95 results about "Implicit knowledge" patented technology

The invention discloses a personalized learning path recommendation system based on artificial intelligence, and relates to the technical field of path recommendation, firstly, the system collects multi-dimensional feature data of a learner, and constructs a personalized feature vector; secondly, in combination with knowledge graph modeling and graph neural network technologies, deeply mining explicit and implicit knowledge point association; then, predicting an optimal learning path by using a sequence recommendation model, and ensuring reasonable sorting of knowledge points; in the learning process, the system combines real-time interaction data, dynamically adjusts a learning path, and continuously optimizes a recommendation strategy through an adaptive optimization algorithm; and finally, based on the learning result and the behavior data, evaluating the effectiveness of the learning path, and updating the knowledge point weight and recommendation strategy through a feedback mechanism, thereby realizing intelligent and self-adaptive personalized learning recommendation, the accuracy and adaptability of learning path recommendation can be effectively improved, learners are helped to master knowledge more efficiently, and the learning recommendation efficiency is improved. And the learning experience and effect are improved.

The invention belongs to the technical field of learner cognitive state interpretation, and particularly discloses a brain-language-vision large model-based cognitive state interpretable method, which comprises the following steps of performing multi-modal data collection for the cognitive state of a learner; performing learner cognitive state characterization based on a multi-scale map convolutional network; brain-vision-language mixed multi-modal time sequence feature fusion based on hierarchical mutual information maximization is carried out; embedding knowledge graph nodes for enhancing the complex task understanding ability of the multi-modal large model; training a brain-vision-language multi-modal large model guided by knowledge; recognizing a learner cognitive state based on a multi-modal large model; the cognitive state of the learner can be explained and analyzed under the support of multi-mode owned reasoning. According to the cognitive state interpretable method based on the brain-language-vision large model, the input multi-modal fusion features are converted into high-quality prompts so as to guide the large language model to extract implicit knowledge, and important support is provided for reasoning generation.

The invention discloses an advertisement copywriting generation method and device based on multi-modal fusion, equipment and a medium, and relates to the technical field of advertisement marketing. The method comprises the steps of obtaining multi-modal data of a target video, the multi-modal data comprising visual data, auditory data and related metadata of the video, and extracting associated data from a local knowledge base; and preprocessing the multi-modal data and the local knowledge base data, and converting the multi-modal data and the local knowledge base data into feature forms which can be used for analysis. According to the method, semantic calibration is carried out on multi-modal input by means of a local knowledge base, it is ensured that generated content strictly follows domain knowledge constraints, the problem of deviation caused by the fact that a traditional model depends on implicit knowledge is solved, multi-modal information ambiguity is eliminated, collaborative semantic generation of texts, images and structured data is achieved, content dimensions are enriched, and the method has the advantages of being high in practicability and easy to popularize. And an efficient solution is provided for the landing of the intelligent generation technology in the vertical field.

The invention discloses a self-adaptive intelligent teaching content recommendation system, and relates to the technical field of intelligent teaching, and the system comprises a data collection and analysis module which is used for collecting multiple types of data of students in the learning process, including learning behavior data, physiological signals, environment data and learning achievement cognition feedback data of the students, and after collection, sending the data to a database; performing preprocessing of noise reduction, standardization and analysis feature extraction on the multi-type data to generate multi-modal data; according to the method, the three-dimensional knowledge graph of knowledge points, error types and thinking paths is constructed, and the TCN analysis is combined, so that explicit knowledge defects can be identified, implicit knowledge vulnerabilities can be diagnosed, students can accurately know knowledge system vulnerabilities of themselves, and targeted defect checking, leak repairing and intensified training are carried out; learning interest information is extracted from multi-modal data, an explicit and implicit multi-dimensional interest model is established, and teaching content is screened in combination with a knowledge short board diagnosis result.

The invention relates to the technical field of intelligent manufacturing, in particular to a milling process intelligent decision-making method based on multi-agent collaboration, which comprises the following steps of: constructing a milling process planning-oriented multi-modal knowledge base, receiving and analyzing a milling processing demand input by a user based on a central large language model agent, and establishing a multi-modal knowledge base; decomposing a process planning task into associated sub-tasks based on knowledge in the multi-modal knowledge base, and distributing the associated sub-tasks to corresponding professional agents; and obtaining related knowledge based on a retrieval enhancement generation technology, and executing the subtask. Through the LLM-driven multi-agent collaborative system and the RAG technology, autonomous dynamic optimization of the process scheme is realized to improve the intelligence level, a distributed architecture is adopted to enhance the flexibility to adapt to frequent changes, a multi-modal knowledge base is constructed to capture and reuse expert implicit knowledge to ensure the consistency of the scheme, and the method has the advantages of being high in practicability and high in practicability. And multi-dimensional knowledge is integrated and deeply applied to cope with complex process requirements, so that the defects in the prior art are effectively overcome.

The invention discloses a field large model geological survey report generation method based on a knowledge graph, and the method comprises the following steps: S1, building an engineering survey field data set through multi-source heterogeneous data collection and structured preprocessing, and the engineering survey field data set comprises five text dimension tags divided according to engineering survey specifications; s2, constructing an engineering investigation knowledge graph; and S2A, knowledge extraction, wherein a bidirectional encoder presentation layer-bidirectional long short-term memory network-conditional random field joint extraction model is adopted. According to the method, a bidirectional encoder presentation layer-bidirectional long short-term memory network-conditional random field joint extraction model is improved on knowledge modeling to perform high-precision entity-relation joint extraction, a geological knowledge map with consistent semantics and clear structure is constructed based on RDF, rule reasoning and graph neural network reasoning mechanisms are fused, and the method has the advantages of high-precision entity-relation joint extraction and high-precision entity-relation joint extraction. Deep mining and complementation of explicit and implicit knowledge are realized, and the ability of the prior art in knowledge expression granularity and reasoning breadth is improved.

The invention provides a transformer area line loss intelligent algorithm decision-making method based on a knowledge graph and a large model agent, semantic retrieval in the algorithm calling process is supported, dominant and implicit knowledge such as expert experience, technical standards and historical cases is coded in a unified mode, a mapping relation is established between algorithm nodes and process nodes in the knowledge graph, and the algorithm is optimized. An integrated reasoning path is formed, so that the intelligent agent can automatically combine and call an algorithm according to a task target, a large model is embedded into a transformer area line loss analysis process, and a hierarchical structure of'task analysis intelligent agent-algorithm decision intelligent agent-result analysis intelligent agent 'is formed; the task analysis agent is responsible for converting a problem input by a user into a structured task; the algorithm decision agent is responsible for algorithm selection and adaptive parameter adjustment; the result analysis agent is responsible for report generation and decision recommendation based on the knowledge base; according to the method, organized association can be carried out on scattered algorithm tools, expert experience and business rules, and a unified transformer area line loss agent tool integration framework is constructed.

The invention discloses an agent interaction method and system for enhancing specific industry knowledge, and belongs to the technical field of artificial intelligence. According to the method, multi-modal original data of a target industry is collected and preprocessed, dominant entities and relationships are extracted based on an industry ontology to generate a triple, and implicit knowledge is fused to construct an iterative mixed knowledge graph; converting a user query into a vector, retrieving a Top-K candidate knowledge vector, and then performing interpretable scoring in combination with a query vector, a candidate vector and a triple to obtain an enhanced feature; and dynamically determining an expert sub-network set, and fusing reasoning results of the experts to generate a final answer. The system comprises a knowledge graph and a feature enhancement and reasoning module, and dynamic updating and accurate reasoning of knowledge are achieved. According to the method, the industry knowledge coverage degree, the reasoning accuracy and the interpretability are improved, the illusion problem of a large language model is relieved, and the method is suitable for intelligent interaction services in the professional fields of medical treatment, finance and the like.

The invention discloses a remote sensing knowledge distillation method and system based on dual-mode feature spectrum decoupling, and the method comprises the steps: inputting a remote sensing image, carrying out the parallel extraction of a full-frequency feature map through a teacher model and a student model, and decomposing the full-frequency feature map into a low-frequency component and a high-frequency component through two-dimensional discrete wavelet transform; performing adaptive spatial feature enhancement on the low-frequency component and the high-frequency component through a density-independent spatial weighting mechanism to generate explicit distillation loss; inputting each component feature map output by the teacher model into a corresponding pre-trained knowledge amplifier to generate a thermodynamic diagram, predicting and capturing a decision boundary confidence implied by the teacher model through the thermodynamic diagram, and constructing implicit knowledge constraints based on student feature responses; target detection loss, explicit distillation loss and implicit knowledge constraint are combined, student model parameters are optimized through gradient propagation, and meanwhile, teacher model and knowledge amplifier parameters are frozen to guarantee knowledge migration stability.

The invention discloses an explicit-implicit relationship collaborative extraction method and device based on a large model and a pre-training model, and the method comprises the steps: 1, obtaining a word vector of a text through a pre-training language model, building a word-level table labeling frame based on the word vector of the text, and building a table labeling frame based on the word level; obtaining an explicit entity relationship triple in the text; wherein the pre-training language model is BERT; 2, acquiring implicit knowledge in the text from the GPT model as an external knowledge source; 3, performing instruction fine tuning by fusing the open source large language model with implicit knowledge and prompts, and performing reasoning through texts and implicit knowledge to obtain an implicit entity relationship triple; and 4, systematically integrating the explicit and implicit entity relationship triples obtained from the text by the open source large language model fused with implicit information fine adjustment and the pre-training language model, and finally obtaining a combined triad.

The invention belongs to the technical field of computer aided design (CAD), and particularly relates to an automatic shaft part size mark generation method applying Ontology. The method specifically comprises the following steps: (1) constructing a shaft part dimension mark generation body; (2) establishing a size mark automatic generation SWRL inference rule; (3) extracting the surface structure and related constraint information of the part; (4) constructing and generating an instantiated ontology model; and (5) combining a Drools inference engine with an SWRL rule, reasoning the size marks and outputting a generation result. Constructing an ontology reasoning knowledge framework for the dimensioning domain knowledge by using the ontology, and reasoning implicit knowledge according to dominant domain knowledge; and in combination with a semantic network rule language rule base, performing reasoning on the specific structure and constraint conditions of the shaft part to generate size marking information. According to the method, a consistent knowledge description framework generated by the size marks of the shaft parts can be provided, so that a computer can automatically generate related size mark information according to structure information and constraint conditions of the shaft parts, and a quick and effective method is provided for intelligent generation of the size marks of the shaft parts.

The invention relates to the technical field of intelligent water conservancy, and discloses and provides a hydraulic structure large language model fine tuning and graph retrieval enhanced generation method based on knowledge distillation, and the method specifically comprises the steps: obtaining a hydraulic structure instruction fine tuning data set in combination with a knowledge distillation method; performing supervised training on the large language model by using a low-rank decomposition technology to obtain a hydraulic structure large language model; identifying a question intention of a user by using a hydraulic structure large language model, and converting a question statement into a knowledge graph query language; and enhancing the professional and credibility of water conservancy generated by the answer of the large model of the hydraulic structure by using a knowledge graph retrieval result. According to the method, the hydraulic structure knowledge is embedded into the large language model, the large language model customized for the hydraulic structure field is obtained, and the explicit knowledge stored in the water conservancy knowledge graph and the implicit knowledge of the large language model are effectively combined, so that the answer is more accurate and traceable, and an effective technical means is provided for intelligent water conservancy construction.

The invention belongs to the technical field of computer aided process design (CAPP), and particularly relates to a body-based shaft part fixture scheme automatic generation method. The method specifically comprises the following steps: (1) constructing a shaft part clamp scheme generation body; (2) establishing a shaft part clamp scheme to generate an SWRL inference rule; (3) extracting relevant feature constraint information of the part; (4) constructing and generating an instantiated ontology model; and (5) combining a Drools inference engine with an SWRL rule, and carrying out inference selection and adjustment on a shaft part fixture scheme. Constructing an ontology reasoning knowledge framework by utilizing ontology pair shaft part clamp scheme domain knowledge, and reasoning implicit knowledge according to dominant domain knowledge; and reasoning an optimal clamp scheme according to the feature constraint condition of the geometric product in combination with a semantic network rule language rule base. According to the method, a consistent knowledge description framework generated by the shaft part clamp scheme can be provided, so that a computer can automatically select a proper clamp scheme and the like according to various constraints of geometric product parts, and a quick and effective method is provided for intelligent selection of the shaft part clamp scheme.

The invention relates to the crossing field of artificial intelligence and hotel informatization management, in particular to a hotel knowledge base self-learning correction method, system and device and a medium. The method comprises the following steps: acquiring a client call voice stream, and preprocessing to obtain voice data; inputting the voice data into a preset multi-modal analysis model and an incremental learning model to generate a knowledge correction decision; updating a knowledge base version and an AI response generator based on the knowledge correction decision, and obtaining customer satisfaction feedback; and adjusting the knowledge correction decision through the feedback. And a knowledge correction decision is automatically generated by using a multi-modal analysis model and an incremental learning model, and a knowledge base version is quickly updated based on the decision. By means of the process, automatic correction of knowledge is achieved, the knowledge updating period is greatly shortened, the knowledge base can reflect latest service information and customer requirements in time, and the timeliness and accuracy of the knowledge base are improved. By incorporating the implicit knowledge into the knowledge base, the knowledge base is richer and more comprehensive.

The invention relates to the technical field of machine learning, in particular to a knowledge distillation method and system based on view alignment. The invention aims to solve the main limitations in the traditional distillation technology, including the problems of excessive confidence of a teacher model, confirmation deviation and the like. The invention provides a novel knowledge distillation framework based on logarithmic probability, which is called KDVA; specifically, z-score standardization is applied to the logarithmic probability of a model for smooth output, so that transmission of more teacher implicit knowledge is promoted. In addition, a same-view-angle and cross-view-angle alignment mechanism is introduced into the KDVA, and comparison information of weak and strong image transformation is utilized to enlighten a student model to obtain more knowledge. In addition, the student model is supervised by using the real label of the sample, so that the student model can obtain more information about each sample target category. According to the method, when different network architectures are applied to different data sets, high effectiveness and stability are shown.

The invention discloses a knowledge base construction method and system based on urban planning implicit knowledge deduction, relates to the technical field of urban planning, and aims to solve the problems that an existing planning knowledge base is insufficient in implicit knowledge mining, poor in scene adaptability and lack of dynamic optimization capability. The method comprises the steps of obtaining multi-source data and performing standardization processing to obtain an enhanced data pool; optimizing the large language model to extract explicit and implicit planning knowledge; constructing a planning knowledge association network with association strength; building an initial knowledge base containing classification, retrieval and implicit knowledge deduction units; generating an adaptability evaluation result through a multi-scene test; constructing a knowledge evolution rule based on an evaluation result and completing knowledge base iterative reconstruction; and establishing a real-time spatial data dynamic triggering mechanism to realize continuous optimization of the knowledge base. According to the method, accurate association and deduction of explicit and implicit knowledge are realized, the scene adaptability and dynamic updating capability of the knowledge base are improved, scientific and efficient knowledge support is provided for urban planning decision making, and the method is suitable for various planning scenes.

The invention relates to the technical field of artificial intelligence, provides a decision generation method, device and equipment based on knowledge distillation and a medium, is applied to financial and medical health care service scenes, can extract multi-modal features in target input data, and realizes extraction and preliminary integration of multi-modal effective features. Knowledge distillation is carried out on the multi-modal initial features, more implicit knowledge is learned for the multi-modal initial features, and the quality and expression ability of the features are improved; the hierarchical knowledge graph is constructed according to the multi-modal knowledge enhancement features, so that the multi-modal knowledge can be subjected to structured representation from different hierarchies, and a clear and organized knowledge basis is provided for subsequent reasoning and decision making; the hierarchical reasoning decision is carried out according to the multi-modal knowledge enhancement features and the hierarchical knowledge graph, analysis and decision making can be carried out on multi-modal information from different granularities, the processing capacity and decision making accuracy of complex tasks are improved, and effective reasoning from local features to global decision making is achieved.

The invention relates to a disease prediction method and device based on implicit knowledge enhancement, equipment and a medium. The method comprises the following steps: calculating a comprehensive representation of a patient according to a historical doctor-seeing record and a static information representation, and training by utilizing the comprehensive representation of the patient to obtain a backbone network; a patient representation matrix is obtained through backbone network calculation, and K similar patient representations with the highest similarity with the target patient are screened out through cosine similarity; according to the disease type and the relationship type, constructing a doctor-seeing time sequence heterogeneous graph, and encoding the doctor-seeing time sequence heterogeneous graph by adopting a heterogeneous graph encoding algorithm to obtain a doctor-seeing time sequence heterogeneous graph representation; and constructing disease comprehensive characterization by using the patient comprehensive characterization, the similar patient characterization and the diagnosis time sequence heterogeneous graph characterization, training a disease prediction model according to the disease comprehensive characterization, and outputting a disease prediction result by using the trained disease prediction model. Patient modeling is enhanced by utilizing the group intelligence of similar patients and the potential complex relationship between diseases, and the accuracy of disease prediction is improved.

The invention provides a circuit design optimization method based on knowledge rule induction and implicit knowledge mining. The method comprises the following steps: 1) classifying and sorting design rules; 2) extraction and modeling of dominant design rules; 3) mining and structured expression of the implicit design rule; 4) constructing and optimizing a knowledge document; 5) establishing and applying a design rule base; and 6) reasoning and verifying the key links in the electrical design process. According to the method, explicit knowledge such as electrical design specifications and periodical papers is systematically summarized, and implicit knowledge such as design experience and skills is extracted in combination with a data mining and knowledge reasoning technology, so that systematic management of key knowledge information in the electrical design process is realized, the intelligent level of an electrical design software platform can be effectively improved, and the design efficiency is improved. The knowledge sharing and multiplexing capability in the design process is enhanced, so that the cable wiring scheme is optimized, and the reliability of the design result in the aspects of safety, economy, scheme consistency and the like is improved.

The invention relates to the technical field of wisdom education, and discloses a personalized question and answer method based on a general large language model and a knowledge graph, and the method specifically comprises the steps: carrying out the entity recognition of student questions through a large language model, and carrying out the matching of knowledge entities in the knowledge graph through an entity matching algorithm; each knowledge entity in the relevance entity set is used as a core entity, the core entity is used as a center to search the knowledge graph, and the search process comprises neighbor search and path search; placing all the triads together to form an aggregation graph of the knowledge sub-graphs; optimizing the aggregation graph by using a large language model to obtain a knowledge graph; the big language model is prompted to learn the knowledge sub-graph set and the structure of the knowledge graph, the big language model is required to combine existing knowledge with knowledge obtained from the knowledge graph, questions are answered, and a reasoning process is provided. According to the method, the implicit knowledge of the large language model and the explicit knowledge of the knowledge graph are combined, and transparent and reliable collaborative reasoning is achieved.

The application discloses a kind of self-adapting intelligent teaching content recommendation system, it is related to intelligent teaching technical field, including data acquisition analysis module: collection student in the learning process multiple types of data, including student's learning behavior data, physiological signal, environmental data and learning achievement cognitive feedback data, after acquisition, multiple types of data are preprocessed with noise reduction, standardization and analysis feature extraction, generate multimodal data;The application is analyzed by constructing knowledge point, error type, three-dimensional knowledge graph of thinking path and time sequence convolution network TCN, not only can identify dominant knowledge defects, but also can diagnose implicit knowledge leaks, so that students can accurately know their own knowledge system leaks, targeted to check and fill the gap and strengthen training;By extracting learning interest information from multimodal data, an explicit and implicit multidimensional interest model is established, and the teaching content is filtered in combination with the knowledge gap diagnosis result.

The invention discloses a knowledge distillation method based on reverse feature fusion and classifier multiplexing, and relates to the technical field of computers. The method at least comprises the following steps: S1, obtaining an original data set, and dividing the original data set into a training set and a test set; s2, preprocessing the original data set; s3, pre-training the teacher network model by using the training set, and storing the trained teacher network model; s4, pre-training the weight by freezing the teacher network, introducing a decoupling attention projection module and a reverse feature fusion module into the student network, and training a student model by multiplexing a teacher classifier and a feature distance loss function; and S5, in a reasoning stage, retaining parameters of a student network architecture and a teacher classifier layer. Through the design of the method, the problem that the student network is difficult to fully learn the implicit knowledge of the teacher network interlayer feature due to the large expression difference between the teacher and the student can be solved. And through multiplexing a teacher classifier, output distribution closer to teachers is obtained.

The invention discloses a ship lockage rolling pre-planning automation system, and belongs to the technical field of intelligent transportation and waterway transportation. According to the system, firstly, a digital twin navigation environment synchronized with a physical world in real time is constructed; in the environment, an initial scheduling strategy is generated through forward reinforcement learning training, the system allows a scheduling expert to intervene a pre-plan generated by the initial strategy, and operation of the pre-plan is collected to form an expert demonstration track; and then, analyzing the track based on reverse reinforcement learning, reversely reasoning the implicit decision preference of the expert and quantifying the implicit decision preference into an expert reward function, and finally, fusing the expert reward function with the initial reward function. The ship lockage scheduling method solves the technical problems that an existing scheduling system depends on a solidified rule, cannot learn expert implicit knowledge and is poor in adaptability, and the intelligent level, efficiency and safety of ship lockage scheduling in a complex dynamic environment are remarkably improved by constructing a closed-loop learning framework of man-machine co-evolution.

The invention discloses a method for verifying dominant knowledge, electronic equipment and a storage medium. The method comprises the following steps: converting implicit knowledge in a language model into dominant knowledge; searching data contents reflecting the implicit knowledge from the model knowledge base according to the precondition fields of the dominant knowledge and the field values of the precondition fields; judging whether the field value of the conclusion field of the dominant knowledge is matched with the data content or not; and in response to matching of the conclusion field of the dominant knowledge and the data content, determining that the dominant knowledge passes verification. Through the scheme disclosed by the invention, data positioning of the model knowledge base can be performed through the precondition field in the dominant knowledge so as to efficiently find the data content capable of reflecting the corresponding implicit knowledge, and then data comparison is performed through the conclusion field of the dominant knowledge so as to generate a reliable and accurate verification result.

The present invention discloses a method and terminal device for evaluating adversarial robustness based on class activation maps. The method comprises adding perturbations to original samples using an adversarial attack algorithm to obtain adversarial samples; obtaining class activation maps of a deep learning model based on the original samples and class activation maps based on the adversarial samples; performing adversarial defense training on the deep learning model using the original samples and the adversarial samples to obtain a trained deep learning model; obtaining class activation maps of the trained deep learning model based on the original samples and class activation maps based on the adversarial samples; and evaluating the adversarial robustness of the deep learning model based on the class activation maps of the original samples and the adversarial samples before and after training. The method and terminal device of the present invention can mine implicit knowledge in the deep learning model, evaluate adversarial robustness from the perspective of the model itself rather than direct model results, and introduce key position information to enhance the interpretability of the evaluation results.

The invention discloses an implicit knowledge mining method and device and electronic equipment, and relates to the technical field of implicit knowledge mining, and the device comprises a perception data collection module, a preprocessing module, an implicit knowledge extraction module, an implicit knowledge conversion module and a knowledge structured display module; the perception data collection module collects different types of perception data, including video, audio and physiological data, and all the collected data are transmitted to the preprocessing module for subsequent processing and analysis; the preprocessing module cleans and processes the original data, and standardizes the time data by using a timestamp formatting tool; according to the method, the knowledge graph constructed by the implicit knowledge conversion module is integrated with the tagged data, and the Elasticsearch is combined to realize quick query and real-time push based on time, emotion and behavior dimensions, so that the data retrieval efficiency is greatly improved, the user is helped to timely obtain the required implicit knowledge, and the decision process and the user experience are optimized.

The invention belongs to the technical field of multimedia information retrieval, and particularly relates to a cross-media retrieval method and system based on adaptive prompt and implicit knowledge enhancement. The method comprises the following steps that S100, a cross-media retrieval data set is established, and the cross-media retrieval data set comprises data of two different media types; step S200, training a cross-media retrieval network based on adaptive prompt and implicit knowledge enhancement by using the cross-media retrieval data set, wherein the cross-media retrieval network comprises a potential excitation mechanism and a prompt pool mechanism based on instance-level query; and step S300, performing a cross-media retrieval test by using the trained cross-media retrieval network to realize matching between different media. Through a potential excitation mechanism and a prompt pool mechanism based on instance-level query, alignment errors caused by asymmetry of information between media and diversity of image distribution can be flexibly dealt with, so that the accuracy and robustness of cross-media retrieval are remarkably improved.

This invention relates to artificial intelligence and big data processing technologies, aiming to provide a method for constructing multimodal intelligent knowledge cards and self-organizing links by topic. The method includes: collecting data from multiple sources, preprocessing it to extract multimodal information, and constructing several standard structured data objects as knowledge cards; encapsulating the data in a unified format to build a knowledge card database; constructing multidimensional hybrid embedding vectors based on the attributes of the data in the knowledge card database using different knowledge cards; establishing a multimodal semantic adaptive topology network under spatiotemporal constraints to achieve self-organizing links by topic; storing the knowledge cards as nodes in a backend knowledge graph and establishing connections with the frontend display / interactive interface. This invention can be used for intelligent processing of data extraction, structured encapsulation, and dynamic association. Through vector space alignment, it can discover implicit knowledge and potential connections, achieving dynamic evolution and adaptability.

The present invention provides a method and system for identifying multiple hardware fittings for power transmission lines that integrates implicit knowledge reasoning. The method belongs to the field of image recognition technology and includes the following steps: obtaining an image of hardware fittings to be identified; performing feature extraction on the image of hardware fittings to be identified, determining scene features, multiple region suggestion box features, and the categories of each region suggestion box feature; performing scene enhancement on the region suggestion box features based on the scene features to obtain scene enhancement features; performing spatial enhancement on the region suggestion box features based on the categories of the region suggestion box features to obtain spatial enhancement features; and determining the location and type of hardware fittings in the image of hardware fittings to be identified based on the features of each region suggestion box and the scene enhancement features and spatial enhancement features corresponding to each region suggestion box feature. Through scene enhancement and spatial enhancement, the implicit relationships between hardware fittings are deeply explored, the visual features of obscured hardware fittings are enhanced, and the accuracy of hardware fitting identification is improved.

The invention discloses a pre-training language model post-interpretation method and system based on framework knowledge detection, and particularly belongs to the field of artificial intelligence and natural language processing. Comprising a framework-based semantic analysis module, a framework-based knowledge graph construction module and an interpretable knowledge detection module. The method is a post-interpretation mechanism based on a framework semantic linguistics cognitive mechanism, and the core of the method is to evaluate and interpret internal parameter concepts of a pre-training language model which cannot be understood by human beings by using framework cognitive concepts which can be understood by the human beings. By introducing a framework, framework elements and framework relationship concepts, a framework knowledge graph capable of representing implicit knowledge behind a text is realized based on a given text. And then a knowledge detection module is combined to generate multiple types of detection problems, and understanding and reasoning of the model on text back implicit knowledge are evaluated. According to the method, the limitation of the model in the aspects of knowledge representation and reasoning can be effectively identified, and support is provided for model optimization and research of a credible artificial intelligence model.