51 results about "Parallel computing architecture" patented technology

The invention provides an instruction-level simulation and performance modeling system for a parallel computing architecture, and belongs to the technical field of computer architecture and simulation verification, and the system comprises an instruction modeling layer which is used for analyzing and executing an intermediate instruction set defined by the architecture; the scheduling execution layer is used for simulating a multi-thread and multi-core parallel execution process; the storage access layer is used for constructing a hierarchical storage access and bandwidth and delay model; and the performance analysis layer is used for collecting and counting key indexes such as an execution period, an instruction utilization rate and memory access delay, and realizing accurate performance modeling of the parallel architecture. According to the method, the performance bottleneck of the design scheme can be rapidly evaluated in the early stage of architecture design, the simulation speed is high, the module configurability is high, the modeling precision is adjustable, and the method is suitable for functional verification, micro-architecture exploration and compiler performance analysis of parallel computing architectures, accelerator chips, heterogeneous multi-core processors and the like.

The invention, which relates to the technical field of mine geology and urban safety, discloses a refined collaborative prediction method and system for subsidence of a global goaf, comprising the steps of establishing a hierarchical storage system, and correcting a working face calculation boundary; extracting the maximum influence radius of all the working faces and four-dimensional coordinates of the mining boundary of each working face, and calculating to obtain a global prediction range to generate a global surface prediction grid; point set insertion and Delaunay triangulation are adopted to obtain an effective triangular unit, a local influence domain range of the effective triangular unit is calculated, and an influenced sub-grid is screened from the global surface prediction grid; and calculating a predicted point movement deformation value based on a parallel computing architecture, accumulating the predicted point movement deformation value to a global result array through a coordinate index mechanism, synthesizing a deformation component in a specific direction, generating a visual result, and performing statistical analysis. According to the method, the problems of poor adaptability and low efficiency of a traditional scheme are effectively solved, and the prediction precision and efficiency are remarkably improved.

The invention discloses a hierarchical simulation modeling system for a new energy station cluster and a real-time verification method, belongs to the technical field of new energy power generation simulation, and solves the problems of high simulation calculation complexity, difficulty in real-time operation and closed-loop interaction verification with an external control system of a large-scale new energy station cluster. According to the technical scheme, the method comprises the steps that a digital dynamic real-time simulation platform configured with a multi-thread parallel computing architecture is adopted, and a new energy station detailed model comprising a wind power generation unit model, a photovoltaic power generation unit model, an energy storage power generation unit model and a current collection system model is deployed on the platform; generating an independent wind speed sequence for each fan through a wind resource distribution model; and constructing a large-base station cluster model composed of a plurality of station simplified models through aggregation equivalence. The system is mainly used for real-time simulation testing of a large-scale new energy base and closed-loop verification of a control system.

The invention relates to the field of geographic information system data processing, in particular to a vector tile cutting and publishing system which comprises a heterogeneous data self-adaptive access and normalization subsystem used for receiving multi-mode heterogeneous input; the spatial-temporal feature analysis and index construction middleware is used for generating a metadata index reflecting data spatial distribution features; the vector tile streaming production engine drives a tile generation process based on the metadata index, converts the intermediate state data into a vector tile data packet by adopting a parallel computing architecture, and writes the vector tile data packet into a tile storage warehouse; and the standardized service publishing bus is used for monitoring the change state of the tile storage warehouse in real time and externally providing a vector tile network service interface which accords with a standard protocol. According to the method, the structure barriers of heterogeneous data sources are eliminated, the stability of data production is ensured through topology self-healing, the defect that one set of rules cut all is overcome through a density-based parameter inversion mechanism, and the tile size and the visualization precision are balanced.

The present disclosure provides a kind of income processing method and system based on event-driven and multilayer parallel architecture, the method comprises: receiving and processing multi-source trigger signal, generates the standardized trigger context of income calculation task;According to the standardized trigger context, the income calculation task is split into multiple independent subtasks, encapsulated as a standardized income event message and published;Subscription and processing standardized income event message, split each independent subtask into atomic subtask, execute distributed income calculation, generate customer income raw results;Customer income raw results are converged and persisted to unified income accounting table.The present disclosure effectively solves the bottleneck of traditional system in performance and expansibility through event-driven and parallel computing architecture, and further through the innovative initiative audit repair mechanism, builds endogenous, automated data quality guarantee system, realizes the overall upgrade of reliability, accuracy and operation efficiency of income processing system.

This invention discloses a method, apparatus, device, and storage medium for predicting transformer overload. By calculating the multi-dimensional dynamically coupled adjacency matrix of the transformer, this adjacency matrix can be used in conjunction with a spatiotemporal attention bidirectional feedback network to effectively improve the accuracy of overload prediction and reduce the false judgment rate under extreme conditions. Furthermore, both the multi-dimensional dynamically coupled adjacency matrix and the spatiotemporal attention bidirectional feedback network are lightweight parallel computing architectures, resulting in a short prediction time for the target transformer's load rate and low resource consumption, which can meet the real-time dispatching requirements of the power grid. The invention also obtains overload and overload benchmark thresholds based on the historical number of overloads in the target transformer's static equipment dataset. These thresholds are used to predict whether the target transformer is overloaded or overloaded, allowing for threshold settings based on the specific conditions of the target transformer to reflect individual differences and further improve prediction accuracy.

The invention provides a distributed gas compressor margin calculation method and device, and belongs to the field of gas compressor pneumatic optimization. The method provided by the invention comprises the following steps: building a distributed parallel computing architecture; constructing a flow field convergence judgment rule based on an efficiency residual error; and adopting a surge boundary self-search strategy for dynamically adjusting the back pressure, carrying out multi-round full three-dimensional flow field parallel calculation by virtue of the distributed parallel calculation architecture, verifying a calculation result of each round in combination with the flow field convergence judgment rule, and iteratively adjusting the outlet back pressure to approach to a gas compressor near surge point, so as to achieve the purpose of reducing the flow field convergence judgment rule. Until a near surge point of convergence and divergence critical states is determined and performance parameters are acquired; and total pressure ratio and flow performance parameters of the design point of the gas compressor are extracted, and the margin of the gas compressor is calculated by combining the performance parameters corresponding to the near surge point. According to the distributed compressor margin calculation method and device, the margin calculation precision and calculation efficiency are both improved, and meanwhile calculation resources are utilized to the maximum extent.

The application discloses a kind of multi-objective collaborative optimization method and device of coal blending of thermal power plant, belong to energy management and intelligent control technical field.Integrating the coal quality of thermal power plant, operation, emission and scheduling data, construct unified feature library, and based on this, set the initial weight of economic, safety, environmental protection target, establish multi-objective optimization model;Adopt parallel computing architecture to solve model, generate multiple candidate blending scheme;Subsequently, combined with the artificial adjustment result of operator and actual combustion feedback data, carry out secondary prediction and comparative analysis;Finally, through continuous learning adjustment behavior and operation effect, drive model automatically update weight and optimization algorithm combination, form closed loop self-learning mechanism, to realize multi-objective collaborative optimization and adaptive decision of thermal power plant.The application improves the intelligence, economy and environmental protection of system operation.

The invention discloses a CUDA (Compute Unified Device Architecture)-based GPU (Graphics Processing Unit) parallel grating generation system and a CUDA-based GPU parallel grating generation method, pixel-level mapping calculation in a grating generation process is executed by GPU multithreads by adopting a CUDA-based parallel computing architecture, and better generation efficiency can be kept when high-resolution images and multi-parameter combinations are processed. A pre-verification mechanism of configuration management is matched, calculation interruption and result deviation caused by invalid parameters can be reduced, the generation process is more stable, repeated reproduction is more convenient, meanwhile, input images, output paths, grating parameters and mapping options are presented in a centralized mode through a graphical user interface, progress and state feedback is provided, and the generation efficiency is improved. Therefore, a user can complete configuration, execution and output management in the same process. In combination with compatible processing of Chinese paths and multi-format images and file output modes such as DFF, engineering access cost can be reduced, and convenience of result delivery can be improved.

This invention discloses a graph computing optimization method and system based on multi-centrality metrics and globally optimal ranking. The method first calculates the multi-centrality metric of vertices based on an industry knowledge graph, generating candidate ranking sequences adapted to different business operations, and then filters out efficient sequence subsets through performance testing. Subsequently, a parallel computing architecture and a business-region combined partitioning strategy are employed to perform a breadth-first search in parallel, starting from key nodes, generating locally optimal sequences. By incorporating sequence similarity calculations with industry chain weights, the filtered efficient sequence subsets are merged with the locally optimal sequences to form a globally optimal sequence. This effectively overcomes the limitations of traditional single ranking in adaptability across multiple business scenarios and significantly reduces computational complexity. The constructed globally optimal sequence demonstrates higher accuracy and adaptability in businesses such as supply chain risk identification and precise investment attraction, providing an efficient and stable graph computing solution for industrial cloud platforms in the equipment manufacturing field.

The invention relates to a multi-array-element airspace zeroing anti-interference virtual method of a MobileNet architecture, and belongs to the field of Beidou and the field of radio communication and artificial intelligence crossing technologies. The method comprises the steps of radio-frequency signal acquisition and digital preprocessing, MobileNet model improved design, virtual array element extension implementation, airspace zeroing weight calculation and optimization, FPGA hardware acceleration execution, beam forming and anti-interference output. According to the virtual array element expansion technology, the number of effective array elements can be increased by 2-4 times, the spatial freedom degree is increased to 15-31 from 7, 4-8 independent nulls can be formed at the same time within the range of + / -60 degrees, and the multi-interference-source suppression ratio is increased to 35 dB or above from 20 dB of a traditional method; by means of the MobileNet lightweight model, the weight calculation complexity is reduced, and by combining the parallel calculation architecture of the FPGA, the real-time performance is obviously improved, the hardware resource consumption is greatly reduced, and the dynamic adaptive capacity is good.

The invention discloses a GPU (Graphics Processing Unit)-based large-breadth SAR (Synthetic Aperture Radar) image quick positioning method, which adopts a CPU and GPU heterogeneous parallel computing architecture and utilizes the many-core characteristics of a GPU to ensure that the efficiency is higher and the time consumption is greatly reduced in the large-breadth SAR image quick positioning process and can reach a minute level or even a second level; the parallel advantage of the GPU is fully exerted, the generation of a plurality of parameters is parallelized, the degree of parallelism is improved, and the operation efficiency is greatly improved; the characteristics that SAR images obtained through multi-mode imaging are located in different coordinate systems, and transplantation and development are tedious are considered, the relation between a first-level SAR image and a second-level correction image is directly established through fitting, processing in the different coordinate systems can be completed only by updating the coordinate conversion relation, and the processing efficiency is improved. Therefore, positioning processing of the SAR image obtained by multi-mode imaging is completed, transplantation and development are facilitated, and the method is more universal.

The invention discloses a balance adjustment method and system for a sound console volume controller, and belongs to the field of sound console control. The method comprises the steps that audio signals and environment noise are collected and preprocessed; audio state perception and noise identification are carried out by using a deep learning model combining a time sequence convolutional neural network and a long short-term memory network; a volume control and frequency equalization dual-channel parallel computing architecture is adopted, and controlled variables are generated based on a first-order active-disturbance-rejection control algorithm and a second-order active-disturbance-rejection control algorithm; using an improved particle swarm optimization algorithm to carry out collaborative optimization on the dual-channel control parameters; and finally synthesizing and outputting a high-quality audio signal. The system correspondingly comprises an audio acquisition module, a deep learning reasoning module, a dual-channel control module, a parameter optimization module and the like, and can be integrated with a hardware acceleration unit. The method effectively solves the problem of coupling of volume and frequency balance control, and has the advantages of high environmental noise adaptive capacity, intelligent parameter adjustment, high processing real-time performance and the like.

The invention relates to the crossing field of marine geochemistry and computational mathematics, in particular to an optimization method for global analysis of a marine multi-dimensional mixing process, which comprises the following steps: S1, data acquisition; s2, carrying out data initialization processing; s3, calculating data; s4, analyzing a result; s5, outputting data; through collaborative design of multi-stage step length optimization and parallel computing architecture, the key problem that calculation efficiency and precision of a traditional OMPA model in a high-dimensional parameter space are difficult to consider at the same time is effectively solved. The core technology breakthrough is embodied in three aspects: firstly, a three-stage step optimization mechanism (0.05-0.01-0.001) with an adaptive characteristic is developed, and the calculation complexity is greatly reduced on the premise of ensuring a global optimal solution; secondly, a parallel computing framework based on MATLAB parFor is constructed, so that the computing efficiency of the 3-6-dimensional end member hybrid model is improved by an order of magnitude; and finally, a standardized data processing flow (z-score method) is matched with a modular architecture design, so that the expandability and applicability of the system are enhanced.

This invention relates to the field of aerospace technology, and particularly to a method and apparatus for multimodal fault diagnosis of momentum exchange devices based on trajectory sensitivity analysis. The method includes: connecting multiple types of sensors on the momentum exchange device to acquire multimodal monitoring data in real time; running a trajectory sensitivity analysis algorithm to calculate the residuals of each channel and generate normalized multi-channel residual signals; performing fusion analysis on the multi-channel residuals based on a parallel computing architecture, outputting anomaly scores and comparing them with thresholds to determine the fault type and location. This invention uses a sensitivity analysis algorithm to calculate the derivative of the influence of parameter changes on the system's output trajectory in real time, and guides the generation and normalization of residual signals. This amplifies and highlights minute deviations caused by slight increases in friction, bearing wear, etc., thereby achieving timely detection of faults in their early stages and overcoming the shortcomings of traditional methods in terms of insufficient sensitivity to initial weak anomalies.

The invention discloses a method and a device for realizing radar data processing, which adopt a parallel computing architecture, perform data throughput with multiple parallelism degrees, optimize a data access mode, further improve throughput rate, improve efficiency and flexibility of data access in a radar sliding window detection process, reduce cache overhead and improve data access efficiency. High-speed, parallel and clear-structure neighborhood data extraction operation is realized, and rapid and accurate data access support is provided for target detection.

This invention discloses a feature-preserving mesh processing method and system based on high-performance parallel computing. The input is an unlabeled point cloud dataset, used to train a point cloud feature generator and a prediction head MLP. α The input is a grid dataset with semantic segmentation labels. The grid surface is sampled to generate a sparse point cloud, and noise is added to the grid to represent the redundant structure of the grid geometry clipping task. Then, the trained prediction head MLP is removed. α Train prediction head MLPs separately β With MLP γ The user inputs the mesh to be processed, all vertices are converted into a sparse point cloud, and the point cloud feature generator and prediction head MLP are used. β MLP γ The invention generates semantic segmentation labels and redundant structure category labels for the sparse point cloud, respectively; finally, it performs a culling operation on the points marked as redundant structures to complete the mesh pruning. This invention can effectively understand the features in the mesh data, and the semantic segmentation results are more accurate. The noise addition process of this invention uses a CUDA parallel computing architecture to ensure the high efficiency of the entire computation process.

This invention discloses a method, apparatus, medium, and product for a multi-engine asynchronous parallel computing architecture. The method includes: acquiring interval marker pairs, which indicate the start and end of a target program segment in a preset program; in response to the interval marker pairs, determining the boundary notification corresponding to the target program from among multiple notifications sent to the control core when each asynchronous engine related to the target program segment performs an operation; and determining the actual execution time of the target program segment based on the sending times of all boundary notifications sent by all asynchronous engines related to the target program segment.

The invention relates to the technical field of a cesium feed-in system in a magnetic confinement nuclear fusion negative neutral beam system, in particular to an optimization method for DSMC numerical simulation of a cesium feed-in system based on CUDA parallel. According to the technical scheme, the method comprises the following steps: setting a molecular initial state at a host end, and preparing initial conditions for analog computation; allocating a memory space for the molecular array and the related data structure at the equipment end; molecular motion is calculated in parallel through a GPU at an equipment end, and collision between molecules and a wall surface is processed. According to the method, the simulation efficiency is improved to the minute level through a CUDA parallel computing architecture, a grid-based molecular index and memory access mechanism is optimized, the statistical reliability is guaranteed by adopting a thread-independent random number generator, near-vacuum molecular index is accurately restored by means of a decoupled physical model, and the statistical reliability is improved. Therefore, an efficient and practical analysis tool is provided for cesium feed-in system engineering design on the premise that the numerical precision is guaranteed.

The invention relates to the technical field of general parallel computing and floating-point computing, and discloses a floating-point computing method and system in a general parallel computing architecture based on a memristor memory, and the floating-point computing method comprises the steps: determining the information of a computing stage based on an operand and floating-point computing information, the operands and the floating-point operation information are sent to a first operation stage of floating-point operation and a matching processing unit, the matching processing unit comprises an operand matching table, and a matching result is obtained; under the condition that the operands and the operation result information of the floating-point operation information exist in the operand matching table, ending each operation stage after the first operation stage of the floating-point operation; and determining that the operands and the operation result information corresponding to the floating-point operation information do not exist in the operand matching table, enabling the operation process of the floating-point operation, and obtaining the operation result of the floating-point operation unit, so that the operation delay of the high-frequency floating-point operation can be reduced, the repeated calculation of the high-frequency floating-point operation is avoided, and the calculation speed of the floating-point operation is improved.

The application discloses a service processing method and device, equipment and a storage medium. The method comprises the following steps: in the embodiment, each deep learning model is allocated at least one first process, all deep learning models are collectively allocated a second process, each deep learning model is loaded into at least one third process, the first process receives a request for calling a deep learning model, and service data in the request is transmitted to the second process; when the second process accumulates service data to meet a preset condition, all accumulated service data are combined into a batch of source data, and the source data is transmitted to the third process; and the third process calls the deep learning model to batch-process the source data, and obtains target data. The division of labor between the processes in the embodiment is clear, and the repeated occupation of resources is reduced. The deep learning model is called for batch processing through a parallel computing architecture, and the efficiency of operation can be significantly improved in the case of limited resources.

The invention discloses a building surface layer defect visual diagnosis system, and relates to the technical field of building surface layer quality detection, the building surface layer defect visual diagnosis system comprises a multi-modal data acquisition module and a diagnosis analysis module, the multi-modal data acquisition module comprises a thermodynamic detection unit, an imaging unit and a three-dimensional detection unit; the diagnostic analysis module comprises a heterogeneous data processing module which is used for executing a space-time registration process on the data acquired by the multi-modal data acquisition module by adopting a parallel computing architecture and extracting multi-scale fusion features by applying a feature pyramid network; the dynamic learning optimization module is used for processing fusion features through a defect diagnosis model with an incremental learning mechanism and implementing correlation between local features and a global structure; and the diagnosis decision output module is used for generating a diagnosis report. According to the invention, the defect positioning accuracy can be improved.

The present application relates to the technical field of FPGA hardware acceleration and neural network calculation, in particular to an image acceleration system based on neural network. The present application constructs an acceleration architecture composed of multiple DMA controllers and modular hardware operators. The operators adopt a unified interface and a ping-pong buffer design, and are scheduled by a register unit according to instructions. By fusing dequantization, convolution, addition and re-quantization into a single integer calculation, and using a pre-computation lookup table method to realize efficient quantization reasoning, the calculation delay and resource consumption are significantly reduced. Through quantization fusion and parallel computing architecture, the throughput is greatly improved. The hardware resource consumption is significantly reduced, which is suitable for low-power edge scenarios. The modular design supports flexible configuration of network structure, which meets the high real-time detection demand while ensuring the accuracy.

The invention discloses a label rendering system and method based on a parallel computing architecture, and the method comprises the steps: calling a main thread in response to a label creation request, and generating a corresponding label computing task message; calling a working thread, and generating each texture image based on each piece of label data; calling a main thread, and generating instance attribute data of each to-be-established label based on the label data and the texture image of each to-be-established label; and calling a main thread, generating a drawing instruction based on preset geometric prototype data and each instance attribute data, and triggering the graphics processor to execute the drawing instruction, so that the graphics processor concurrently performs instantiation drawing on each to-be-established label to realize rendering of each label. According to the method, on the basis of the main thread and the working thread, the fluency of a browser in a three-dimensional visual scene large-batch label rendering scene is ensured, the graphics processor is triggered to execute the drawing instruction and instantiate rendering of the multiple to-be-built labels is achieved, and the label rendering efficiency is further improved.

The present application relates to the technical field of power system relay protection, in particular to a hydropower and new energy integration grid-related setting value checking method and system, the system comprises: a panoramic modeling module, which performs parameter input of hydropower station and new energy station equipment; a fault simulation module, which performs fault current simulation under multiple working conditions based on a distributed parallel computing architecture; a setting value calculation module, which generates setting parameters of protection devices; and a grid-related checking module, which constructs a coordination graph of power generation equipment capacity curve and protection action characteristics and outputs checking conclusions. In the present application, by constructing a high-precision mathematical model of multi-energy integration and a parallel computing mechanism, the problem that the traditional single-computer calculation mode is difficult to cope with complex topological changes and dynamic characteristics after new energy is connected is solved, accurate matching and visual verification of grid-related protection setting values and equipment capacity boundaries are realized, protection misoperation or refusal to operate risks are effectively avoided, and the safety defense level and operation and maintenance efficiency of new-type power systems are significantly improved.

Apparatuses, systems, and techniques to compute cyclic redundancy checks use a graphics processing unit (GPU) to compute cyclic redundancy checks. For example, in at least one embodiment, an input data sequence is distributed among GPU threads for parallel calculation of an overall CRC value for the input data sequence according to various novel techniques described herein.

The application discloses an aviation part round hole and end face cooperative detection method, device, equipment and medium, and relates to the aviation part detection field.The method comprises the following steps: performing outlier elimination on the surface three-dimensional point cloud data of the target aviation part by using the 3σ criterion, and obtaining a round hole effective point set and an end face effective point set; based on a double-thread parallel computing architecture, a first thread is used to perform iterative optimization on the round hole effective point set based on the linear least square method and the 3σ criterion, a round hole fitting result is obtained, a second thread is used to extract an end face plane normal vector based on the end face effective point set and the minimum eigenvalue method of the covariance matrix, and an end face fitting result is obtained; based on the fitting result, a normal error component in a three-dimensional round center vector is eliminated by using a vector projection method, so that the projection round center distance and the eccentricity of the nested round hole in the end face plane are calculated, and the concentricity of the nested round hole is evaluated.The application can realize the aviation part round hole and end face cooperative detection in a non-damage, accurate and efficient manner.

The invention discloses a large-loop source transient electromagnetic fast forward modeling method based on GPU acceleration, and the method comprises the steps: firstly carrying out the parameter setting and preprocessing, then segmenting a large-loop source into a north edge, a south edge, an east edge and a west edge, and carrying out the discretization of each edge into N electric dipoles through employing a Gaussian-Legendre quadrature method; constructing a loop edge-frequency sampling point-wave number-time channel four-level parallel computing architecture to compute each edge; wherein each side in the GPU adopts a frequency-wave number-time parallel computing architecture, and parallel computing is performed on each electric dipole, so that the frequency-time domain response of each electric dipole is obtained; and finally, after calculation of all the electric dipoles is completed, vectorization calculation and summation are carried out on transient responses of all the electric dipoles on the GPU, a large loop source total field response at the current observation point is obtained, and a final time domain electromagnetic field value is output. According to the method, the advantages of large-scale parallel computing of the GPU can be efficiently and fully played, so that the computing efficiency is effectively improved on the premise of ensuring the computing precision.

The application discloses a power system wiring detection method based on block parallel processing and relates to the current detection field, which comprises the following steps: collecting real-time operation data through sensors arranged at key nodes, constructing a basic data set after Kalman filter denoising and normalization processing; establishing a sub-block identification system based on topological structure blocks, and extracting electrical and topological feature vectors; adopting a master-slave parallel computing architecture, assigning tasks by priority by the master node, and performing sub-block level anomaly preliminary screening by using an improved random forest model; realizing cross-sub-block collaborative verification by checking boundary tie switch state, branch current and bus voltage; finally, constructing a global wiring state atlas for difference analysis, accurately positioning abnormal equipment and types, and verifying the results through high-precision re-measurement. The application has the advantages that the efficiency is improved through scientific block and distributed parallel computing, the accuracy is guaranteed by combining multi-dimensional detection, boundary checking and re-measurement mechanism, wiring abnormalities are efficiently located, and reliable support is provided.

The invention discloses a batch plasma data processing method suitable for real-time profile inversion of an X-mode polarized microwave reflectometer, and belongs to the technical field of plasma diagnosis. The method comprises the four steps of system environment preparation, vacuum data processing, plasma data processing and result storage and output. A GPU parallel computing architecture is constructed by adopting PyTorch DDP and CuPy, and batch reading, spectral analysis, beat frequency signal extraction, time delay calculation and density inversion whole process acceleration of original I / Q time domain data are realized. Compared with a traditional CPU serial method, the method has the advantages that the processing efficiency is remarkably improved, multi-time-point and multi-band mass data inversion can be completed in real time, electron density data at different radial positions can be rapidly output, and the real-time performance and accuracy requirements of plasma density profile inversion in a fusion experiment can be met.