172 results about "Task network" patented technology

The invention discloses a medical risk intelligent prediction system based on time series data mining. The system comprises a multi-dimensional time sequence data acquisition and preprocessing module, a time sequence mode deep mining engine, a multi-dimensional risk assessment engine, an intelligent intervention decision support system and a real-time monitoring feedback module. A time sequence mode mining engine adopts a layered architecture, and short, medium and long-term time sequence modes are respectively analyzed through a bidirectional LSTM-attention network, a wavelet transform-convolutional network and a seasonal decomposition-gating circulation network. The risk assessment engine integrates an isolated forest, an auto-encoder, a Transform multi-task network and knowledge graph reasoning, and realizes all-around risk quantification. The decision support system generates a personalized intervention strategy based on deep Q network reinforcement learning and case reasoning. According to the system, early prediction and accurate intervention of medical risks are realized, and the prediction accuracy and the medical safety level are remarkably improved.

The invention relates to the technical field of automatic driving, in particular to an automatic driving task processing method and device, equipment and a medium. According to the method, firstly, the navigation guidance information is acquired and directly incorporated into beyond visual range contents (such as remote steering and distance), so that the limitation of the sensing range of a vehicle-mounted sensor is broken through, and the problem that an existing system lacks beyond visual range information is solved. And secondly, semantic analysis is performed based on navigation information and multi-view visual data to obtain text probability distribution, so that association of global navigation context and real-time scenes is realized, and a semantic basis is provided for humanization-like planning. Then, performing cross-modal fusion on the aerial view angle features and the text probability features, so that global navigation logic and real-time environment perception are deeply coupled, and decision is ensured to consider both a short-term environment and long-term planning. And finally, fusing the features and inputting the features into a task network, so that sensing, prediction and planning are more suitable for an actual scene, the utilization capability of beyond-visual-range information in a complex scene is improved, and the adaptability and reasoning capability of the system are enhanced.

The invention provides an intelligent task scheduling and optimizing method and system, a medium and equipment, and the method comprises the steps: analyzing task metadata through a database storage process, generating a task dependency graph according to an obtained task dependency matrix, and converting the task dependency graph into a visual interaction interface; identifying a key link with longest time consumption and a bottleneck task on the key link through the created time prediction model, and generating a multi-dimensional optimization suggestion based on an identification result; automatically adjusting a task scheduling script according to the multi-dimensional optimization suggestion and generating a standardized configuration file, dynamically allocating resources according to the standardized configuration file, and monitoring an execution state of a task through a fault-tolerant mechanism; and performing intelligent early warning according to the task log data collected in real time and the system performance index, and generating a multi-dimensional analysis report. The system is an intelligent scheduling system integrating dependency analysis, link optimization and execution monitoring, and automatic analysis and optimization of a complex task network are achieved.

The invention belongs to the technical field of remote sensing image processing. The invention provides a multi-source remote sensing image class incremental learning method based on prompt fine tuning. According to the embodiment of the invention, a mode-specific prompt pool is constructed, an instance-based prompt query mechanism is designed, and proper prompts are dynamically selected for different input characteristics; a learnable modal sharing global prompt is added to each attention layer of the frozen Vision Transform network, and modal public information specific to a task is extracted; carrying out cross-modal prompt conversion and fusion; freezing a part of the modal mapping network to reserve a modal conversion relation of the old task; establishing a prompt-guided knowledge aggregator module, freezing a knowledge aggregation token in an old task network, and jointly guiding the knowledge aggregation token, a knowledge aggregation token of a new task and a modal specific prompt to guide the aggregator module to learn image features; and training the established incremental learning network model.

The invention belongs to the technical field of computing power distribution, and particularly relates to a dynamic computing power distribution method and system based on reinforcement learning, and the method comprises the following specific steps: S1, covering cloud, edge and end full-node scenes, and collecting computing power resource states, task demand features and cross-domain network condition data in real time; s2, on the basis of standardized data output by a cross-domain computing power sensing module, by constructing a state space fusing computing power, tasks and a network, defining an action space of computing power scheduling direction and proportion, and designing a multi-target reward function for balancing the resource utilization rate, the task satisfaction rate and long-term conflict avoidance; and realizing self-learning and self-iteration scheduling strategy generation based on a reinforcement learning algorithm. According to the invention, the reinforcement learning agent autonomously learns the computing power demand of the emergency scene and the new type of task, the rule does not need to be manually preset and modified, and the method has the advantage of realizing dynamic adaptation of computing power distribution to complex and changeable scenes.

This application provides target recognition method, a multi-task network model training method, and an electronic device. The target recognition method includes: inputting video images into a multi-task network model one by one to obtain a predicted feature map; performing post-processing on the predicted feature map to obtain a target detection result; judging whether a target class confidence degree is greater than a preset confidence degree; if so, judging whether a target image quality score is greater than a preset score; if so, cropping out a target image from the video images according to a target detection box; and inputting the target image into a target recognition model corresponding to the target class to obtain a target name. In this way, this application decreases the number of calls of the recognition model, and also reduces a training duration of the model.

The invention discloses a rock slag multi-dimensional intelligent identification and real-time early warning method for tunnel boring machine construction. The method comprises the steps that continuous rock slag images are acquired and preprocessed; inputting the preprocessed rock slag image into the trained multi-task network, and outputting a multi-task result; wherein the task network adopts a unified encoder and multi-head decoder architecture, the multi-head decoder comprises a segmentation head, a detection head, a regression head and an anomaly scoring head, the segmentation head outputs a pixel mask, the detection head outputs a rock slag frame and category, the regression head outputs a particle size distribution parameter, and the anomaly scoring head outputs a frame level anomaly score; synchronously carrying out sliding window statistics based on output results of the regression head and the abnormal score head; and performing multi-level early warning judgment based on a sliding window statistical result. According to the method, multiple functions of rock slag instance segmentation, target detection, particle size distribution regression, flow estimation, anomaly scoring and the like can be realized, and multi-stage early warning and parameter suggestion are realized through time sequence statistics.

The invention discloses a small sample aerodynamic modeling method based on multi-task learning. Comprising the following steps: constructing a multi-task prediction model, integrating an auxiliary task network and a target task network, and enhancing the feature extraction capability of an encoder through an SE layer and an attention layer; acquiring multi-source aerodynamic force data and carrying out standardized preprocessing on the multi-source aerodynamic force data; a dynamic weight mechanism is designed, the influence of auxiliary task prediction on target task output is adaptively adjusted according to input features, and effective fusion of low-fidelity data and high-fidelity data is achieved; two-stage training is carried out to ensure the efficiency and stability of multi-task learning; the prediction capability of the model under the limited sample condition is verified; through the synergistic effect of multi-task knowledge migration, feature enhancement and dynamic task integration, in combination with the feature optimization characteristics of an SE layer and an attention mechanism, high-precision aerodynamic prediction is realized under the condition of limited samples, and an efficient and accurate aerodynamic prediction method is provided for aircraft design.

The invention discloses a pig segmentation and posture detection method, device and equipment, and the method comprises the steps: obtaining pig farm image data, and carrying out the marking of a bounding box, a segmentation mask and a posture key point for each pig instance in each pig farm image data, so as to construct training data; a multi-task network model based on the YOLOv8 framework is constructed, and the network architecture of the multi-task network model comprises a backbone network, a neck feature fusion module, a detection head, a segmentation head and a key nodding head, and the detection head, the segmentation head and the key nodding head are arranged in parallel; inputting the training data into the multi-task network model, and training based on a preset loss function to obtain a trained pig segmentation and posture detection model; and inputting an acquired to-be-detected image into the pig segmentation and posture detection model for reasoning to obtain a segmentation and posture detection result corresponding to each pig. According to the method, the processing efficiency is remarkably improved, and the requirement of a real-time monitoring scene of a large-scale pig farm for low delay can be met.

The invention provides an information recommendation method and device, electronic equipment and a storage medium, and relates to the field of data processing, in particular to the field of artificial intelligence. According to the specific implementation scheme, feature extraction is carried out on user features of a target user, a user historical click sequence and resource features of candidate resources through a first processing network, and first fusion features are obtained; scene related information corresponding to a recommended scene of the target user is determined, feature extraction is performed on the scene related information through a second processing network to obtain a scene related vector, and the recommended scene is a new user scene or an old user scene; performing feature extraction on the first fusion feature and the scene correlation vector through a scene processing network to obtain a second fusion feature; performing multi-task prediction through a multi-task network according to the second fusion feature to obtain a prediction result of each task, the tasks including a click task and a conversion task; and determining a comprehensive score of the candidate resources according to the prediction result, and determining a resource recommendation list according to the comprehensive score.

The invention provides a multi-agent collaborative cognitive calculation method based on point cloud feature marking, and belongs to the field of Internet of Vehicles. The method specifically comprises the following steps: firstly, inputting original point cloud data collected by a sensor of a vehicle intelligent agent into a point cloud feature mark generator, converting the original point cloud data into a one-dimensional point cloud feature mark sequence semantic perception dynamic encoder for encoding a mark sequence, and generating a feature sequence; packaging the feature sequence, the space coordinates and the pose information of the self-vehicle intelligent body into a message data packet; converting the feature mark coordinate space of the neighbor intelligent body into a coordinate system with the self-vehicle intelligent body as the center by a point cloud mark aggregation module, and generating a unified sequence; and the semantic perception dynamic fusion module carries out global context modeling and dynamic fusion on the unified sequence, corrects feature mark position deviation, generates a refined sequence, and inputs the refined sequence into a downstream task network for generating final prediction. According to the method, the perception robustness is obviously improved.

The invention belongs to the technical field of network attack and defense simulation, and discloses a network attack and defense simulation engine system based on discrete event driving, which comprises an event scheduling module, a simulation network construction module, an attack and defense simulation module, a simulation kernel module and a panoramic situation awareness module, the event scheduling module is in the form of MITRE ATTamp; the CK is constructed as a theoretical base and is used for converting all network activities into a dispatchable discrete event sequence; the network activities comprise data packet sending, protocol timeout and attack triggering; and the simulation network construction module can construct a simulation network containing complete service logic. According to the application, the MITRE ATTamp is scheduled by the event scheduling module; the CK tactical intention is converted into an ordered event stream, a hierarchical task network planner in the attack and defense simulation module dynamically decomposes and re-plans a high-level target, and ATTamp can be utilized; and the CK technology ID reconstructs an attack chain in real time.

The invention discloses a full life cycle management system for an enterprise product supply and demand order contract, relates to the technical field of enterprise supply and demand contract management, and is used for solving the problem of low efficiency of supply and demand performance collaboration. Through multi-source business event collection and unified coding time reference, records dispersed in a business system are integrated into an event stream sorted according to time, a supply and demand event chain and a life cycle unit corresponding to a contract are automatically identified, the performance state is visible and the abnormity is traceable, and the efficiency is improved. The performance terms and the settlement terms are analyzed into computable constraints, cooperative control of a performance task network and resources is driven, resource demand intervals are generated according to uncompleted tasks and constraints, key resource states are compared to identify resource conflicts, and processing control items such as production, delivery, purchase or term adjustment are generated, so that stockout and delay risks are reduced; and the performance stability and the resource utilization efficiency of the enterprise are improved.

The invention discloses a multi-agent path planning method, device and equipment and a computer storage medium. The method comprises the following steps: constructing a multi-agent path planning model comprising a subtask network, an execution strategy network and a multi-commentator network; generating sub-task targets of multiple agents in a fixed time span through a sub-task network based on the current global state of the multiple agents; according to the sub-task target, through a sub-task network, controlling a plurality of agents to execute an action sequence of a preset step number; calculating a reward value according to the execution result of the preset step number; updating parameters of the multiple reviewer networks and the strategy network and parameters of each reviewer network in the multiple reviewer networks based on the environment rewards and the internal rewards; updating parameters of a sub-task network through a state transition strategy gradient module according to a relationship between a new state of the multi-agent after the action is executed according to the preset step number and a sub-task target state; and repeating the steps until training convergence to obtain a multi-agent path planning target model.

A method includes generating a synthetic dataset with a generative model. The method also includes tuning the generative model based on feedback from a task network that receives the synthetic dataset as input. The task network may perform image recognition. The synthetic dataset may be generated based on a set of classes and labels of the classes. The method may iteratively generate the synthetic dataset and tune the generative model, based on feedback from the task network.

The invention discloses an improved multi-modal three-dimensional medical image classification method based on fusion assistance. The method comprises the following steps: 1, inputting multi-modal medical image data into ResNet to extract modal features and fuse the modal features; 2, constructing a multi-branch network, inputting a fusion feature into a main classification branch, and extracting a cross-plane global context and a fine-grained feature in combination with cross-plane key slice selection and Transform; 3, introducing discriminant prior knowledge generated by the main classification branch into a fusion auxiliary branch, and extracting enhanced features; 4, fusing the main branch fine features and the auxiliary branch enhanced features to obtain semantic level fusion features; 5, inputting the fused features into a classifier to output category probabilities, and taking a category corresponding to the maximum value as a diagnosis label; and 6, joint loss is constructed based on prediction and real labels, and the multi-branch multi-task network is trained and optimized. According to the method, complementary information of the multi-modal medical image is fully mined, focus perception is enhanced by combining judgment prior guidance and multi-task collaborative optimization, and the diagnosis accuracy and stability are improved.

The invention discloses a paralytic nursing intelligent early warning system and method based on the Internet of Things technology. The system comprises a multi-modal physiological parameter acquisition module, an edge calculation preprocessing unit, an intelligent data transmission module, a multi-scale time sequence feature extraction module, a space-time diagram convolutional network module, a cross-modal attention fusion module, a multi-task risk prediction module and a model training and optimization module. According to the system, multi-mode data such as electrocardio, blood pressure, blood oxygen, eye movement tracks and voice are collected, preprocessed at an edge end and then transmitted to a cloud end; a multi-scale convolutional network is adopted to extract time sequence features, parameter association is modeled through space-time diagram convolution, cross-modal data fusion is realized by using an attention mechanism, and finally risk classification, anomaly detection and trend prediction are completed through a multi-task network. The early-stage, accurate and explainable early warning of the stroke risk is realized, and the early warning accuracy and clinical practicability are remarkably improved.

The invention relates to the technical field of medical image artificial intelligence, in particular to an auxiliary interpretation method for medical image rapid diagnosis of acute abdominal disease in combination with deep learning, and the method comprises the steps: 1, organ targeted segmentation: obtaining CT sequences of an arterial phase, a venous phase and a delay phase, and carrying out the dynamic calculation of an interpolation interval according to the layer thickness parameter of scanning equipment, and carrying out the spatial standardization; synchronously generating an intestinal canal mask, a blood vessel mask and a peritoneum mask by adopting an acute abdominal disease directional segmentation network, wherein the network introduces an anatomical size adaptive multi-scale cavity convolution group into a deep layer of an encoder; 2, quantifying dynamic pathological signs; step 3, performing clinical-image gating fusion; and 4, multi-task cooperative diagnosis: inputting the fusion features into a pre-trained multi-task network, and outputting an acute abdominal disease cause classification probability and an operation urgency evaluation value in parallel. The multi-dimensional analysis capability of acute abdominal disease diagnosis is improved through multi-task cooperative diagnosis, and accurate classification of disease causes and evaluation of operation urgency can be carried out at the same time.

The invention relates to substation inspection, in particular to a transmission line multi-modal inspection data fusion method based on deep learning, and the method comprises the steps: constructing a unified space-time coordinate system, mapping multi-modal inspection data to the coordinate system, and carrying out the space-time alignment of the multi-modal inspection data; according to the characteristics of different modes, a special encoder is used for carrying out feature extraction on inspection data of each mode, and the inspection data are projected to a feature space with a unified dimension; performing dynamic weighted fusion on the high-level semantic features among the inspection data in different modes by using a mutual attention mechanism to obtain a global perception vector; inputting the global perception vector into a downstream task network, and optimizing model parameters by using a corresponding loss function; according to the technical scheme provided by the invention, the defect that the multi-modal inspection data of the power transmission line is difficult to effectively fuse in the prior art can be effectively overcome.

The invention provides an end-to-end multi-task three-dimensional depth and semantic fusion visual perception system and an end-to-end multi-task three-dimensional depth and semantic fusion visual perception method. Comprising an image acquisition module used for outputting image data carrying calibration parameter identification; the fusion preprocessing module is used for processing the synchronous image pair to obtain a registration image pair; the multi-task network module comprises a shared feature extraction layer, a depth estimation branch and a semantic segmentation branch; the multi-task coupling unit is used for applying consistency constraint to the shared features and the branch features and transmitting geometric priori and category priori among branches; and the output interface module is used for packaging the depth map and the semantic mask according to a preset data structure, and outputting the depth map and the semantic mask to a real-time control interface of an external execution control system through an industrial real-time communication interface for calling. According to the invention, end-to-end low delay and deterministic output, time synchronization and calibration consistency maintenance, compact integration and simplified wiring can be realized, and a high-precision depth map and a semantic mask can be stably provided for a control system.

The invention relates to a multi-edge task unloading method combined with task pre-sorting, and belongs to the technical field of edge calculation and task unloading. The method comprises the following steps: firstly, establishing a multi-edge, multi-user and multi-task network architecture comprising a cloud layer, an edge layer and a user layer; based on network architecture support, an application model, a transmission model and an execution model for an application task of a user are established, the application model represents any subtask as a triple, the transmission model defines a corresponding transmission rate, and the execution model comprises a local execution model, an edge execution model and an overall cost model; a task pre-sorting mechanism is introduced, and subtasks which can be parallel are sorted in a descending mode according to priority factors; and finally, based on the ordered task sequence after sorting, performing task unloading strategy decision by adopting a deep Q learning network. According to the method, joint optimization of task execution delay and energy consumption can be realized in a multi-user, multi-task and multi-edge node computing environment.

The invention provides a footprint image intelligent analysis method and system based on a multi-task network, and the method comprises the steps: carrying out the key point marking and preprocessing of a footprint image, and obtaining a key point multichannel Gaussian thermodynamic diagram; matching the footprint image with the key point multichannel Gaussian thermodynamic diagram, and inputting the footprint image and the key point multichannel Gaussian thermodynamic diagram into a deep multi-task network to generate a footprint analysis diagram; and obtaining individual attribute information based on the footprint analysis graph, and carrying out footprint matching and identity discrimination to complete footprint image intelligent analysis based on the multi-task network. According to the method, high-precision footprint segmentation can be realized, an analytic graph can be generated and attribute inference and identity matching can be completed at the same time, and compared with an existing single-task method, the robustness and generalization are remarkably improved. The system has the advantages of being high in accuracy, high in interpretability and good in practicability, and is suitable for the fields of security evidence obtaining, identity recognition, motion behavior research, medical health monitoring and the like.

The application discloses a kind of computing power network green space-time task scheduling and mixed energy collaborative optimization method.The steps are as follows: one, establish the computing power network system model of fusing computing resources, network routing resources and mixed energy, construct multi-objective joint optimization problem;Two, model the problem as a Markov decision process, define the state space containing task, network and energy state, and the action space composed of computing power node selection, routing path selection and forwarding time;Three, use the integrated deep reinforcement learning framework, configure multiple intelligent agents with different preference weights for parallel training, to achieve the best balance between quality of service and carbon emissions;Four, according to the integrated strategy of complete training, combined with the real-time input task flow and carbon emission intensity, output the optimal space-time scheduling decision and energy storage charging and discharging strategy.

The application discloses a kind of based on ring network fusion's collaborative task efficiency analysis method and device, it is related to efficiency analysis technical field.The unmanned aerial vehicle collaborative task system is simplified as four types of node types containing OODA ring;Based on the four types of nodes of OODA ring, determine actual connection edge, obtain task network, extract generalized OODA ring from task network, cover collaborative relationship;Generalized OODA ring node attribute quantization framework based on task demand is built, including task-driven node attribute extraction, quantization method and data acquisition path and data normalization processing;Based on generalized OODA ring node attribute quantization framework, nonlinear aggregation is carried out, and the efficiency of task network is comprehensively analyzed based on the probability of task success.The problem that the prior art cannot accurately capture the efficiency change of unmanned aerial vehicle collaborative task system in dynamic confrontation process, and is limited in practical application, is solved.

The invention discloses a key factor identification method for influencing process industrial load participation in demand response and computer equipment thereof, and relates to the field of power system scheduling and control. Firstly, a state task network is utilized to describe a production link coupling relation, and a maximum total system profit is taken as an optimization target; and constructing an energy optimization scheduling model for the general industrial process. Secondly, common influence factors in the production process are extracted for a power grid and process industrial production interaction scene; the method comprises the following steps of: performing preliminary rapid screening on a large number of influence factors on the basis of a Morris screening method, and finally quantifying global sensitivity coefficients of the screened influence factors on three types of core indexes, namely economical efficiency, productivity and energy efficiency on the basis of a Sobol 'global sensitivity analysis method, so as to identify key factors which influence the participation of the process industrial load in demand response.

This invention discloses an adaptive end-to-end network learning-driven polarization descattering depth estimation method. It proposes a physics-driven end-to-end network to simultaneously acquire clear reconstructed images and high-precision depth images in scattering environments. The method first acquires linearly polarized images of the scene in four directions, extracting light intensity and linear polarization feature maps through decoupling calculations. Then, a dual-task network including a polarization-guided fusion module is constructed. Utilizing the physical sensitivity of DOLP to scattering particle concentration and optical path length, it transforms these into a spatial attention mask, physically constraining and enhancing the shallow texture of the intensity map in the feature space. Finally, dehazing and depth estimation branches are encoded and decoded in parallel. This invention overcomes the intensity-depth ambiguity of traditional RGB images, effectively suppressing the interference of non-uniform fog on geometric perception and visual texture, and achieving high-precision scene depth estimation and high-fidelity dehazing imaging under adverse weather conditions.

The invention provides a traffic sign target recognition method based on deep meta-learning, a medium, equipment and a product, and relates to the technical field of meta-learning, and the method comprises the steps: obtaining a traffic sign image, and dividing the image into a training task set and a test task set; the method comprises the following steps: constructing a deep meta learning model by taking CNN as a meta network of meta learning and a structure of each task network, optimizing an MAML meta learning algorithm by using a ZeroTrick strategy on the basis of Meta-SGD, and setting a linear layer weight parameter as 0 after the algorithm starts and each sampling task cycle ends; and training the deep meta-learning model by using the training task set, testing the trained deep meta-learning model by using the test task set, and using the tested model for traffic sign target identification. According to the invention, the generalization ability and accuracy of the traffic sign recognition model under the condition of few samples are improved.

The application provides an interactive infrared meibomian gland image segmentation method based on graffiti simulation; the method designs a new multi-task network architecture, receives multiple interactive information (clicks and graffiti) as input, realizes gland segmentation, and predicts false positive and false negative regions to guide the model to perform graffiti simulation. The method adopts a two-stage network training strategy and combines uncertainty region simplified masks to improve the accuracy of error region prediction. For each round of user interaction, the method performs prediction in two stages. In the first stage, the network outputs a rough segmentation mask and corresponding false positive and false negative prediction regions based on user clicks; in the second stage, the model simulates graffiti based on the two error regions according to the pseudo-graffiti generation strategy to refine the rough segmentation mask output in the first stage. Compared with the existing method, the method can simultaneously consider the efficiency of graffiti and the simplicity of user interaction, and further reduce the labor cost of users.

The application discloses a kind of mine target area prediction method, system, equipment and storage medium, including obtaining the element content chart of the region to be detected, and according to element content chart constructs chemical data set and is calculated to obtain element feature map, element feature map is carried out to obtain element scale feature map by inflation convolution operation, constructs a well-trained mine target area prediction source task network and multiple same structures target convolutional neural network, element scale feature map is input in corresponding target convolutional neural network with selected weight, to control target convolutional neural network in calculation by selected weight and carry out voting election to obtain final prediction result, can be combined multiple scale features to carry out mine target area prediction, while selectively migrate weight parameters in mine target area prediction source task network are used for each scale network training, solve the problem of few data, improve the accuracy and reliability of intelligent prospecting prediction.

The application discloses a knowledge distillation method and device for reactor nuclear accident deduction and fault diagnosis, and comprises the following steps: obtaining nuclear energy simulation device fault data; building a multi-task network; dividing the obtained data set into a training set, a verification set and a test set, and pre-training the multi-task network by using the training set; fixing the parameters in the multi-task network after pre-training, that is, freezing the pre-training model with large parameters as a teacher model; adopting a multi-task proportional penetration knowledge distillation method to transfer the knowledge of the teacher model to a student model; and obtaining physical quantity deduction results and fault results of a target pipeline by reasoning by using the student model. D The application can simultaneously process multiple fault diagnoses and parameter deductions in a reactor nuclear accident by building a multi-task network based on a physical information neural network, fully utilizes the correlation between different tasks, and improves the accuracy of fault diagnosis and the deduction capability for the development process of a nuclear accident.