52 results about "Knowledge evolution" patented technology

The invention relates to the technical field of knowledge graph construction and maintenance, and discloses an AI knowledge graph construction and maintenance method based on ontology engineering, and the method comprises the steps: obtaining multi-source heterogeneous data to construct an initial graph ontology model; loading the constraint rules and the entity alignment parameters to carry out cross-modal knowledge fusion, and generating a dynamic knowledge evaluation model; executing ontology topology reasoning based on the model, and constructing time sequence knowledge evolution simulation data; training the semantic conflict resolution model to generate a knowledge fusion optimization model, and outputting map maintenance parameters; knowledge increment simulation information is generated, a multi-dimensional optimization model is constructed to iteratively adjust the ontology structure, and optimal knowledge fusion path parameters are generated; and dynamic ontology matching is realized by combining real-time reasoning and historical track updating maintenance strategies. According to the method, the construction efficiency, the fusion quality and the self-adaptive maintenance capability of the knowledge graph are improved, the method is suitable for an intelligent knowledge management scene of multi-source heterogeneous data, and the defects of a traditional method in the aspects of semantic conflict resolution, ontology dynamic evolution and the like are overcome.

The invention relates to the technical field of artificial intelligence, in particular to a large language model compliance reasoning method and system based on domain knowledge graph constraint, and the method comprises the steps: collecting domain literature corpora, extracting an entity relationship triple, labeling a timestamp interval and confidence, and constructing a domain knowledge basic data layer; organizing the state change of the same entity at different time points into a directed evolution link, forming a knowledge evolution tracking graph, and generating a time sequence perception type domain knowledge graph; constructing a semantic alignment detection mechanism, and detecting a semantic drift region; selecting a current effective graph knowledge fragment according to a detection result, and embedding time sequence constraint into a model context through a dynamic knowledge injection channel to realize real-time knowledge calibration in a reasoning process; and establishing a compliance reasoning verification process, comparing the reasoning result with the time sequence constraint rules one by one, marking and correcting, and outputting a compliance conclusion conforming to the current specification.

The invention discloses a verifiable APT attack chain extraction method based on a cognitive distillation and gating technology. The method comprises the steps of input processing and extraction, dynamic gating weight generation, hierarchical prototype decision making, adversarial robustness guarantee and output. An attack chain time sequence modeling and dynamic gating mechanism is created for the first time, causal constraints are injected through phase perception position coding, semantic fragmentation is avoided through full-word masks, and splitting is conducted in the cracking stage; historical dependence and semantic similarity weight reduction are fused to block noise, and the cutting precision is improved. A cognitive distillation dynamic knowledge evolution system is constructed based on prompt engineering prototype matching and self-attention feature decoupling double engines, so that few-sample reasoning and decision determinacy quantification are realized, technical representation is stripped, a kernel is focused and attacked, and real-time evolution of a knowledge base is promoted; a defense closed loop is constructed by parameterized noise injection and KL divergence, a verifiable countermeasure immune system is established, an attack is simulated to optimize a defense benchmark, probability distribution stability is monitored, the misjudgment rate is extremely low, and a traceable audit log is output.

The invention provides an intelligent auxiliary diagnosis and treatment service system and method based on a large language model, and the system comprises a large language model engine and a medical knowledge dynamic updating device, and the medical knowledge dynamic updating device carries out the automatic and quantitative arbitration of the conflict between the new knowledge extracted from the literature and the existing knowledge of a knowledge graph, and carries out the automatic and quantitative arbitration of the conflict. A conflict detection unit identifies conflict knowledge pairs with mutually exclusive logic, an evidence intensity quantification unit calculates and synthesizes document timeliness, source authority and research type evidence grade multi-dimensional evidence intensity scores for two conflict parties, and a grading arbitration decision unit compares a score difference value with a dynamic threshold value. And making a grading decision of automatically adopting a high-score party, generating an artificial review task or marking dispute coexistence, and finally executing updating by a knowledge graph updating unit. The invention further provides a method corresponding to the system, double-chain collaboration of online diagnosis and treatment response and offline knowledge evolution is achieved, and the problems of automation and intelligentization of conflict processing in medical knowledge updating are solved.

The invention relates to the technical field of intelligent animal husbandry, and discloses a mutton sheep quantitative intelligent feeding management system based on the Internet of Things. Comprising a multi-modal sensing module, an edge computing gateway, a metabolic efficiency feedback factor computing unit, an individual nutrition digital twin modeling unit, an active disturbance response verification mechanism execution unit, an intelligent feeding terminal and a group knowledge evolution module. According to the system, an individual nutrition digital twinborn modeling unit is adopted and is combined with an online reinforcement learning algorithm, and an accurate feeding decision is generated according to a metabolic efficiency feedback factor and a nutrition elasticity coefficient matrix; and an active disturbance response verification mechanism execution unit applies controllable tiny disturbance when the model is uncertain, and an elastic coefficient matrix is dynamically updated. According to the method, the physiological state of the individual is sensed in real time by using multi-modal data, dynamic closed-loop control based on metabolic efficiency is realized, the cold start problem of the new individual is solved through knowledge distillation, and the feed conversion efficiency and the culture benefit are remarkably improved.

The invention discloses a green financing-oriented credit large model joint modeling system and method, and belongs to the technical field of green financing, the system comprises a multi-source green data management module, a credible reasoning and calculation module, a large model interaction module and a dynamic knowledge evolution module, and the method comprises the following steps: 1, obtaining original data of a green financing project, generating a knowledge constraint signal; 2, analyzing the structured data to generate a logic decision chain, executing tasks related to environmental benefit measurement and calculation in the structured data, and generating quantitative indexes; 3, integrating the quantitative index and the logic decision chain into a reasoning context, converting the knowledge constraint signal into an output constraint instruction, and generating a credit evaluation report; and 4, receiving feedback information of the credit evaluation report, and updating and optimizing the system. According to the invention, by generating the logic decision chain and the quantitative index, the illusion of the large model is effectively reduced, and the credit assessment accuracy is improved.

The invention provides a network boundary extension sensing method and system based on an AI model. The method comprises the following steps: acquiring multi-modal data such as physical fingerprints, protocol characteristics and flow modes of equipment through an edge sensing agent; generating a device-behavior joint representation vector through a feature decoupling encoder; constructing a dynamic time sequence topological graph sequence by using a space-time diagram neural network to map boundary evolution; threat reasoning is carried out by adopting three channels of rule matching, unsupervised anomaly detection and semi-supervised threat hunting; fusing decisions through a dynamic weighted voting mechanism and triggering an automatic response; and realizing closed-loop self-optimization based on incremental learning. The system correspondingly comprises an edge sensing layer, a central analysis layer, a strategy execution layer, a knowledge evolution module and a model credibility guarantee unit. According to the method, through cloud-edge-end collaborative reasoning and causal attribution analysis, the sensing and handling capability of unknown threats and APT attacks in a dynamic network boundary is remarkably improved.

The invention provides an injection molding quality problem traceability analysis method, which comprises the following steps of: acquiring a surface image and process parameters of a molded product, extracting defect characteristics through methods of image enhancement, edge detection, region segmentation and the like, and fusing the defect characteristics with normalized process parameter data to obtain an injection molding quality problem traceability analysis result; constructing a multi-modal knowledge graph containing entity nodes and causal relationship edges; further introducing implicit causal edges and path diversity scores to realize dynamic sorting and root cause identification of defect causal paths; the system supports knowledge increment updating and scoring model reinforcement learning under actual production feedback, so that the root cause diagnosis accuracy and the system adaptive capacity are continuously optimized, and the automation, accuracy and knowledge evolution efficiency of injection molding defect analysis in a complex scene are improved.

The invention discloses a new energy station event-driven broadband oscillation suppression control method, system, equipment and medium, and belongs to the technical field of power systems and automation thereof, and the method comprises the steps: monitoring the operation quantity of a station in real time, sensing an abnormal trigger event signal, identifying an oscillation frequency band, outputting frequency band characteristics, and forming an optimal damping vector. And injecting the virtual damping signal according to the frequency band priority, carrying out oscillation control, judging the current oscillation control effect, entering a safety mode when detecting that communication is abnormal or overtime, and carrying out parameter rollback. The invention constructs a multi-layer intelligent control system integrating real-time event triggering, spectrum identification, self-learning optimization, coordinated injection and knowledge evolution. The method shows high stability and high robustness under various typical working conditions, the safety margin and the electric energy quality of a new energy grid-connected system can be improved, and a reproducible and extensible technical path is provided for autonomous operation of a large-scale new energy station in the future.

The application provides a project knowledge base-based work order service tracking method, platform and medium, relates to the technical field of data processing, and the method comprises the following steps: constructing a project knowledge base in a project operation process; reading work order information, performing semantic analysis processing, and mapping the analysis result to the project knowledge base to generate a knowledge mapping result; extracting a historical processing path and a standard implementation path associated with the work order and meeting a preset condition, and constructing a service processing baseline track corresponding to the work order; in the work order execution process, real-time collection of work order processing behavior information is performed, continuous mapping to the project knowledge base is performed, and a real-time knowledge evolution track is generated; and work order service tracking analysis is performed, and a service tracking result is established. The application solves the technical problem that work order management in the prior art mostly depends on manual input and processing, lacks an automatic knowledge mining and application mechanism, and work order processing efficiency is low, error rate is high, and project progress and quality are affected.

The invention provides a territorial space knowledge evolution model construction method, and belongs to the technical field of territorial space knowledge evolution models.The territorial space knowledge evolution model construction method includes the steps that a distributed graph storage framework is constructed, sub-graph partitions are divided through a community detection algorithm, and external event information is collected and converted into a graph operation instruction sequence; positioning a k-hop neighborhood sub-graph by using an incremental sub-graph matching algorithm and executing local updating to generate an intermediate knowledge graph, calculating node state embedding representation of a fusion time coding vector, capturing a time sequence dependence mode through a time perception graph attention aggregation algorithm, executing semantic consistency constraint detection to identify conflicting nodes, and obtaining a time perception graph attention aggregation algorithm; when the conflict degree exceeds a threshold value, the multi-version concurrency control protocol is triggered to roll back to the snapshot version, otherwise, the intermediate knowledge graph is reserved as final output, and the problem that it is difficult to give consideration to updating efficiency and semantic consistency guarantee when the territorial space knowledge graph responds to an external event for evolution updating is solved.

The invention provides a work order service tracking method and platform based on a project knowledge base and a medium, and relates to the technical field of data processing, and the method comprises the steps: constructing the project knowledge base in a project operation and maintenance process; reading the work order information, performing semantic analysis processing, and mapping an analysis result into a project knowledge base to generate a knowledge mapping result; extracting a historical processing path and a standard implementation path of which the correlation degree with the work order meets a preset condition, and constructing a service processing baseline track corresponding to the work order; in the execution process of the work order, processing behavior information of the work order is collected in real time and continuously mapped to the project knowledge base, and a real-time knowledge evolution track is generated; and executing work order service tracking analysis, and establishing a service tracking result. The technical problem that the project progress and quality are affected due to low work order processing efficiency and high error rate caused by the fact that work order management mostly depends on manual input and processing and lacks an automatic knowledge mining and application mechanism in the prior art is solved.

This invention presents a knowledge evolution poisoning attack method for graph-enhanced retrieval and generation systems, belonging to the field of retrieval enhancement and generation. Through knowledge evolution forgery attacks and multi-target cross-subgraph collaborative attacks, it can generate evolutionary corpora that satisfy temporal constraints and have stronger structural connectivity without compromising the overall consistency of the knowledge graph. This allows for a more thorough exposure of GraphRAG's vulnerabilities in knowledge extraction, community partitioning, subgraph retrieval, and evidence aggregation stages in authorized evaluation environments. Compared to direct splicing injection, the samples generated by this method more closely resemble the "real knowledge update" distribution and can be used for pre-deployment red team evaluation, post-deployment regression testing, and effectiveness verification and parameter selection for defense modules such as consistency detection, retrieval filtering, and fact verification.

The application discloses a kind of multi-source knowledge fusion's administrative penalty intelligent question and answer model construction method and system, it is related to artificial intelligence and market supervision technical field.The present technique is dispersed in view of the existing technology administrative penalty knowledge, question and answer accuracy is poor, update lag and lack of explainability problem, first acquisition and pre-process multi-source market supervision data construction field corpus;Subsequently based on domain ontology guide and large language model extraction, construct and realize semantic uniformity and entity alignment administrative penalty knowledge graph;Further design by search channel and generation channel collaborative dual-channel intelligent question and answer architecture, based on knowledge graph generation traceable natural language answer;Finally, combined with including automatic incremental update, regulation time limit management and user feedback optimization knowledge evolution mechanism, realize the continuous improvement of knowledge.The application realizes the uniform modeling and intelligent question and answer of multi-source supervision knowledge, significantly improves the accuracy, explainability and real-time update capability of administrative penalty intelligent question and answer.

The invention relates to the technical field of decommissioned battery evaluation and large language models, in particular to a decommissioned battery scene matching and knowledge evolution system based on a large language model, which extracts multi-dimensional characteristics such as SoH and internal resistance of a decommissioned battery and requirements such as scene power requirement and cycle frequency, and performs LLM reasoning on adaptation degree and interpretation and vector retrieval similarity to obtain a decommissioned battery scene matching and knowledge evolution model. Carrying out weight fusion according to 0.6: 0.4 to obtain a comprehensive score for judgment and matching; a knowledge graph is constructed to associate battery-scene-constraint, incremental evolution is realized through semantic deduplication and graph combination, cloud-edge-end deployment is adopted, the method is based on a large language model decommissioned battery scene matching and knowledge evolution system, the Top-3 hit rate is 92.7%, the coverage rate of six-month newly-added scenes is 90.4%, the time delay of 1000 pieces of increment combination is 14.2 s, the interpretation readability is 4.6 / 5, and the method has the advantages of being high in reliability and high in reliability. And local deployment is supported to guarantee enterprise data security.

This invention proposes a psychological diagnosis and treatment dialogue method based on dynamic knowledge evolution and adaptive retrieval, belonging to the fields of natural language processing and intelligent medical technology. The method includes constructing a psychological counseling generation model, an urgency prediction module, and a background process auxiliary model; initializing a patient knowledge base; sending the patient's current dialogue input to the background process auxiliary model to extract key information and determine whether retrieval is needed; if no retrieval is needed, the psychological counseling generation model generates a response, and the current dialogue ends; otherwise, the retrieval yields a set of candidate structured memory units, which are adaptively reordered to obtain context information; the context information is concatenated with the current dialogue input and sent to the psychological counseling generation model to generate a therapeutic response conforming to psychological counseling standards; and the knowledge base undergoes dynamic evolution and incremental updates. This invention overcomes the limitations of the traditional "static profile" of dialogue systems, achieving long-term continuity and dynamic adaptability of diagnostic and treatment memory.

This invention relates to a flexible circuit board patch anomaly detection system and processing method based on multimodal data, specifically in the field of flexible circuit board patch anomaly detection. This system achieves intelligent anomaly detection in flexible manufacturing scenarios through multi-module collaboration: an adaptive knowledge base construction module decouples inter-batch differences, laying a stable foundation against interference; a real-time migration module leverages feature similarity to achieve instantaneous parameter migration, significantly improving response efficiency for new batches; a dynamic optimization module models parameter adjustment as a reinforcement learning process, simultaneously optimizing detection accuracy and system robustness; and a verification and knowledge evolution module perceives distribution drift through adversarial mechanisms, safely injecting new knowledge and avoiding the risk of historical forgetting, ultimately forming a lifelong learning architecture from feature decoupling to closed-loop evolution. This achieves breakthroughs in dimensions such as inter-batch adaptive time consumption, manual maintenance frequency, and small-sample generalization ability, effectively supporting continuous and reliable detection in complex process environments.

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 invention provides a federated learning method and system for proxy feature collaborative synthesis and meta-knowledge evolution oriented to model isomerism, and the method comprises the steps: repeatedly executing multiple iterations of global operation and local operation until a federated learning model meets a convergence condition; the global operation of the server side comprises the following steps: receiving element gradients uploaded by each client, performing security aggregation, and updating global sharing module parameters; the local operation of the client comprises the following steps of: decoupling a local model into a private module and a shared module based on local real data, and generating agent characteristics which are similar to the statistical distribution of the local real data and are decoupled in semantic information by a generator; local heterogeneous real features and proxy features are mapped to a shared potential space of a unified dimension; executing double-layer optimization of internal loop local adaptation and external loop element gradient calculation, and calculating element gradients for shared module parameters; only the element gradient is uploaded to a server side, and local joint optimization, feature mapping and element gradient calculation uploading operation are iteratively executed.

The invention belongs to the technical field of power systems, and relates to an operation risk prediction analysis method under power distribution network fault and abnormal working conditions, which comprises a multi-dimensional data acquisition module used for collecting multi-aspect information of a power distribution network, and a dynamic fault diagnosis module used for constructing a three-dimensional fault feature space and performing intelligent diagnosis according to fault features. The operation state prediction module carries out prediction and life evaluation on the change of each part of the equipment according to the fault feature information and sends a starting instruction to the risk quantitative evaluation module, and the risk quantitative evaluation module is used for calculating the possibility of danger occurrence and evaluating the risk of each part of the equipment. The self-adaptive decision-making module is used for automatically formulating a response scheme according to fault assessment information, and the knowledge evolution module is connected with a communication network, and can perform case library construction and automatic learning experience. The system realizes full-closed-loop intelligence of perception, diagnosis, prediction, assessment, decision-making and evolution through modular design; the system can reduce the power failure time and improve the energy use efficiency.

The invention belongs to the technical field of artificial intelligence and environment monitoring, and particularly relates to a carbon emission intelligent prediction optimization system. The invention aims to solve the problems of low prediction precision, response lag and lack of closed-loop regulation in the prior art. The system collects multi-source data through a carbon source sensing layer, extracts spatio-temporal features through a feature engineering engine, predicts the emission trend through fusion of a physical mechanism and a deep learning two-channel model, generates a dynamic regulation and control strategy based on multi-objective optimization, and realizes closed-loop optimization and continuous learning in combination with execution feedback and a knowledge evolution center. And the prediction accuracy and the operability of emission reduction decision making are improved.

Embodiments of the present application provide a text analysis method and device, electronic equipment and storage medium, and relate to the technical field of data mining. The method comprises: constructing a text citation network corresponding to a set of texts to be analyzed based on the citation relationship between each text in the set of texts to be analyzed in a target field; selecting a plurality of candidate paths from the text citation network; performing at least one clustering operation on the plurality of candidate paths until a preset ending condition is met, and at least two clustering centers obtained when the preset ending condition is met are taken as knowledge evolution paths corresponding to at least two subfields in the target field respectively. Through analysis of the set of texts to be analyzed in the target field, embodiments of the present application obtain the development context corresponding to each subfield in the target field, and the analysis result is more comprehensive; the text semantic information and network structure information of the candidate paths are considered, the information utilization is more comprehensive, and the analysis result is more accurate.

This invention discloses a remote collaborative fault early warning and maintenance support system for semi-autogenous grinding mills. The invention employs a three-layer closed-loop architecture—a perception-execution loop, a diagnostic decision-making loop, and a knowledge evolution loop—to achieve intelligent operation and maintenance from data to knowledge. The perception-execution loop completes data acquisition and operating condition identification at the edge, generating event summaries and evidence chains. The diagnostic decision-making loop performs twin-based precise diagnosis and calibration based on the summaries, outputs fault location, and generates executable work cards with integrated safety interlocks through a two-stage retrieval process. The knowledge evolution loop verifies and consolidates the evidence chains, driving incremental learning and version updates of the model and thresholds, forming an update package for reverse synchronization. Through the bidirectional flow of event summaries, work cards, evidence chains, and update packages, the three loops form a continuous evolutionary closed loop of "perception-diagnosis-operation-optimization," thereby improving early warning accuracy, maintenance efficiency, and recurrence suppression capabilities, and ensuring stable, adaptive, and safe operation of the system under complex operating conditions and weak network environments.

The application discloses an education intelligent agent system and method based on multi-engine cooperation and dynamic knowledge evolution, and the system comprises a data acquisition layer, which is responsible for collecting original data from various data sources and providing data support for the upper layer; a data layer is used for storing various data; a model layer is used for providing different levels of large model capabilities; an intelligent analysis layer uses an artificial intelligence layered large model as the technology, analyzes and processes the collected data, and generates high-level abstract data models and knowledge; an application service layer encapsulates the results of the intelligent analysis layer into reusable services; an application layer directly faces the application interface of the end user and provides specific functions; the application has the advantages that: a real personalized learning path planning is realized, through the deep cooperation of the six intelligent engines, the education mode innovation from "one face for thousands of people" to "one person thousands of faces" is realized, and the intelligent education experience of precise adaptation, real-time response and combination of learning and use is provided for each learner.

The invention discloses an after-sales intelligent question-answering method and device fusing vehicle context perception and knowledge evolution, equipment and a storage medium, and relates to the technical field of vehicle after-sales, and the method comprises the steps: obtaining question information sent by a user according to an adaptation protocol corresponding to a communication platform; acquiring corresponding vehicle context information based on the problem information; according to the problem information and the vehicle context information, performing retrieval from an after-sales knowledge base to obtain target knowledge content, the after-sales knowledge base comprising a double-layer knowledge graph architecture; and obtaining target answer information based on the question information, the target knowledge content and the vehicle context information, and returning the target answer information to the communication platform, thereby solving the problems of response generalization and low accuracy caused by lack of dynamic data support of a vehicle state and an environment scene in an existing after-sales question-answering system. Compared with a traditional question and answer system which only depends on static text matching, the accuracy of fault diagnosis, the personalized degree of answers and the adaptability to complex scenes are remarkably improved.

The present application relates to the technical field of data governance, and particularly relates to a standardized data governance system based on RAG and a large model warehouse, which comprises a data fusion module, a standard knowledge evolution module, a preliminary inspection module and a RAG enhancement and model warehouse module; the data fusion module is used for obtaining a multi-source unified semantic data set; the standard knowledge evolution module is used for forming a standard theme library; the preliminary inspection module is used for generating a first search result based on an external task request; the RAG enhancement and model warehouse module is used for generating a second search result based on a preset multi-modal model, the external task request and the first search result. By constructing a standard theme library to form a first search result, the present application verifies the result formed by the RAG enhancement and model warehouse module as a boundary condition, improves the accuracy of the data generation result, and prevents model reasoning deviation and error knowledge diffusion.

The invention discloses a bidding document generation method and system driven by incremental knowledge evolution and multi-dimensional expert constraints, and the method comprises the steps: incremental dynamic construction and updating of a knowledge base, task attribute perception-based heterogeneous large language model collaboration and text generation, and parallel integrated quantitative review fusing multi-dimensional expert constraints. And quality decision and structured feedback generation based on confidence penalty, iterative optimization of closed-loop driving and final output. According to the method, a full-automatic layered decoupling architecture is adopted, real-time biological growth of a knowledge base is achieved through an incremental knowledge evolution mechanism based on HNSW dynamic graph indexes, and a multi-dimensional negative feedback closed-loop control algorithm based on parallel ensemble learning and a variance penalty mechanism is utilized; and the compulsory generation quality carries out mathematical level convergence on a preset bid invitation review standard.

The application discloses a few-sample citrus knowledge graph completion biomimetic prototype memory attention network method and relates to the technical field of computer artificial intelligence. Firstly, for the few-sample task relations in the citrus knowledge graph, the neighborhood of the head entity and the tail entity is defined respectively, and the semantic representation of the entity pair is generated through an entity pair feature information encoder. A memory module is constructed, and reliable relationship prototypes are generated through memory replay and update. The similarity between the semantic representation of the query triple and the generated relationship prototype vector is calculated, and the rationality of the query triple is evaluated based on the similarity. By constructing a positive and negative query set and minimizing the total loss function combined with the ranking loss and the smoothing loss, the model is trained and optimized to obtain a few-sample citrus knowledge graph completion model. The application solves the technical problems of weak few-sample relationship representation capability and lack of memory mechanism suitable for citrus knowledge evolution in the prior art.

This application discloses a data processing and insight method and system based on a multimodal intelligent assistant, belonging to the field of computer technology. By integrating device physical model deduction and domain knowledge graph retrieval, a dual-track coupled reasoning framework is constructed, effectively solving the problems of decoupling between evidence and the spatiotemporal evolution of the device, and the logical defocusing of knowledge retrieval in traditional diagnosis. Through heterogeneous intelligent agent collaborative data collection and real-time conflict detection, the reliability and consistency of evidence are improved. Dynamic graph fusion and traceable reasoning mechanisms provide a transparent causal attribution chain and comprehensive credibility assessment, overcoming the limitations of black-box diagnosis. The introduction of metacognitive reflection and closed-loop optimization instructions can proactively address uncertainty, achieve continuous knowledge evolution and self-improvement of diagnostic capabilities, thereby reducing the false positive and false negative rates and supporting accurate operation and maintenance decisions.

The invention relates to the technical field of data management, in particular to a standardized data management system based on RAG and a large model warehouse, which comprises a data fusion module, a standard knowledge evolution module, an initial inspection module and an RAG enhancement and model warehouse module. The data fusion module is used for obtaining a multi-source unified semantic data set; the standard knowledge evolution module is used for forming a standard theme library; the initial detection module is used for generating a first retrieval result based on an external task request; and the RAG enhancement and model warehouse module is used for performing reasoning generation based on a preset multi-modal model, the external task request and the first retrieval result to obtain a second retrieval result. According to the method, the standard topic library is constructed, the first retrieval result is formed and serves as a boundary condition to verify the result formed by the RAG enhancement and model warehouse module, the accuracy of the data generation result is improved, and model reasoning deviation and wrong knowledge diffusion are prevented.