90 results about "Metaknowledge" patented technology

The invention relates to the technical field of data mining, and discloses a feasibility research data intelligent mining system which comprises a heterogeneous data acquisition module, a semantic association analysis module, a decision map generation module, a time sequence feature correction module and a knowledge distillation optimization module. The heterogeneous data acquisition module captures data features through a multi-source data sensing node and a dynamic dimension fusion network and constructs a multi-layer topology; the semantic association analysis module analyzes semantic association by using a concept topology modeling unit, a knowledge vector clustering unit and a multi-mode switching link; the decision graph generation module generates a core decision reference framework based on strategy optimization nodes and a rule inference engine; the time sequence feature correction module performs time sequence correction and noise compensation on the semantic association; and the knowledge distillation optimization module detects the deviation through the entity relationship evaluation network and feeds back the optimization decision framework. The system realizes multi-source data intelligent acquisition, semantic dynamic analysis and decision graph adaptive generation, and improves the accuracy and efficiency of feasibility research data mining.

The invention relates to the technical field of artificial intelligence and robots. According to the multi-modal perception and interaction method and device in the body environment, the method comprises the steps that environment entropy estimation processing is carried out through a dynamic weighting multi-modal feature fusion algorithm, and an environment entropy value representing the disorder degree of the environment is generated; performing cross-modal alignment processing to generate a fusion environment understanding map; performing dynamic decision processing through the task adaptive reinforcement learning model to generate an interaction instruction; driving an execution mechanism to execute the interaction action to obtain an execution result of the interaction action; carrying out dynamic adjustment processing on the weight of the environment entropy value to generate an updated environment entropy weight; and performing local knowledge node incremental updating processing on the meta-knowledge base to generate an optimized meta-knowledge base so as to solve the problems of insufficient consistency of cross-modal data and poor environmental understanding robustness caused by large distribution deviation between virtual features generated by a generation model in a noise or data missing scene and a real environment in related technologies.

The invention relates to the technical field of artificial intelligence, and discloses a multi-agent collaborative data question-answering system and method based on a large model, and the system comprises an agent cluster module which is composed of five special agents, namely a data question-answering agent, a data extraction agent, a data analysis agent, a visualization agent and a quality examination agent, and achieves the task decomposition and collaborative execution through dynamic scheduling; the knowledge management module comprises a business knowledge base, a data element knowledge base and a user feedback base, and adopts a hierarchical knowledge fusion technology to provide domain knowledge support for the intelligent agent; and the supporting function module covers a front-end dialogue component and a verification and execution engine and is responsible for interactive interface rendering and result reliability verification. According to the method, the fine tuning requirement on the large model is remarkably reduced, the illusion of the large model is effectively intercepted through a dual verification mechanism, and the accuracy and reliability of question and answer results are improved.

The invention provides a question and answer model training method and device and an intelligent question and answer method and device for a specific field, and relates to technologies such as large models, model training and deep learning in the field of artificial intelligence and the technical fields such as intelligent search and intelligent question and answer. According to the specific implementation scheme, corpus data of a specific field are obtained; meta-knowledge question and answer data are constructed based on the corpus data through a first model, and the meta-knowledge question and answer data comprise meta-questions and answers corresponding to the meta-questions; based on the meta-knowledge question-answer data, domain knowledge training is carried out on the to-be-trained question-answer model; on the basis of the meta-knowledge question and answer data, a semantic-related question set is constructed for the meta-questions, and structured reasoning training data is generated through a second model on the basis of the question set of the meta-questions; and based on the structured reasoning training data, performing reasoning ability enhancement training on the question and answer model to be trained after domain knowledge training. The question and answer performance of the model in the vertical field can be remarkably improved.

The invention relates to the technical field of deep learning and small sample learning, in particular to a small sample capacity training method based on deep learning, which comprises the steps of 1, cross-domain data adaptation and feature alignment, 2, meta-knowledge distillation and prototype enhancement, 3, attention-guided small sample fine adjustment, and 4, model uncertainty quantification and iterative optimization. According to the small sample capacity training method based on deep learning, through cross-domain feature alignment, meta-knowledge distillation, prototype enhancement and dynamic iterative optimization, the problems of model overfitting and weak generalization ability in a small sample scene are solved, high-precision model training when the sample size is less than or equal to 50 is realized, and the training efficiency is improved. The method is suitable for data scarce scenes such as medical images and minority language processing.

The invention discloses a marketing content generation method and system based on big data, and relates to the technical field of big data processing and artificial intelligence, and the system comprises a multi-source data collection and fusion module which is used for receiving original multi-source data from different behavior sources, content sources and business scene sources, and performing structured processing, time relationship coordination and feature fusion on the original multi-source data to generate a fused portrait data set for describing the relationship between the user and the product. In the invention, through the multi-source data acquisition and fusion module, deep integration of heterogeneous multi-source data and unified representation of multi-modal features are realized, a high-quality data basis is provided for subsequent knowledge graph construction and content generation, and the problems of isolated island and insufficient feature utilization of traditional marketing data are solved; through a ternary knowledge graph construction module, information such as users, products, scenes and the like is constructed into a structured knowledge graph, and the semantic understanding ability and relevance of content generation are greatly enhanced.

The invention relates to the technical field of intelligent recommendation, and discloses an AI intelligent matching method based on a knowledge graph, and the method comprises the steps: constructing a quaternary knowledge graph containing a time dimension; identifying legal entities and semantic relationships by adopting an entity identification and relationship extraction technology; multi-level semantic features are extracted through a two-layer progressive semantic matching algorithm of a grammar layer, a semantic layer and a reasoning layer; constructing lawyer ability portraits based on a heterogeneous graph neural network and a time sequence perception graph convolution technology; progressive matching calculation is adopted, the optimal matching weight is learned through a multi-layer attention mechanism, and dynamically optimized intelligent matching is achieved. The technical problems of cold start, insufficient semantic understanding ability and poor timeliness processing ability in the existing legal consultation matching system can be solved, and the matching precision and the user satisfaction are improved.

System and method for using knowledge fabric based on knowledge ontology, designed for deep language understanding and mechanistic causal reasoning, and meta-knowledge repository for auditable question answering. The method includes receiving an input text from a user, building a knowledge graph that represents real world facts and associations in the form of contextually tagged and weighted knowledge propositions, in multiple knowledge domains. The knowledge graph in combination with causal path knowledge and metadata describing digital sources containing answers constitutes the knowledge fabric. The method includes resolving ambiguity and determining actual intent of the user for the input text, from a plurality of interpretations of intent for sentences using the knowledge graph in conjunction with logical inference to achieve deep natural language understanding. The method includes finding / delivering response to the input request as to why / how unknown factors resulted in known outcome, or what outcomes are likely given known causal factors.

The invention relates to the technical field of intelligent teaching, and provides a self-adaptive personalized teaching system based on AI and a knowledge graph, and the system comprises a knowledge graph construction module, a student portrait module, a self-adaptive recommendation module, a teaching interaction module, and a management module. The knowledge graph construction module comprises a data processing unit, a knowledge extraction unit and a graph storage unit; the data processing unit is used for carrying out preprocessing such as word segmentation and stop word removal on the teaching text; and the knowledge extraction unit is used for extracting knowledge point entities and relationships between the entities from the preprocessed text through a natural language processing technology. The knowledge points are subjected to fine-grained modeling through the knowledge graph, the AI algorithm is combined to analyze student learning behaviors and evaluation data, knowledge vulnerabilities and learning characteristics of students can be accurately captured, the error rate is lower than that of a traditional method, the self-adaptive recommendation module generates personalized learning paths based on the reinforcement learning algorithm, and the learning efficiency is improved. And repeated learning of students on invalid knowledge points is avoided.

The invention relates to the technical field of artificial intelligence, in particular to a software component analysis system based on artificial intelligence, which comprises a data acquisition and preprocessing unit, an AI-driven component identification and analysis unit, a risk detection and evaluation unit, a knowledge base and dynamic updating unit and a result visualization and disposal suggestion unit. According to the method, through a collaborative mechanism of multi-modal semantic fingerprint generation, cross-language component matching and dynamic and static feature dual verification, the bottleneck that traditional software component analysis is insufficient in recognition precision of high-confusion, cross-language and secondary packaging components is effectively broken through, and precise recognition and analysis of the components and key attributes thereof are achieved; therefore, a more reliable technical support which better meets actual business requirements is provided for software component security analysis.

The invention relates to the technical field of intelligent coal mine safety production, and discloses an intelligent coal mine safety early warning method and system based on deep learning, and the method comprises the steps: constructing a difficulty evaluation function, and achieving the progressive learning from simple to complex; based on a difficulty assessment result, a model-independent meta-learning algorithm is realized, so that the model quickly adapts to new mining area characteristics; constructing a privacy protection federated learning framework by using the meta-learning model, and realizing multi-mining-area cooperative training; a continuous learning module is constructed, and original experience is reserved when new knowledge is learned; constructing a meta-knowledge evaluation module to realize cross-mining-area safety knowledge sharing; according to the invention, the security risk identification accuracy is improved; multi-mining-area cooperative training is realized on the premise of protecting data privacy; the method has continuous optimization capability and effectively solves the problem of model drift; and efficient sharing and migration of cross-mining-area safety knowledge are realized.

The invention provides a construction method of a business element knowledge base and a local data analysis method based on the business element knowledge base. The business meta-knowledge base comprises table structure definitions for a plurality of business scenes, business rule documents and analysis templates. The local data analysis method based on the business meta-knowledge base comprises the following steps: receiving a user demand; generating a prompt instruction based on the user demand and the business meta-knowledge related to the user demand; sending the prompt instruction to the large language model, and receiving a structured data operation statement which is returned by the large language model and is generated based on the prompt instruction; and executing the structured data operation statement locally to obtain an answer to the user demand. According to the method, the business meta-knowledge base is constructed, then interaction between the local data and the large language model is achieved through the business meta-knowledge base, efficient data analysis is achieved through the large language model, and meanwhile the privacy security of the local data can be protected.

The invention provides a large model illusion relieving method and device, a medium and a product, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining an input sample which comprises meta-knowledge and dialogue history; screening the meta-knowledge by using a sample filter based on reinforcement learning, and performing model training by using the screened meta-knowledge to obtain a knowledge graph embedding model; inputting the dialogue history into the knowledge graph embedding model, and obtaining a knowledge graph embedding vector output by the knowledge graph embedding model; fusing a local knowledge vector and a global knowledge vector according to the knowledge graph embedded vector to obtain a fused knowledge graph vector; embedding the fused knowledge graph vector into an encoder-decoder model to obtain a large model; according to the method, the interference of data noise on a knowledge graph embedding method can be reduced, so that the illusion problem of a large model is relieved.

The invention relates to an airborne resource limited heterogeneous unmanned aerial vehicle cluster dynamic alliance and cooperation task planning method. Firstly, a double-layer coding scheme is designed, an outer layer optimizes a grouping strategy of heterogeneous unmanned aerial vehicles, and an inner layer completes specific distribution and task planning of the unmanned aerial vehicles. Secondly, an unmanned aerial vehicle cooperation strategy based on the dynamic alliance idea is provided, the limitation of a traditional fixed cooperation relation is broken through, and a more flexible and efficient cooperation mode is achieved. In order to solve the problem that the task position in the actual disaster environment is difficult to accurately obtain, an investigation difficulty classification formula based on the shape of a detection area is constructed and is used for quantifying the task investigation difficulty in the disaster environment. In addition, a meta-knowledge migration strategy is improved, the evolutionary state is judged by calculating the distance between the population centroid and the optimal solution, the migration radius is determined in a self-adaptive mode, and efficient distribution scheme migration is achieved. In conclusion, according to the method, multiple strategies are fused, resource allocation is optimized and calculated, the heterogeneous unmanned aerial vehicle resource utilization rate is improved through a dynamic alliance mechanism, high-value tasks are scheduled preferentially in combination with an investigation difficulty classification formula, and finally the sub-population task planning efficiency is improved by using an improved meta-knowledge migration strategy.

The invention discloses a battery health state detection method and device and a medium, and relates to the technical field of battery health management, and the method comprises the steps: collecting historical operation data and full-band EIS scanning data of a battery, constructing a battery data set, and independently collecting state parameters; training a deep reinforcement learning DRL intelligent agent through the battery data set, performing scanning decision on state parameters of the battery by using the trained DRL intelligent agent, and outputting a sparse frequency band decision vector; utilizing the sparse frequency band decision vector to control the EIS equipment to carry out sparse scanning on the target battery to obtain sparse frequency band EIS data; the method comprises the following steps: constructing a meta-knowledge base through various types of batteries, and training the meta-knowledge base through a meta-learning algorithm to obtain a reference migration model; and carrying out few-sample adaptation processing on the sparse frequency band EIS data by using the reference migration model. According to the method, sparse EIS scanning is carried out by dynamically selecting frequency points, and efficient real-time diagnosis is achieved.

The invention provides a method, a device and equipment for generating schema files in a low-code platform, and belongs to the technical field of artificial intelligence. The method comprises the following steps: in response to an operation instruction for a low-code application program, calling a large language model, and identifying the operation instruction to obtain at least one keyword; generating at least one feature vector based on the at least one keyword; based on the at least one feature vector, at least one description file is obtained from a platform meta-knowledge base, the platform meta-knowledge base is used for storing the corresponding relation between different feature vectors and the description file, and the description file is used for describing at least one attribute of the low-code application program; based on the at least one description file, a schema file of the low-code application program is generated, and the schema file is a structured description file corresponding to the low-code application program and is used for generating the low-code application program. According to the method and the device, the generation cost of the schema file can be reduced.

The invention discloses an information retrieval method of a multivariate knowledge base in the field of constructional engineering based on a large model, and belongs to the technical field of information retrieval, the knowledge base in the field of constructional engineering including multivariate knowledge such as texts, drawings, specifications and standards, cases and the like is constructed, and cross-modal and cross-type accurate retrieval is realized by adopting a large model technology; the method specifically comprises the steps of preprocessing and structured representation of multivariate knowledge, fine-tuning training of a large model adapted to the field, semantic analysis of a user retrieval request, collaborative retrieval of multi-modal knowledge, intelligent sorting and optimization of retrieval results and dynamic updating and maintenance of a system. According to the method, the problems of limited knowledge types, insufficient semantic understanding, poor professional adaptability and the like in traditional constructional engineering information retrieval are solved, the retrieval accuracy, comprehensiveness and efficiency are remarkably improved, and professional and intelligent knowledge support is provided for constructional engineering employees.

The embodiment of the invention provides a dynamic model training optimization method and device, processing equipment, a product and a medium, and is applied to the technical field of model training. The method comprises the following steps: collecting at least one environment variable in a target scene in real time; according to the at least one environment variable, generating an environment change intensity index corresponding to the target scene, the environment change intensity index being used for quantifying an environment change degree in the target scene; under the condition that the environment change intensity index is larger than or equal to a preset threshold value, fast adaptation priori is obtained, and the fast adaptation priori is meta-knowledge generated based on an MAML algorithm in advance; based on the fast adaptation priori, the strategy of the intelligent agent is optimized, the intelligent agent can perceive the environment in the target scene and autonomously take action to achieve the preset target, and the fast adaptation priori is used for guiding and adjusting the input characteristics and strategy updating speed of the strategy network of the intelligent agent. The method solves the problem that the strategy of the intelligent agent in the non-stationary environment is easy to fail quickly.

The invention discloses a multi-modal intelligent garbage detection method based on dynamic adaptive meta-learning, and the method comprises the steps: constructing a framework based on a Faster R-CNN two-stage detector fusing uncertainty estimation and multi-modal features, fusing a dynamic adaptive meta-learning mechanism, and supporting and querying a set to achieve robust adaptation through environment interaction; an enhanced garbage proposal module is designed, multi-modal feature fusion is adopted, and deep interaction with query features is supported; and constructing an advanced garbage classification module, and introducing a dynamic soft attention mechanism to realize space-semantic alignment. In the training stage, mixed meta-learning is adopted, an extended FSOD data set is combined with self-supervised pre-training, N-way K-shot subtask training is performed, meta-knowledge is acquired, and dynamic fine tuning is performed by using a labeled junk image in combination with real-time environment data. In the optimization stage, real-time detection of various types of garbage is realized in the test stage through a multi-task joint loss function supervision framework.

The invention discloses a document knowledge management method and system based on text retrieval enhancement generation, and belongs to the technical field of computer intelligence. The method comprises the steps that a document uploaded by a user is preprocessed, knowledge text blocks are divided, meta-knowledge is extracted and vectorized, and a document knowledge system is constructed; when a user puts forward a question, analyzing the question through a knowledge calling agent, and constructing a knowledge reference table containing meta-knowledge, knowledge text blocks and knowledge sources in combination with meta-knowledge and vector similarity retrieval; the user question and the knowledge reference table are fused to form a context, a large model is driven to generate an accurate answer, and knowledge tracing is provided; the question and answer effect is evaluated according to user feedback and knowledge reference accuracy, if the effect is good, question and answer pairs are stored as new knowledge, document slice parameters and the knowledge retrieval range are dynamically adjusted according to the evaluation result, and continuous optimization of a knowledge system is achieved. According to the method, the accuracy of semantic comprehension and retrieval can be improved, and the utilization capability of document knowledge is enhanced.

The invention discloses a science popularization content intelligent retrieval interaction system and method combined with a knowledge graph. The system comprises a knowledge graph construction unit, a multi-layer multi-head attention mechanism collaborative Transform coding unit, a knowledge graph embedding and Transform feature fusion unit, a retrieval result sorting unit based on fusion features, an interactive intention capture unit and a result optimization output unit. The method comprises the following steps: constructing a knowledge graph for multi-source science popularization data, extracting retrieval keyword features by applying Transform coding, fusing the knowledge graph and the retrieval keyword features, and calculating the similarity with science popularization content to finish initial sorting; and meanwhile, capturing user interaction behavior analysis intention change in real time, and readjusting and outputting an initial result in combination with knowledge graph semantic association. The method corresponds to operation steps of all units of the system, and intelligent retrieval interaction of popular science content is achieved. According to the system and the method, the problems of insufficient semantic understanding, poor interaction experience and the like of traditional retrieval are effectively solved, and the accuracy and the interaction intelligence of popular science content retrieval are improved.

A topic for building a new intent on which to train a chatbot can be received. A database of chatbot training data can be searched for a candidate intent having meta-knowledge similar to the received topic. Utterances associated with the candidate intent can be extracted. The received topic and the extracted utterances can be input to a trained machine learning model. The trained machine learning model generates example utterances for the new intent. The new intent with the generated example utterances can be used as training data for training the chatbot.

A knowledge-creation assistance device (100) comprises: a knowledge-structuring unit (111) that, on the basis of a measurement analysis result of a sample outputted from an analysis device, generates pre-complementary structured knowledge that is configured to include one or more items pertaining to measurement; a missing-knowledge estimation unit (113) that adds an item to the pre-complementary structured knowledge to generate post-complementary structured knowledge; and a knowledge-editing unit (114) that receives an editing instruction for the post-complementary structured knowledge and generates confirmed structured knowledge. The missing-knowledge estimation unit (113) adds the item to the pre-complementary structured knowledge by using an estimation model that is a machine learning model in which an item included in the pre-complementary structured knowledge is used as an explanatory variable, and an item included in the confirmed structured knowledge is used as an objective variable.

The invention discloses a sewage treatment lift pump AI intelligent regulation and control method and system based on working condition perception, and relates to the field of sewage treatment.The sewage treatment lift pump AI intelligent regulation and control method comprises the steps that multi-source heterogeneous working condition data are collected and preprocessed, and a regular real-time perception data sequence is output; carrying out calculation and feature extraction on the regular real-time sensing data sequence, and outputting a structured feature vector; inputting the structured feature vector into a preset lightweight causal graph model for causal inference, and outputting a causal task descriptor; taking a causal task descriptor as a retrieval key, performing matching in a historical operation case library, and outputting a similar historical strategy-result pair set; and jointly inputting the causal task descriptor, the similar historical strategy-result pair set and recent real-time interaction data into a meta-learner online adaptation module. According to the method, asynchronous updating is carried out on a historical operation case library, a lightweight causal graph model and a meta knowledge base of a meta learning device based on full-process data of a current control period.

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 relates to the technical field of data mining, and discloses an intelligent mining system for research data, which comprises a heterogeneous data acquisition module, a semantic association analysis module, a decision graph generation module, a time sequence feature correction module and a knowledge distillation optimization module. The heterogeneous data acquisition module captures data features and constructs a multi-layer topology through a multi-source data perception node and a dynamic dimension fusion network; the semantic association analysis module analyzes semantic association by using a concept topology modeling unit, a knowledge vector clustering unit and a multi-modal switching link; the decision graph generation module generates a core decision benchmark framework based on a strategy optimization node and a rule reasoning engine; the time sequence feature correction module performs time sequence correction and noise compensation on semantic association; and the knowledge distillation optimization module detects deviation through an entity relationship evaluation network and feeds back an optimized decision framework. The system realizes intelligent acquisition of multi-source data, dynamic analysis of semantics and adaptive generation of a decision graph, and improves the precision and efficiency of research data mining.

The application discloses a dynamic multimedia data deployment method based on reinforcement learning, comprising the following steps: establishing a meta-learning model and acquiring dynamic multimedia data; a meta-pretraining process: selecting days N to be meta-pretrained, days i for online meta-adaptation and a task environment sequence to be dynamically adapted from the dynamic multimedia data; selecting initial model parameters for the task environment in different sequences; inputting state variables, training according to a loss function of the meta-learning model after generating actions and rewards, and obtaining dynamic meta-knowledge; an online meta-adaptation process: inputting a new dynamic task environment, initializing a model by using the dynamic meta-knowledge; updating the meta-learning model on the new task environment, performing generalization training of the dynamic meta-knowledge, and obtaining a new model and meta-knowledge. The application can avoid the performance decline of edge content cache hit rate caused by a dynamic request mode under the constantly changing video popularity.

The present invention discloses a feature enhancement method, device, equipment and storage medium for cross-domain generalized image recognition, wherein the method includes: constructing a triplet input image; extracting causal features and non-causal features from the triplet input image; encoding the causal features and non-causal features to obtain two feature-level enhancement meta-knowledge; generating a feature-level implicit enhancement strategy based on the two feature-level enhancement meta-knowledge; using causal features, non-causal features and the feature-level implicit enhancement strategy to generate enhanced causal features and enhanced non-causal features; using a classifier to identify causal features, non-causal features, enhanced causal features, and enhanced non-causal features respectively, and combining the classification results and a preset loss function to reversely update the feature extractor, encoder, enhancer and classifier. The present invention effectively expands the source domain and learns cross-domain invariant causal features by learning two feature-level enhancement meta-knowledge and causal feature intervention, thereby improving the single-source domain generalization performance of the model.

The invention discloses a language model retrieval data processing method and device, a server and a storage medium, relates to the technical field of integrity verification of a model system knowledge base, and constructs a self-adaptive architecture from automatic verification of retrieval data consistency, isolation of inconsistent retrieval data and restoration of the retrieval data. Comprising the steps of performing risk assessment on dynamic knowledge vectors retrieved in real time; when the risk assessment reaches a preset risk level, calling a data verification contract from a preset distributed account book according to the unique identifier of the dynamic knowledge vector; the data verification contract is generated by executing an encryption process based on a meta-knowledge vector corresponding to the dynamic knowledge vector; the verification data verifies whether the contract is consistent with a dynamic data contract, wherein the dynamic data contract is generated by executing an encryption process based on the dynamic knowledge vector; and when the data verification contract is inconsistent with the dynamic data contract, calling a database interface to carry out failure marking on the dynamic knowledge vector in a database, and isolating the dynamic knowledge vector.

This invention discloses a gait recognition method based on sample-adaptive representation. By incorporating meta-knowledge into gait recognition, this method achieves sample adaptation, enabling the model to better perceive a wide range of complex scenarios, such as angles and conditions. The method specifically involves the following steps: acquiring gait data; defining an optimization objective; learning meta-knowledge using a meta-hypernetwork; applying attention to the spatial, temporal, and channel dimensions using meta-knowledge; integrating temporal information using meta-knowledge; and iterative training. This method is suitable for gait recognition in complex scenarios, leveraging meta-knowledge to achieve good results and generalize to diverse internal and external conditions.