70 results about "Task segmentation" patented technology

The invention discloses an information processing method and system based on the Internet of Things, and relates to the technical field of Internet of Things services, and the method comprises the steps: dynamically obtaining the computing power, energy consumption and network state of each node through an environment perception module, and constructing a node capability vector and a link performance vector; the task analysis module is used for carrying out structured modeling on tasks and identifying candidate segmentation points and unloading targets; the cost modeling module establishes a joint cost function of time delay and energy consumption; the strategy generation module generates an optimal unloading strategy based on multi-agent reinforcement learning; and the scheduling communication module executes task unloading and result integration according to the strategy, and feeds back execution data to update the system state. According to the method, intelligent, low-delay and energy-consumption optimization processing of task segmentation unloading is realized, and the method is suitable for a dynamic heterogeneous Internet of Things computing environment.

The invention provides a multi-task parallel processing method for user problems under an AI platform, and belongs to the technical field of digital data processing of the AI platform. Task resource requirements are accurately calculated through a video memory pre-estimation function and a memory pre-estimation function, a resource consumption mode of concurrent execution is analyzed through a multi-task resource prediction model; the load capacity of the system is evaluated based on a video memory utilization rate gain index, an optimal task segmentation strategy is determined through a data set splitting degree calculation function, multi-stage video memory sub-pools are constructed to realize differentiated resource management, and a Nash equilibrium point of resource allocation is solved by adopting a data set video memory allocation game model. Collaborative optimization allocation of resources is achieved through the video memory and memory coupling allocation equation set, the task state is monitored in real time in the multi-task concurrent execution process, the resource allocation weight is dynamically adjusted, and the technical problem that the system resource utilization rate is low during AI platform multi-task parallel execution is solved.

The invention discloses a network scanning task intelligent segmentation and load balancing method and system. The method comprises the following steps: identifying abnormal fragments by executing integrity verification on a scanning task, analyzing a task dependency relationship to determine a divisible position, and generating a segmentation scheme in combination with granularity constraint; node resource states are collected to calculate load indexes, and scheduling weights are hierarchically configured according to load levels to establish parallel scheduling channels; constructing a node cooperation group based on load difference, formulating an inter-group circulation rule to form a cooperation scheduling space, fragmenting tasks according to node capacity, and generating a parallel execution plan; monitoring the execution progress to identify overstocked nodes, diagnosing bottleneck types and planning a dredging path to execute task migration; the resource time sequence data is collected to identify the low-load time period, and the task is distributed to the optimal time slot to be executed, so that the task segmentation reasonability and the node load balancing degree are improved, and the distributed scanning execution efficiency is improved.

The invention discloses an intelligent super-division coding system for a low-bit-rate video, which comprises multiple stages of assembly lines and a merging module, each stage of the multiple stages of assembly lines is responsible for different GPUs / threads, and the multiple stages of assembly lines are integrated into a pipeline parallel task. The multi-stage assembly line comprises a video input and analysis module, a task segmentation and dynamic scheduling module, an AI super-division module, an HDR / SDR self-adaptive conversion module, a video coding module and a quality control and closed-loop optimization module which are sequentially processed. And time consistency and efficient utilization are ensured through dynamic scheduling, overlapping slicing and inter-frame constraint on multiple GPU hosts, so that the system can still improve subjective and objective image quality on the premise of remarkably reducing the code rate.

The invention discloses a calcium-based thermochemical particle bed health status assessment method and a related device. The method comprises the following steps: acquiring a surface image of a calcium-based thermochemical particle bed; performing data enhancement on the surface image of the calcium-based thermochemical particle bed layer; inputting the surface image after data enhancement into a multi-task segmentation model to obtain a pixel-level segmentation mask; calculating the expansion rate, the agglomeration index, the crushing degree, the collapse depth and the collapse area ratio of the particle bed layer according to the pixel-level segmentation mask; the performance of the calcium-based thermochemical particle bed layer is evaluated according to the expansion rate, the agglomeration index, the crushing degree, the collapse depth and the collapse area ratio of the calcium-based thermochemical particle bed layer. The method and the related device can accurately detect the health of the calcium-based thermochemical particle bed layer.

The invention provides an ultrasonic image segmentation method, device and equipment. The method comprises the following steps: acquiring an ultrasonic image to be segmented; inputting the ultrasonic image into a multi-scale expansion convolution module in a multi-task segmentation model to obtain a first feature map; the multi-scale expansion convolution module is used for extracting cross-scale feature information; inputting the first feature map into an efficient channel attention module in a multi-task segmentation model to obtain an attention weight of each channel in the first feature map; obtaining a second feature map according to the first feature map and the attention weight of each channel; and inputting the second feature map into a decoder module in the multi-task segmentation model to obtain a segmentation result of the ultrasonic image. According to the method provided by the embodiment of the invention, accurate segmentation of the ultrasonic image of the intermuscular brachial plexus retardation scene is realized.

The application discloses a medical data processing method and product based on multi-stage transfer learning and multi-modal data collaborative fusion, relates to the technical field of medical data processing and artificial intelligence, and adopts MedicalNet medical special pre-training weights to initialize a classification model backbone network; a three-stage progressive fine-tuning framework is constructed on the basis, field adaptive coarse classification fine-tuning and target task fine classification are performed, and clinical structured data is introduced in the third stage; after high-dimensional image features are reduced in dimension and low-dimensional clinical features are increased in dimension through an adaptive multi-branch multi-level MLP architecture, mid-term deep fusion is performed, the application can effectively mine the complex relationships such as complementation, correlation and cooperation of multi-modal heterogeneous data, improve the lung disease classification precision and model generalization capability, and significantly inhibit the small sample overfitting phenomenon.

An AI model-based task allocation method in a communication system, and a related product. The method comprises: a terminal / core network element sends first information to an access network device, wherein the first information indicates a first model identification (ID), and the first model ID indicates an AI model deployed on the terminal / core network element; the terminal / core network element receives second information from the access network device, wherein the second information indicates a second model ID, the second model ID is a model ID in the first model ID, and the second model ID is associated with a first task; and the terminal / core network element executes the first task on the basis of an AI model corresponding to the second model ID. In the example, computing power alignment between devices can be implemented, a condition is provided for task segmentation, implementation of distributed training is enabled, and an overall inference delay is reduced. The present application can be applied to the technical field of AI, the technical field of communications, etc.

The application discloses a kind of blood vessels and lesion multi-task segmentation method based on ultra-wide-angle fundus image, belong to image processing field.The steps include as follows:S1.UWF fundus image dataset is obtained, and is assigned as training set and test set;S2.build multi-task semi-supervised learning network based on weight control mechanism, including encoder, decoder, cross-level non-local graph module and loss weight control mechanism, and training set image enters encoder and carries out feature extraction;S3.feature that encoder output is decoded and feature reconstruction by decoder part;S4.combined loss function and total loss are optimized to network by training, obtain the blood vessels and lesion multi-task segmentation model based on ultra-wide-angle fundus image;S5.data in test set are input into model, and segmentation result is obtained.The application can improve the ability of model to extract image detail features, realize fine and accurate multi-task segmentation.

The invention discloses a language model intelligent training task scheduling method based on reinforcement learning, and the method comprises the following steps: constructing a task pool, and generating a task attribute set; performing rhythm coding and memory priority processing to generate a training state vector, inputting the training state vector to the improved PPO model, and outputting scheduling action distribution; executing task segmentation, and inserting fragment boundary mark information to generate a scheduling action set; calculating a step length difference value, a training duration difference value and a rhythm difference value between tasks, generating a conflict weight, and executing scheduling conflict constraint and buffer control; scheduling behaviors and training feedback are collected, training efficiency, parameter stability, task switching smoothness and task distribution diversity scores are calculated and fused to generate a smooth reward vector, and a task pool is updated and refreshed. According to the method, self-adaption, high efficiency and rhythm coordination of task scheduling are realized, and the self-adaption capability and execution efficiency of training task scheduling can be improved.

The application discloses a multi-granularity and multi-level computing power scheduling method in an end-side computing power network, which comprises the following steps: deploying an online deep reinforcement learning agent and a system state monitoring module on a base station side; detecting and collecting channel states and computing resources and sending them to the deep reinforcement learning agent; outputting a task segmentation strategy of each user and a scheduling decision of each subtask; a task-type device (TD) segments tasks according to the instruction and schedules the tasks to target computing nodes according to the instruction for parallel processing; the task-type device (TD) receives the computing results of all the subtasks scheduled out in parallel; and the scheduling method segments the tasks of each user in multiple granularities according to current system state information, including channel gain, idle resources provided by a resource-type device (RD) and resources allocated to each user by an edge computing server, and schedules the segmented subtasks to suitable computing nodes in parallel, so that the execution time delay of an application program is further shortened by taking advantage of parallel execution.

The invention discloses an intelligent marine environmental pollution early warning method based on a biomarker, and belongs to the technical field of marine ecotoxicology, and the method comprises the steps: building an image quality control standard, obtaining a marine mussel histopathology image, building a data set according to the standard, carrying out the data marking and quality control, and carrying out the preprocessing. A model is constructed based on a YOLOv11 architecture, a multi-task segmentation function is realized by taking an image segmentation module as a core, and model training, verification and evaluation are carried out; different feature pixel points in the histopathology image are extracted through the trained model, then the shortest distance between the different feature pixel points is calculated through the Euclidean distance, and a result image containing distance marks is generated after averaging; and the marine environmental pollution early warning grade is divided by calculating the response index H. According to the method, the tissue pathology image is accurately analyzed by means of deep learning, identification and segmentation of different tissue structures of the marine mussels are realized, and an efficient technical means is provided for marine environment monitoring.

The invention provides a dynamic fragmentation method for a front-end rendering task. The execution pressure degree of the rendering task can be quantified in real time by monitoring the style calculation time consumption and the layout calculation time consumption in the rendering process of the front end of the browser. Based on the execution pressure degree, the system dynamically determines the segmentation granularity of the current front-end rendering task, so that the task is divided into a plurality of sub-tasks, and the sub-tasks are scheduled and executed according to priorities. According to the method, task segmentation can adaptively respond to actual rendering pressure, and the problem of unreasonable resource allocation caused by solidification of a static fragmentation strategy is avoided. Through accurate task division and scheduling, the system can effectively reduce blockage of a main thread by a long task, the rendering efficiency and the response speed of user interaction are improved, and meanwhile, the rendering stability and fluency in a complex scene are ensured.

The invention discloses a postoperative abdominal organ ischemia risk assessment method and system based on image analysis, and relates to the technical field of image analysis. The method comprises the following steps: acquiring an abdomen enhanced CT image of a target patient, and outputting the abdomen enhanced CT image after standardization processing; an improved 3D U-Net + + architecture of multi-task collaborative learning is adopted to perform joint segmentation on organs of a target patient and a target area, and geometric prior and a topological reasoning mechanism are fused; the method is technically characterized in that multi-task segmentation is adopted to enable RPPR calculation to focus on a real ischemic area, average dilution signals of all organs are avoided, and topological correction ensures that blood vessel features reflect real anatomy; in addition, through conjoint analysis of a hypoperfusion area prediction mask and a blood vessel VTIF, two ischemic subtypes of structural occlusion and functional hypoperfusion are disclosed, individualized development of a clinical intervention strategy is promoted, and the condition of required blood vessel intervention or conservative treatment is defined, so that the whole scheme has innovativeness and clinical landing value.

The application discloses a layered accumulation floating-point matrix multiplication deterministic method, device and medium, relates to the field of high-performance computing and chip technology, and the method comprises the following steps: task segmentation is performed on a floating-point matrix multiplication calculation task according to a reduction dimension, and the floating-point matrix multiplication calculation task is distributed to multi-thread parallel processing under a plurality of computing clusters; intermediate results generated by thread operation are cached to shared memory of the computing cluster; a main thread of each computing cluster completes sequential accumulation of the intermediate results in the cluster according to a preset order to obtain a local accumulation result; and the local accumulation result is sequentially accumulated to global memory according to a predetermined order by controlling a global accumulation sequence number, so that the parallel calculation efficiency is ensured, the determinacy of the floating-point matrix multiplication operation result and the stability of the calculation precision are considered, and the application scene with high requirements on the reproducibility and precision consistency of the operation result, such as artificial intelligence reasoning and scientific calculation, can be adapted.

The invention relates to the technical field of resource scheduling, and discloses an intelligent resource scheduling supply method and system in a heterogeneous computing environment, and the method comprises the steps: obtaining core heterogeneous data, carrying out the exponential smoothing denoising, obtaining a smooth state value, combining an HAT extension tuple, and carrying out the analysis to obtain a comprehensive capability score; the contribution degree is automatically adjusted through the similarity between the static features and the dynamic features; constructing a coupling correction function, calculating a mixing precision matching degree, and reflecting nonlinear correlation between targets in combination with an exponential product; a dynamic penalty function is constructed, a quantum genetic algorithm fitness function is optimized, and multi-objective collaborative optimization is carried out; constructing a complexity coupling and dynamic adaptation mechanism, and dynamically adjusting a task segmentation proportion; optimizing FPGA bit stream preloading according to the index ratio of the design frequency to the reconfiguration time; constructing a synchronous delay coupling model, and performing state synchronization optimization; balancing multi-dimensional feedback through a combined reward function; the influence of decision factors is quantified through contribution degree entropy, and interpretability is improved.

A task allocation method based on an AI model in a communication system and related products, the method comprising: a terminal / core network element sends first information to an access network device, the first information indicating a first model identifier (ID); wherein the first model ID indicates an AI model deployed by the terminal / core network element. And the terminal / core network element receives second information from the access network equipment, wherein the second information indicates the second model ID. Wherein the second model ID is a model ID in the first model ID, and the second model ID is associated with the first task. And the terminal / core network element executes the first task based on the AI model corresponding to the second model ID. According to the embodiment, alignment of computing power between devices can be achieved, conditions are provided for task segmentation, distributed training is enabled to be achieved, and the overall time delay of reasoning is reduced. The method can be applied to the technical field of AI, the technical field of communication and the like.

The invention provides an affinity-based edge computing network damage-resistant dependency task scheduling method and system, and the method comprises the steps: receiving a dependency task, judging whether to carry out task segmentation or not according to a resource demand, constructing a directed acyclic graph, and merging repeated subtasks; screening alternative nodes and detecting schedulable subtasks; sorting the tasks based on a multi-factor priority model; performing task-node affinity and task-task anti-affinity judgment, identifying key subtasks and initiating redundancy calculation; extracting enhanced features by using a multi-head attention map neural network, and generating a scheduling decision in combination with a reinforcement learning model; scheduling is executed, the state is monitored, and rescheduling is started for failed tasks. According to the method, the execution time delay of a large computing power dependency task can be effectively reduced, the robustness and generalization ability of the system in a dynamic edge environment are enhanced, and the damage resistance is remarkably improved.

The invention relates to a GPU (Graphics Processing Unit) maximum clique enumeration-oriented exposure-pull type load balancing method, which comprises the following steps of: decomposing a search space of a maximum clique enumeration task into a plurality of sub-search trees, and creating a plurality of parallel working units on a GPU; in response to the fact that the working state of the working unit is a preset busy working state, setting an exposed node and executing a preset enumeration task until a preset descending condition is met, and switching to a preset idle working state; in response to the working state being a preset idle working state, traversing atomic counters of other working units and executing a preset pulling task; and in response to successful pulling, switching to a preset busy working state based on the exposed node information of the pulling working unit and the candidate branch index until the maximum clique enumeration task is completed. Therefore, the problems that in the related technology, due to the fact that frequent data transmission, task segmentation and synchronous operation are limited, calculation is interrupted, extra memory management overhead is needed for maintenance, and the calculation efficiency is limited are solved.

The invention discloses a self-attention task self-adaptive segmentation method and device based on multiple cards and multiple core particles, and relates to the technical field of computer science, and the method comprises the steps: determining a self-attention model structure according to input self-attention model parameters; determining a hardware topological structure according to the input hardware parameters; aiming at each pre-configured task segmentation strategy, determining the task quantity allocated to the single core according to the self-attention model structure and the hardware topological structure; aiming at each pre-configured task segmentation strategy, respectively determining a to-be-calculated scale of the self-attention model structure under each task segmentation strategy according to the self-attention model structure and the task quantity allocated to the single core; and determining the calculation delay of each task segmentation strategy based on the to-be-calculated scale, and determining the optimal task segmentation strategy based on the calculation delay of each task segmentation strategy so as to segment the self-attention task according to the optimal task segmentation strategy, so that the task division strategies of card granularity, core particle granularity and single core granularity can be flexibly selected.

The invention discloses a virtual advertisement putting method under a meta universe, and relates to the technical field of virtual reality advertisements, and the method comprises the steps: obtaining interaction behavior data of a target user and a virtual advertisement in a meta universe platform, and carrying out the quantization processing of the interaction behavior data, and generating quantum state superposition data; establishing a quantum entanglement channel network among a plurality of meta universe platforms according to excited state probability components in the quantum state superposition data; constructing a three-dimensional advertisement model according to the collaborative task segmentation scheme, and allocating sub-tasks to the target user; interaction track data generated in the sub-task distribution process is collected, a quantum advertisement entropy value is generated through a von Noiemann entropy algorithm, and a non-interchangeable token is cast on the block chain based on the quantum advertisement entropy value. According to the method, the intention of the user is accurately captured through the quantum state superposition data, and the dynamic optimization of the advertisement resources is realized based on the quantum entanglement channel network.

This invention discloses a method and system for assessing postoperative abdominal organ ischemia risk based on image analysis, belonging to the field of image analysis technology. The method includes acquiring enhanced abdominal CT images of the target patient and outputting them after standardization processing; employing an improved 3D U-Net++ architecture with multi-task collaborative learning to jointly segment the target patient's organs and target regions, integrating geometric priors and topological inference mechanisms; its key technical points are: using multi-task segmentation to focus RPPR calculation on the real ischemic area, avoiding average dilution of the signal across all organs, and topological correction to ensure that vascular features reflect the real anatomy; furthermore, through the joint analysis of low-perfusion area prediction masks and vascular VTIF, it reveals two ischemic subtypes: structural occlusion and functional hypoperfusion, promoting the individualization of clinical intervention strategies and defining the necessary vascular intervention or conservative treatment, making the overall solution both innovative and clinically applicable.

The application provides a wavelength granularity task segmentation scheduling method and system for large-scale graph computation, comprising: obtaining a user-submitted graph computation task and network topology information, establishing a system architecture, and completing system initialization; performing feature analysis on the graph computation task; applying a multi-level graph segmentation algorithm to divide the graph data into multiple subgraph units; analyzing the available wavelength resource state in the optical network, establishing a mapping relationship between the subgraph communication demand and the wavelength resource; configuring an optical cross-connect device to establish a dedicated wavelength channel, and distributing the subgraph task to the corresponding computing node; and performing data exchange between the subgraphs through the dedicated wavelength channel, while performing parallel computation within the subgraphs. The application combines graph segmentation and wavelength resource allocation, realizes the collaborative optimization of the computation task and the communication resource, solves the performance bottleneck problem caused by the separation of computation and communication in the traditional distributed graph computation, and significantly improves the efficiency and scalability of large-scale graph computation.

The invention relates to the technical field of GPUs and swarm intelligence, in particular to a task segmentation device which comprises the steps that a calculation task is obtained, and the calculation task is segmented into a plurality of subtasks; the task allocation device polls each subtask and allocates a calculation address and a storage address to each subtask; the calculation execution device is used for sending the subtasks to the corresponding streaming processors according to the calculation addresses of the subtasks; and the memory storage device is used for adjusting the storage position in the memory according to the storage address of each subtask so as to optimize the storage efficiency of the memory. According to the system, a computing task is dynamically cut into sub-tasks matched with a streaming processor (SM), and SM-level fine-grained scheduling is carried out based on real-time task characteristics and a video card state, so that video memory and computing power resource holes caused by traditional whole card allocation are thoroughly eliminated.

The invention relates to a method for segmenting tasks, a computing device, a medium and a program product. The method comprises the steps that task grids are divided based on the sizes of thread clusters so as to obtain the total number of the thread clusters, and each thread cluster comprises a plurality of continuous thread groups; determining the number of thread clusters allocated to each calculation task construction module in the plurality of calculation task construction modules; calculating an initial coordinate of the thread cluster allocated by the current calculation task construction module; and calculating original coordinates of a plurality of thread groups in a thread group cluster allocated by the current calculation task construction module in the task grid based on the initial coordinates, so as to allocate the thread groups to the calculation cores based on the original coordinates. According to the invention, the communication frequency can be obviously reduced, and the task segmentation efficiency is improved.

The invention relates to the technical field of artificial intelligence data processing and privacy protection, in particular to a cloud collaborative AI data annotation task real-time segmentation and privacy protection merging system, which comprises a dynamic semantic perception task segmentation module used for receiving a complete AI data sample to be annotated and an AI training target of an annotation task; dynamic semantic perception segmentation and millisecond-level distribution are adopted, task response delay is reduced by 62%, the problem of lag caused by whole sample distribution in a traditional scheme is solved, core semantic blocks (10%-30% of data volume) are distributed to high-cost experts based on a core-edge block differentiation distribution strategy, labeling precision is guaranteed, and the method is suitable for large-scale popularization and application. Edge feature blocks are distributed to low-cost primary annotation personnel to complete basic annotation, the whole personnel do not need to be provided with experts, experimental verification shows that the manpower annotation cost can be saved by 30% or above, a double-layer encryption-zero knowledge verification mechanism achieves no decryption verification, federal learning and differential privacy control information leakage risk, and the GDPR compliance requirement is met.

The application discloses a network scanning task intelligent segmentation and load balancing method and system, which identifies abnormal fragments by performing integrity check on scanning tasks, analyzes task dependency to determine a segmentable position, and generates a segmentation scheme in combination with granularity constraints; collects node resource states to calculate a load index, configures and schedules weights according to load levels to establish parallel scheduling channels; constructs node cooperation groups based on load differences and formulates inter-group flow transfer rules to form a cooperative scheduling space, fragments tasks according to node capacities and generates a parallel execution plan; monitors execution progress to identify backlog nodes, diagnoses bottleneck types and plans a dredging path to execute task migration; collects resource time sequence data to identify low-load time periods, and distributes tasks to optimal time slots for execution, which improves the rationality of task segmentation and the degree of node load balancing, and improves the distributed scanning execution efficiency.

The invention relates to the technical field of data processing, in particular to a night light data automatic downloading and processing system, which is characterized in that a parameter input module is used for inputting limit data related to night light data to be downloaded; the task segmentation module segments the downloading task according to the limited data to obtain at least one downloading queue; the automatic operation module downloads the night light data in the data downloading platform according to the limited data based on a Selenium framework, and stores the downloaded night light data in a corresponding folder according to a downloading queue; the downloading monitoring module monitors the downloading process of the automatic operation module and monitors whether data downloading is completed or not at the data storage end; and the data processing module unifies file formats of the stored night light data. According to the invention, the problems of tedious operation, low efficiency, poor stability and lack of post-processing capability in the prior art are solved, and automatic, intelligent, high-efficiency and extensible data downloading and processing are realized.

A multi-task sea ice segmentation method based on a deformable space-frequency state space network comprises the following steps: (1) constructing input data including a synthetic aperture radar channel, a brightness temperature channel and an auxiliary channel for multi-source sea ice remote sensing image data; (2) performing double-flow feature extraction on the input data by using a deformable space-frequency encoder; (3) constructing a sea ice category text prompt library, encoding the text prompt library by using the pre-trained visual language model to obtain a text embedding vector, and calculating the pixel-level similarity between the text embedding vector and the visual features; and (4) inputting the semantic enhanced fusion features into a visual state space decoder, and modeling a long-range context dependency relationship by using a multi-direction selective scanning mechanism for decoding to obtain a multi-task segmentation result. According to the multi-task sea ice segmentation method based on the deformable space-frequency state space network, accurate segmentation of multi-task sea ice can be realized, and the precision and robustness of multi-task sea ice segmentation are improved.

The present application relates to the technical field of data analysis, in particular to a self-adaptive task segmentation pipeline optimization method and system, which establishes a heat conduction parameter set of each computing node through the physical topology of a liquid-cooled computing cluster; collects the operation data of the computing node and the cooling system in real time, calculates the performance trajectory of each computing node according to the heat conduction parameter set and the operation data; when it is determined according to the performance trajectory that a computing node will trigger frequency reduction, a new task segmentation decision is generated after the unexecuted task flow is intervened cooperatively; the pipeline execution is regulated according to the new task segmentation decision; the execution efficiency of the pipeline task in the liquid-cooled computing cluster can be effectively improved.