481 results about "Graph spectra" patented technology

The invention discloses a multi-modal knowledge graph construction and intelligent fault diagnosis method and system for a communication power supply system. The method comprises the steps of multi-modal knowledge graph construction, graph increment updating and intelligent fault diagnosis. The multi-modal knowledge graph construction adopts a unified data acquisition semantic specification and a heterogeneous data fusion strategy, a dynamic evolution heterogeneous graph is established, and equipment full life cycle state perception and causal link modeling are realized; the efficient, atomicity and consistency updating of the atlas is realized by the hierarchical atlas increment through shadow composition, structural difference rate calculation and a subgraph replacement mechanism; according to the intelligent fault diagnosis, an alarm propagation sub-graph is constructed, a path convergence and multi-dimensional attribute scoring mechanism is adopted, and deep joint verification is carried out by using a multi-modal evidence fusion network; and in combination with a reinforcement learning optimization strategy embedded based on a graph structure, adaptive scheduling and diversity constraint of a diagnosis path are realized, and the fault positioning accuracy and the system intelligence in a complex scene are remarkably improved.

The invention provides a self-adaptive network topology dynamic reconstruction method and system based on deep reinforcement learning, and relates to the technical field of deep reinforcement learning, and the method comprises the steps: obtaining the topology state information, service flow distribution information and historical reconstruction records of a current network; extracting topological correlation characteristics among nodes through graph convolution operation, and generating fusion state representation in combination with service flow information; inputting the fusion state representation into a deep reinforcement learning model to identify bottleneck nodes and redundant links, and outputting a reconstruction action candidate set; searching and evaluating the long-term cumulative income of the candidate actions through a Monte Carlo tree, and screening an optimal reconstruction action sequence; a graph coloring algorithm is utilized to allocate time slots and process resource conflicts, and a resource-feasible topology adjustment scheme is generated; and extracting a network evolution rule through tensor decomposition, and constructing a topological optimization association mapping graph. According to the method, the network bottleneck can be intelligently identified, the network topology structure is dynamically optimized, and the network performance and the resource utilization rate are effectively improved.

The invention relates to the technical field of industrial component detection, in particular to a transformer iron core detection method based on computer vision, which comprises the following steps of: acquiring an iron core image, extracting key pixel characteristics, screening a directional scattering abnormal region to generate an interference map, extracting a consistent gradient region correction image to generate a reconstruction map, and positioning a symmetric disturbance generation structure map by integral gray difference. And analyzing an overlapping relation by a superposition structure graph to generate an abnormal component graph, and evaluating a risk level by matching a reference index to generate an early warning graph layer. Interference reflection and structural features can be distinguished through linkage analysis of the pixel direction vector and the brightness change frequency, correction of a distorted area in an image is realized based on a gray statistical stable value, and the distortion of the image is corrected by constructing a symmetric point map and analyzing the change trend of a gradient difference value sequence. And the structural overlapping relation is quantitatively judged by combining a component mapping profile diagram, so that the relevance between an abnormal region and a key component is clearly expressed, and the grading evaluation capability of various fault risks in the iron core is improved.

The invention discloses a supply chain risk identification method and system based on a knowledge graph, belongs to the technical field of supply chain management and artificial intelligence crossing, and aims to solve the technical problem of how to realize dynamic monitoring, accurate identification and active early warning of supply chain risks, improve full star, real-time performance and interpretability of supply chain risk identification, and improve the risk identification efficiency. According to the technical scheme, the method comprises the following steps: collecting and treating multi-source data: collecting static background information and dynamic risk information of a supplier, and carrying out highly intelligent data treatment on the collected static background information and dynamic risk information of the supplier through a data treatment engine to ensure data quality and consistency; constructing a dynamic knowledge graph; intelligent risk identification: based on a graph topological structure and dynamic attributes, identifying key risk nodes and communities, tracing in time, marking risks, and performing early warning; decision support and visualization are carried out; and dynamically optimizing and feeding back.

The invention discloses a power distribution cabinet maintenance system based on artificial intelligence, and belongs to the technical field of intelligent power distribution cabinet diagnosis. Normalization, time sequence feature extraction and denoising are carried out on the collected data, an electrical topological graph is automatically constructed, node states are vectorized, text semantics are coded by utilizing a BERT class model, and a maintenance knowledge graph is generated; through fusion of multi-source data, a multi-branch neural network is constructed, state perception and risk determination are realized, and a fault trend and structure degradation are identified. Calculating a potential fault risk coefficient, and triggering a structure health monitoring mechanism; analyzing the structural health based on a topological graph and a graph neural network, and starting semantic strategy retrieval when the structure is abnormal; comparing the semantic conformity between the state and the historical strategy, and assisting in generating a precise maintenance strategy; according to the system, dynamic monitoring, intelligent early warning and strategy recommendation of the operation state of the power distribution cabinet are realized, and the operation and maintenance efficiency and the equipment reliability are improved.

The invention relates to the technical field of intelligent manufacturing and production management, in particular to a self-adaptive production scheduling system based on artificial intelligence, which comprises a data acquisition and reference construction module for analyzing process data to construct a directed acyclic graph representing a non-interference state as a reference map; the theoretical disturbance simulation module is used for converting the interference rule into a graph change instruction, generating a theoretical damaged state graph and obtaining a theoretical difference feature vector; the theoretical difference feature vector comprises, but is not limited to, a vector form obtained after a difference matrix is expanded according to rows or columns in terms of mathematical representation; the real deviation extraction module is used for collecting real-time state data to construct a real-time operation state diagram and calculating a real difference feature vector; a double-domain coupling decision module; an adaptive scheduling execution module; according to the method, the causal relationship is verified by comparing the form of theoretical deduction and actual observation, non-systematic noise is effectively filtered, and accurate response to real faults is realized while the stability of the production rhythm is maintained.

The invention discloses a power generation equipment state fault diagnosis method and system based on artificial intelligence, and the method comprises the steps: actively injecting a mechanical excitation signal of a preset frequency spectrum into a key part according to a physical topological structure of power generation equipment, and carrying out the fusion to generate a time-space-frequency three-dimensional data volume; inputting the three-dimensional data volume into a physical embedded variational auto-encoder, and outputting an equipment state pure feature tensor; inputting the pure feature tensor into a graph space-time causal reasoning network to generate a fault propagation causal graph with probability weight; performing multi-agent diagnosis on the fault propagation causal atlas, and outputting a fault diagnosis report which has a credibility interval and comprises fault positioning and root cause analysis; and mapping the fault diagnosis report to the digital twin of the equipment in real time, and outputting a self-adaptive maintenance strategy sequence which minimizes the expected value of the whole life cycle operation and maintenance cost. According to the embodiment of the invention, the accuracy and anti-interference capability of fault diagnosis can be improved, and the operation and maintenance cost can be effectively reduced.

The invention discloses an intelligent management system and method for knowledge graph quality evaluation and self-repairing, belongs to the technical field of knowledge graphs, and aims to solve the problems that in traditional knowledge graph management, manual auditing efficiency is low, an effective automatic repairing means is lacked, and data complexity and real-time changes are difficult to deal with. The system firstly collects multi-source heterogeneous data in a target field, cleans the data through a deep learning noise recognition model, extracts entities and relationships by using a natural language processing technology, and adds metadata to convert the entities and relationships into graph structure data; then, a graph framework is defined based on the ontology, entity semantic alignment is achieved in combination with a graph neural network, and a knowledge graph is constructed by complementing implicit relations with the help of a pre-training language model. Then, the quality of the atlas is quantitatively evaluated through a four-layer quality evaluation system, meanwhile, a repair scheme is generated based on vulnerability feature extraction, knowledge base matching and decision fusion, and intelligent self-repair is achieved; the map can be monitored in real time and evaluated regularly, a repair strategy and a knowledge base are optimized through reinforcement learning, it is ensured that the map is kept accurate and time-efficient for a long time, and the practical value is improved.

The invention discloses a network security intrusion detection system and method fusing a graph neural network, and the method comprises the steps: collecting multi-source flow data in a network operation process, constructing an induction topological graph through employing a graph convolution modeling technology, and revealing a deep incidence relation between network nodes; a hidden attack mode is identified by adopting a multi-layer graph propagation and resonance enhancement technology, and high-quality node embedding representation is generated through a graph attention mechanism and abnormal resonance amplification; an attack behavior map is extracted by combining depth map learning and a map pooling technology, and a self-adaptive protection strategy is generated through map inversion mapping and dynamic topological transformation; an interlaced detection sequence is constructed by adopting a multi-layer detection rule decomposition and graph optimization sorting technology, and a self-adaptive response signal is generated through active defense prediction and time difference bottleneck analysis, so that intelligent detection, accurate analysis and self-adaptive protection of network intrusion behaviors are realized, and the intelligent level and protection effect of network security protection are improved.

The invention relates to the technical field of big data, in particular to an intelligent operation and maintenance management method based on a big data algorithm, and the method comprises the steps: constructing and continuously updating a dynamic fault association graph through inputting multi-source heterogeneous operation and maintenance data; starting full-graph scanning based on a predefined period, detecting an abnormal topological structure through a graph pattern recognition algorithm, and marking potential risk nodes; executing dynamic influence diffusion simulation on the potential risk nodes, calculating a business influence severity quantized value after the fault, and marking fault propagation vulnerabilities according to the quantized value; taking the potential risk node as a starting point, executing a reverse traceability algorithm for preferentially exploring a path pointing to a fault propagation vulnerable point, and outputting a fault propagation path and a source fault node identifier; and finally generating and executing a fault processing strategy. The process solves the problem that traditional operation and maintenance cannot quantitatively evaluate and discriminate the highest priority disposal object from numerous potential risks, and realizes accurate positioning and active prevention and control of weak links of fault propagation.

The invention discloses an information reasoning method, system and equipment based on knowledge enhancement and a medium. The method comprises the following steps: constructing a target knowledge graph and a target knowledge graph index according to a target entity, a target attribute and a target constraint condition in to-be-reasoned information; determining an atlas sub-graph of the target entity through the first-level entity hash index; screening an entity attribute set matched with the target attribute from the atlas sub-graph based on the secondary attribute classification index; filtering the entity attribute set matched with the target attribute according to the three-level space-time dimension index to obtain a target dynamic attribute; recalling a target retrieval fact from the target knowledge graph according to the graph sub-graph, the entity attribute set and the target dynamic attribute; the to-be-reasoned information and the target retrieval facts are input into the preset large language model to obtain the target reasoning result, knowledge enhancement reasoning can be carried out by constructing the multi-level index target knowledge graph and fusing the dynamic attribute constraints in combination with the large language model, and then the accuracy, the real-time performance and the field adaptability of the reasoning result are improved.

The invention relates to the technical field of computers, and discloses a chip mounter material tracing management method and system based on a knowledge graph. The method comprises the following steps: constructing a dynamic knowledge graph covering the whole process of a patch; injecting multi-source data in real time to drive dynamic updating of the atlas; when the materials are abnormal, reverse tracing and forward influence double-path reasoning are started; isolation, rework and reset instructions are automatically generated and pushed to a manufacturing execution system; and synchronously updating the atlas state to form an auditable record. The system comprises a dynamic knowledge graph construction module, a real-time data injection module, a bidirectional inference engine module, an automatic processing instruction generation module and a graph state synchronization module. According to the scheme, millisecond-level error positioning and closed-loop processing are realized, and the tracing efficiency and the quality guarantee capability are improved.

The invention discloses a power distribution terminal equipment fault intelligent diagnosis method and system based on the Internet of Things. The method comprises the following steps: S1, collecting multiple items of operation data of a power distribution terminal to construct a time sequence sample; s2, extracting power disturbance characteristics based on a sliding window, generating a behavior coupling matrix and a topological connection matrix, and calculating node redundancy; s3, fusing the two types of relationships to construct a dynamic graph; s4, inputting a graph diffusion network, fusing a structure and a behavior diffusion result, and generating a node state feature vector; s5, inputting a multi-label classification model to identify the fault type and probability; s6, generating a response instruction in combination with the fault type and the redundancy; s7, response is executed, the relation matrix and the classification model are updated, and the diagnosis process is optimized in a closed-loop mode. According to the invention, accurate fault identification and quick response of the power distribution terminal are realized, and the intelligent operation and maintenance level of a power supply system is improved.

The invention relates to an industrial internet multilayer causal motif abnormal propagation path identification method and system, and the method comprises the steps: firstly carrying out the construction and extraction of a multilayer high-order motif, extracting a motif unit which expresses the local high-order structure features through the construction of a semantic hierarchical graph structure in combination with a frequent sub-graph mining and cross-layer motif alignment mechanism, and carrying out the recognition of the abnormal propagation path of the multilayer causal motif. Stable and uniform multi-layer motif representation is formed; then, on the basis of the structural equation model, motif variables are regarded as endogenous variables of a causal model, a causal path between motifs is mined by introducing conditional mutual information and a Bayesian structure learning algorithm, an average causal effect is calculated to construct a causal consistency matrix, and causal community division is realized in combination with a weighted modularity optimization method; and finally, quantifying the dynamic change of a community causal structure by constructing a causal deviation graph between an expected causal graph and an observed causal graph, and assisting in identifying a causal-driven abnormal propagation path. According to the method and the system, accurate detection and causal traceability of equipment-level and subsystem-level abnormal modes in an industrial system can be realized.

The invention relates to the technical field of artificial intelligence in the education industry, in particular to an intelligent composition quality evaluation method and system based on a large language model, and the method comprises the steps: carrying out the text normalization and semantic unit segmentation of a composition, extracting a semantic vector through a first large language model in combination with a context enhancement strategy, and positioning a semantic fracture risk position; recognizing composition core elements through a second large language model, and mapping the composition core elements back to the semantic unit sequence; constructing a demonstration logic diagram, extracting a core demonstration path and abstracting the core demonstration path into a logic role topological graph; in combination with a pre-constructed writing specification knowledge graph, comparing structural compliance, connection strength and an expected support relationship, identifying and demonstrating logic defects, and generating a global deduction item list; semantic clustering is carried out on illegal items to form an error label set, comprehensive weight is calculated in combination with historical data of students, and core weak items are positioned; according to the application, the logic analysis depth of intelligent evaluation of the argument is remarkably improved, and the pertinence and practicability of teaching feedback are improved.

The embodiment of the invention provides a method, device and system for constructing an attack graph, and a storage medium, and relates to the technical field of network security. The method comprises the steps that based on a test case knowledge base of a target system, an attack graph information expansion algorithm is executed through a knowledge graph technology, and expanded information is obtained; utilizing the expanded information to construct an attribute attack graph of the target system; and carrying out attack path description on vulnerabilities of the target system based on the attribute attack graph. An attack graph information expansion algorithm is executed through a test case knowledge base based on a target system and by means of a knowledge graph technology, information needed for constructing an attack graph can be obtained, potential attack paths can be mined, the attack graph information is expanded, and then a comprehensive and accurate attribute attack graph is constructed. According to the method, effective description of the vulnerability attack path of the target system is realized, so that the constructed attack graph can reflect the security condition of the network system more truly, more scientific decision support is provided for network security management personnel, and the network security protection effect is improved.

The invention relates to the technical field of cognitive inference, and discloses a cognitive inference system fusing a dynamic knowledge graph and deep learning. The system comprises a cognitive event stream processing module which is used for acquiring a real-time event stream and a cognitive target stream, identifying entity state change and extracting a reasoning intention; the graph mapping generation module is used for receiving the entity state change and the reasoning intention, constructing a mapping relation with knowledge graph topology and generating a multi-dimensional graph mapping library; the cognitive path decision-making module is used for extracting key topological characteristics from the multi-dimensional map mapping library, calculating a reasoning path migration probability and generating an initial cognitive path set; the time sequence evolution prediction module is used for carrying out time sequence evolution modeling on the knowledge graph topology based on the initial cognitive path set, identifying node state offset and updating a multi-dimensional graph mapping library; and the reasoning optimization module is used for analyzing a reasoning stage and a reasoning dependency relationship of the cognitive target flow according to the updated multi-dimensional map mapping library, and generating a dynamic cognitive reasoning library.

The invention belongs to the technical field of knowledge graph construction, and discloses a supply chain knowledge graph construction method based on time sequence dynamic perception and a large language model. Constructing a time sequence dynamic sensing model, and respectively generating a time-sensitive embedding matrix for the entity and relationship of each sub-graph in a historical time window; inputting the time-sensitive embedded matrix into an aggregator to further mine structure and semantic information of entities and relationships; establishing a dependency relationship of an autoregression model learning sub-graph in a time sequence, and generating a time sequence evolution representation; and inputting the time sequence evolution representation into a pre-trained large language model, generating candidate entities or candidate relationships to complement the fact tetrad, and updating the sub-graph sequence of the current timestamp. According to the method disclosed by the invention, the supply chain domain knowledge is adapted while the general semantic understanding capability is reserved, and the balance of dynamic evolution modeling, long-period dependency capture and efficient utilization of the domain knowledge is realized, so that the reliability and interpretability of a construction result are ensured.

The invention discloses an operation and maintenance workflow cooperation system and method, and relates to the technical field of business process.The method comprises the steps that after a natural language operation and maintenance requirement is received, a subtask set containing task attributes is extracted through a semantic model built based on a pre-training operation and maintenance field language model; inputting the sub-tasks into a causal mining model, capturing an implicit dependency relationship between the tasks through an attention mechanism which takes task types and resource demands as weight regulation factors, and generating an operation and maintenance relationship graph which contains dependency confidence coefficients and dependency types and does not have cyclic conflicts; splitting the atlas into a task chain set and a free task point set by adopting a causal-oriented greedy pruning algorithm based on a directed edge association subtask maximum aggregation and task chain set scale minimization principle; task chains are distributed through a weighted matching algorithm in combination with the chain overlap ratio and the to-be-handled task amount of the intelligent agent, remaining free task points are distributed according to the balance principle, and accurate disassembly and efficient cooperation of operation and maintenance tasks are achieved.

The invention relates to the technical field of data processing and mode recognition, in particular to a bearing fault diagnosis method based on multi-scale feature fusion, which comprises the following steps: fusing multi-source data such as vibration, acoustic emission and rotating speed, performing angle domain resampling by using rotating speed data, generating a two-dimensional order spectrogram, and stacking to construct a three-dimensional working condition information tensor; a master-slave modulation heterogeneous neural network is adopted, high-dimensional spatial-temporal features are extracted through a main branch three-dimensional convolutional network, time sequence details are extracted from an original sequence through an auxiliary branch one-dimensional convolutional network, affine transformation parameters are generated, and dynamic modulation is achieved on the high-dimensional features; and the output state vector is mapped to a fault evolution knowledge graph, probability prediction is carried out through a graph attention network and by introducing a Monte Carlo discarding mechanism, a probability mean value is calculated as a fault classification result, and the diagnosis confidence is quantified by a probability variance. According to the invention, through multi-scale feature fusion and dynamic modulation, the problem of insufficient feature discrimination caused by scale mismatch under variable working conditions is solved.

The invention discloses a homework mark correcting method and system with a sorting and concluding function, and the method comprises the steps: obtaining student homework text data, dividing the data according to question blocks, and generating difficulty labels; inputting the operation unit into a BERT model to generate an embedded vector; comparing the standard answers in the same vector space to obtain an error prototype; error clustering is completed based on a graph structure and an ant colony algorithm; finely adjusting an output layer of the BERT model by utilizing a clustering result to form a classification boundary; constructing a heterogeneous graph containing students, questions, error categories and difficulty levels, and generating a multi-level mark-leaving graph; and performing structured storage on the data according to the multi-dimensional index. According to the invention, the homework correction intelligence, classification and traceability are improved.

The invention discloses a graph structure-based domain penetration attack path generation method, which belongs to the technical field of network security, and comprises the following steps of: constructing a multi-level relation graph comprising a host node, a service node and a user node through automatic detection by taking any host in a domain as a starting point; assigning a weight attribute to the atlas edge based on a vulnerability library and an attack pattern library; an improved heuristic graph search algorithm is adopted, all feasible attack paths and threat scores thereof are generated by integrating the path length, attack difficulty and permission improvement effect, and the problem that the threat scores of all the feasible attack paths are influenced in various scenes and dynamic change domain environments is solved. The technical problems of realizing comprehensive automatic penetration testing, accurately identifying potential attack paths and establishing a systematic threat assessment mechanism are solved, the automation, intelligence and high-efficiency level of domain penetration testing is remarkably improved, and the method has good adaptability, expansibility and practical value and is suitable for popularization and application. And attack path discovery and risk early warning work in a dynamic network environment with high security requirements can be effectively supported.

The invention relates to the technical field of knowledge graph completion, provides an implicit relation perception time sequence knowledge graph completion method based on dynamic embedding and self-attention, and aims to improve the inference and completion capability of missing facts in a time sequence knowledge graph. According to the method, time evolution modeling, a graph neural network and semantic similarity calculation are combined, and dynamic embedding representation fusing static, trend and periodic characteristics is constructed. Explicit structure information is extracted through a multilayer relational graph convolutional network, and meanwhile, an implicit semantic similarity relationship under synchronous and asynchronous time is introduced to construct a sparse semantic graph. Structural information and semantic information are fused through GRU, multi-time step features are aggregated by adopting a time perception self-attention mechanism, and key time information is highlighted. And finally, entity prediction is completed by using a ConvTransE decoder, and the model is optimized through cross entropy loss. According to the method, a static structure and implicit semantics can be modeled at the same time, the time sensitivity is enhanced, and the method is suitable for large-scale dynamic graph completion and has better reasoning ability and generalization performance.

The invention discloses an energy storage system fault database indexing method, and particularly relates to the technical field of fault prediction. The method comprises the following steps: firstly, constructing an energy disturbance vector sequence matrix in a unified time window, extracting features based on a continuous variation rate and energy residual distribution, and generating an energy disturbance feature spectrogram; establishing an energy propagation path atlas in combination with a system module topological relation, and introducing a time sequence consistency identifier; a fault evolution fingerprint is generated through graph embedding coding, similarity index matching is carried out in combination with a standard fault trajectory, and a fault type and a positioning weight are output; real-time energy indexes are further fused, a micro-fault position prediction map is generated, and dynamic early warning is achieved; according to the method, accurate identification and visual positioning of the micro-fault of the energy storage system in a complex scene can be realized, and the operation safety and the intelligent operation and maintenance capability of the system are improved.

The invention relates to the technical field of data processing, and discloses a full-life-cycle-oriented engineering technology data governance method and a full-life-cycle-oriented engineering technology data governance system. The method comprises the steps that project object attributes are divided into an anchor point attribute set and an extended attribute set, a locking mark is added to the anchor point attribute set, the extended attribute set is layered according to stages to obtain a data object meta-model, a dependency relationship mapping table is established according to the data object meta-model to achieve automatic verification when the attributes are changed, and the attribute change is completed. And establishing a spatial topological graph and a process graph to diagnose inconsistent defects, establishing a weighted graph to quantitatively analyze change influences, evaluating data quality according to scene types, and intelligently complementing missing attributes. According to the method, the integrity and consistency of cross-stage transmission of engineering data, the real-time performance and accuracy of multi-professional data collaboration, the depth and automation degree of data quality diagnosis, the scientificity and comprehensiveness of change impact analysis and the flexibility and efficiency of data utilization are improved.

The invention belongs to the technical field of fan arrangement, and discloses a fan point location automatic arrangement method and system based on artificial intelligence, and the method comprises the steps: firstly receiving a plurality of spatial constraint layers and deployment parameters, and unifying the coordinates; a conflict relation graph is constructed based on the processed constraint layers, nodes of the graph are different constraint layers, and edges are conflict strength among constraints; inputting the map into a pre-training map attention network, and reasoning to obtain a conflict slow-release factor which quantifies a constraint relaxation degree; then the factors are fed back to a point location search algorithm, and candidate point locations are evaluated by using a scoring function of the fusion factors; and finally, outputting a final arrangement scheme according to a scoring result. According to the method, through artificial intelligence middleware, such as a graph attention network, an artificial intelligence-based search algorithm and the like, the problem of excessive region deletion of a traditional method is solved, and the area and arrangement reasonability of a deployable region are improved.

The invention relates to the field of knowledge graph data processing, and discloses an indoor space automatic layout and rendering method and system based on a design knowledge graph, and the method comprises the steps: maintaining an entity knowledge graph containing a variation path, and discretizing a continuous geometric space into an environment topological graph; executing node mapping and resource topology handshake verification based on subgraph isomorphism; and responding to the mapping deadlock, triggering recursive topology rewriting along the variation path to reconstruct the query sub-graph, and generating a configuration instruction. According to the method, continuous geometric search is converted into discrete atlas isomorphism and attribute propagation, the high-dimensional space layout calculation complexity is reduced, the deadlock problem under the over-constraint condition is solved by utilizing a recursive variation mechanism, and the configuration efficiency is improved. And the logic leakproofness and engineering compliance of the layout scheme are ensured.

The invention discloses an industrial product surface defect traceability method, system and device, and relates to the technical field of industrial product surface defect traceability, and the method comprises the steps: collecting actual defect time sequence data of a product and supplementary data generated by a digital twin system, and building a multi-view topological expression; extracting consistency characteristics of each view to obtain space-time integration characteristics of defect evolution; performing instance mining according to the multi-view topological expression and the space-time integration characteristics to establish a surface defect quality analysis knowledge graph; defining type vectors of head entities and tail entities for various relationships in the knowledge graph, and generating initial semantic representations of newly added entities; constructing a knowledge graph evolution process of dynamic graph updating to perform sub-graph level updating, and performing graph enhancement by generating soft facts and confidence thereof; a reinforcement learning recommendation framework is constructed, and product surface defect traceability is realized; according to the method, the reasoning capability of the model under the sparse atlas is improved, and the reliability and interpretability of a reasoning path are maintained.

The invention relates to the technical field of colloidal gold detection, and discloses a colloidal gold multi-index synchronous detection system for AI multi-task scheduling. The system comprises a spectral feature decoupling module which separates overlapped spectral responses based on a graph convolution network and generates a spectral fingerprint spectrum; the multi-index quantization module analyzes the nonlinear mapping relation through a variational auto-encoder to generate a quantization decision vector; the fluid dynamic modeling module is combined with a Navier-Stokes equation to invert a sample diffusion path; the signal drift suppression module suppresses background interference by applying a generative adversarial network; the task scheduling engine module adopts a Monte Carlo tree search strategy to allocate computing resources; the cross interference compensation module generates a compensation coefficient matrix by using a tensor decomposition algorithm; and the feedback module controls the micro-valve array to optimize the detection synchronism. All the modules cooperate to achieve multi-index synchronous detection, the detection precision, efficiency and anti-interference capability are improved, and the system is suitable for the fields of medical diagnosis, food safety detection and the like.

The invention relates to the technical field of knowledge fusion, and discloses an interactive health science popularization question and answer guidance system for constructing a medical knowledge graph, comprising: a guidance instruction generation module extracting attribute variation dimensions between to-be-aligned data and candidate nodes and generating a guidance instruction; the feedback analysis module converts the feedback data into a feedback correction vector; the atlas fusion actuator determines an initial fusion weight according to the coordinate distance, determines a weight correction coefficient based on the topological centrality of the target node, constructs a weighted feature update function by using a feedback correction vector and a local topological relation to calculate a feature increment, and completes feature convergence fusion; according to the method, a priori constraint force balance feedback feature pointing value provided by a map structure is utilized, it is ensured that heterogeneous knowledge fusion conforms to medical ontology logic, and the global stability of science popularization map evolution is guaranteed.