3955results about "Processor architectures/configuration" patented technology

The invention discloses a method for automatically drawing an OpenGL (Open Graphics Library) program by using Vulkan. The method comprises the following steps of: creating a context used by the Vulkan, initializing each module, processing an OpenGL instruction related to texture and data buffering, and managing storage of texture and data buffering resources in a video memory; a shader program used by the OpenGL is preprocessed into a format acceptable to Vulkan, and an OpenGL shader program instruction is created and destroyed; processing an OpenGL (Open Graphics Library) instruction related to frame buffering to generate structural body information required by Vulkan dynamic rendering; an OpenGL instruction of the sampler is also created; processing an OpenGL (Open Graphics Library) instruction for creating a vertex input format and managing a vertex data buffer area, and maintaining vertex input information, a vertex buffer area and an index buffer area required by Vulkan; and finally, drawing or calculating, distributing and calling Vulkan on the basis of all the instructions.

The invention discloses a Direct3D memory model compatible method based on adaptive placeholder resources, which comprises the following steps: establishing resource metadata, accessing a scene library, checking an instruction template and double sandboxes when a DXVK is started, describing related resources by the DXVK through the metadata after a D3D application is started, completing resource mapping and metadata dynamic updating, and allocating the resources to the corresponding sandboxes; the DXVK compiles an application shader code, identifies a resource access instruction, matches an access scene and a check template by combining a pipeline stage and binding slot query metadata, instantiates the check instruction and adds the check instruction to the front of a target instruction; according to the unbound resources, adaptive generation of corresponding occupied resources is carried out, the authority and the state are configured, the unbound resources are bound to a Vulkan descriptor set to replace VKNULLHANDLE, and adaptation of the shader interface and the pipeline state parameters is completed to create PSO (Particle Swarm Optimization); and intercepting an application rendering instruction analysis parameter to construct a command buffer area, submitting the command buffer area to a Vulkan queue to trigger a GPU (Graphic Processing Unit) to execute rendering, and finally completing complete conversion and adaptation from the D3D rendering logic to the Vulkan.

The invention is suitable for the field of GPU management, and provides an intelligent dynamic management method for GPU computing power and a cloud platform, and the method comprises the following steps: collecting task data and GPU state data, and generating a task queue and a resource allocation strategy in combination with a scheduling plug-in; based on the task queue and the GPU real-time load, dynamically adjusting a resource allocation proportion through reinforcement learning to analyze a task dependency relationship and generate a migration plan; optimizing a communication path and adjusting asynchronous transmission delay according to the task dependency relationship and the GPU communication topology, and outputting a synchronous state mark; and monitoring abnormity in combination with the synchronization state and the GPU hardware state, executing thermal migration according to the migration plan, performing video memory recovery, and updating the resource idle list. According to the invention, through algorithm innovation and hardware collaborative optimization, intelligent, dynamic and efficient resource scheduling in the multi-GPU system is realized.

The invention discloses a lecturer video automatic generation method based on AI speech synthesis and animation driving. The method comprises the following steps: performing structured analysis on a PPT or a text script through an improved interior point method and an incremental shortest path algorithm; performing semantic grouping by applying a full-dynamic parallel single-link clustering algorithm and generating an enhanced script with an expressive mark; a CosyVoice technology is combined with a low-rank approximation method to generate a high-quality voice data stream; establishing a mapping relation between contents and action expressions through semantic analysis, and generating a complete action expression instruction set; and driving the digital human model by using the msueTalk technology, and generating a final lecturer teaching video through a parallel rendering algorithm. According to the invention, the method achieves the efficient and automatic generation of the education video, remarkably improves the content production efficiency, reduces the production cost, and guarantees the specialty and expressive force of the teaching video.

The invention relates to the field of scene rendering, and particularly discloses a three-dimensional scene optimization method and system for collaborative rendering of dynamic LOD and view cone rejection based on space-time prediction, and the method comprises the steps: dynamically calculating the visibility frequency of an object, and dynamically adjusting the loading and rendering modes of objects with different priorities; predicting a view cone range of multiple frames in the future by using an LSTM network architecture; dividing the scene into uniform grid blocks, and then performing grading elimination; optimizing a heterogeneous computing pipeline; rendering the execution process; the system comprises a dynamic LOD and visual cone rejection collaborative optimization module, an LSTM visual cone prediction module, a block-level mixed rejection module, a heterogeneous calculation pipeline module, an optimization CPU-GPU task allocation and data transmission module and a rendering execution control module. According to the method, the technologies of collaborative optimization of dynamic LOD and view cone removal, block-level mixed removal, heterogeneous calculation assembly line optimization and the like are adopted, so that unnecessary rendering calculation and resource loading are reduced, and the rendering efficiency is improved.

The invention belongs to the technical field of augmented reality (XR) and computer vision crossing, relates to fusion application in immersive digital performance, and provides a virtual actor reconstruction and immersive presentation system based on 4D Gaussian splash modeling and XR space positioning. The system comprises a set of spherical multi-camera-position high-synchronization camera shooting matrix used for capturing dynamic images of actors; performing dynamic modeling on the multi-angle image through a 4D Gaussian splashing technology, and outputting a virtual actor point cloud model which can be used by XR equipment; vPS visual positioning and an SLAM tracking module are combined, and precise mapping positioning of a performance space is achieved in AR / MR equipment. The method supports the immersive watching of the actor image at the audience end at a 360-degree free visual angle, and realizes the natural presentation of the virtual actor without dead angles and wearing in cooperation with shielding judgment and a real-time rendering engine. The system is widely applicable to on-site entertainment scenes such as immersive theaters, text travel performances, brand activities, concerts and television programs.

Apparatus and method for efficient storage of BVH nodes in blocks. For example, one embodiment of an apparatus comprises: bounding volume hierarchy (BVH) construction circuitry to construct a BVH based on primitives of a graphics scene; and block allocation hardware logic coupled to or integral to the BVH construction circuitry, the block allocation hardware logic to allocate a plurality of nodes of the BVH into a plurality of blocks for storage in a cache or memory subsystem, the block allocation hardware logic to maximize a number of blocks which include a leading parent node and one or more corresponding child nodes of the plurality of nodes.

The invention discloses a Direct3D rendering model compatible method based on a dynamic template pool, which comprises the following steps: establishing three types of mapping tables between D3D and Vulkan when compiling DXVK, constructing resource metadata, and creating a core, extended and temporary three-level template pool according to the mapping tables after starting; when the D3D application creates resources, metadata is initialized, parameters are verified, physical memories are allocated and grouped, resource groups are pre-verified, a batch binding command is generated, memory binding is completed, and the metadata is updated; when a resource view is created, a template is matched from a template pool, a handle is generated after instantiation, a resource handle is associated, and a descriptor is bound; when a rendering state is set and a rendering instruction is executed, the rendering state and the rendering instruction are respectively converted into a Vulkan related state and a Vulkan related instruction through a mapping table, a handle is bound after a PSO cache is inquired, a command buffer area is submitted to a GPU queue to execute drawing, and compatible operation of the D3D application on a platform supporting a Vulkan operating system is realized under the condition that the GPU does not support VKKHRmaintence5 and VKKHRmaintence6 extension.

The invention relates to the technical field of artificial intelligence chips, and discloses a model performance estimation method and device and computer equipment, and the method comprises the steps: determining model configuration data and candidate distributed strategies of a target model; converting the original calculation graph corresponding to the single-card deployment state based on the model configuration data and the strategy configuration data of the candidate distributed strategies to obtain a distributed overhead calculation graph corresponding to the multi-card deployment state; and performing performance estimation on the candidate distributed strategy according to the basic overhead and the additional overhead in the distributed overhead calculation graph to obtain strategy performance data of the target model under the candidate distributed strategy, thereby realizing conversion of a single-card model into a multi-card model. And based on the multi-card model, simulation calculation of the multi-card interconnection mode is realized on the premise of limited hardware resources, so that the influence of a communication operator and a topological structure corresponding to the multi-card interconnection mode in the overall operation of the model is reflected, and the upper limit of the model performance can be accurately evaluated in a simulator verification stage before silicon is applied.

The invention provides a high-resolution machine vision detection method and system for precise chromatic aberration detection and a medium, and the method comprises the steps: carrying out the calibration of a camera based on a camera calibration tool, and synchronously calibrating a light source parameter and a color parameter; setting shooting parameters based on the calibrated camera, obtaining a workpiece image in real time, preprocessing the workpiece image, mapping the preprocessed image to a standard color space from an original RGB space, and extracting color features of the preprocessed image; comparing the color feature with the color feature of the standard sample, calculating a color difference index, and comparing with the color difference index based on a set color difference threshold to obtain a detection result; by calibrating various parameters of the camera, the visual detection precision is ensured, and by performing color space mapping on the workpiece image and analyzing the difference between the color difference index of the color feature and the color difference threshold value, the color abnormal area is accurately analyzed, and the detection precision of the abnormal color is improved.

The invention discloses a heterogeneous GPU resource management scheduling method, a computer device, a medium and a product. The method comprises the following steps: acquiring computing power resources of each node, including a GPU model, a GPU video memory, a GPU number, a computing power segmentation scheme and a GPU use condition; automatically segmenting the heterogeneous GPU of each node according to the computing power segmentation scheme of each node to obtain resource segmentation information; obtaining an expected computing power and an expected video memory of a to-be-executed task; screening out target nodes meeting the expected computing power and the expected video memory according to a node analysis strategy; and according to the sub-resource analysis strategy, screening out sub-resources meeting the expected computing power and the expected video memory, recording the sub-resources as target sub-resources, and allocating the to-be-executed task to the target sub-resources. According to the method, GPU resource fragmentation is effectively reduced through global and node resource conjoint analysis scheduling, meanwhile, pooling management, dynamic configuration and intelligent scheduling of heterogeneous computing equipment can be achieved, and the resource utilization rate is effectively increased.

A method or system for implementing a machine learning pipeline on a chipset comprising a plurality of hardware compute elements. The system accesses a hardware-agnostic functional description of the machine learning pipeline, wherein the description specifies a plurality of functional modules, including at least one machine learning model. Hardware specifications of the chipset are accessed to identify the available hardware compute elements. Based on the hardware specifications, the functional modules are synthesized into a plurality of interconnected executable components configured to execute on at least two different hardware compute elements. An implementation package is generated, comprising the executable components and metadata describing interconnections between them. The implementation package is then deployed to the chipset, where the executable components are executed by the identified hardware compute elements.

The invention relates to an application method of a GPU (Graphics Processing Unit) in design rule inspection in the field of integrated circuits, in particular to a layout design rule inspection method, system and equipment, a medium and a product. The layout design rule checking method comprises the following steps: inputting initial layout data, analyzing a design rule checking task to be executed, and marking the priority and complexity level of the task; the method comprises the following steps: dividing layout data into a plurality of data blocks according to regions on the basis of regional characteristics of the layout data, distributing tasks and corresponding data blocks to a GPU computing core according to task priorities and complexity levels, and distributing new data blocks according to the overall load condition of the GPU computing core after the GPU computing core completes data block processing, and repeating the steps until all tasks are executed, and outputting a layout design rule check result. The system, the computer equipment, the computer readable storage medium and the computer program product have the same beneficial effects as the layout design rule checking method.

The invention relates to the technical field of image processing, and discloses an infrared image enhancement method and system based on local phase correlation, and the method comprises the steps: obtaining a plurality of continuous frames of infrared images, carrying out the intelligent partitioning of a reference frame, and calculating the variance feature, the method comprises the following steps: selecting regions of interest with rich information, independently executing phase correlation operation in each region to extract a local translation vector, obtaining global displacement estimation through weighted fusion, adopting an abnormal value detection algorithm to improve robustness, and finally realizing sub-pixel-level image alignment and intelligent weighted fusion. The method is suitable for real-time enhancement processing of satellite-borne infrared remote sensing images, the resource constraint requirement of an embedded platform is met while the processing quality is guaranteed, and an efficient and reliable technical scheme is provided for space remote sensing image processing.

The invention discloses a WebGL-based mass data particle system rendering method and device, and belongs to the technical field of computer graphic processing. The method comprises the following steps: firstly, selecting the type of a particle emitter according to scene requirements, then constructing a primitive object based on points or quadrangles, and configuring three-dimensional coordinates and texture parameters; aggregation rendering is carried out on a single-frame primitive array through a WebGL vertex shader and a fragment shader, and the drawing calling frequency is reduced; the method comprises the steps of dynamically adjusting particle attributes, including position compensation, color gradient and explicit-implicit control, determining a rendering form and playing time of an animation according to requirements of an actual application scene, and supporting fine configuration of a single particle, including texture animation, dynamic scaling and rotation parameters. When a large amount of data is processed, on the premise of ensuring the rendering quality, the rendering efficiency can be remarkably improved, the consumption of a memory and GPU resources can be reduced, the response time of the system can be optimized, and meanwhile, the interaction experience of a user can be improved.

The invention discloses a communication method between graphics processors, a product, equipment and a medium, relates to the technical field of high-performance calculation and artificial intelligence acceleration, and is applied to a stand-alone system comprising a plurality of graphics processors and a central photoelectric hybrid switching chip constructed based on interconnection of an electric switching matrix and an optical switching matrix. The graphics processor is connected with the central photoelectric hybrid switching chip through an optical link and an electric link; the method comprises the following steps: performing data classification on to-be-transmitted data of a source graphics processor; if the to-be-transmitted data is a control flow, the source graphics processor is controlled to send the to-be-transmitted data to an electric switching matrix through an electric link, and then the to-be-transmitted data is routed to a target graphics processor in the graphics processors; and if the to-be-transmitted data is a data stream, the source graphics processor is controlled to send the to-be-transmitted data to the optical switching matrix through the optical link, and then the to-be-transmitted data is routed to the target graphics processor. Interconnection between graphics processors is optimized to improve communication efficiency between graphics processors.

The invention discloses a GPU Box hardware decoupling system based on a Retimer card and a PCIe Switch chip, and belongs to the technical field of computer hardware architecture and high-speed interconnection. According to the system, a Retimer card and a PCIe Switch chip are integrated in an independent GPU Box, and a decoupling link of a CPU server and a GPU acceleration card is constructed; the Retimer card realizes 30-meter long-distance PCIe signal transmission and breaks through physical distance limitation; the PCIe Switch chip pools GPU resources through a dynamic routing and MRIOV technology, supports flexible allocation of computing power by multiple servers, and realizes Peer-to-Peer direct connection communication between GPUs. Aiming at a large model reasoning scene, the system optimizes KV cache bandwidth allocation and video memory and memory cooperative scheduling, so that the 100B parameter model reasoning throughput is greatly improved; and meanwhile, the usability of the system is greatly improved through fault isolation and hot plug design. According to the method, the problems of physical binding of the CPU and the GPU, limited transmission distance, rigid resource allocation and the like in a traditional architecture are solved, and the method is suitable for large-scale AI calculation and distributed GPU cluster deployment.

The invention discloses a graphic rendering system and method based on OpenGL SC and a related product. The system comprises a rendering interface module used for providing a high-order graphic rendering function interface for an application program to receive graphic modeling data; the data conversion module is used for converting the graphic modeling data into intermediate drawing data conforming to the OpenGL SC standard; the graphic processing module is used for executing graphic calculation and rendering logic based on the intermediate drawing data and generating a rendering instruction; and the graph drawing module is used for calling the underlying OpenGL SC function library to execute the rendering instruction so as to complete graph rendering. According to the graphic rendering system, the underlying graphic pipeline is packaged inside, the development threshold and complexity are reduced through the high-order graphic interface, and the development efficiency and reliability are improved. All the modules are standardized and coordinated to realize complete process packaging from data to rendering, so that optimization and maintenance are integrated inside, and the maintainability, expandability and stability of the system are enhanced.

In various examples, systems and methods are disclosed relating to coordinating and synchronizing the actions of different types of processors with low latency. Different types of processors may perform better at different types of tasks. By coordinating the processing of a one-dimensional processor such as a vector processing unit (VPU) and the processing of a two-dimensional processor such as a pixel processing engine (PPE), an overall speed of task completion can be improved.

The invention relates to a map visualization rendering method and system based on a WebGPU engine and computer equipment, and the method comprises the steps: obtaining map data in a unified format; inputting the map data into a rendering layer, and performing layer division on the map data according to spatial levels and element types to obtain multiple pieces of layer data; distributing an independent rendering thread for each layer data; performing block processing on the map data to obtain picture blocks with pyramid structures; and calling a corresponding rendering thread in the current view range and the scaling to load the corresponding picture block for rendering display. By means of the method and device, the problem that the map visualization rendering performance is poor is solved.

The invention discloses a graphic processing unit cooperative processing method and system for accelerating a three-dimensional generative model. The model comprises a diffusion processing stage and a Gaussian splash rendering stage. In the diffusion processing stage, the time step number and the data precision calculated by the neural network are dynamically adjusted according to the visual angle sensitivity, and a specific graphic processing unit core is scheduled for execution. In the Gaussian splash rendering stage, pixel block tasks are predicted and intelligently scheduled to a stream processor based on historical data; and in the stream processor, the processing sequence of the Gaussian ball by the rendering unit is optimized according to the geometric distance, and the close-range unit is preferentially processed. During back propagation, the built-in logic of the stream processor preaggregates the gradient contributions of the same Gaussian ball, and then updates the gradient contributions through a single write operation. Through collaborative optimization of software and hardware, the efficiency bottleneck of the 3D generation model on a graphic processing unit is effectively solved, the calculation speed, the throughput and the hardware utilization rate are remarkably improved, and the model generation time is shortened.

The invention provides an image real-time filter processing method based on edge calculation, and the method comprises the steps: obtaining the local texture feature distribution of an input image, calculating the gradient variation of each pixel point in the horizontal and vertical directions, and calculating a quantitative index value reflecting the image texture complexity based on the statistical distribution feature of the gradient variation; executing a parallelized filter effect rendering operation based on the optimized processing task allocation scheme, and applying corresponding filter parameters to each image block through a multi-thread cooperative processing mechanism to form filter rendering configuration information suitable for the performance of the current mobile device; and carrying out fusion reconstruction on brightness, saturation and hue component data after filter processing, generating a final filter processing image through a reverse conversion process from HSV to RGB, and recording parameter configuration information in the processing process at the same time for rapid processing of subsequent similar images.

The invention discloses a digital twin intelligent rendering method and system based on GPU load optimization and camera visual angle self-adaption, and the method comprises the steps: obtaining the basic information of a GPU, and evaluating the actual rendering capability of the GPU through a performance test; pre-defining a plurality of surface number grades according to the complexity of the digital twin model; performing LOD calculation based on the visual angle of the camera; the system performance is monitored in real time; on the basis of LOD calculation of the view angle of the camera, the GPU load condition is combined, a self-adaptive adjustment or predictive adjustment strategy is adopted, and the rendering complexity of a non-key area is optimized; user manual adjustment options are provided, allowing the user to adjust the rendering quality according to personal preferences, and providing visual feedback at the time of adjustment. According to the method, the GPU load is reduced while high-precision rendering of the key area is guaranteed, LOD switching is optimized in combination with historical inspection data, and the rendering efficiency and the system stability are improved.

The invention discloses a multi-view collaborative 3D Gaussian splash optimization method and system, and the method comprises the steps: constructing a multi-level heterogeneous video memory pool, and dynamically dividing a video memory in 3D Gaussian reconstruction into a view exclusive memory block and a global shared memory pool; a mixed rendering-gradient pipeline is designed, and hardware-level pipeline parallelism in 3D Gaussian reconstruction is realized through a double-buffer asynchronous switching mechanism based on a CUDA Warp-level parallel primitive fusion forward rendering and back propagation thread group; performing multi-view gradient joint optimization, screening an effective gradient path in 3D Gaussian reconstruction based on the visibility mask matrix, and performing projection error weighted fusion on a multi-view gradient tensor; and implementing a multi-modal densification decision, generating a 3D Gaussian candidate splitting position in 3D Gaussian reconstruction through Monte Carlo sampling, calculating a joint optimization objective function by combining a multi-view projection residual error and a gradient contribution factor, and finally realizing 3D Gaussian reconstruction. According to the invention, high-precision and low-delay large-scale scene real-time rendering and training can be realized.

The invention aims to provide a SiamFC-pytorch tracking model pre-processing and post-processing acceleration method and a SiamFC-pytorch tracking model pre-processing and post-processing acceleration system based on an NPU (Network Processing Unit). The system is realized based on a Yulong810SOC, and comprises a video input module (1), a hardware decoding module (2), a detection module (3), a tracking module (4) and a turntable control module (5), the method comprises the following steps: a, a video input module receives a video stream, and the video stream is decoded into an original image in a YUV format through a hardware decoding module; b, the detection module runs a YOLOv5 model through a neural network engine of the NPU; c, an NPU pre-processing and post-processing unit of the tracking module executes template branch pre-processing, search branch pre-processing and search branch post-processing on the YUV image and the target initial bounding box; and d, the turntable control module adjusts the posture of the camera turntable according to the real-time coordinates of the target. The method is applied to the technical field of computer vision and artificial intelligence hardware acceleration.

The invention discloses an ALU, a processor, a chip, equipment and a texel index calculation method, and belongs to the technical field of chips. The ALU comprises a first comparator, a first divider, a first summator and a texel adjusting unit. A first comparator to determine a first value, the first value being determined based on a smaller value of the integer portion of the input texture coordinate and the maximum offset; the first divider is used for calculating a ratio between the input offset and the first divisor to obtain a first ratio; the first summator is used for calculating the quotient of the first ratio and the sum of the first numerical value and the second numerical value to obtain a first calculation result; and the texel adjusting unit is used for determining a target texel index corresponding to the input texture coordinate according to at least one of the first calculation result and the addressing mode. According to the method, the first calculation result can be determined based on the smaller value in the two, and the value of the integer part of the input texture coordinate can be reduced within a certain limit, so that the bit width of the calculation unit is reduced.

The invention discloses a generative AI model real-time rendering engine construction method and related equipment thereof, and relates to the technical field of man-machine interaction, and the method comprises the steps: obtaining multi-modal semantic information input by a user, and converting the multi-modal semantic information into a patent vector and a constraint parameter; constructing a lightweight generation model, and directly generating a renderable intermediate representation by adopting the lightweight generation model; constructing a rendering pipeline based on the generated intermediate representation, and converting the intermediate representation into a visual image; capturing user operation in real time, dynamically updating constraint conditions of a generated model, triggering increment generation, and optimizing system performance by adopting a model lightweight, hardware acceleration and hybrid rendering strategy; through the time filtering and space constraint technology, the generated content is consistent in time and space. According to the method, the problems of low generation speed and low rendering efficiency in the fusion process of the generative AI model and the real-time rendering engine are solved, and the efficiency and quality of visual image generation and rendering are improved.

In various examples, systems and methods are disclosed relating to generating each initial feature map of a plurality of initial feature maps based on a respective input image of an input dataset, each initial feature map, incorporating depth data of the respective input image, corresponds to a plurality of pixels of the respective input image, generating a sparse feature point cloud including a plurality of features determined using the plurality of initial feature maps, transforming the sparse feature point cloud into multi-resolution sparse grids, each of the multi-resolution sparse grids comprising a plurality of voxels, modeling, using a plurality of neural networks according to a hierarchal architecture, the multi-resolution sparse grids to construct a hierarchical volume representation, and providing constructed content based on the hierarchical volume representation.

The invention discloses a low-resource and low-energy-consumption remote sensing image brain-like computing system based on an FPGA (Field Programmable Gate Array), which belongs to the cross technical field of brain-like computing and remote sensing information processing, and comprises a data interaction module which is used for high-speed data transmission between a PC (Personal Computer) end and the FPGA and comprises a PCIe (Peripheral Component Interconnect Express) interface sub-module and a data transmission control sub-module; the storage control module is used for data interaction control of the off-chip DDR and the on-chip BRAM and providing efficient data supply for the processing engine; the processing engine module is used for simulating a brain-like pulse neural network to complete convolution calculation of the remote sensing image; comprising a convolution processing sub-module, a data storage sub-module, an input buffer sub-module and an output buffer sub-module. According to the low-resource and low-energy-consumption remote sensing image brain-like computing system based on the FPGA, the brain-like pulse neural network is adopted, the BRAM is allocated to store the intermediate computing result, and the computing unit is designed in combination with SNN characteristics, so that energy consumption and resource waste are effectively reduced, and computing efficiency is improved.

The invention provides a real-time processing method for endoscopic image blood vessel enhancement and microcirculation evaluation, which comprises the following steps of: processing an original endoscopic image, and locally enhancing a brightness channel through a contrast-limited adaptive histogram equalization technology; self-adaptive frequency domain-space domain decomposition is realized based on local texture complexity analysis; performing multi-scale Hessian matrix blood vessel detection on the low-frequency component; applying a directional Gabor filter bank to the high-frequency component; spatial-temporal feature fusion is realized through multi-resolution pyramid optical flow calculation; synchronously completing blood vessel probability prediction, blood vessel diameter estimation and blood flow direction prediction by using a lightweight multi-task deep learning network; the blood flow velocity is analyzed and calculated based on a speckle mode, the perfusion density is subjected to accelerated statistics through an integrogram, and vascular morphological parameters are extracted by adopting an improved skeleton algorithm. According to the invention, an enhanced blood vessel visualization effect and a real-time microcirculation quantitative evaluation function can be provided, and the overall improvement of the endoscope image processing quality and efficiency is realized.