244 results about "Inference machine" patented technology

The invention relates to a multi-modal data and RAG technology fused power design knowledge base construction method, and belongs to the technical field of power software development. The method comprises the following steps: carrying out collection and information extraction on multi-source heterogeneous original data; the method comprises the following steps of: constructing a multi-dimensional knowledge element structure containing parameters, specifications and case relationships by carrying out classification, specialized and precise processing and cross-modal association on data; based on a vector, graph and relational database mixed storage architecture, semantic vector efficient retrieval, knowledge graph relation management and business data synchronization are achieved respectively; and a dynamic optimization result is subjected to hybrid retrieval, a dual-drive reasoning mechanism outputs compliance conclusions and bases, and a retrieval enhancement generation service ensures that output contents conform to specifications. And systematic management and intelligent application of the electric power design knowledge are realized.

The invention discloses an intelligent substation safety measure checking method and system, and the method comprises the steps: obtaining a secondary system topological structure, equipment information and historical safety measure ticket data of a substation, and constructing a quaternary knowledge graph; according to the quaternary knowledge graph, extracting multi-modal features of the maintenance task and identifying the type of a maintenance scene by combining a memory guide reflection decision reasoning mechanism, optimizing a rule reasoning process through a distributed guide local search algorithm, and generating an optimal safety measure operation set for a specific maintenance scene; automatically identifying a correlation loop and determining a minimum safety isolation range through a memory guide decision reasoning mechanism, dynamically generating a minimum safety operation set according to the real-time state of the equipment, and adaptively adjusting operation steps; and an operation dependency relationship model is constructed in combination with unwrapping variational multi-graph representation learning and a distributed guide local search algorithm, and operation sequence compliance verification, risk level assessment and dynamic visual early warning are realized. According to the invention, accurate formulation, dynamic adjustment and risk early warning of safety measures of the intelligent substation are realized.

The invention discloses an agent collaborative optimization data center management system based on knowledge graph driving, and relates to the technical field of data center management. The system specifically comprises the following modules: a modeling and entity management module, a cross-regional global scheduling optimization module, an agent game negotiation optimization module, an agent trust management module, a game negotiation conflict identification module, a reasoning evidence management module and a cross-regional consistency verification module. By establishing a complete knowledge graph model, systematic modeling of resource attributes, constraints, historical decisions and strategy preferences is realized, a unified data basis is provided for negotiation among multiple agents, the problem of a suboptimal solution caused by information asymmetry is effectively solved, a hierarchical reasoning mechanism is adopted, and the probability of resource disruption is reduced. In combination with global-region-node three-level reasoning and multi-round game negotiation, recursive optimization from global to local is realized, the reasoning complexity is effectively reduced, and the negotiation efficiency is improved.

The invention belongs to the technical field of computer data security, and discloses an enterprise data encryption storage system based on strategy optimization. The system is composed of a strategy map construction and optimization module, a sensitivity identification and encryption strategy generation module, an encryption execution and ciphertext structure processing module, a strategy-driven storage scheduling module and an audit recording and feedback optimization module. According to the method, a strategy graph containing multiple types of nodes such as users, data, behaviors, risks and resources is constructed, the edge weight and the node confidence coefficient are dynamically adjusted through the behavior frequency, the access relation and historical audit information, a graph structure oriented to behavior contexts is formed, and on the basis, an encryption strategy label is generated in combination with a fuzzy semantic reasoning mechanism; a traditional static recognition mode based on field rules can be broken through, potential high-sensitivity data and abnormal behavior paths are recognized, and the generation accuracy and the self-adaptive capacity of a data encryption strategy are improved.

The invention provides a method and a system for intelligently generating and dynamically adjusting a public emergency plan, which are oriented to the field of public safety emergency. The method comprises the following steps: fusing multi-source heterogeneous data to construct an extensible domain knowledge graph, establishing a standardized emergency instruction library, and carrying out multi-dimensional information labeling; automatic extraction of disaster elements is realized based on an entity recognition model of deep learning; analyzing association rules among the emergency entities through a semantic relationship mining technology; key information such as a disaster chain and resource distribution is rapidly obtained by using a map reasoning mechanism; carrying out cross-department resource collaborative allocation by adopting a multi-objective optimization algorithm to generate an optimal disposal scheme; a high-dynamic adjustment mechanism is constructed, and an emergency plan is dynamically optimized based on situation evolution prediction and real-time monitoring data; and finally, automatic generation and versioning management of the plan are realized. Through multi-mechanism cooperation of knowledge modeling, intelligent element analysis, semantic reasoning, dynamic optimization and predictive adjustment, the emergency plan generation efficiency, situation adaptability and resource allocation rationality are remarkably improved, and support is provided for quick response and scientific decision under emergencies.

The invention discloses an AI low-code development component automatic generation method based on a knowledge graph, and relates to the technical field of AI low-code development, and the method comprises the following specific steps: constructing an AI development knowledge graph: constructing a knowledge graph containing a five-layer structure, collecting and preprocessing multi-source data, and extracting various entities and association relationships, so as to obtain an AI development knowledge graph; constructing an ontology structure, importing the ontology structure into a database, and forming a final knowledge graph through manual auditing and machine reasoning optimization; according to the method, the multi-dimensional AI development knowledge graph is constructed, user requirements are analyzed in combination with the optimized natural language processing model, the component configuration scheme is generated by introducing the multi-level reasoning mechanism, and finally the standardized low-code component is automatically generated, so that full-automatic conversion from the user requirements to the low-code component is realized, and the user experience is improved. The defect that an existing low-code platform depends on manual component configuration is overcome, the development period is greatly shortened, and the development cost and the technical threshold are reduced.

The invention relates to an intelligent optimization method for an industrial waste transportation path, and the method comprises the steps: firstly collecting multi-source and multi-dimensional input variables, such as vehicles, roads, orders and waste attributes, achieving the dynamic simulation of a whole transportation process through digital twin modeling, and carrying out the structural processing of data through combining with space and environment labels; when an abnormal event is monitored, an abnormal correction action is dynamically generated and optimized through a multi-agent cooperation and game reasoning mechanism, and an optimal scheduling decision aiming at different working conditions is realized; and continuously monitoring and executing feedback by the system, returning the actual deviation to the model and the knowledge base, and driving the self-learning and continuous evolution of the anomaly correction strategy.

The invention discloses a Beidou-based new energy electric vehicle battery remote state monitoring system, and relates to the technical field of battery monitoring, and the system comprises a multi-dimensional data flow construction unit; a digital twin layer generation unit; the causal situation map establishing unit is used for generating a joint embedding vector, embedding the symbol constraint rule set into a preset graph neural network architecture, constructing a neural symbol causal discovery engine, directionally adjusting the joint embedding vector in combination with an anti-fact reasoning mechanism, and establishing a causal situation map; the contribution degree of each type of degradation inducement to the service life of the battery is evaluated; and the service life prediction and report generation unit is used for constructing a prediction generator and a verifier, outputting a consensus residual service life prediction value in combination with the real-time vehicle operation data, and generating a battery state report. According to the invention, the comprehensiveness and the real-time performance of data acquisition are improved, and the intelligent level and the application efficiency of remote monitoring and management of the battery are improved.

The invention relates to a meteorological data set automatic construction method and system based on modal bridging, and aims to meet the multi-modal large model training requirement in the meteorological field, and the method and system realize automatic conversion from an original meteorological image to a structured expert reasoning text through deep fusion of image and text information. The method comprises five stages of data preprocessing, image-text semantic modeling, causal reasoning generation, consistency screening and parallel processing, key meteorological elements are extracted by using a multi-modal model, a chained thinking process is constructed through a language model with meteorological knowledge, chained reasoning annotation is realized, cross-modal semantic alignment and a multi-round reasoning mechanism are introduced, and a multi-modal semantic model is established. The method is advantaged in that high-quality samples are screened in combination with rules and models, automation, high consistency and good expansibility are realized, manual annotation cost is substantially reduced, and the method is suitable for large-scale meteorological reasoning multi-modal data set construction.

The apparatus may be a wireless device configured to estimate, for a first transmission in a first slot, a first channel associated with the first transmission, wherein the first transmission is associated with a first precoding, receive, in a second slot following the first slot, a second transmission associated with a second precoding, and estimate, based on the received second transmission and at least one of the received first transmission or the estimated first channel, a second channel associated with the second transmission.

The invention belongs to the technical field of wireless communication, and discloses an edge computing and cache enabling meta-universe system and an intelligent optimization method, and the technical scheme of the invention comprises the following core optimization objectives: firstly, through an intelligent content cache strategy, a user cache hit rate is maximized, and unnecessary data transmission is reduced; secondly, task unloading decisions are optimized, computing resources are reasonably distributed, and energy consumption of user terminals is reduced; and thirdly, dynamic intelligent allocation of computing resources is realized, and the overall resource utilization efficiency of the system is improved. The ADRL algorithm provided by the invention has the following unique advantages: 1, by introducing an active reasoning mechanism, the decision ability of the algorithm in an uncertain environment is enhanced; secondly, in combination with preference information of the intelligent agent, an optimization strategy is more targeted; and thirdly, comprehensive balance of a multi-dimensional optimization target of the system is realized, and powerful technical support is provided for efficient operation of the element universe network.

The invention discloses a three-dimensional physical field real-time prediction method and system based on geometric deep learning and physical constraint, and relates to the technical field of computer-aided engineering and artificial intelligence. The method comprises the following steps: directly extracting native boundary representation data (B-Rep) of a three-dimensional model from a computer aided design system; constructing a heterogeneous dual graph taking a parameterized curved surface as a graph node, skipping finite element grid division, and aggregating local and global topological features by using a graph neural network; in combination with a physical information driving mechanism, a partial differential equation (PDE) residual error is introduced as a loss function for constraint training, and generalization prediction of a novel geometric structure is realized; and finally, the physical field state quantity is predicted through direct regression and is rendered in real time. An incremental reasoning mechanism based on a local topology subgraph is adopted, millisecond-level physical field real-time feedback under design modification is achieved, and the method is suitable for scheme rapid screening and trend prediction in the initial stage of design.

The invention discloses a personnel examination answer sheet identification method based on multi-agent collaborative perception. The method comprises the steps of obtaining an answer sheet scanning image, constructing a microstructure correction engine through reinforcement learning to process an answer sheet, scoring a correction result in combination with an unsupervised quality evaluation system, and adjusting correction parameters through a dynamic feedback iterative optimization mechanism. Deploying sensing multi-agent cooperative work, combining graph structure modeling and a template-free reasoning mechanism of a dynamic topology knowledge base, and combining a contribution degree evaluation mechanism to optimize the agents; entering a recognition stage of filling intention collaborative perception, modeling a recognition decision into a Markov decision process, quantifying recognition values of different scenes through a hierarchical reward mechanism, constructing a scene memory playback pool to store filling cases, and combining a lightweight online strategy optimization mechanism to obtain a recognition result and generate an evaluation report. According to the method, high-precision and high-robustness recognition of the complex answer sheet is realized, and the recognition accuracy and the paper marking efficiency are improved.

The invention relates to an equipment predictive maintenance management method and system based on multi-feature fusion, belongs to the technical field of equipment health management, and is used for solving the problems that existing sensor data is easily disturbed and distorted, and a potential causal structure of an equipment degradation path is difficult to reveal due to lack of a comprehensive modeling framework. The method comprises the steps of collecting multi-source data in real time, extracting a causal contribution degree of the data to a fault to generate a causal significance feature value, generating a three-dimensional health feature vector through an anti-fact neural network model in combination with a physical failure model, fusing the two to obtain an equipment state risk feature matrix, inputting the model to output a risk score, and obtaining an equipment state risk result. And finally, dynamically adjusting the monitoring frequency and the maintenance level and iteratively optimizing the strategy. According to the method, data interference can be filtered out, multi-class reasoning mechanisms are deeply fused, the equipment degradation law is accurately revealed, and full-life-cycle self-adaptive maintenance is achieved.

The invention discloses a vehicle-mounted distributed intelligent cooperative reasoning method. The method comprises the following steps: acquiring natural language input of a user in a vehicle; analyzing the natural language input through the vehicle-mounted large model to obtain a user intention; generating a plurality of reasoning tasks according to the user intention, and calling a plurality of reasoning models based on the plurality of reasoning tasks; generating a plurality of preliminary reasoning results based on the plurality of reasoning tasks through the plurality of reasoning models; the plurality of reasoning tasks are in one-to-one correspondence with the plurality of reasoning models; and generating a target reasoning result based on the plurality of preliminary reasoning results by using the vehicle-mounted large model. According to the method, the results after independent reasoning of each module are integrated to the vehicle-mounted large model in a centralized manner, and deep fusion and final decision making are performed on multi-system data by means of the vehicle-mounted large model, so that a cross-module collaborative reasoning mechanism is constructed, the user operation complexity is remarkably reduced, and the system response efficiency is effectively improved.

The invention belongs to the technical field of contract review, and particularly relates to an intelligent contract review analysis method and system based on a large language model. According to the method, dynamic generation of rules and a neural symbol reasoning mechanism are mainly fused, and the core is to realize automatic and precise risk identification and evaluation of contracts by utilizing the powerful language understanding and generation capability of a large language model (LLM); the method comprises the specific steps of generating a contract review rule, extracting facts from a contract to be reviewed, performing review reasoning and generating a review report. According to the method, the inherent logic preciseness of symbol deduction and the powerful semantic understanding capability of a large language model are organically combined, so that core elements and potential risks in a contract can be more accurately identified, and contract terms which are complex in expression or have hidden agreement can be effectively processed and expressed.

The invention belongs to the technical field of power transmission channel hidden danger recognition, and particularly relates to a power transmission channel hidden danger intelligent judgment method and device based on knowledge graph and multi-modal large model fusion. The method comprises the following steps: constructing a knowledge graph based on a power industry standard and a historical disposal record; acquiring a detection result of the hidden danger position of the power transmission site by using a target detection algorithm, mapping the detection result to the knowledge graph, and performing context verification and hidden danger level preliminary judgment based on a multi-level reasoning mechanism to obtain a corresponding graph reasoning result; a multi-modal large model is utilized to fuse visual information, a map reasoning result and a historical case, and a comprehensive evaluation report with professional logic is generated, so that automatic filtering of false alarms and accurate judgment of hidden danger levels are realized. The key defects in hidden danger identification and evaluation of the power transmission channel in the prior art are overcome.

The invention relates to the technical field of semantic understanding, in particular to a three-dimensional scene semantic understanding method and system based on multi-modal deep learning, and the method comprises the following steps: collecting a point cloud image and a depth map in an automatic driving scene, carrying out the normalization standardization and deletion filling, extracting texture geometric space features, and carrying out the fusion through an attention mechanism; a multi-time-step state vector is introduced to calculate change features, a spatial relation between road participation objects is modeled, a dynamic instance graph structure is constructed, semantic tags are reasoned, and fusion features are compared to generate a three-dimensional scene semantic understanding result. According to the method, the fusion quality is guaranteed through multi-source data normalization standardization, the semantic complementarity is enhanced through collaborative extraction of image texture and point cloud geometric features, the dynamic scene perception ability is improved through state vector modeling, the object interaction semantic relation is described through a spatial relation graph, and the recognition accuracy and consistency are improved through a semantic label reasoning mechanism. And the integrity and robustness of three-dimensional semantic understanding are integrally enhanced.

The invention relates to the technical field of text data processing, and discloses a text data extraction method, system, equipment and medium based on multi-modal fusion and self-evolution learning, which comprises the following steps of: performing feature extraction and spatial alignment on a printed text, a handwritten annotation and a dynamic table of a mixed format document to obtain a semantic feature of an image-text table, and inputting the semantic feature into a dynamic analysis layer; analyzing metaphor expressions and synonymous heterogeneous fields through field extraction and a context semantic reasoning mechanism, and outputting structured data; performing grammar compliance verification by adopting a regularization engine, and performing comparison verification through a federal learning mechanism; and inputting the verified data into the reinforcement learning model, updating the analysis rule and the model parameters through strategy iteration, and feeding back the updated analysis rule and model parameters to the dynamic analysis layer to complete closed-loop optimization. According to the method, the processing precision and efficiency of the complex document are greatly improved, the manual intervention requirement is remarkably reduced, and meanwhile, the privacy protection and compliance requirements are met.

The apparatus may be a wireless device configured to estimate, for a first transmission in a first slot, a first channel associated with the first transmission, wherein the first transmission is associated with a first precoding, receive, in a second slot following the first slot, a second transmission associated with a second precoding, and estimate, based on the received second transmission and at least one of the received first transmission or the estimated first channel, a second channel associated with the second transmission.

The invention discloses a monitoring data trend analysis and decision suggestion generation method based on a large language model agent. The method comprises the following specific steps: step 1, constructing a domain knowledge enhancement module; 2, developing and registering a callable tool set, and compiling a series of performance functions by using Python; 3, constructing an agent arrangement platform and a core agent, and creating a master control agent in the agent arrangement platform as a scheduling center of an analysis task; step 4, executing an intelligent agent analysis process, and when an analysis request is received, enabling the intelligent agent to autonomously run according to the following process; 5, system integration and interface calling are carried out, the whole agent is packaged into RESTful API service and deployed in an enterprise server, by introducing a large language model agent architecture, an RAG enhanced reasoning mechanism and a callable tool set, the technical transition from passive data display to active insight generation is achieved, and the method has the advantages of being high in practicability and easy to popularize. And the analysis efficiency, the decision accuracy and the system intelligence level are remarkably improved.

The invention discloses a text risk identification method and device fusing a fine-grained knowledge graph and deep learning, and the method comprises the steps: carrying out the combined entity identification of a standardized text set, and extracting evaluation dimensions and viewpoint contents to form an entity pair set; performing sentiment polarity analysis on the entity pair set to obtain sentiment tendency, and constructing a triple (evaluation dimension, sentiment tendency and viewpoint content); constructing a fine-grained emotional knowledge graph based on the triple, and optimizing the fine-grained emotional knowledge graph by adopting structure perception and joint loss; based on the to-be-recognized text, retrieving from the atlas to construct an enhanced text, and inputting a fusion atlas perception reasoning model comprising a BERT coding layer, an atlas attention module, a cross-layer residual attention mechanism and a KAN reasoning layer to obtain a text risk recognition classification result. According to the method, an emotion structure modeling and semantic reasoning mechanism is introduced, the accuracy, interpretability and robustness of text risk recognition are improved, and the method is suitable for multi-field text analysis tasks under complex contexts.

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

The invention is suitable for the technical field of three-dimensional target detection, and provides a 3D target detection method and device based on inter-modal bidirectional interaction and progressive reasoning. According to the invention, independent feature extraction is carried out on an input laser radar point cloud and a multi-view image; performing cross-modal attention interaction, residual refinement and intra-modal local self-attention calculation to obtain enhanced feature data; performing cross-modal reasoning and target decoding to generate fusion feature data; and inputting to a feedforward neural network, and generating a final three-dimensional target detection result through a full connection layer. Bidirectional dynamic compensation of geometric information and semantic features can be realized, cross-modal feature expression is optimized layer by layer by adopting a progressive reasoning mechanism, and a scene adaptive dynamic fusion strategy is designed. The integrity of small target detection in a shielding scene, the accuracy of target positioning under a complex illumination condition and the stability of long-distance target sensing are effectively improved, and meanwhile, the robustness of a multi-mode sensing system in a dynamic environment is guaranteed.

The invention provides an intelligent log assertion reasoning method and system driven by a knowledge graph, and relates to the technical field of log reasoning, and the method comprises the steps: carrying out segmentation processing and semantic analysis on heterogeneous log data, constructing a knowledge graph space, and establishing a time sequence and a causal relationship between log event nodes; and a multi-hop reasoning mechanism is adopted to generate a fault diagnosis report and a processing suggestion, and an automatically optimized operation and maintenance knowledge rule set is realized. According to the invention, the fault diagnosis accuracy can be improved, the fault processing time is shortened, and intelligent operation and maintenance management is realized.

The invention discloses a natural language structured query generation method and system based on pattern reasoning, and relates to the technical field of natural language processing and database query. The method comprises the steps of mode knowledge graph construction in an offline stage and three-step reasoning generation in an online stage. In the offline stage, through structure analysis, semantic enrichment and data portrait processing, a database mode is converted into a knowledge graph Schema-KG rich in structure and semantic information. The online stage adopts three-step reasoning: firstly, decomposing a natural language of a user into an intention component through LLMParser, and linking the intention component to a graph entity; secondly, reasoning an optimal JOIN path on the Schema-KG based on a graph search algorithm, and generating a mode perception intermediate representation SA-IR; finally, the SA-IR is compiled into an SQL, and pre-verification is executed. According to the method, the accuracy of complex query is remarkably improved through a structured reasoning mechanism, errors during operation are effectively avoided through pre-execution verification, a clear reasoning process is provided through SA-IR intermediate representation, and the interpretability and debugging efficiency of the system are greatly improved.

The invention provides a rainstorm flood disaster chain monitoring field knowledge graph construction method, and relates to the technical field of knowledge graph construction, and the method comprises the steps: carrying out the preprocessing of original multi-source disaster data, and obtaining various types of quality-controlled data; calling a disaster field fine-tuning large model and an ontology inference engine based on the preliminary corpus and the multi-class quality-controlled data to generate a core ontology and a sub-document set, screening the sub-document set to construct a document association graph, and calling a mapping model to match a structured data field and a core ontology attribute; performing triple extraction according to the sub-document set and the constructed three-level cue word template; ontology instance attributes are vectorized and clustered, a three-layer collaborative system and an atlas core structure are generated through domain conflict repair and pruning density adjustment, a data calling disaster domain fine-tuning large model is obtained based on a processing process to complement an instance implicit relationship of the atlas core structure, and a target knowledge atlas is constructed. According to the invention, construction of the knowledge graph in the disaster field is realized.

The invention provides a remote sensing image interpretation method based on large model reasoning and tool enhancement, which comprises the following steps: generating an initial reasoning node of a reasoning tree by using a large language model as an active decision maker based on system cue words and input remote sensing image features; based on the current inference tree state, the active decision maker executes inference and generates a new inference node, so that the inference tree is expanded; based on node scores, adopting a pruning strategy to eliminate reasoning paths with low scores; repeating the steps until a reasoning termination condition is met, and selecting a path with the highest accumulated score from the reasoning tree as an optimal reasoning path; and key information is extracted and organized into a structured intelligence report to be output. According to the method, a complete interpretation chain of'thinking-tool-observation 'is explicitly modeled through a tree reasoning mechanism, so that the generation logic of each intelligence conclusion is clear and visible, and the problems of trust crisis and insufficient interpretability caused by'black box' decision of a traditional deep learning model are solved.

The invention provides an enhanced generation method based on topology perception graph coding and adaptive sub-graph retrieval, which comprises the steps of multi-granularity semantic perception segmentation and knowledge graph construction, structural feature matrix construction and explicit topology position code generation, and constrained sub-graph diffusion and dynamic pruning based on semantic-topology joint scoring. Generating a graph-text consistency loss constraint based on attention matrix structured alignment; explicit topological position coding is adopted in a coding layer to avoid overhead and excessive smoothness caused by online GNN aggregation; in a retrieval layer, a connected sub-graph instead of a fragment node is used as an enhanced context; in an alignment layer, an internal attention matrix of a large language model is directly constrained to be consistent with a sub-graph adjacent matrix, and structured guidance is realized from a reasoning mechanism level, so that logic illusion is inhibited, and the multi-hop reasoning accuracy is improved.

The invention relates to the field of aero-engine fault diagnosis, in particular to an aero-engine fault diagnosis method based on deep contrast learning. By constructing a fault mapping and reasoning mechanism with self-learning and self-adaptive capabilities, accurate intelligent diagnosis from multi-source health parameters to fault physical positions is realized. According to the scheme, the method comprises the steps of fusing multi-source sensor data by adopting a multi-head self-attention mechanism and a time sequence convolutional network, outputting a discriminative health state representation vector, constructing a fault-parameter comparison learning framework, and embedding the health state representation vector and a fault mode into the same semantic space through positive and negative sample pair training, end-to-end fault mode recognition and mapping are achieved, a fault knowledge graph is constructed, learning updating and associated reasoning are conducted, a deep reinforcement learning mechanism is introduced, the decision-making process of a maintenance expert is simulated, multi-hop reasoning and path searching are conducted in the knowledge graph, and the most possible fault source and detection path are recommended. The method is suitable for aero-engine fault diagnosis.