204 results about "Knowledge learning" patented technology

The invention discloses an intelligent question-answering system training method based on machine learning, and the method comprises the steps: optimizing a knowledge learning sample to obtain optimized knowledge, determining a knowledge weight, constructing an industry knowledge base, fusing multi-modal question content to obtain fused question content, and carrying out the training of the intelligent question-answering system. Obtaining industry description information and a user portrait according to the fused question content and the user information, and inputting the fused question content, the industry description information and the user portrait into a question and answer reinforcement learning model to obtain answer content, and obtaining a first reward signal, a second reward signal and a dynamic reward weight according to the answer content and the user question response information, adjusting the question and answer reinforcement learning model, inputting the multi-modal question content into the adjusted question and answer reinforcement learning model to obtain optimized answer content, and updating the industry knowledge base by adopting a user interaction log and the knowledge weight. According to the method, intelligent question and answer system training can be accurately and efficiently carried out, and meanwhile powerful support is provided for intelligent question and answer services of all industries.

The embodiment of the invention provides a self-adaptive detection and detection system for low-altitude flight inspection, and aims to improve the efficiency and safety of unmanned aerial vehicle inspection operation. Firstly, a plurality of unmanned aerial vehicle description information sets, unmanned aerial vehicle cluster description information and flight inspection task description information are acquired; the information covers performance parameters and task requirements of the unmanned aerial vehicles in different inspection scenes, then the information is loaded into a target adaptive inspection decision model for knowledge learning in advance, and target estimation confidence is generated by analyzing the information so as to evaluate the success probability of the unmanned aerial vehicle cluster in executing a specific inspection task. And when the target estimation confidence meets the set requirement, the corresponding unmanned aerial vehicle cluster is intelligently scheduled to execute the inspection task, so that efficient completion of the task is ensured, and the adaptability and flexibility of the unmanned aerial vehicle inspection operation are effectively improved through intelligent evaluation and dynamic scheduling.

The invention provides a distillation method and device based on AI large model knowledge and electronic equipment, and belongs to the technical field of artificial intelligence, in the method, a dynamic adjustment mechanism is adopted for the temperature, and along with the increase of a training period, the preset initial temperature corresponding to the training period is gradually reduced; besides, in each training period, the preset initial temperature is finely adjusted based on the distillation loss, so that the target temperature corresponding to each training sample is obtained, namely the temperature most suitable for the current distillation effect. The target distillation loss and the student loss obtained by recalculating based on the target temperature are more scientific, so that the total loss for updating the weight parameters of the student model is more accurate and robust, the student model is prompted to learn the knowledge of the teacher model more effectively, and a scientific and efficient learning process is learned. The finally obtained student model has good generalization ability, and the robustness of the model is significantly enhanced, so that the overall effect of knowledge distillation is significantly improved.

The invention provides a robot social adaptive navigation knowledge learning and migration method and system, and relates to the field of mobile robot navigation. Aiming at the problems that an existing path planner lacks time sequence memory and neglects pedestrian social intent, a man-machine co-fusion scene is constructed, a training set containing an expert demonstration path is made, and a recursive generation model is input; designing a recurrent neural network embedded RRT, generating an RNN-RRT planner, and fusing historical information and pedestrian convergence probability in training; new scene loading training parameters are finely adjusted to realize knowledge migration, loss convergence or output RNN final parameters after reaching a preset round number. According to the method, the path anthropomorphism and generalization ability are improved, and the method is suitable for complex human-computer interaction scenes.

The invention discloses an intelligent diagnosis method for large model knowledge annotation, and the method comprises the steps: carrying out the knowledge injection of a large model, and obtaining the formatted semantic knowledge of each fault type; constructing a self-supervised pre-training network by using all fault data to train an encoder to extract class-independent features; constructing and training a health feature comparison and semantic knowledge prediction network; using the trained encoder and the health feature comparison and semantic knowledge prediction network to obtain semantic knowledge of a fault sample without category, adjusting the predicted semantic knowledge based on a double-view dynamic semantic knowledge learning strategy, dividing test data into a high-probability sample and a low-probability sample, the semantic knowledge of the low-probability samples is adjusted by using the spatial distribution of the high-probability samples, and the fault is diagnosed cooperatively. According to the method, semantic knowledge labeling can be carried out by using professional documents under the condition that fault data is limited, spatial distribution of semantic knowledge is adjusted, and the accuracy and credibility of fault diagnosis are improved.

The invention discloses a software process arrangement system and method based on a large model, and particularly relates to the technical field of software process arrangement. The intelligent process design module is used for realizing intelligent and low-threshold process design and optimization through natural language understanding and a visualization technology; the Agent collaborative execution module is used for dynamically distributing tasks, monitoring states and coordinating a plurality of Agents to efficiently complete process execution; and the knowledge learning enhancement module is used for constructing a domain knowledge system and providing intelligent support for process decision and optimization. According to the method, the demand described by a natural language of a user is converted into a structured process definition through the intelligent process design module, rapid design is carried out by utilizing a process pattern library and a visual editing tool, and meanwhile, compliance check and optimization suggestion are carried out, so that compliance and high efficiency of process design are ensured, and the process design efficiency is improved. And the Agent collaborative execution module dynamically allocates tasks to appropriate Agents according to process requirements, monitors the execution state in real time, and performs coordinated processing in case of abnormality to ensure efficient execution of the process.

The invention provides an enterprise employee safety knowledge learning method based on AI and an incentive mechanism. The method comprises the following steps: firstly, developing a safety knowledge learning platform by using uniapp and Node.js, and designing a recommendation system through an AI technology training model and a collaborative filtering algorithm to form an intelligent learning platform; the employees input work types after logging in, an AI model generates a safety knowledge learning graph, personalized learning is carried out through a mobile terminal, and the platform recommends related contents according to learning history and interests. Introducing an incentive mechanism, setting incentive measures such as learning milestone rewards and point ranking, and implementing positive and negative incentive according to the learning condition of the employees; the platform formulates a personalized learning plan and notifies through intelligent learning reminding in combination with learning habits of employees and social media use behaviors, and an enterprise establishes a perfect assessment mechanism and supervises the learning progress; the platform supports safe knowledge sharing and communication, a feedback mechanism is formed by using intelligent analysis, and learning results and suggestions are pushed regularly, so that a good continuous learning atmosphere is created.

The invention discloses an enterprise innovation risk assessment system based on virtual-real fusion common knowledge learning, and the system comprises a data collection and preprocessing module which is used for collecting and preprocessing a real operation standardized data set; the simulation environment construction and disturbance generation module is used for constructing a virtual simulation environment and generating a virtual data set; the semantic alignment and fusion module is used for performing semantic alignment and weighted fusion to generate a virtual-real fusion data set; the risk relation graph construction module is used for constructing an enterprise innovation risk relation graph; the multi-order structure embedding module is used for executing multi-order graph structure embedding; the common knowledge migration module is used for extracting a common feature structure and executing migration adaptation; and the risk assessment module is used for carrying out risk assessment. According to the method, enterprise atlas and multi-semantic structure embedding are fused, cross-enterprise risk migration assessment is achieved, and the method has the advantages of being high in expression ability, high in adaptability and accurate in assessment.

The invention discloses a lightweight power transmission line insulator defect detection algorithm based on SGA and SE-F. Firstly, a brand new attention mechanism SGA is introduced on the basis of a YOLOv5 network, and the saliency of a defect target to be detected is enhanced, so that the problem that small insulator defect features are difficult to extract is solved; secondly, a same-scale weighted fusion module SE-f is provided and used for replacing a Concat module in the feature fusion network, and the fusion capability of the network on features of low-level position information and high-level semantic information is improved; thirdly, carrying out sparse training and channel pruning on the improved YOLOv5, and further optimizing a model structure; the knowledge distillation guide model is used for knowledge learning, and the detection precision is improved. The beneficial effects of the invention are that the method firstly introduces an attention mechanism, constructs a novel feature fusion mode to improve an initial model, then achieves the light weight of the model in combination with a channel pruning algorithm, and finally constructs a knowledge distillation model to improve the detection precision of the pruned model.

The invention discloses a continuous learning method and system for a power transformation equipment fault diagnosis model, belongs to the technical field of image processing, and solves the problem of how to enhance the recognition capability of a visual language pre-training model for new and old knowledge. According to the method, fine-grained image semantic knowledge extraction is realized in a weak supervision mode, and the corresponding relation of a plurality of images with similar local features can be mined by calculating global semantic score alignment loss and local semantic feature comparison loss, so that the model can fully understand a new semantic concept and decouple knowledge, and the semantic knowledge extraction efficiency is improved. According to the method, new knowledge learning is carried out more accurately, general knowledge and special knowledge in new data are decoupled by calculating the matching degree of fine-grained text features and fine-grained semantic features, new semantic concepts are decoupled, the relation between the new semantic concepts and existing knowledge is established, and the knowledge learning efficiency is improved on the premise that the general knowledge is kept. And special knowledge of a new semantic category is better learned, good compatibility of new knowledge and old knowledge is realized, and the model has better forward compatibility capability.

The invention provides an AI-based three-dimensional medical image intelligent labeling method and system. The accuracy and efficiency of medical image labeling are remarkably improved. By combining an image classifier of preliminary knowledge learning with a basic lesion labeling network, knowledge information from different sources can be synthesized, and a more comprehensive and reliable lesion semantic knowledge point sequence is generated. Furthermore, through three types of training image generation strategies, complementarity among different networks is effectively utilized, the recognition capability of common lesion areas is enhanced, and lesion areas which are respectively and uniquely found are explored, so that diversity and representativeness of training data are enriched. Finally, deep knowledge learning is carried out on the basic lesion labeling network based on the diversified training data, the recognition precision and generalization ability of the generated target lesion labeling network are remarkably improved, the workload of manual labeling of doctors is relieved, and the medical image analysis process is accelerated.

The invention relates to the technical field of cross-border finance, and discloses a cross-border finance compliance examination intelligent engine based on a large model, which comprises a data acquisition layer, a data preprocessing layer, a large model core processing layer, a compliance examination decision-making layer and a user interaction layer. According to the cross-border financial compliance inspection intelligent engine based on the large model, the strong natural language processing ability and knowledge learning ability of the large model are utilized, unstructured text information can be effectively processed, fast learning is achieved, the engine adapts to constantly changing supervision policies, the large model further has the autonomous reasoning and analysis ability, and the large model can be widely used. Complex cross-border financial businesses can be comprehensively judged, and the defects of the traditional technology in the aspects of examination efficiency, accuracy and adaptability are overcome.

The invention discloses a knowledge learning management method and device based on data analysis, and the method comprises the steps: obtaining a scene behavior data set, the scene behavior data set comprises the behavior data of each pharmacy employee in different business scenes, and the business scenes at least comprise a drug recommendation scene, a drug taboo reminding scene and a special medication consultation scene; constructing a behavior model according to the scenarized behavior data set, and extracting behavior feature information of the drugstore employees in different business scenes through the behavior model; determining a knowledge point set and a knowledge blind area which need to be mastered by the drugstore employee in each business scene based on the behavior feature information; constructing a knowledge graph according to the scenarized behavior data set, the knowledge blind area, the knowledge point set and the association relationship between the knowledge point set and the business scene; and based on the knowledge graph, determining professional learning contents of the drugstore employees and priorities corresponding to the professional learning contents. The professional knowledge level of drugstore employees can be comprehensively improved.

The invention relates to the technical field of large-scale language models, and discloses a method for converting a natural language to an SQL (Structured Query Language) task by combining fine tuning and Prompt. According to the method, the database mode knowledge learning module is constructed, so that the database mode can be efficiently understood and memorized, the length of input tokens is remarkably reduced, and the token efficiency is improved. Meanwhile, the module utilizes a LoRA model to carry out fine adjustment on a large language model, the performance of the model on text-to-SQL tasks is optimized, and the model can generate SQL statements more accurately; besides, an example selection module is also constructed, and the most similar example is selected as a prompt by calculating the similarity between the problem and the candidate example, so that the accuracy of generating the SQL statement by the model is further improved. According to the method, the generalization ability of the model is improved, and the model can more flexibly cope with natural language queries in different fields.

The invention provides an intelligent curriculum recommendation method and device based on a knowledge graph and a storage medium, and the method comprises the steps: constructing a multi-dimensional knowledge graph which comprises a curriculum skeleton knowledge sub-graph, a user capability sub-graph and a scene demand sub-graph; generating a knowledge dependence matrix based on the course skeleton knowledge sub-graph, and generating user learning sequence features based on historical learning data of the user; acquiring a learning target, a learning time constraint and a learning scene input by a user, and determining a first knowledge learning path and task sequence set and a second knowledge learning path and task sequence set of the user; and based on the user learning sequence features, the course skeleton knowledge sub-atlas, the user capability sub-atlas and the scene demand sub-atlas, using a fusion model to perform fusion processing on the first and second knowledge learning paths and the task sequence set to obtain a course meeting the user demand. The accuracy of course recommendation is improved.

The invention discloses a voice and music collaborative generation method and system based on a dynamic mixed attention and expert architecture, terminal equipment and a medium, and relates to the technical field of audio generation. The method comprises the following steps: acquiring multi-modal input containing at least one of audio, text and vision, performing embedding processing on the multi-modal input, and mapping the multi-modal input to a unified space to obtain a fusion sequence; setting a plurality of attention heads, dynamically selecting the attention heads to activate experts through attention head routing gating, and weighting and aggregating calculation results to obtain mixed attention features; generating expert probability distribution through expert set routing gating, dynamically selecting an expert set to activate experts, and calculating task adaptation features; and performing audio generation processing based on the audio language modeling head to obtain a voice or music result. According to the method, collaborative generation of voice and music, dynamic allocation of computing resources, balance of domain-specific and cross-domain general knowledge learning are realized, the problems of task conflict and data imbalance are solved, and the audio generation quality and efficiency are improved.

The invention belongs to the technical field of electric power artificial intelligence, and discloses an electric power knowledge question-answering method based on a knowledge reasoning thinking chain embedded large model and a related device. The method comprises the steps of obtaining a power problem text; and inputting the power question text into a pre-trained knowledge reasoning thinking chain embedding large model, and outputting a text answer of the power question. According to the method, a supervised learning and preference optimization mechanism is fused, and collaborative optimization of large-model professional knowledge learning and thinking path modeling is realized by constructing an electric power knowledge question and answer and analyzing a thinking chain data set; the technical problems that an existing large model is weak in knowledge, inconsistent in thinking process and insufficient in generalization ability in power professional text questions and answers are solved.

The invention provides a mine safety virtual practical training system, which comprises a virtual environment construction module for constructing a highly realistic virtual mine scene according to a real mine operation environment by using a virtual reality technology; the man-machine interaction module is used for realizing interactive operation between the student and the virtual scene, including simulation of operation equipment and simulation of accident emergency disposal; the safety knowledge learning module is used for providing theoretical knowledge learning related to mine safety; and the practical operation module is used for carrying out practical operation in the virtual scene and simulating various operations in the actual mine production process. The comprehensive, efficient and safe mine safety training environment is provided for trainees through fine three-dimensional modeling, rich man-machine interaction modes, comprehensive safety knowledge content and highly simulated practical operation tasks.

The invention discloses a team-type dialogue generation and tutoring learning method and system based on a large language model, and the method comprises the steps: constructing a rule-based expert course knowledge base according to the course demands and a course knowledge structure; secondly, entering an observation type learning situation, generating personalized knowledge conversations between different teachers and fixed students according to the generation type teacher prompt instruction set, and generating personalized knowledge conversations between the fixed teachers and different students according to the generation type student prompt instruction set; then, entering an interactive learning situation, generating teaching statements of experts and teachers according to a generative interaction initiating prompt instruction set, and generating personalized feedback statements of the experts and teachers according to feedback of students and a generative interaction feedback prompt instruction set; and finally, according to the use experience feedback of the students, knowledge learning effect analysis is carried out, an accurate team-type learning situation is optimized and generated, and personalized tutoring of the students is realized.

The invention belongs to the field of sugar detection, and particularly relates to a litchi sugar detection method based on machine learning, and the method comprises the steps of complete information collection, priori knowledge learning, feature fusion, litchi model updating, litchi reflection spectrum scanning and recording, preprocessing, interpretability analysis and model output. According to the scheme, litchis automatically roll before passing through a detection area by adopting the rotating roller, and cameras are respectively mounted at different angles of the front and rear sections of the rotating roller and the left and right sides of the conveying belt, so that errors caused by single-angle shooting are reduced, and the sugar detection accuracy is improved; according to the scheme, a nondestructive, rapid and low-cost near-infrared spectral imaging technology is adopted, near-infrared wavelengths are screened according to the importance of the wavelength to a sugar detection result, redundant information is removed, features contributing to detection and grading are extracted, an interpretable artificial intelligence technology is adopted, and the design of a return model is optimized.

The invention discloses a multi-substation equipment defect identification method and system based on federated learning, belongs to the technical field of image processing, and solves the problem of feature offset caused by invisible client data in a substation equipment defect identification process. In the federal learning process, a visual branch and a text branch of the CLIP model do not need to be transmitted between the local end and the server end, knowledge learning and knowledge fusion of different server ends can be realized only by transmitting text cue words and parameters of an encoder and a decoder, and compared with a CLIP model with a large amount of parameters, the method has the advantages that the knowledge learning efficiency is improved, and the cost is reduced. According to the method, the transmission parameter quantity is greatly reduced, the global model of the server end with the optimal effect is finally obtained through multiple times of loop training of the local end and the server end, visual features of any category, especially unseen categories, can be generated for the local end model, and the visual features of the local end model can be obtained. The phenomenon of feature offset caused by invisible server data in the substation equipment defect identification process is reduced, and the generalization ability of the model is improved.

The invention is suitable for the technical field of education, and provides a content recommendation method and system based on children cognition, and the method comprises the steps: obtaining historical learning data, carrying out the construction of a knowledge graph based on the historical learning data, and generating a knowledge structure graph; performing vector mapping processing on the plurality of knowledge nodes and the edges connecting the plurality of knowledge nodes by using a graph embedding algorithm to generate a knowledge vector set; according to the knowledge vector set, calculating a semantic association degree among the plurality of knowledge nodes, and if the semantic association degree is greater than a preset threshold value, extracting a key knowledge node from the plurality of knowledge nodes by using a hierarchical clustering algorithm; determining a learning sequence corresponding to the key knowledge nodes according to a preset knowledge learning strategy, and generating a knowledge progressive sequence; generating a target knowledge node sequence based on the knowledge progressive sequence and the historical learning data by using a preset reinforcement learning algorithm; and the target recommendation content is generated according to the target knowledge node sequence and recommended to the user, so that more appropriate learning content can be recommended.

The invention discloses a robot manipulator grabbing and docking method and device, a medium and a product, and relates to the field of robot and sensor perception.The method comprises the steps that a visual-tactile joint data set of a manipulator in the process of grabbing a filter hose is constructed; establishing a tactile priori knowledge learning model according to the visual-tactile combined data set; outputting a current grabbing configuration according to the tactile priori knowledge learning model; motion planning information of the manipulator is obtained according to the current grabbing configuration; the manipulator is controlled to be in butt joint with the filter hose according to the motion planning information of the manipulator, mechanical modeling is conducted on the contact state of a butt joint of the filter hose and a butt joint of a machine tool, and a mechanical control model is obtained; and according to the mechanical control model, a DDPG parameter identification algorithm is adopted, and pose information and contact force information in the mechanical arm butt joint process are obtained. According to the flexible pipe grabbing and butting device, grabbing and butting of the flexible pipe of the cutting fluid filtering device can be more efficiently and stably carried out.

The invention provides a highway service area traffic flow scheduling method and system based on an intelligent algorithm, and the method effectively utilizes a to-be-learned template set comprising a plurality of template scheduling learning data through a deep knowledge learning algorithm. According to the method, in a target training stage, aiming at each template scheduling learning data, an effective labeling training value and an ineffective labeling global training value of a corresponding training traffic flow scheduling network relative to a traffic flow scheduling network to be called are dynamically evaluated, so that the pertinence and the high efficiency of a training process are ensured. And through continuous optimization learning, when the training error does not continue to decrease any more, deep knowledge learning is terminated, and finally, a target traffic flow scheduling network capable of accurately determining target traffic flow dynamic data scheduling knowledge description is obtained. According to the method, the intelligent level of traffic flow scheduling is improved, the traffic flow scheduling effect of the highway service area is optimized, the traffic jam of the service area is relieved, and the operation efficiency and the user experience of the service area are improved.

The invention discloses a deep learning model-based knowledge learning agent training method, and relates to the technical field of auditing, and the method comprises the steps: carrying out the optimal disassembly task classification through a multi-model fusion knowledge classification method, and obtaining defined auditing knowledge and undefined auditing knowledge; optimal disassembly task classification is carried out by using a multi-model fusion knowledge classification method, and undefined audit knowledge is analyzed through an adaptive improved iteration mechanism, a fusion NLP algorithm and a deep learning audit agent to generate a question answer; the data security in the use process of the audit knowledge learning agent is controlled through a dynamic authority control and question and answer tracing mechanism; when facing large-scale and multi-level auditing tasks, the system can quickly position the core of a problem, redundant operation is reduced, and the response speed is increased.

The invention discloses a knowledge learning method for intelligent machine common sense reasoning. The method comprises the following steps of 1, normalizing common sense reasoning data; 2, constructing a combination normal form; step 3, multi-dimensional feature common knowledge learning; and 4, model testing and common sense reasoning. According to the method, the problems that a traditional common sense reasoning method only depends on learned shallow semantics, deep common sense semantics contained in sentences are difficult to master and the like can be solved, and common sense knowledge is captured by utilizing a combined normal form and multi-dimensional features, so that the common sense reasoning capability of the model is enhanced.

The invention discloses a two-stage knowledge distillation method based on dynamic sampling prompt. The two-stage knowledge distillation method comprises the following steps of image encoder distillation and decoder distillation. Based on the design of two-stage knowledge distillation, compression and light weight of a large model are realized, synchronous distillation is carried out on an SAM image encoder and a decoder, efficient alignment of encoder extraction features and a mask decoder is realized, and the calculation cost is effectively reduced while the excellent generalization ability of the model is reserved. According to the method, a dynamic sampling prompt strategy is designed in decoder knowledge distillation and is used for efficiently selecting prompt points, so that the model learning process is optimized. The strategy particularly focuses on incorrect regions in the segmentation result to guide the student model to be closer to a true value. By dynamically updating the prompt point set, knowledge learning of the student model to the teacher model is gradually enhanced, and performance loss possibly caused in the distillation process is effectively reduced.

The invention discloses a big language model knowledge editing system based on mixed experts, and belongs to the field of natural language processing. According to the method, the hybrid expert architecture and the keyword attention router are combined, so that the model knowledge is dynamically updated under the condition that the original parameters of the large language model are kept unchanged; according to the single-layer bypass hybrid expert adapter provided by the invention, only single-layer additional experts are introduced into a model, and input with similar knowledge requirements is routed to the same expert through a keyword attention router, so that the experts can efficiently distinguish and process different types of knowledge information; the invention further provides a data batch processing method based on semantics, similar instances are grouped in the training stage, specialization of an expert model is promoted, and knowledge learning preference of a large language model is better met. According to the method, excellent performance is shown on models of various types and scales and in various editing tasks, and balance between generalization ability and local optimization is achieved.

The invention belongs to the technical field of intelligent education, and provides a subject knowledge learning method planning system, which comprises an input data acquisition module, a knowledge graph analysis module, a knowledge graph planning classification module, a content classification module, an association combination output module, an abstract concept learning path module, an evaluation module and a planning module. Wherein functions of the output module and the planning module are associated and combined through the content classification module. And the content classification module performs third-level classification on the data subjected to second-level classification to obtain hierarchical abstract concept data information. And the association combination output module outputs learning content and abstract result data by using a neural network model, and performs grading according to difficulty. And finally, a planning module plans learning methods of abstracted and non-abstracted contents of the current subject for the user step by step according to the answering accuracy of the evaluation module.

The invention relates to a large language model-based job matching scene query understanding method. The method comprises the following steps of: receiving a job name, a job description and job requirement information input by a user; performing noise filtering and coding normalization processing on the input post information to generate a standardized text; geography and organization entities are extracted through a named entity recognition model, cue words are finely adjusted through a large language model, structural constraint conditions are formed by technical recognition educational background, working years and ages, and a condition analysis template is dynamically generated. And finally, through a large language model, verifying the accuracy of the keyword items, and allocating weight coefficients for the lexical items. Therefore, the efficiency is remarkably improved. Through automatic semantic analysis of a large language model, manual deep intervention of professional knowledge learning of posts is not needed, and core information such as post responsibilities and skill requirements can be rapidly extracted and converted into semantic retrieval features.