87 results about "Knowledge learning" patented technology

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 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 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 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.

The invention relates to the technical field of data processing, in particular to a model training method and device, equipment and a storage medium. The method comprises the following steps: acquiring first input data and first output data of a first model; inputting the first input data as second sample input data into a second model, and obtaining second output data output by the second model; obtaining a first loss value based on the first output data and the second output data, and obtaining a second loss value based on the second output data and the target answer data; and training the second model based on the weighted sum of the first loss value and the second loss value to obtain a trained target second model. According to the invention, one-sided knowledge learning of the second model in the process of distilling knowledge from the first model to the second model can be avoided, the influence of error noise of the first model is avoided, and the training effect of the second model is improved.

The invention relates to the technical field of simulation practical training, in particular to a scene perspective simulation platform and a practical training method thereof, and the simulation platform comprises a theory and model simulation module and a practical training operation training module. The theory and model simulation module comprises a theoretical knowledge learning sub-module for outputting theoretical knowledge points; the model operation sub-module is used for displaying and executing splitting, combining and restoring operations of an operation user on the three-dimensional equipment model; the simulation analysis sub-module is used for identifying and recording operation behaviors of the operation user; the practical training operation training module comprises a practical training knowledge learning sub-module used for outputting practical training knowledge points; the practical training operation sub-module is used for realizing state transition of the three-dimensional equipment model according to the first practical training instruction; and the practical training recording sub-module is used for recording all first practical training instructions and operation results of the operation user. Through the theory and model simulation module and the practical operation training module, theory teaching and practical operation training can be effectively linked.

The present disclosure relates to a drift detector to detect a drift in features of a diagnosis, the drift detector comprising a first collecting device configured to collect relevant explanations and raw data, a first database compiling the relevant explanations and raw data collected by the first collecting device, a second collecting device configured to collect relevant available expert knowledge from reliable sources, a second database compiling the relevant available expert knowledge from reliable sources collected by the second collecting device, a learned feature extractor configured to group initial data of the second database into a characteristic feature pattern for different diagnoses and to subsequently group the data from the first database into first feature patterns by diagnosis and from the second database into second feature patterns by diagnosis, a comparator configured to compare the empirical distributions of each of the grouped feature patterns in the databases with each other, and a processor configured to generate a report if results of the comparison do not comply with a predefined condition. Applications include medical applications such as recognizing new diseases.

This invention relates to the field of intelligent training technology, specifically to an internet-based intelligent training system for the prevention and control of respiratory infectious diseases. In this invention, a long short-term memory network is used to deeply model learners' learning progress, identify weaknesses in the learning process, and accurately push corresponding content, effectively providing personalized learning path recommendations. A graph neural network-based learning resource scheduling and content push method dynamically adjusts the frequency and order of learning content pushes to match learners' progress, ensuring that learning content is pushed at the appropriate time and with suitable difficulty. By constructing an interaction graph among learners and combining it with influence assessment, key prevention and control knowledge is disseminated preferentially through learners with high influence, optimizing the knowledge dissemination path. Combined with the application of the SIR (Self-Improving Influence) propagation model, the system predicts learners' progress in learning prevention and control knowledge, identifies learning bottlenecks, and significantly improves learning effectiveness.

The application provides a knowledge learning method and device of a machine translation model, comprising: constructing a migration dataset; respectively using a machine translation teacher model and a machine translation student model to translate the original text of each sample of the migration dataset to obtain the first candidate word probability distribution and the second candidate word probability distribution of each sample of the migration dataset; determining the migration loss of each sample of the migration dataset according to the first candidate word probability distribution, the second candidate word probability distribution and the standard candidate word probability distribution of each sample of the migration dataset; and realizing the knowledge learning of the machine translation student model to the machine translation teacher model based on the first candidate word probability distribution of each sample of the migration dataset and the migration loss. The application enables the machine translation student model to learn complementary knowledge from the machine translation teacher model through the migration loss, realizes the knowledge accumulation of the machine translation student model, and further improves the translation performance of the machine translation student model.

The invention relates to a distributed intelligent question and answer method based on a novel federated knowledge learning model, and belongs to the field of natural language processing, and the method comprises the steps: each client carries out the fine tuning of a private downstream task large model through local privacy data, and a server carries out the fine tuning of a global model through a public data set, the knowledge understanding ability is enhanced; the central server decomposes the task flow into required demands and issues the demands to the client; the client generates corresponding knowledge fragments according to the received requirements and uploads the knowledge fragments to the server; the server aggregates the fragmented knowledge received from the client to form complete task auxiliary knowledge; the server large model generates a task response in conjunction with task assistance knowledge. According to the method, task decomposition, knowledge aggregation and identification removal modules are introduced, so that high performance is ensured, and powerful privacy protection is provided.

The application provides an intelligent substation anomaly detection method based on meta-learning technology, first, a multi-working condition task library is constructed for meta-training. Secondly, a double meta-knowledge learning framework is designed to learn task-level model initialization parameters suitable for rapid adaptation. Subsequently, online reconstruction error driven anomaly detection is deployed, and the adapted model is used to calculate data reconstruction error in real time, and combined with the adaptive threshold to accurately distinguish and alarm grading. Finally, the system supports a dynamic updating mechanism, which continuously collects verified samples and periodically triggers meta-updating. The application significantly reduces the data dependence and deployment cost in new scenarios, improves the model's generalization ability to multiple working conditions and sensitivity to rare anomalies through meta-knowledge sharing, and ensures the long-term effectiveness of the detection system through online learning mechanism, providing an efficient, adaptive and evolutionary security protection solution for intelligent substations.

The application discloses a kind of based on low-rank self-adaption's federal remote sensing big model training method and system, comprising: using respective remote sensing data, pre-training big model and local low-rank self-adaption model is carried out individualized heterogeneous data knowledge learning;Using the local low-rank self-adaption model parameter initialized global shared low-rank self-adaption model parameter after training, based on pre-training big model and global shared low-rank self-adaption model is carried out collaborative federal training, learns global remote sensing task field knowledge;By heterogeneous rank alignment fusion mode, the alignment of local heterogeneous data knowledge and global remote sensing task knowledge is realized after, output is composed of pre-training big model and the low-rank self-adaption model parameter of alignment after local by federal remote sensing big model, such as greatly reduce the parameter quantity and communication overhead of federal training upload, guarantee the efficient training of model, also realize local heterogeneous data and global remote sensing task self-adaption alignment, guarantee the versatility and robustness of model.

The application discloses a multi-dimensional student state knowledge tracking method fusing emotion and forgetting mechanism, relates to the field of intelligent education, and aims at the problems that the existing method ignores the dynamic influence of emotion on forgetting and lacks a learning feedback mechanism.The application proposes a multi-dimensional state collaborative evolution framework.The method firstly constructs an independent forgetting state which is explicitly regulated by emotion and establishes a knowledge state which is inhibited by the forgetting state;secondly, the knowledge learning gain is innovatively introduced into the emotion updating process, realizing deep coupling of the three-dimensional states of knowledge, emotion and forgetting;finally, the states are fused through cross-dimensional attention, and the basic prediction probability is dynamically calibrated by using subjective difficulty and a prediction uncertainty coefficient.The application effectively simulates a real cognitive psychological process, and significantly improves the accuracy, explainability and individualization level of knowledge state tracking.

The invention discloses a Unity-based cable accessory installation training system and a construction method thereof. The system comprises a basic knowledge learning module, a professional knowledge learning module, a training examination module, an examination evaluation module and the like. The basic knowledge learning module provides content such as identification graphs related to cable accessory installation, electrical instruments, tool use and safety specifications and the like, and teaching is assisted in a visual mode of images, texts, voice and structures. The professional knowledge learning module is used for displaying key installation processes and process standards in the forms of three-dimensional animation, video explanation, structure explosion diagram and the like; the training examination module is provided with basic examination and practical operation examination, and supports question bank import, interactive answering and action monitoring; and the assessment and evaluation module performs statistical analysis on assessment data and generates visual assessment reports such as a radar map and the like. According to the invention, the learning efficiency of trainees can be improved, the training cost is reduced, the training safety and flexibility are enhanced, and standardization, intelligence and high efficiency of cable accessory installation skill training and assessment are realized.

This invention discloses a sequence recommendation method, system, device, and medium based on attention mechanisms and persistent memory, comprising: defining item categories within the system; collecting and processing sequence data within the system to obtain processed sequence data; constructing a sequence recommendation model, wherein the constructed sequence recommendation model includes an embedding generation module, a location information generation module, a self-attention module based on persistent memory, and a prediction module; training the sequence recommendation model using the processed sequence data as samples; and model deployment and prediction, wherein the model deployment and prediction involves inputting the target sequence data into the trained sequence recommendation model and predicting the next item based on the model output. This invention, by introducing a persistent memory mechanism, enables the model to focus on global cross-sample knowledge throughout the task and learn general knowledge of the task, which plays a significant role in improving recommendation performance.

The invention discloses a multi-source heterogeneous data continuous learning evaluation method based on context knowledge enhancement, and relates to a continuous learning technology and a model performance evaluation method in machine learning. The method mainly aims at evaluating the ability of a depth model to continuously learn sequence tasks on multi-source heterogeneous data; the method is an evaluation method which is based on context knowledge enhancement and focuses on key new knowledge learning efficiency and precision. According to the method, a human forgetting curve is simulated, the defect that new knowledge and old knowledge are not distinguished in an existing evaluation mode is broken through, higher calculation weights are given to knowledge needed by the model for processing problems in the current context environment, and the continuous learning ability of the model on multi-source heterogeneous data is evaluated in a mode more fitting the reality.

This invention discloses a weakly supervised open-pit mining site identification method based on adaptive SAM and automatic knowledge learning. The method includes: S1, collecting images of the study area and extracting three-band images using an input spectral adapter; extracting depth features using the feature extractor of the MobileSAM model; constructing a multi-scale perception and geometric adaptation module and obtaining image features by applying N² dilated convolutional units with different dilation rates to the depth features; S2, a scene classifier using predefined prototype features to guide image features in scene classification; then, a cue generator using class activation mapping to obtain a heatmap for scene classification and selecting a set of high-confidence cue points; and a pseudo-label generator constructing triplet contrast constraints; S3, the mask decoder of the MobileSAM model combining cue information and image features to perform cue decoding and obtain the open-pit mining site identification result. This invention combines a set of high-confidence cue points with image features for cue decoding to obtain efficient and high-precision open-pit mining site identification results.

The application belongs to the field of image processing, and provides a method and system for automatically guiding a standard section of cardiac ultrasound based on motion capture, which captures images of a subject, determines a human posture through acquired image information, and performs motion capture on an ultrasound probe to determine a motion trajectory and posture of the ultrasound probe; real-time ultrasound image information of the subject in a target body position and the ultrasound probe is acquired; trajectory conversion is performed according to the motion trajectory of the ultrasound probe, knowledge learning and feature extraction are performed according to the real-time ultrasound image information, a gap between a current detection section and a standard section is determined based on an extraction result and a conversion result, and a moving angle and a moving distance of the ultrasound probe are predicted; and automatic acquisition of the standard section of cardiac ultrasound and action guidance of the probe can be realized according to a prediction result.

The application discloses a kind of personalized federated continuous learning method and system based on dynamic clustering and multi-scale prototype, to solve the problem of spatiotemporal catastrophic forgetting in personalized federated continuous learning.The server initializes global model containing prompt parameters and cluster-level multi-scale prototype library, and the client generates routing histogram representing local data distribution and incremental prototype through a gated routing network and uploads it.The server calculates JS divergence based on routing histogram to detect drift, triggers K-Means online re-clustering, and dynamically updates the multi-scale prototype library containing short-term, long-term and drift prototypes in combination with the drift signal.The client uses long-term prototype to construct a joint loss optimization model.The application balances new knowledge learning and old knowledge retention while protecting data privacy and reducing overhead, and adapts to scenarios where data distribution continues to evolve.

The invention discloses a large-model multi-agent-based bridge management and maintenance knowledge graph construction method, system and device and a medium, and the method comprises the steps: processing a bridge inspection report, converting an original text into a unit through a decomposition agent, and building a text annotation database; the method comprises the following steps: taking a subject text block as input, extracting a triple by an extraction agent, then checking whether the extracted triple conforms to a bridge detection domain ontology and domain knowledge or not by a verification agent, finally correcting the triple with errors by a correction agent, iteratively extracting, verifying and correcting the triple, and outputting a high-quality triple; establishing a dynamic knowledge learning mechanism, and updating a knowledge base; constructing an initial knowledge graph according to the verified knowledge triad; and the review agent reviews the initial knowledge graph and feeds back a review result to the construction agent for iterative updating, and finally, the bridge management and maintenance knowledge graph is constructed. According to the method, the accuracy and the fact consistency of the atlas data can be ensured.

The invention provides an agricultural courseware knowledge point segmentation and combination method and device, equipment and a storage medium. The agricultural courseware knowledge point segmentation and combination method comprises the steps of obtaining an agricultural courseware video comprising a plurality of key frame images and explanation voices corresponding to the key frame images; performing voice recognition on the explanation voice to obtain an agricultural knowledge text corresponding to the key frame image; extracting a key entity from an agricultural knowledge text corresponding to the key frame image, and performing semantic association on the key entity and an entity node in an agricultural field knowledge graph to obtain a semantic association result of the key frame image; based on the semantic association result of each key frame image, dividing the agricultural courseware video into a plurality of agricultural knowledge point segments, and labeling knowledge point tags for the agricultural knowledge point segments; and on the basis of a user use demand, extracting knowledge point fragments to be used from the plurality of agricultural knowledge point fragments, and combining to generate a new agricultural knowledge learning video. According to the invention, intelligent segmentation, labeling and personalized on-demand combination of agricultural courseware video knowledge points are realized.

1. Name of the product in this design: Graphical User Interface (Business Knowledge Guide) for Water Resources Business Analysis and Management System for Electronic Equipment. 2. Purpose of this design: An electronic device. 3. The key design feature of this product is its graphical user interface. 4. The image or photograph that best illustrates the design's key features: the front view. 5. Uses of the graphical user interface: for building a learning system for project-related knowledge, evaluating work statistics, compiling and querying information on honors and awards, and assisting in work. 6. Human-computer interaction method of graphical user interface: Click "Knowledge Map" in the upper left corner of the main view to enter the interface change state diagram 1, which can view mind maps of various knowledge categories. Clicking "Add Knowledge Category" on the upper right of the interface change state diagram 1 will take you to the interface change state diagram 2. This interface allows you to add a root node. By entering information such as "Knowledge Title", "Sorting" and "Knowledge Content Type", you can add a new knowledge category. In the interface, "XXX" represents text, numbers, letters, or symbols.

The invention discloses a household garbage intelligent classification identification and question answering system based on deep learning. The system comprises an image acquisition module, a garbage detection module, a classification mapping module, an intelligent question answering module, a data storage module, an interactive question answering module and a user interface module. The image acquisition module supports picture uploading and camera real-time acquisition; the garbage detection module adopts a YOLO model to realize garbage image real-time detection, and the false detection rate is reduced through stability filtering; the classification mapping module is used for mapping detection categories into standard four classifications; the intelligent question and answer module integrates a large language model and returns structured classification guidance; the data storage module realizes persistent management and tracing of detection records; the interactive answering module provides a garbage classification knowledge learning function; and the user interface module adapts to screens with different resolutions. The system is high in recognition efficiency, good in interaction experience and traceable in detection result, supports multi-source input, can effectively improve the intelligent level of garbage classification, and is suitable for multiple fields of environmental protection, smart cities and the like.

The embodiment of the application provides a kind of based on digital model's converter station unmanned plane cruise route collaborative processing method and system, obtain network knowledge learning data sequence including multiple template collaborative control data and each prior abnormality mark corresponding to multiple template collaborative control data;Load basic exception label diagnostic network, and the basic exception label diagnostic network is the exception label diagnostic network generated by knowledge learning long short-term memory network model based on initialization weight parameter;Based on knowledge domain self-adaptability training, the prediction unit of the basic exception label diagnostic network is learned by network knowledge learning data sequence, and the exception analysis network is obtained;Prediction unit includes one or more multilayer perceptron;According to exception analysis network, abnormal node positioning is carried out, and the high-performance exception analysis network is generated using the technical solution, so that subsequent abnormal node positioning accurate, fast can be realized.

The application provides a highway service area vehicle flow scheduling method and system based on an intelligent algorithm, which effectively utilizes a to-be-learned template set containing multiple template scheduling learning data through a deep knowledge learning algorithm. In the target training stage, the method dynamically evaluates the effective annotation training value and invalid annotation global training value of the corresponding training vehicle flow scheduling network relative to the to-be-called vehicle flow scheduling network for each template scheduling learning data, thereby ensuring the pertinence and efficiency of the training process. Through continuous optimization learning, when the training error no longer continues to decline, the deep knowledge learning is terminated, and finally the target vehicle flow scheduling network capable of accurately determining the target vehicle flow dynamic data scheduling knowledge description is obtained. The method improves the intelligent level of vehicle flow scheduling, optimizes the vehicle flow scheduling effect of the highway service area, helps to relieve traffic congestion in the service area, and improves the operation efficiency and user experience of the service area.

This invention relates to the field of knowledge recommendation technology and provides a personalized medical knowledge recommendation method and system for medical students. The invention acquires periodic test data; performs phased progress learning planning; compares the overlap of knowledge digestion; acquires reference learning data from other medical students; analyzes knowledge learning gaps; selects multiple gap-filling medical knowledge areas; and recommends relevant knowledge to the target medical student. Based on the target test data, the invention compares the periodic test data for phased progress learning planning and knowledge digestion overlap, selects reference medical students, acquires reference learning data, analyzes knowledge learning gaps between the reference and target learning data, selects multiple gap-filling medical knowledge areas, and recommends relevant knowledge to the target medical student. This allows the invention to uncover the commonalities and differences in learning among different medical students, providing them with forward-looking and targeted learning improvement directions for rapid academic improvement, effectively enhancing the learning efficiency of the target medical students.

The application provides a knowledge graph-based cognitive ability analysis method and system, which can construct a corresponding knowledge graph according to the historical knowledge learning records of different target objects, determine the cognitive analysis error of the target object according to the knowledge graph, and correct the knowledge graph, so as to effectively avoid the occurrence of knowledge graph construction deviation caused by the data defects of the historical knowledge learning records, finally, re-determine the cognitive ability evaluation value of the target object according to the corrected knowledge graph, accurately and reliably evaluate the target object, and adjust the knowledge data learning progress of the target object according to the cognitive ability evaluation value, thereby maximizing the learning efficiency and learning quality of the target object.

An object of a system according to an embodiment is to learn know-how of an excellent banker and to perform a loan determination fairly and efficiently.SOLUTION: A system according to an embodiment includes a know-how learning unit and a loan determination unit. The know-how learning unit learns know-how of an excellent banker. The loan determination unit makes a loan determination based on the know-how learned by the know-how learning unit.SELECTED DRAWING: Figure 1