13 results about "Metaknowledge" patented technology

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 artificial intelligence and industrial simulation, in particular to an evolvable industrial simulation question and answer intelligent agent system and a simulation question and answer method thereof. The evolvable industrial simulation question and answer intelligent agent system comprises a knowledge base module, a tool calling module, an intelligent agent core module, a user interaction module and a log and audit module; the knowledge base module comprises a multi-element knowledge collection unit, a document loading and cutting unit, a vectorization embedding and storage unit, a semantic retrieval unit, a knowledge evolution unit, a rule base and a case base; the application has the beneficial effects that (1) knowledge is dynamically evolved and updated autonomously and timely; (2) the system has the capability of autonomously refining knowledge from simulation results; (3) tool calling is intelligentized, and the system realizes "question and answer as calculation"; (4) the system has strong capabilities of complex problem disassembly and iterative solution; and (5) the system has excellent adaptability and expandability.

The application discloses an unsupervised meta-learning method based on visual causal intervention, which comprises an unsupervised meta-training stage and a supervised meta-testing stage. Specifically, in the meta-training stage, the method uses unlabeled support set samples to learn the causal relationship among input data, prior knowledge and predicted output and the causal relationship between hidden variables in the prior knowledge, eliminate the negative influence of context bias in the input data, and finally obtain unbiased meta-knowledge. In the meta-testing stage, the method uses the above obtained meta-knowledge as the initialization parameters of the model, combines supervised and unsupervised learning under the condition of a small amount of labeled support set samples, further eliminates the influence of data bias, and can accurately predict the label of the query set sample. The method can accurately predict the category of the output image in the few-shot image classification problem of the downstream task.

The application discloses an e-commerce API interface security vulnerability detection method based on a knowledge graph, which comprises the following steps: collecting API gateway logs and API document data of an e-commerce system, and constructing a business knowledge graph; obtaining an initial vulnerability detection rule library, and forming a rule knowledge graph; constructing a meta-knowledge graph; calling rules in the rule knowledge graph for matching detection; storing conflict events in the meta-knowledge graph; creating an embryo node, splitting a new rule node when the aggregation amount reaches a maturity threshold, and storing the new rule node as a birth event in the meta-knowledge graph; periodically calculating an effectiveness index, and storing the effectiveness index as a death event in the meta-knowledge graph when the effectiveness index is lower than an apoptosis threshold; analyzing the correlation mode between events, detecting API calling requests, and generating a vulnerability detection result. The application constructs business, rule and meta-knowledge graphs to realize API vulnerability detection self-evolution, and has the advantages of wide coverage, high accuracy and continuous optimization.

The application relates to a causal DAG discovery method for an online service system based on fusion of soft priori. The method comprises the following steps: obtaining observation data and text meta-knowledge of the service system, preprocessing to form a standardized sample set, identifying variable types and semantics and outputting; generating a natural language description according to the variable semantics, querying a large language model for an ordered variable pair, analyzing to obtain three types of causal probability vectors, and calibrating to obtain an edge-level priori probability. An appropriate conditional independence test method is selected, high-confidence independent / dependent sentences are divided, and weights are assigned. A candidate directed acyclic graph is selected as an initial structure, parameters are estimated by linear regression, and data fitting scores are calculated, language priori scores, conditional independence penalty terms and counterfactual self-consistency penalty terms are calculated. Fusion is carried out into a hybrid score function, discrete optimization is carried out under the constraint of a directed acyclic graph, and a causal graph structure with the optimal score is output. The method can improve the efficiency and accuracy of a smart operation and maintenance system.

The application relates to the technical field of knowledge graph construction and intelligent information retrieval, and discloses a retrieval method based on a knowledge graph and related equipment. The method comprises the following steps: constructing a triple basic unit knowledge graph of entity-relation-entity; receiving a query sentence, and performing deep semantic segmentation on the query sentence to obtain semantic units; analyzing the query intention of a user according to the segmented semantic units and the triple basic unit knowledge graph, and mapping the query intention of the user to the triple basic unit knowledge graph; according to the mapped query intention, finding the optimal retrieval result on the triple basic unit knowledge graph based on a mapping relationship, and feeding back the optimal retrieval result to a retrieval end, thereby completing the retrieval work based on the knowledge graph. Through a recommendation algorithm, the possible results are compared according to the sorting of a specific algorithm, and a higher-precision retrieval result is obtained, so that accurate result matching is further realized.

The application discloses a brain effect connection learning method based on a causal autoencoder and meta learning, first encodes input fMRI time sequence data through a causal autoencoder (CAE), extracts time sequence space information, then learns causal connections between brain regions by using a structural equation modeling (SEM) in the CAE, finally, uses a model-agnostic meta-learning (MAML) reptile to learn brain effect connection meta knowledge (refers to brain effect connections commonly possessed by most subjects) shared between different subjects, and migrates to the CAE to improve the recognition ability of the model on small sample data. The application uses meta learning to utilize the brain effect connection information commonly shared between different fMRI subjects, effectively alleviates the problem that small sample data is difficult to provide sufficient data. Experimental results on real resting state fMRI data show that the application can provide an alternative auxiliary means for medical researchers to analyze the mechanism of the human brain.

A computer-implemented digital knowledge management system for institutional learning and decision support, comprising the following: A user interaction interface configured to receive structured institutional information from a variety of users connected to a university. This structured institutional information includes strategic issues, institutional goals, performance indicators, academic activity data, and decision-relevant input. an input processing unit that is operationally connected to the user interaction interface and is configured to capture, classify, and convert the received structured institutional information into machine-readable data sets; a collaborative culture assessment unit consisting of at least one processor and a memory in which executable instructions are stored, wherein the collaborative culture assessment unit is configured to collect and process organizational behavior data representing loyalty, engagement, cohesion, well-being and sense of community among members of the institutional leadership; a knowledge generation unit that is operationally linked to the collaborative culture assessment unit, wherein the knowledge generation unit is configured to analyze received knowledge records and generate new institutional knowledge by exploring alternative ideas, identifying new insights, and combining individual and collective institutional contributions; a knowledge exchange unit that is communicatively linked to the knowledge generation unit, wherein the knowledge exchange unit is configured to distribute the generated institutional knowledge to authorized users via controlled communication channels and structured knowledge dissemination procedures; a knowledge application unit designed to transform distributed institutional knowledge into decision support tools, including proposed strategic actions, operational recommendations, and measurable performance targets; an institutional learning repository consisting of a permanent digital storage system configured to store knowledge records, decision outcomes, institutional experiences, and insights gained from implemented decisions; a decision quality assessment processor that is operationally linked to the institutional learning repository and the knowledge application unit and is configured to evaluate generated decisions against predefined institutional criteria, including problem-solving effectiveness, strategic value contribution, achievement of institutional goals, and contribution to organizational learning; and a coordination processor configured to coordinate the operational interaction between the collaborative culture assessment unit, the knowledge generation unit, the knowledge sharing unit, the knowledge application unit, the institutional learning repository, and the decision quality assessment processor, such that the collaborative organizational conditions influence knowledge generation, knowledge distribution, knowledge utilization, and the subsequent evaluation of strategic decisions.

The application relates to the technical field of artificial intelligence and industrial simulation, in particular to an evolvable industrial simulation question and answer intelligent agent system and a simulation question and answer method thereof. The evolvable industrial simulation question and answer intelligent agent system comprises a knowledge base module, a tool calling module, an intelligent agent core module, a user interaction module and a log and audit module; the knowledge base module comprises a multi-element knowledge collection unit, a document loading and cutting unit, a vectorization embedding and storage unit, a semantic retrieval unit, a knowledge evolution unit, a rule base and a case base; the application has the beneficial effects that (1) knowledge is dynamically evolved and updated autonomously and timely; (2) the system has the capability of autonomously refining knowledge from simulation results; (3) tool calling is intelligentized, and the system realizes "question and answer as calculation"; (4) the system has strong capabilities of complex problem disassembly and iterative solution; and (5) the system has excellent adaptability and expandability.

The application discloses a photovoltaic and energy storage power grid power generation power prediction method based on a model-agnostic meta-learning method, solves a photovoltaic and energy storage power grid power generation power prediction problem through a meta-learning technology, designs a generation model, combines task characteristics, splits prediction problems of all devices into power generation device power prediction problems in different power station ranges, and through a meta-learning method, meta-knowledge learned from one task can be transferred between different tasks, and the conversion of the meta-knowledge between different tasks plays a key role in power generation power prediction. The application jointly models dynamic time sequence characteristics and static characteristics related to power generation efficiency, learns a representative model initialization parameter value, and significantly improves fitting speed and prediction accuracy of the model in a new scene.

This invention discloses an intelligent product cognitive modeling and multilingual document generation and verification system and method, belonging to the fields of knowledge engineering and natural language processing. The system adopts a three-layer architecture of "knowledge planning layer + knowledge production layer + knowledge service layer," and also includes two core components: "workflow engine + testing framework." The knowledge planning layer is used to construct a meta-knowledge system and quantitatively evaluate its completeness through coverage metrics; the knowledge production layer is used to perform document generation, terminology standardization, structure optimization, fact verification, and automated testing; the knowledge service layer is used to perform multilingual release, version archiving, and feedback collection; the workflow engine performs intelligent scheduling, using task priority sorting to form a closed-loop processing flow; the testing framework is used to generate execution evidence and calculate hash digests for audit trails. This invention improves document accuracy from unverifiable to 100% observable, achieves knowledge coverage of user intent of 1, and increases development efficiency by 18 times.

The application discloses a language model retrieval data processing method and device, a server and a storage medium, relates to the technical field of model system knowledge base integrity verification, and constructs a self-adaptive architecture from automatically checking retrieval data consistency to isolating inconsistent retrieval data and then repairing retrieval data. The application comprises the following steps: performing risk assessment on a dynamic knowledge vector of real-time retrieval; when the risk assessment reaches a preset risk level, calling a data verification contract from a pre-set distributed ledger according to a unique identifier of the dynamic knowledge vector; the data verification contract is generated based on an encryption process performed on a meta-knowledge vector corresponding to the dynamic knowledge vector; verifying whether the data verification contract is consistent with a dynamic data contract, wherein the dynamic data contract is generated based on an encryption process performed on the dynamic knowledge vector; when the data verification contract is inconsistent with the dynamic data contract, calling a database interface to mark the dynamic knowledge vector as invalid in a database and isolating the dynamic knowledge vector.