50 results about "Task adaptation" patented technology

The embodiment of the invention provides an intelligent learning path recommendation method and system based on a dynamic state. The method is applied to the technical field of intelligent learning recommendation, and comprises the following steps: calculating a skill adaptation weight, a load weight and an emergency degree weight in real time based on a dynamic state of a target employee, including a current skill improvement condition, a workload and a task emergency degree; combining the skill matching degree, the learning strength and the task association degree of each learning unit in the candidate learning path, and performing comprehensive evaluation by using a multi-weight scoring function; and sorting the paths according to the comprehensive score, generating a personalized recommended learning path set and sending the personalized recommended learning path set to an employee terminal, thereby realizing intelligent learning path recommendation with dynamic adaptation and accurate matching. According to the scheme, personalized learning path pushing aiming at actual post requirements and working states of the employees can be realized, correlation, urgency and acceptability of learning contents are improved, learning efficiency and task adaptability are remarkably enhanced, and the employees are helped to quickly compete with post targets.

The invention relates to a time sequence lightweight analysis method which comprises the following steps: 1) constructing a time sequence lightweight adaptive network model which comprises a basic input layer, a core function module, an optimization training module and a task adaptation layer; 2) inputting the time sequence data into a basic input layer, processing the time sequence data, and outputting a patch sequence; 3) in the core function module, performing core noise filtering and frequency domain feature enhancement on the patch sequence to obtain a time domain enhancement feature; 4) performing feature fusion on the time domain enhancement features to obtain interaction enhancement features; and 5) inputting the interaction enhancement feature into a task adaptation layer to obtain a time sequence analysis result. According to the method, accurate capture of nonlinear features and complex structures of time sequence data, fusion of short-term and long-term dependency relationships and universal adaptation of multiple types of time sequence tasks can be effectively achieved, model lightweight and calculation efficiency are considered at the same time, and balance of performance and efficiency is achieved.

The invention discloses a large language model task adaptation method and system based on multi-modal prompt learning, and relates to the technical field of multi-modal learning and prompt-driven task adaptation, and the method comprises the steps: collecting multi-modal data, carrying out the unified coding, fusing the multi-modal data into shared semantic representation, and analyzing a task instruction and a context to construct a task state vector. A structured prompt is generated based on the fused representation and task semantics, and an optimizer is introduced to dynamically adjust the prompt content to adapt to feedback changes. And jointly embedding prompt and input into the large model, scheduling a fine tuning mechanism to execute reasoning, and outputting an evaluation index to be compared with a threshold value for feedback. According to the method, through multi-modal semantic fusion, task state modeling and prompt optimization regulation and control, accurate adaptation and dynamic response of a large language model in a complex task are achieved, the self-adaptability of a prompt structure, the consistency of model reasoning and the stability of task execution are improved, and the method has remarkable generalization ability and engineering practical value.

The invention discloses a heterogeneous large language model multi-agent-oriented multi-scale task collaborative orchestration method and device, and the method comprises the steps: firstly obtaining a to-be-orchestrated task processing demand and the task adaptation capability of each agent, and constructing a heterogeneous interaction graph containing agent nodes and task nodes; thirdly, clustering and grouping nodes in the graph according to the task adaptation capability of the intelligent agent and the similarity degree of task processing requirements; then, performing coarse-grained processing on the grouped graphs by using a pre-trained multi-scale collaborative task arrangement model to obtain a coarse-grained collaborative arrangement scheme; and inputting the task nodes into a task scheduling priority model to obtain priorities, and carrying out fine-grained processing on the coarse-grained scheme by using the priorities to obtain a fine-grained collaborative arrangement scheme between the intelligent agent and the corresponding task. The invention aims to solve the problem of low scheduling efficiency caused by agent isomerism, high communication overhead and task dynamic complexity in task arrangement.

The invention discloses an urban traffic cooperative scheduling method and system based on a large model and multiple agents. The method comprises the steps that natural language task description is converted into a task semantic graph and a structured prompt; generating a plurality of strategy candidates by using a large language model, and performing language scoring and reasoning arbitration; based on the scoring result, selecting an optimal strategy for multi-agent execution; behavior execution data are collected and evaluated and fed back, and strategy closed-loop updating is achieved through parameter optimization and Prompt fine tuning; and a distributed task embedding mechanism and a lightweight migration module are combined, so that the multi-task adaptability and the training efficiency of the system are improved. The cooperative training system constructed by the invention has the capabilities of natural language interaction, strategy interpretability, behavior controllability and task migration, and is suitable for various complex urban tasks such as traffic jam dispersion, emergency response, signal lamp linkage and the like.

The invention discloses a recommendation system user behavior sequence feature enhancement method based on a big language model, which comprises the following steps: acquiring a recommendation data set, performing feature structure analysis according to the use of the recommendation data set in a recommendation system and a conventional field to construct a first text, and inputting the big language model to generate a data set description; constructing a second text according to the target task and the sequence feature type, and inputting the second text and the data set description into a large language model to generate feature semantics; constructing a third text based on the feature semantics, and inputting the large language model to generate feature codes; feature codes are embedded into a prediction model training environment, and automatic generation and loading of new features in training data are achieved; and carrying out a new feature experiment in the prediction model framework, and evaluating and generating a new round of sequence features. The method has higher expressive power, context perceptibility and task adaptability in a behavior data modeling-oriented recommendation system and a personalized system, and can effectively solve the technical bottlenecks of an existing method in the aspects of structure, expression, generalization and the like.

The invention relates to the technical field of energy management, in particular to a fourth-generation residence intelligent energy management and energy saving system and method, and the system comprises a response feature recognition module, a response gradient calculation module, a task adaptation sorting module, an instruction time sequence regulation and control module and an inertia correction module. In the method, the response fluctuation equipment is dynamically identified and labeled by identifying the response time offset of the equipment in the continuous task, extracting the starting and finishing offset characteristics, finishing response inertia classification in combination with the equipment type and the operation cycle, calculating the frequency change of the offset change and identifying the equipment with an unstable response state; response scheduling priority weights are distributed, control tasks of equipment are ranked, response lag records are extracted in combination with sending of control signals and response time difference, dynamic correction of equipment control precision and ordered adjustment of execution rhythm are achieved, the phenomena of multi-equipment operation conflict and response out-of-control are effectively avoided, and the service life of the equipment is prolonged. And the coordination of equipment regulation and control and the flexibility of system energy efficiency scheduling are improved.

The present application provides a task adaptation and federated learning method and system for edge side visual analysis, for task adaptation, in the scene of federated learning, by preventing negative transfer to ensure the high precision of the model in visual analysis, so that the model can be trained through local samples, and can also interact with other edge devices to learn the relevant task information of other edge devices, thereby improving the precision of the model in visual analysis of the edge device; at the same time, the overhead in federated learning is reduced, the communication overhead is large when the edge device interacts, the resources of the edge device are limited, by using local task knowledge accumulation, the communication overhead and calculation overhead of the whole training process in response to task adaptation are reduced under the condition of ensuring high learning precision, and the communication size of the edge device will not increase with the increase of the task.

The invention provides a small sample training method and device for an OCR model, electronic equipment and a medium, and the method comprises the steps: carrying out the data enhancement of a small sample training data set, generating a plurality of meta-tasks, and enabling each meta-task to comprise a plurality of image samples and corresponding task domain identifiers and text recognition result labels; training an initial OCR (Optical Character Recognition) model by taking the plurality of image samples of each meta-task and the corresponding task domain identifiers as training samples and taking the text recognition result labels as sample labels; the initial OCR model comprises a task adaptation layer, an initial text detection layer, an initial text recognition layer and an initial correction layer; the task adaptation layer takes the task domain identifier as input to generate task adaptation parameters, and the task adaptation parameters are used for adaptively updating the parameters of the initial text detection layer, the initial text recognition layer and the initial correction layer. According to the invention, model training can be carried out based on small samples, the training efficiency is high, the generalization ability of the model is improved, and the training cost is reduced.

The embodiment of the application provides a kind of based on large language model humanoid robot industrial task scene generation method and system, belong to industrial automation and artificial intelligence technical field.The method comprises: obtaining industrial task database;According to the task parameter extraction and transformation of industrial task database, obtain first task prompt information;Thought chain task decomposition is carried out to industrial task, and subtask sequence information is obtained;Large language model is trained, and initial task execution strategy is obtained;The initial task execution strategy is evaluated in multidimension, and evaluation result is obtained;According to evaluation result, first task prompt information is optimized, and second task prompt information is obtained;According to second task prompt information, large language model is adjusted, and target task execution strategy is obtained.The embodiment of the application can improve the task adaptability and strategy reliability of humanoid robot in complex industrial scene, significantly improve the flexibility and efficiency of industrial automation production.

This invention relates to the field of model adaptation technology, specifically to a multi-task adaptation method based on a hybrid sparse expert network. The method includes: establishing a pre-trained model on a large-scale dataset; establishing a downstream multi-task dataset; constructing a hybrid sparse expert network, including: building a backbone network based on the pre-trained model on the large-scale dataset, and adding an adaptation network and a multi-task head to the backbone network; training the parameters of the adaptation network and the multi-task head in the hybrid sparse expert network based on the downstream multi-task dataset; and applying the trained hybrid sparse expert network to various downstream tasks. This invention can be used for various visual models and different downstream tasks, achieving improved multi-task adaptation performance under low computational cost conditions.

This invention discloses a robot autonomous configuration service system built around a large-model agent framework, including a robot autonomous task system, a large-model agent framework, an application system, and an evaluation system. The large-model agent framework receives user instructions, parses task requirements, and generates task execution strategies. The robot autonomous task system drives the robot to complete physical actions based on the task execution strategies and introduces an agent extension mechanism to adapt the large-model agent framework to different types of robots and task environments. The application system is used for data transmission, storage, and visual interactive operation. The evaluation system collects task execution data in real time and outputs quantitative evaluation results to optimize the execution strategy. This system effectively reduces the threshold of human-machine interaction and the difficulty of understanding two-way intentions, significantly improving the robot's task adaptation flexibility and execution reliability, and is suitable for various robot autonomous operation needs in multiple scenarios such as inspection, delivery, and search.

The application provides a full-process dynamic task planning AI agent scheduling method and system for SEO, relates to the technical field of agent scheduling, and preconfigures rule configuration information after building an agent core architecture, connects the preconfigured rule configuration information to a plurality of LLM models and a full-network information collection interface, and obtains preparation architecture building information; the preparation architecture building information is used for creative task analysis through a retrieval hub, creative task analysis data is obtained, the creative task analysis data is analyzed for task allocation through the retrieval hub, task allocation analysis data is obtained; the keywords, outline and content are sequentially generated and verified and analyzed through the multi-agent architecture building information, and agent scheduling data is obtained, the application effectively solves the contradiction between the plurality of LLM models and task adaptation and the problem of scheduling hub computing power overload under high concurrency, breaks the vicious circle of the two, does not require manual intervention, and greatly improves the full-process automation level of SEO content creation.

The invention belongs to the field of machine learning, and particularly relates to a method and system for supplementing task adaptation of a large model and a readable storage medium. The method comprises the following steps: inputting task data to be processed into a specific layer; the specific layer comprises at least two selected specific small models respectively fitting different distributions; the specific small model is a small model trained by using a training set corresponding to the task to be adapted; judging whether the confidence coefficient of the input task data processed by the corresponding specific small model is greater than or equal to a first set confidence coefficient threshold value or not according to the sequence of the performance of the specific small model in the specific layer from high to low, if so, adopting a processing result of the specific small model, and if not, continuing to judge; and if the confidence coefficient processed by no specific small model in the specific layer is greater than or equal to a first set confidence coefficient threshold value, processing the input data by a large model or an enhancement layer. Tasks difficult to adapt to the large model are supplemented through the small model, and parameters of the large model do not need to be modified.

The invention discloses a space-time big data processing method based on a pre-training-fine tuning architecture, and solves the problems of complex relation and high calculation cost when a traditional method processes space-time multi-attribute data by combining a pre-trained Transform model and a prompt fine tuning mechanism. Specifically, the model framework comprises a time encoder, a space encoder and a head network, and common features among a plurality of time-space attributes are efficiently captured in a parameter sharing mode. In the pre-training stage, the model learns a general spatio-temporal pattern in a large-scale spatio-temporal data set, and then fine tuning is performed on specific spatio-temporal attributes by using a trainable spatio-temporal prompt token through a prompt fine tuning stage, so that efficient task adaptation and low calculation cost are realized. The method not only can improve the space-time prediction precision, but also has good mobility, adapts to unseen space-time attributes, and has wide application prospects.

The invention discloses a cooperative tray scheduling control method based on multiple AGVs (Automatic Guided Vehicles). According to the method, intelligent task release is realized by establishing a digital identifier for a tray and calculating a task priority; each AGV evaluates the ability of itself and obtains a task through distributed bidding; when the single AGV is insufficient in capability, dynamic alliance collaborative operation is formed; the alliance plans a path through a space-time diagram and eliminates conflicts; during execution, a hierarchical control architecture is adopted to keep synchronization; after the task is completed, performance is evaluated, and the alliance is disintegrated; and the system continuously monitors the health state of the AGV and realizes predictive maintenance. Through three innovations of tray intelligence, dynamic alliance and predictive maintenance, the limitation of traditional centralized scheduling is broken through, the system robustness, task adaptability and operation reliability are improved, and an efficient technical scheme is provided for intelligent logistics.

The invention discloses a robot strategy learning method and a related device, and the method comprises the steps: constructing a training data set based on a sharing control framework of teleoperation; the training data set is utilized, a course training method for gradually weakening visual input disturbance is adopted, the robot strategy is trained and learned, the trained and learned robot strategy is obtained, the method and the related device can improve the data collection efficiency and naturalness, and meanwhile the generalization ability and the task adaptability of the robot strategy are improved.

This invention discloses a method, apparatus, device, and storage medium for dynamic allocation of warehousing tasks. The method includes: receiving a warehousing task request; determining a target task to be processed based on the warehousing task request; determining multiple candidate execution devices currently available for executing the target task based on preset resource status data; determining a task adaptation value corresponding to each candidate execution device based on the task attributes of the target task and the device attributes of the candidate execution devices; selecting a candidate execution device that meets preset conditions as the target execution device based on the task adaptation value, and issuing the target task to the target execution device. Because this invention determines the task adaptation value corresponding to each candidate execution device based on task attributes and device attributes, and then selects the target execution device based on the task adaptation value, compared with the prior art, this invention improves the scientific nature of warehousing task allocation, thereby improving the overall warehousing operation efficiency and system resource utilization.

The invention belongs to the field of artificial intelligence, and discloses a multi-modal large language model task adaptation fine tuning method and a related device, and the method comprises the steps: introducing a parameter-trainable adapter layer into a preset weight layer of a multi-modal large language model; obtaining a lexical element sequence of the training sample based on a preset weight layer of the multi-modal large language model, calling a preset lexical element classification model to obtain each lexical element type in the lexical element sequence, and processing each lexical element in the lexical element sequence according to the lexical element type to obtain the output of the training sample, parameters of the adapter layer and parameters of the lexical element classification model are updated according to output of the training sample; and finally, obtaining a multi-modal large language model for task adaptation fine tuning. According to the method, fine-grained and lexical-level task adaptation is achieved in the fine tuning process, the problem of disastrous forgetting in the fine tuning process is remarkably relieved, and the performance of a multi-mode large language model on an original pre-training task is effectively maintained while the adaptability of the multi-mode large language model to a new task is improved.

The invention provides a digitized marketing management metering method and system for a micro-grid, and the method comprises the steps: constructing an equipment behavior state graph which comprises an equipment unique number, an initial state, a life cycle event set which is arranged according to a time sequence, and a state attribute set, the event set comprises goods arrival, verification, assembly and disassembly, allocation and return events; constructing an error sequence, calculating a health score of the equipment based on an error change trend, a time fluctuation penalty and a local mutation sensitive item, and generating an error trend label according to the health score; calculating an allocation adaptability score of the equipment based on the health score, the error trend tag and the current state of the equipment, and generating a judgment result of whether the equipment is allowed to be allocated or reused in combination with a fluctuation risk regular item and a state consistency entropy item; and screening available equipment according to the judgment result, and calculating a task adaptation score in combination with task context features.

The invention provides a multi-agent cooperation interaction method and system, and the method comprises the steps: firstly, enabling a decision agent to obtain an execution environment and a subtask set corresponding to a to-be-executed instruction in response to the received to-be-executed instruction; the decision-making agent generates a target dynamic interaction graph corresponding to a to-be-executed instruction based on a preset interaction graph and the sub-task set, and decomposes the target dynamic interaction graph into interaction sub-tasks which execute at least one interaction operation with at least one element feature in an execution environment in sequence; and finally, sequentially executing the page interaction agent according to the sequence of the interaction subtasks so as to complete the execution of the to-be-executed instruction. According to the method and the system, compared with a traditional browser automatic script which only depends on a preset rule, complex tasks of multi-step decision making can be processed, the intelligent level of man-machine interaction is remarkably improved, the task application range is remarkably widened, and meanwhile the response speed of task processing is also improved.

The invention relates to a multi-mode perception-assisted communication decision joint learning method and device and a medium, and the method comprises the steps: generating a controllable mask mark according to a to-be-executed communication decision task, and carrying out the mask processing of a designated mode and a designated part; respectively inputting each piece of modal sensing data subjected to mask processing into a corresponding modal encoder for feature extraction and mapping to obtain a multi-modal feature representation with a unified embedding dimension; inputting the multi-modal feature representation and a corresponding learnable potential representation vector into a fusion module, aggregating multi-modal information to a low-dimensional shared potential space through cross attention calculation, and generating a unified potential representation; task query vectors of different communication decision tasks are utilized, task related information is extracted through interaction with the unified potential representation, and after the task related information is processed by the corresponding task adaptation modules, results of the communication decision tasks are output. Compared with the prior art, the method has the advantages of high flexibility, high efficiency, high robustness and the like.

The invention discloses a self-adaptive adjusting method and system for an intelligent conveying tray, and relates to the technical field related to intelligent conveying systems.The method comprises the steps that a task recognition model is integrated in the intelligent conveying tray, and task issuing equipment carries out task broadcasting and carries out self-correlation recognition; reading the current task state of the intelligent conveying tray, carrying out conflict identification, and establishing a conflict factor; carrying out broadcast task adaptation optimization by utilizing a topological evolution encoder; scheduling adaptation optimization of the intelligent conveying tray is carried out, and a scheduling adaptation optimization result is established; and configuring a tray task of the intelligent conveying tray, and executing a topological combination of the intelligent conveying tray so as to execute a broadcast task. The technical problems of insufficient task conflict dynamic detection capability, poor scheduling efficiency and flexibility, low resource utilization rate and limited multi-agent cooperation efficiency in the prior art are solved, and the technical effects of improving the intelligent conveying response speed, the task execution efficiency, the resource utilization rate and the task execution reliability are achieved.

The invention discloses an AI agent task rapid adaptation method based on meta learning, which comprises the following steps: acquiring task basic data, and encoding to generate an initial representation vector; folding the task space through a reversible mapping mechanism to generate task manifold coordinates; local disturbance calculation is executed to obtain curvature information, and parameters are updated in the ridge line direction; executing a task by using the adapted parameters, collecting performance and generating a task factor vector; generating reconstructed manifold coordinates through factor mapping, and constructing a consistency constraint and optimization target; and updating the mapping mechanism according to the optimization target, and updating the factor module and the initial parameter. According to the method, through a reversible mapping mechanism, a ridge direction rapid adaptation mechanism and factor-manifold closed loop consistency optimization, a rapid, stable and high-generalization-ability task adaptation process of the AI intelligent agent in a brand new task is realized.

The application discloses a task adaptation result confirmation method and system based on large language model fine tuning, the method comprises the following steps: obtaining model parameters of a large language model, updating the model parameters by using an optimization algorithm, and then updating the learning rate of the large language model by using the updated model parameters; iteratively training the large language model by using the updated learning rate and model parameters until the final model parameters are output, updating the large language model according to the final model parameters, executing a plurality of target specific tasks to obtain corresponding target results, and determining the task adaptation result according to all target results. By introducing an adaptive learning rate adjustment mechanism, the application uses feedback information of the model in the training process to dynamically adjust the learning rate, enhances the model adaptability, enables the large language model to better adapt to specific tasks, and improves the performance of the model in actual application.

The invention discloses a task model switching method and device, electronic equipment and a storage medium. The method comprises the steps of judging whether a target task model is in a preset abnormal state or not when executing a target task; when the target task model is in a preset abnormal state, determining a reference task model based on the similarity configuration information; the similarity configuration information records the similarity between every two task models in the plurality of task models, the plurality of task models at least comprise a target task model, and the similarity between the task models is determined based on the similarity between target information of the task models; the target information comprises an input feature, an output feature and a task adaptation degree; and switching the target task model into a reference task model, and continuing to execute the target task by adopting the reference task model. According to the scheme, the reference task model is determined based on the similarity configuration information, and the target task model is switched into the reference task model, so that the task model switching accuracy can be effectively improved.

This invention relates to an efficient fine-tuning method for translation parameters of large model based on hierarchical activation difference perception, belonging to the field of natural language processing and large model fine-tuning technology. To address the problems of pre-training bias dominance and hierarchical functional imbalance in existing large language models (LLMs) for low-resource machine translation tasks, this invention includes: firstly, accurately locating model modules sensitive to the machine translation task using cumulative activation intensity and cross-layer activation variance; then, dividing the model hierarchy into task-adaptive layers and legacy inert layers based on activation difference indices; introducing learnable latent gating parameters to generate hierarchical-specific modulation factors; and during efficient parameter fine-tuning, using the modulation factors to amplify the signal in the task-adaptive layer while suppressing the legacy inert layers. This invention effectively improves translation performance from Chinese to low-resource languages ​​such as Lao and Khmer by structurally differentiating the optimization direction.

This invention, entitled "An Adaptive Method for Infrared Image Tasks Based on Cue Learning and Adapters," belongs to the fields of transfer learning and edge computing. The technical problem it addresses is the high cost, strong data dependence, and susceptibility to catastrophic forgetting associated with full-parameter fine-tuning of pre-trained infrared base models during downstream task adaptation, coupled with the heavy burden of edge deployment. Existing lightweight fine-tuning techniques suffer from limited performance or insufficient flexibility. The key technical solution involves: freezing the backbone parameters of the pre-trained model; adding learnable visual cue vectors before the input embedding layer; inserting an adapter module and configuring a lightweight task header in the Transformer layer; fine-tuning newly added parameters using only a small number of labeled samples; and deploying the model to edge devices after pruning and quantization.

The application provides a cockpit end multi-vehicle task adaptation method, equipment and vehicle. The cockpit end multi-vehicle task adaptation method is applied to a large model provided with a public processing layer, at least two task input adaptation layers and at least two task output adaptation layers. The method comprises task format adaptation of a corresponding first vehicle task through each task input adaptation layer; task data calculation of each task format data through the public processing layer; format conversion of intermediate feature data through the task output adaptation layer corresponding to the task input adaptation layer, and adaptation of the converted intermediate feature data and vehicle functions. In the application, the public processing layer is reused to calculate the task data of each task format data, which can effectively reduce the number of large models required in the multi-vehicle task execution process, reduce the computing power required by the vehicle chip, and avoid simultaneous execution of multiple vehicle task analysis and vehicle function adaptation.