34 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 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.

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 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 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 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.

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 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.

The application discloses an interpretable decision system for single-cell multi-omics data integration analysis, comprising: a standardized data interface layer for receiving single-cell multi-omics raw data in different formats; a modular analysis layer for managing an algorithm module library encapsulated based on containerization technology; a multi-algorithm result evaluation and fusion layer for objectively evaluating parallel algorithm results based on predefined quantitative indicators, and performing fusion strategies of optimization, consensus extraction or difference identification; an interactive biological interpretation and verification environment module for associating analysis results with external biological knowledge databases, supporting user interactive operation, and hypothesis-driven reanalysis; and a reproducible analysis report generator for capturing and storing traceability information of the whole analysis link and generating a visual structured project report. The application constitutes a standardized, automated and feedback mechanism analysis workflow, so that the analyzed data has objectivity, interpretability and reproducibility.

The invention discloses a new energy power plant power generation acquisition terminal network security defense method. The method comprises the following steps: performing security threat analysis on a power generation acquisition terminal; constructing a power generation acquisition terminal network security protection framework; aiming at attack threats existing in the power generation acquisition terminal, attack association rules are mined based on a parallel algorithm, and association rules in a specific attack scene are generated offline; on the basis of similarity calculation, matching of online abnormal events and association rules is achieved, and specific network attacks are recognized; and analyzing security risk indexes of the power generation acquisition terminal, predicting a possible influence range and severity of a current attack according to hazard assessment and anomaly recognition results, and constructing and optimizing an anti-permeation strategy for the terminal in the influence range. According to the invention, the risks of hijacking, eavesdropping, interference, denial of service attack, forgery of control instructions and tampering of measurement data of wireless communication between the power generation acquisition terminal and the station level are reduced, and the network security defense capability of the power generation acquisition terminal is effectively improved.

The invention discloses an Internet of Things intelligent feeding control method and system based on multi-algorithm cooperation, and belongs to the technical field of intelligent feeding of live pigs. According to the method, pig house environment data and live pig physiology and ingestion data are collected through multiple types of Internet of Things sensing nodes, and high-quality effective data are screened through forest isolation, linear interpolation, Z-score standardized multi-algorithm parallel preprocessing and consistency verification; and the cloud constructs a parallel algorithm decision-making layer containing a BP neural network, random forest regression, LSTM and K-means clustering, and deploys logistic regression, a support vector machine and a DBSCAN parallel early warning model at the same time, thereby realizing timely early warning of health risk, equipment fault and stress response. According to the method, the limitation of traditional weight distribution algorithm fusion is abandoned, the method is suitable for various breeding scenes such as large-scale fattening, nursing, free-ranging and multi-stage linkage, the feed conversion ratio can be reduced by 8%-15%, the risk response time is shortened to be within 5 minutes, and the refinement level and economic benefits of pig breeding are remarkably improved.

The invention discloses an automobile spraying track generation method and system, and relates to the field of path planning, and the method comprises the steps: collecting a workpiece three-dimensional model, material and regional data, and constructing a comprehensive data set; based on the curved surface curvature and boundary conditions, spraying sub-areas are divided in a self-adaptive mode, and a partition mapping table is generated; determining spraying parameters of each sub-region through a multi-factor matching algorithm; an initial track is generated by adopting a contour parallel algorithm, thickness uniformity is optimized through finite element simulation, track parameters are adjusted, collision detection and boundary correction are carried out in combination with an obstacle model, finally, the track is smoothly processed through a B spline curve, time parameters are integrated, and an executable time sequence instruction set is generated and output to spraying equipment. The method has the beneficial effects that intelligent and accurate planning of the spraying track is achieved, the method can be matched with the complex curved surface of the automobile workpiece, spraying uniformity is guaranteed through multi-link optimization, the collision risk is avoided, and spraying quality and operation efficiency are both considered.

The invention discloses an automatic land cover change detection method and system in a river and lake management range. The method comprises the following steps: making a land cover change training data set by using multi-source remote sensing change detection data; cutting the change detection remote sensing image pair based on a multi-thread parallel algorithm; constructing a change attention enhanced multi-scale change detection model, and realizing pixel-level image change detection through end-to-end training; performing fine-tuning migration on the change detection model based on historical remote sensing image data in a river and lake management range to obtain a high-precision model meeting river and lake management area land coverage change detection; and carrying out lossless splicing and vectorization conversion on a change detection result, and removing small changes and noise spots through area constraint to obtain a final change detection vector pattern spot. By combining the open source change detection data set and the change detection model migration method, the automatic detection of the land coverage change in the river and lake management range is realized, and the automatic interpretation precision and efficiency of the river and lake monitoring information are improved.

The application relates to the technical field of environmental science, and discloses a simulation method and system for large-scale gas diffusion. The method comprises the following steps: determining a simulation purpose and collecting relevant data, constructing a geometric model of a simulation area, performing multi-scale grid division, estimating a gas diffusion path, speed and concentration through preliminary simulation, subsequently correcting the estimation result and selecting a suitable physical model, coupling different scale physical models through a multi-scale coupling strategy, designing a parallel algorithm to simulate the multi-scale coupling model, evaluating the simulation result in combination with measured data, analyzing errors, optimizing, analyzing the simulation result, evaluating environmental risks, and displaying a gas diffusion process through three-dimensional visualization. The application improves simulation accuracy, environmental adaptability and response flexibility, and realizes precise simulation and risk evaluation of large-scale gas diffusion in a complex environment.

The application discloses a kind of medium and long-term power system partition and local power grid balance ability evaluation and early warning method and system.Method includes: based on real-time power system model data, superimposed medium and long-term power system generation and consumption boundary data, equipment maintenance / put into operation / retirement information, generate the model and section data of medium and long-term power system that meet stable operation condition;Identify the power supply partition grid and local power grid and its internal power equipment information of each period;Using multi-period parallel algorithm, the balance of generation and consumption of partition grid and local power grid, positive and negative standby and power supply margin are calculated;Further evaluate the balance of medium and long-term power system and standby and power supply margin condition, provide medium and long-term partition and local power grid balance monitoring and early warning function.The application provides medium and long-term power grid balance ability analysis data, effectively supports the monitoring of future power grid balance ability operation characteristic change trend, improves power grid long-period stability analysis ability, improves future risk early warning ability.

The invention provides a power grid large-scale grounding grid state evaluation method, which belongs to the technical field of grounding grid detection, and comprises the following steps of: extracting a resistance component and an inductance component through frequency domain impedance analysis to construct a sparse impedance characteristic matrix, and establishing an aggregation grid distribution matrix by adopting a self-adaptive grid strategy; potential distribution and current density distribution are calculated through a regional decomposition parallel algorithm to obtain a conversion equivalent circuit parameter vector, a solving strategy is dynamically adjusted through nuclear space and image space decomposition, and the conversion equivalent circuit parameter vector and a standard grounding grid parameter vector are compared to calculate an abnormal state evaluation index vector; a corrosion area is marked to generate a corrosion position distribution matrix, a grounding grid state evaluation report is output in combination with an adaptive measurement strategy and an automatic correction mechanism, and the technical problems that the detection cost is high, the workload is large and comprehensive detection cannot be achieved due to the fact that excavation verification is needed for large grounding grid corrosion state detection are solved.

The invention relates to a resource scheduling method and device and a storage medium, and the method comprises the steps: obtaining streaming data, and obtaining an available resource for executing the streaming data; based on initial frame data in the stream data, traversing a plurality of preset initial algorithm modules, and positioning a target algorithm module from the plurality of initial algorithm modules; determining an algorithm type corresponding to the target algorithm module; obtaining a target resource corresponding to the target algorithm module based on the algorithm type; when it is detected that the available resources are greater than or equal to the target resources, creating a parallel algorithm module based on the algorithm type and the target algorithm module, and allocating resources to the parallel algorithm module; and the parallel algorithm module and the target algorithm module execute data processing on the stream data in parallel. Through the method and the device, the problems of waste of computing resources and low data processing efficiency are solved.

The application discloses a kind of high-efficiency parallel PIC / MCC fast calculation method and system.The method divides PIC / MCC calculation flow into four modules of charge distribution, electric field solving, particle propulsion and collision processing, and is executed in parallel on GPU with independent kernel function.By making all simulation data resident GPU memory, structured array and row-major layout are used to optimize storage access.In charge distribution, GPU atomic operation is introduced to ensure data consistency;In electric field solving, parallel algorithm is selected according to dimension to accelerate Poisson equation solving;In particle propulsion, electric field is obtained by interpolation and particle motion state is updated;In collision processing, Monte Carlo method is used to judge collision type in parallel.Each module is synchronized and controlled through GPU event mechanism to ensure physical process self-consistent.The application breaks through the bottleneck of traditional serial calculation, realizes more than 30 times speedup while maintaining calculation accuracy, and significantly improves the efficiency and scalability of plasma numerical simulation.

The invention provides a fault early warning method and system for a hydrogen power system of a hydrogen energy high-speed train, and belongs to the technical field of train fault monitoring. And a pre-trained anomaly detection model is utilized to process the obtained key component operation state data, and an anomaly detection result of the hydrogen power system of the hydrogen energy high-speed train is obtained. According to the method, the limitation of a fixed threshold value method is overcome, and misjudgment and missed judgment are effectively reduced; early warning can be carried out in advance when a potential fault is not obvious, so that the safety of train operation is greatly improved; and by utilizing distributed calculation and a parallel algorithm, the processing efficiency of large-scale real-time data is remarkably improved, and a powerful technical guarantee is provided for efficient and safe operation of the train.

The application provides a kind of remote sensing satellite in-orbit video encoding method and system based on heterogeneous computing platform, belongs to remote sensing satellite technical field, including: based on virtual node dynamic mapping algorithm and consistent hash dynamic allocation task algorithm, dynamically adjust the on-board computing resources of remote sensing satellite;The elastic allocation is carried out to on-board computing resources, and the parallel processing of the elastic allocation on-board computing resources is carried out using multi-unit stacking pipeline parallel algorithm, the directional model based on satellite video image and the geometric correction model between video image frames are constructed, and the optical satellite video image stabilization with geographic coding is obtained;The optical satellite video stabilization is compressed and encoded to shorten the input-output time consumption, hide the time delay of input-output time consumption, and output video stream.The application changes the traditional remote sensing satellite video encoding task needing ground processing into on-board in-orbit processing mode, significantly reduces the time consumption of remote sensing video encoding, and provides remote sensing satellite "fast, accurate and flexible" service.