1118 results about "Task learning" patented technology

Provided in the present application is an intelligent question answering method based on a collaboration between a large language model and a knowledge graph, relating to the technical fields of artificial intelligence and natural language processing, the method comprising: decomposing a complex question into a plurality of simple questions, and analyzing the degree of association between the simple questions and a basic function so as to form a multi-hop reasoning path; automatically extracting structured information from the simple questions on the basis of a multi-task learning framework of a large model, so as to construct a knowledge graph; and constructing a cumulative reasoning learning framework on the basis of a logic reasoning large model, and performing iterative verification on a process result formed by the knowledge graph on the basis of the multi-hop reasoning path, so as to correct the reasoning path until a correct answer is inferred.

The invention belongs to the technical field of intelligent perception and mode recognition, and particularly relates to a human body behavior recognition method based on millimeter wave radar and multi-task learning. The method comprises the steps that millimeter wave radar point cloud data and RGB video streams are synchronously collected, and a five-dimensional point cloud scene is generated through three-dimensional analysis and dynamic target extraction; a hierarchical Point Transform network is constructed to extract radar space-time features, and human body key point features are generated by using visual auxiliary attitude estimation; radar and key point features are dynamically fused through a cross-modal attention mechanism, a multi-task joint optimization strategy is combined, behavior classification serves as a main task, attitude estimation serves as an auxiliary task, and a behavior recognition result is output. According to the method, through multi-task cooperation and cross-modal feature interaction, on the premise of ensuring privacy security, the accuracy and robustness of human behavior recognition in a complex scene are remarkably improved, and efficient and reliable technical support is provided for the fields of intelligent monitoring, human-computer interaction and the like.

The invention relates to the technical field of distributed resource management and optimization, in particular to a distributed resource life cycle evaluation and dynamic updating method, which comprises the following steps: data acquisition: collecting multi-modal data of distributed resources, including resource state data, use behavior data, environment data and historical maintenance data; feature extraction: carrying out feature engineering processing on the multi-modal data, and extracting key indexes such as a resource health state, a degradation rate and environmental sensitivity; building a life cycle evaluation model, building the model by using the time sequence data of the resources based on a multi-task learning framework, and predicting the health state and the residual life of the resources; and optimizing a dynamic updating strategy, and monitoring and dynamically adjusting in real time. The method provided by the invention overcomes the problems of insufficient life cycle dynamics and rigid updating strategy in a traditional method, has the characteristics of high efficiency, intelligence and accuracy, and is suitable for intelligent management and optimization of new energy equipment, industrial assets and other distributed resources.

The invention belongs to the technical field of medical image processing, and discloses a CT image segmentation and classification system based on segmentation feature guidance, and the specific technical scheme is as follows: the system adopts a shared encoder to extract general features, realizes collaborative optimization of segmentation and classification through a double decoding path, adopts a partial decoder in a segmentation path, and adopts a partial decoder in the segmentation path; in combination with a local feature attention module, through multi-scale feature fusion and a boundary perception mechanism, the region consistency of a global segmentation map is gradually optimized, edge detail information is supplemented, and a classification path generates a space attention weight through a segmentation feature guide module by utilizing segmentation prediction; the classification network is guided to focus on a focus area and suppress background interference, a self-adaptive loss weighting strategy based on multi-task learning is adopted, the double-task gradient flow is dynamically balanced, and the gradient competition problem in the multi-task learning is effectively relieved.

The invention belongs to the technical field of knowledge maps, and discloses a traditional Chinese medicine knowledge map fusion system based on semantic alignment. Comprising a multi-modal semantic embedding and entity alignment layer, a structure embedding and attribute fusion service presentation layer, an enhanced TransR model and knowledge graph construction layer, an intelligent rule engine and conflict resolution layer, an end-to-end automatic assembly line layer, a multi-task learning framework and incremental dynamic updating. Through innovative combination of a multi-level semantic alignment technology and an intelligent traditional Chinese medicine rule engine, end-to-end automatic fusion of traditional Chinese medicine knowledge maps is realized, and heterogeneous knowledge maps from a plurality of data sources are automatically converted into a standardized unified knowledge map through an assembly line. Compared with a traditional character string matching method, the accuracy rate of the method is about half, and the alignment accuracy rate is improved to 80-90%. More importantly, under the same data condition, the fusion time is greatly shortened, and the incremental updating efficiency is remarkably improved.

The invention provides an airfoil flow field prediction method based on multi-task learning, and the method comprises the steps: firstly building an airfoil flow field prediction model based on multi-task learning, which comprises an encoder, a backbone network and a multi-head decoder; then training the established model by using an airfoil flow field data set and adopting an optimization strategy based on multi-task learning; and finally, performing geometric parameterization and grid generation processing on an airfoil which actually needs flow field prediction to obtain standardized airfoil flow field data, and inputting the airfoil flow field data into the trained airfoil flow field prediction model for prediction to obtain full flow field physical quantity distribution and lift-drag coefficient information. According to the method, a multi-task loss optimization strategy is used in the prediction model training process, so that the problem of conflict between airfoil profile surface loss and volume loss optimization can be effectively solved, and then the construction of an airfoil profile proxy model and the accurate prediction of flow field information and aerodynamic parameters are realized.

The invention relates to an intelligent computing resource allocation method based on reinforcement learning. According to the technical scheme, the method comprises the steps that monitoring data from different sources including hardware monitoring, software logs and network bandwidths are fused, and a multi-dimensional time sequence state space is formed; carrying out dimensionality reduction and de-noising processing on the multi-modal data by using a depth auto-encoder; multi-task learning MTL is introduced into time sequence modeling, and resource requirements and state evolution of multiple tasks are predicted at the same time; generating a plurality of predictive resource scheduling strategies by using a GAN (Generative Adversarial Network), and dynamically selecting a strategy scheme when a load changes; each strategy is realized by an independent sub-network, and part of core knowledge is shared; through a strategy evolution mechanism, according to a historical feedback optimization strategy combination including task completion time and resource consumption, in a heterogeneous resource environment including a plurality of cloud computing platforms and edge computing nodes, based on difference of resource types, fine-grained scheduling of strategies is carried out; and scheduling the decision by using a distributed Q-learning mechanism in the reinforcement learning model.

The invention provides a personalized fitness system and method based on multi-modal data and an adaptive large model. According to the method, multi-modal data are integrated, and heterogeneous data collaborative analysis is realized by using a space-time alignment algorithm and a confidence coefficient weighting mechanism. A multi-task learning framework is adopted, physiological prediction, exercise risk early warning and psychological incentive strategy generation tasks are synchronously processed, a user stage target is adapted through a dynamic attention mechanism, and federal learning and transfer learning technologies are combined. A nonlinear periodic planning engine based on reinforcement learning dynamically adjusts training load and action difficulty according to real-time biomechanical simulation results and recovery state evaluation, and meanwhile, a lightweight model compression technology and end-side reverse dynamics calculation are adopted. Based on the scheme, the exercise performance prediction precision and the early warning timeliness are improved, and the exercise loss risk caused by overtraining is reduced; the cold start data bottleneck is broken through; low-delay virtual coach interaction and privacy protection are realized; and dynamically adjusting the training intensity and the incentive strategy.

The invention relates to the technical field of robot training. The robot self-adaptive training method based on reinforcement learning comprises the steps that task sub-target information is generated through a high-level strategy network, the task sub-target information is input into a low-level execution network, an action control instruction is generated according to the task sub-target information, interaction feedback information is collected in the execution process, and the action control instruction is sent to a robot through a robot. Calculating a reward value according to the interaction feedback information, carrying out association processing on the reward value and the scene complexity parameter, executing a dynamic reward shaping operation, generating an adjusted reward signal, generating a strategy model optimized by meta-learning based on the adjusted reward signal, loading the strategy model in a simulation environment, and carrying out dynamic reward shaping. A target strategy model optimized through simulation training is generated, the target strategy model is loaded to the robot, and the robot is controlled to execute task operation in the actual interaction scene. The method has the effect of realizing adaptive task learning of the robot in a multi-interaction scene.

This specification describes systems and methods, implemented as computer programs on one or more computers in one or more locations, for controlling an agent to perform multiple different tasks in an environment. The described techniques partition the architecture of a controller into a dispatcher that understands the environment and an executor that understands how to control the agent, with a control channel between them that structures the partitioning. This allows implementations of the controller to generalize better.

The invention relates to the field of data processing, and particularly provides a big data mining method and system based on digital enterprise management, and the method comprises the steps: obtaining multi-modal session data generated in the process of carrying out digital service interaction between a target enterprise and a user, extracting a user demand feature set from the multi-modal session data, and based on a preset multi-task learning framework, carrying out joint training on the user demand feature set, generating a demand analysis model, calling the demand analysis model to analyze the user demand feature set, generating an optimization strategy set associated with the target enterprise service process, and sending the optimization strategy set to the server. And updating the digital service execution logic of the target enterprise according to the optimization strategy set, and performing incremental training on the demand analysis model based on user feedback data. According to the invention, the response efficiency and the function adaptability of the digital service process can be improved.

The invention relates to the technical field of computer vision, and particularly discloses an adaptive pyramid and multi-stage path aggregation-based lightweight multi-task small target detection algorithm, which comprises an adaptive feature pyramid network, a multi-stage path aggregation module, a lightweight Transform module, an optimized channel attention mechanism, a multi-task learning head and an algorithm training method. According to the lightweight multi-task small target detection algorithm based on adaptive pyramid and multi-stage path aggregation, dynamic weight adjustment of different levels of features is realized by combining an adaptive FPN, and the expression ability of multi-scale features is enhanced. The MPAM module introduces a lightweight Transform module on the original basis, global feature modeling is performed by using an axial attention mechanism, the perception ability of the model to a small target is improved, and a channel attention mechanism is optimized to reduce calculation overhead.

The invention discloses a power equipment fault prediction system based on big data analysis. The method comprises the following steps: acquiring initial equipment multi-dimensional data; constructing a dynamic topology network of the power equipment, including a dependency relationship between the equipment and a fault propagation path, performing embedded learning on the dynamic topology network by using a GNN graph neural network, and extracting equipment collaboration features in the initial equipment multi-dimensional data; a multi-task learning framework is constructed in combination with the equipment cooperation features to predict the equipment fault probability and the remaining service life, and an equipment health index is obtained; and acquiring environmental parameters, dynamically adjusting a fault judgment threshold based on the equipment health index and the environmental parameters, generating a prediction result, integrating the prediction result with an SCADA system, and triggering graded early warning. And the influence of environmental factors on the operation state of the equipment is fully considered. Under different environmental conditions, the equipment fault risk can be judged timely and accurately.

The invention discloses a machine generated text detection method, a terminal, a medium and a program product in the field of natural language processing, and the method comprises the steps: inputting a to-be-detected target text into a trained text detection model, and obtaining a detection result outputted by the text detection model; the text detection model comprises a first semantic feature extraction network, a second semantic feature extraction network and a fusion classification network; the first semantic feature extraction network is used for extracting semantic features of keywords in a target text to generate a first representation vector; the second semantic feature extraction network is used for extracting overall semantic features of the target text and generating a second representation vector; the fusion classification network processes the first representation vector and the second representation vector to generate a detection result about whether the target text is a machine-generated text; the training of the text detection model adopts a multi-task learning strategy. According to the method, multi-task training is introduced, so that the accuracy and robustness of machine generated text detection are effectively improved.

The invention relates to the field of big data analysis, in particular to an intelligent recommendation system based on user behaviors. The method comprises the following steps: acquiring user explicit behavior data and user implicit behavior data in a system, acquiring interaction depth index data of a user in the system, adding noise to sensitive behaviors in the data by using a differential privacy technology, establishing a variable time attenuation function, segmenting a behavior sequence according to sessions by using a hierarchical Transform encoder, and obtaining a variable time attenuation function; establishing a GNN user intelligent recommendation model based on a heterogeneous graph neural network, connecting the multi-task learning framework with the heterogeneous graph neural network to obtain a target GNN user intelligent recommendation model, optimizing a recommendation result of the model by using a multi-target optimization algorithm, inputting feature multi-granularity behavior data into the user intelligent recommendation model for recognition, and performing recommendation on the target GNN user intelligent recommendation model. And obtaining a user recommendation result. The problem of recommendation homogenization caused by a single target can be avoided, and intelligent recommendation efficiency and recommendation accuracy are improved.

The invention provides a city calculation basic model and equipment based on a Mama time sequence, and relates to the technical field of data processing. In the model provided by the embodiment of the invention, a Mama framework is combined with multi-task time sequence analysis. Aiming at the non-stationarity of urban time series data, a frequency prompt network is designed for decoupling in combination with a Koopman operator theory and dynamic mode decomposition, a steady mode and non-stationary fluctuation are analyzed from the angles of time and frequency, and a frequency mode memory pool (FPMP) is introduced for realizing cross-scene feature reuse. The advantage of a Mama model in capturing long-term time dependence is utilized, and an attention mechanism similar to Mama is designed to simulate channel dependence in a multivariable time sequence. A learnable supplementary sequence and a multi-task prompt mark are introduced to support dynamic multi-task adaptation, and the contradiction between urban data isomerism and task diversity is relieved. Shared knowledge and specific task knowledge are balanced through shared experts and specific task experts of the multi-task attention module, the negative migration problem in multi-task learning is relieved, and the generalization ability of the model is enhanced.

The invention discloses a farmland field segmentation method and system based on multispectral SAM model and multitask learning guidance, and belongs to the field of field segmentation. The problem that in the prior art, channel number matching is conducted in a simple channel compression or deep convolution mode, and the challenges that the boundaries of cultivated land parcels are complex, diversified and subtle are difficult to effectively deal with is solved. The method comprises the following steps: performing spectrum compression on a multispectral remote sensing image through a U-Net structure fused with a CBAM attention mechanism to generate a three-channel pseudo visible light image; the image is input into an SAM-ViT encoder subjected to LoRA fine tuning, and global semantic features are extracted; a task token sequence is constructed, explicit interaction between the token and the image features is realized through a bidirectional Transform module, and high-resolution fusion features are generated; calculating cross-task attention, and outputting a multi-task mask through inner product operation; and performing post-processing by using a watershed algorithm to generate a field boundary. The method is used in the field of cultivated land identification.

The invention discloses a streaming detection method for harmful output of a large language model, and the method comprises the steps: carrying out lexical element level labeling on the collected harmful output of the large language model, and obtaining lexical element level labeling data; based on the lexical-level annotation data, a multi-task learning framework is adopted to train a streaming detection model, and the streaming detection model comprises a feature extractor, a global scoring device and a lexical scoring device; and carrying out stream detection on the stream detection model along with the lexical element output stream of the large language model, and detecting and judging the output harmfulness in the output process of the large language model according to a threshold value set by a user so as to realize stream detection based on incomplete output. According to the method, the harmful output stream is stopped in advance while the detection performance is ensured.

The invention discloses a game level generation recommendation method, system, device and medium, and the method specifically comprises the steps: carrying out the combined modeling of multi-modal data through a multi-task learning network, and generating a dynamic user portrait vector, the main task of the multi-task learning network is used for predicting the skill level of a player, and the auxiliary task of the multi-task learning network is used for predicting the skill level of the player; auxiliary tasks of the multi-task learning network are used for classifying interest labels and analyzing emotional tendencies; inputting the dynamic user portrait vector into a dual-channel generative adversarial network, generating a candidate level set, and screening initial recommended levels through a playability evaluation algorithm; and calculating the matching degree between the skill score of the user and the level challenge degree in real time according to a difficulty-skill dynamic balance model, and dynamically adjusting the level parameters of the initial recommendation level through the matching degree to obtain a first recommendation level. According to the method, key problems in traditional game level design and recommendation are effectively solved, and efficient, personalized and real-time game level generation and recommendation with optimized user experience are realized.

A method includes obtaining image frames capturing one or more people in at least one scene and identifying features of the image frames. The method also includes providing the identified features to a trained spatiotemporal transformer machine learning model configured to generate a set of features for each of the one or more people. The set of features for each person includes facial features of the person and pose features of the person over time. The method further includes performing face identification using the facial features to generate one or more first embeddings representing at least one face of at least one person and performing gait identification using the pose features to generate one or more second embeddings representing at least one gait of at least one person. In addition, the method includes identifying at least one of the one or more people based on the first and second embeddings.

The invention relates to the field of artificial intelligence and big data processing, and discloses a professional field agent construction method and system based on a multi-modal hybrid expert model and a knowledge graph. The method comprises the following steps: collecting multi-source heterogeneous data such as user behaviors, texts, images, structured data and voices, and vectorizing and coding the multi-source heterogeneous data; performing feature extraction on different modal data and fusing to form unified representation; constructing a hybrid expert network based on a gating mechanism to perform task specialization processing on the fusion representation; introducing a domain knowledge graph and performing semantic enhancement through a graph neural network; on the basis of fusion representation, tasks such as recommendation, intention recognition and question and answer are executed at the same time through a multi-task learning framework; and an output result is fed back through the multi-modal interaction interface, and user feedback is collected to update the model. According to the method, the semantic understanding, task prediction and personalized recommendation capabilities of the model in a complex scene can be improved.

The invention relates to a cross-domain voice classification method and device based on feature decoupling and multi-task learning. The method comprises the following steps: firstly, acquiring a multi-data-domain voice file, and preprocessing the multi-data-domain voice file to obtain a cross-domain voice classification data set; then, constructing a cross-domain voice classification model which comprises a voice feature encoder module, a data domain classification module, a supervised comparative learning module and a multi-task classification module; then, a joint optimization loss function is constructed based on data field classification loss, supervised contrast learning loss and task classification loss, and a gradient descent algorithm is adopted to train and optimize the cross-domain voice classification model based on the cross-domain voice classification data set and the joint optimization loss function; and finally, inputting to-be-classified voice into the trained cross-domain voice classification model to obtain a voice corresponding category. The discrimination ability and generalization ability of the model in a cross-domain scene are significantly improved, so that the model still maintains high classification precision in a complex multi-source voice environment.

The invention discloses a sewage treatment plant effluent prediction method based on multi-task learning. The method comprises the following steps: acquiring sewage treatment data; based on the sewage treatment data, establishing an effluent prediction model; the input of the effluent prediction model is inflow water quality data, process data, environmental data and sewage treatment unit data, and the output of the effluent prediction model is predicted effluent index data; predicting the water outlet index data in future time based on the water outlet prediction model and the input of the water outlet prediction model; according to the method, the water outlet prediction model is constructed in combination with multi-task learning, and the model can comprehensively consider the time sequence dependence and mutual influence relationship among the input data to perform prediction, so that the calculation redundancy is reduced, and the prediction demand that an actual process needs to cooperatively consider multiple targets is met; the effluent quality prediction precision and the process regulation and control efficiency are remarkably improved, and a solid foundation is laid for promoting intelligence of operation management of a sewage treatment plant.

The invention relates to a medical training analog simulation system based on big data analysis, in particular to the field of medical training analog simulation, the medical training analog simulation system provides an efficient, accurate and dynamically adaptive training environment through cooperative work of a plurality of modules; the data processing module ensures high quality of input data, the construction module improves the generalization ability of the model through transfer learning and multi-task learning, the dynamic optimization module improves the model prediction precision through self-supervised learning and learning rate adjustment, and the real-time feedback module monitors student operation and provides personalized tutoring; the whole system combines high-quality data, intelligent training and real-time feedback, provides accurate and personalized support for medical education and practical training, and improves operation skills and coping ability of students.

The invention belongs to the technical field of multi-dimensional data processing, and particularly relates to a policy recommendation-oriented multi-dimensional graph data recall policy system and method.The multi-dimensional graph data is cleaned and structured through data preprocessing, and the multi-dimensional graph data comprises enterprise portrait data, policy data and other related data; feature extraction: encoding the policy text by adopting a deep semantic analysis model, and extracting a feature vector containing context semantics; dynamic feature interaction: adjusting the importance of the user and policy features through a dynamic weight mechanism, and optimizing the feature interaction effect; multi-dimensional task collaboration: based on a multi-task learning framework, processing a plurality of policy objectives at the same time, and generating a comprehensive matching result; a recall strategy is generated, a multi-dimensional graph data recall strategy suitable for policy recommendation is generated in combination with the processing result, and real-time synchronization between a recommendation system and enterprise requirements and policy environment changes is ensured.

The invention discloses a thermal power generating unit control parameter adjusting method and device based on a large model and digital twinning. The method comprises the steps that variable selection and data preprocessing oriented to multi-task learning are carried out; based on results of variable selection and data preprocessing, establishing a data-driven thermal power generating unit control parameter optimization adjustment large model; and carrying out real-time feedback and self-adaptive adjustment of a fusion digital twinborn technology based on a data-driven thermal power generating unit control parameter optimization adjustment large model. The device comprises a data preprocessing unit, a model establishing unit and a feedback adjusting unit which are connected in sequence. According to the invention, the continuous sensing, analysis and evaluation of the operation state of the unit and the intelligent sensing and self-optimization of the control parameters are realized, and a technical basis is provided for a few-person-on-duty power plant or an unattended-on-duty power plant in the future.

The invention discloses a satellite orbit forecasting method based on deep learning physical constraint loss, and the method comprises the following steps: 1, carrying out the normalization preprocessing of input data, forming a training data set and a test data set, and constructing batch processing training data; and 2, performing dimension expansion on sample data points in each window in the batch processing data formed in the step 1, constructing a multi-dimensional feature space of the sample points, and forming a batch processing input data format capable of being introduced into the model. And 3, performing forward reasoning on the batch data formed in the step 2 by using a model, and obtaining a batch processing orbit prediction value output by the model at the next moment through a CNN lightweight spatial-temporal feature extraction module and a BiLSTM bidirectional time sequence neural network module. And 4, taking the track prediction value obtained in the step 3 and the truth value label in the training set obtained in the step 1 as input, calculating to obtain a loss value of a current training iteration batch through a multi-random learning loss module fusing physical constraints, and performing reverse updating of model parameters to complete model training. And step five, through the steps two to four, performing reasoning verification on the model by using the test set formed in the step one, and comparing with a truth value in the test set to obtain a model test result.

Techniques for generating recommendations include generating, based on one or more features and a short-term time window, an input feature sequence, generating, based on the input feature sequence, one or more user intent embeddings using a first machine learning model; and generating, based on the input feature sequence and the one or more user intent embeddings, one or more recommendations using a second machine learning model.

Techniques are disclosed for training a model, using multi-task learning, to transform natural language to a logical form. In one particular aspect, a method includes accessing a first set of utterances that have non-follow-up utterances and a second set of utterances that have initial utterances and associated one or more follow-up utterances and training a model for translating an utterance to a logical form. The training is a joint training process that includes calculating a first loss for a first semantic parsing task based on one or more non-follow-up utterances from the first set of utterances, calculating a second loss for a second semantic parsing task based on one or more initial utterances and associated one or more follow-up utterances from the second set of utterances, combining the first and second losses to obtain a final loss, and updating model parameters of the model based on the final loss.

The invention relates to the technical field of psychological health, and discloses a psychological health multi-modal evaluation method, system and device and computer equipment, and the method comprises the steps: obtaining multi-modal data, and carrying out the feature extraction of the multi-modal data, and obtaining multi-modal features; performing emotion-oriented cross-modal attention mechanism analysis on the multi-modal features to respectively obtain a preset-dimension emotion scale vector, an emotion semantic feature, a multi-dimensional emotion feature and an uncertainty quantitative feature; fusing the preset dimension emotion scale vector, the emotion semantic feature, the multi-dimensional emotion feature and the uncertainty quantitative feature to obtain a multi-modal fusion feature; and obtaining a mental health assessment result based on the multi-modal fusion features and a preset multi-task learning framework. Through a cross-modal attention mechanism, deep semantic fusion of five modals of vision, audio, physiology, text and behavior is realized, and intelligent mapping from original multi-modal data to accurate psychological state judgment is realized through a deep learning technology.