprocessor feeds back an instruction execution result to the main processor, and for the other instructions, the vector coprocessor writes the instruction execution result into the data memory to be called by the main processor. According to the invention, through the coupling processing of the main processor and the vector coprocessor, the processing capability of multi-channel satellite navigation signals is enhanced, and the response speed of the system is improved.

The invention discloses an intelligent optimization method based on a multiphase flow LBM parallel algorithm. The intelligent optimization method comprises the following steps: 1) constructing a data dependency model of the multiphase flow LBM parallel algorithm; 2) constructing a multiphase flow LBM parallel algorithm memory access performance analysis model; 3) proposing a kernel fusion rule and a performance change estimation method after fusion for the multiphase flow LBM algorithm according to the data dependency relationship model, the GPU memory access characteristics and the data read-write relationship; 4) combining the fusion rule and a performance change estimation method into a grouping genetic algorithm, and searching an optimal fusion scheme; and 5) performing optimization according to the optimal fusion scheme, and comparing the performance before and after optimization. According to the method, CUDA kernel data transmission and memory access characteristics are comprehensively considered, performance gains under various CUDA kernel fusion strategies are explored through an intelligent search algorithm, finally, the optimal CUDA kernel fusion strategy is obtained, and the acceleration effect is remarkably improved compared with a traditional multiphase flow LBM parallel method.

The application provides a kind of automobile energy management method based on container cloud and model predictive control, parallel multi-block ADMM algorithm and container cloud are fused to design the calculation model predictive control cloud architecture for the nonlinear system of plug-in hybrid electric vehicle, to improve the real-time feasibility of nonlinear model predictive control;For non-convex problem with nonlinear constraint, a parallel multi-block ADMM algorithm is designed, and under reasonable assumptions, the algorithm converges to the local optimal solution of the problem, using this high-parallel distributed optimization algorithm, a computational model predictive control is developed, which can convert the NMPC optimization problem of nonlinear system into a series of sub-problems related only to the decision variables at this time in a prediction step, and using the cloud with rich computing resources, a cloud-based computing MPC architecture is designed.The architecture fully integrates parallel algorithms and container cloud technology, so that each sub-problem is solved in parallel in the corresponding container, thereby improving the computing efficiency.

A method and system for accelerating deep neural network hybrid parallel inference in heterogeneous trusted execution environments are presented. This method first acquires the hardware parameters of the heterogeneous cluster and establishes a segmented model of a secure memory paging penalty. Second, it performs active parallel partitioning based on a directed acyclic graph, identifying Fork and Join nodes and transforming them into independent scheduling boundaries to generate macro-level graph partitions. Next, for overloaded operators, a cost-aware operator-level parallel algorithm is employed to construct a three-dimensional cost function integrating computation, paging, and communication, determining the optimal parallelism and dividing the data into multiple micro-operator slices. Finally, a two-level hybrid parallel cooperative scheduling is executed, constructing a unified heterogeneous task dependency graph through virtual synchronization nodes and utilizing priority assignment of the global critical path and cooperative group constraints to complete physical node mapping. This invention avoids loss of topological concurrency, overcomes the bottleneck of single-point physical memory limits, eliminates deadlock in heterogeneous scheduling, and significantly reduces end-to-end inference latency.

The present invention provides a parallel analysis method for watershed flow direction and cumulative flow using the D8 algorithm in a CUDA architecture. The method comprises: constructing a read and storage model for watershed DEM raster data based on GPU texture memory; setting parallel grids and thread blocks based on GPU global memory, mapping threads to DEM raster cells, and obtaining the current thread identification number; each thread continuously picking the elevation values of the center grid and adjacent grids of the DEM raster data according to the read and storage model; calculating the maximum distance-weighted drop based on the elevation values of the center grid and adjacent grids, and determining the flow direction of the center grid based on the maximum distance-weighted drop; constructing a flow direction grid cell based on the flow direction of the center grid, mapping threads to flow direction grid cells, and reading thread identification numbers and flow directions; and each thread performing serial flow calculations according to the flow direction to obtain a cumulative flow matrix. The present invention proposes a parallel method for fast computation of the D8 algorithm in a CUDA environment, solving the problems of raster pixel access and computation conflicts in parallel computation of the D8 parallel algorithm.

The application provides a corneal topographic alignment method based on Placido ring image and laser ranging fusion, belongs to the technical field of image processing of ophthalmic diagnostic equipment, and utilizes a single auxiliary sensor-free main camera to collect an RGB original image; a centering branch process and a focusing branch process are synchronously and in parallel executed on the same frame of RGB original image to solve XY axis centering deviation and Z axis focusing deviation; wherein the centering branch process adopts two sets of DCSA-Unet models with consistent architecture to sequentially complete coarse positioning of a pupil and fine positioning of a Placido inner circle; horizontal centering deviation threshold value, vertical centering deviation threshold value and focusing deviation threshold value are synchronously checked, and when the three deviations are all less than the corresponding preset threshold values, corneal image shooting is triggered; if any one of the deviations exceeds the threshold value, the position of the main camera is adjusted to re-collect the RGB original image. Thus, only by relying on single camera homologous image collection, three-axis full-automatic closed-loop alignment can be realized through a double-branch parallel algorithm combined with a lightweight DCSA-Unet network.

Devices, systems, and methods for providing more accurate and reliable sensor data and for detecting sensor failures. Two or more electrodes can be used to generate data, and the data can be subsequently compared by a processing module. Alternatively, one sensor can be used, and the data processed by two parallel algorithms to provide redundancy. Sensor performance, including sensor failures, can be identified. The user or system can then respond appropriately to the information related to sensor performance or failure.