1707 results about "Standardization" patented technology

The invention discloses an industrial innovation knowledge graph dynamic construction method based on a large language model, and belongs to the technical field of data processing. According to the method, multi-source heterogeneous data are integrated, cleaning, format conversion and standardization processing are performed, entities, relationships and attributes are automatically extracted by adopting a large model driven zero sample extraction technology, a high-precision triple library is constructed by combining entity embedding alignment and context anaphora resolution, and a knowledge graph is generated based on a graph database. According to the method, incremental data are captured in real time through a dynamic sensing layer, graph dynamic updating is achieved through an atomization updating mechanism, and data timeliness is guaranteed in combination with timestamps and multi-source verification. According to the method, the knowledge graph construction efficiency and coverage rate are improved, the defects that a traditional method is high in manual dependency degree, long-tail knowledge is missing, updating lags and the like are effectively relieved, and accurate support is provided for technical innovation decision making.

The invention provides an MES-based power distribution cabinet quality inspection analysis and process regulation and control method and system, and the method comprises the steps: converting a quality inspection standard into an executable rule, generating a standardized quality inspection template, evaluating the quality of a single project and a batch, monitoring the mean value and fluctuation of a production process based on a statistical process control theory, and calculating a process capability index. A dynamic threshold value formula is combined to adjust a control limit in real time, process stability is evaluated, equipment data is standardized, production batches are associated, data consistency is verified and ensured, graded early warning is performed according to the deviation degree, a machine learning model is combined to realize predictive maintenance, process parameters are dynamically corrected, historical data are analyzed through machine learning, and the process quality is improved. According to the method, high-order feature quantification influence factors are extracted, abnormal parameter combination is detected in combination with an isolated forest, a process strategy library is updated, closed-loop optimization is formed, full-process automation from data acquisition, real-time analysis and parameter adaptive adjustment to predictive maintenance is realized, and quality inspection efficiency and process stability are improved.

The invention relates to the technical field of building engineering, in particular to a building data processing method and system based on multiple building specifications, and the method comprises the steps: building model data standardization processing: extracting geometric attributes, material attributes and spatial topological relations of components through a BIM software interface, and generating model data in a standard format; constructing a multi-source specification rule tree, performing semantic analysis on national standard, local standard and industrial standard provisions, extracting triple constraint conditions, and fusing to generate a unified rule tree containing hierarchical relationships and conflict marks; on the basis of a spatial topology mapping relationship between the model and the rule tree, identifying a specification conflict and generating an optimization rule set according to a specification effectiveness level and a spatial attribute priority; and finally, real-time compliance verification is executed, and a three-dimensional compliance report including conflict positioning, article basis and correction suggestions is output. According to the method, multi-source specifications can be automatically adapted, model violation components are accurately recognized, a correction scheme is given, and the specification compliance and the verification efficiency in the design stage are improved.

The invention relates to a building information intelligent management method and system based on a BIM technology, and the method comprises the steps: intelligently analyzing a user demand into a standardized parameter set, and generating an initial BIM model through combining with a rule engine; model parameters are automatically adjusted through parameter matching and an AI rule engine, and a standard compliance BIM model is output; performing structure, energy consumption and space utilization rate simulation analysis on the compliance model to generate a performance report containing a normalized deviation value; taking the deviation value as deep Q network state input, iteratively optimizing parameters to reach the standard through a predefined action space, and outputting a performance optimization BIM model; construction and operation and maintenance data are obtained in real time, digital twin comparison with the optimization model is carried out, deep Q network secondary optimization is triggered, a dynamic update BIM data flow is generated, and all stages of the whole life cycle are synchronized, so that closed-loop management of building whole life cycle dynamic management and control is achieved, and the design efficiency, the engineering compliance and the cross-stage cooperation capability are improved.

The invention discloses a BIM-based building electromechanical full life cycle management platform and an optimization strategy thereof, relates to the technical field of building informatization management, and solves the defects of the existing BIM technology in the aspects of processing efficiency, model precision, data synchronization and change management. According to the invention, a data layer is used for data cleaning and standardization through a machine learning model, and distributed storage is realized through a cloud management system; the model layer constructs and updates a high-precision BIM model; the synchronization collaboration layer ensures the consistency and traceability of data by using a real-time synchronization mechanism and a block chain technology; the decision analysis layer performs data analysis and prediction through a deep learning algorithm; the full life cycle management layer integrates historical data and current data and automatically triggers a management process; according to the method, the data processing efficiency and the model precision are greatly improved, and the seamless sharing capability of information and the transparency of data synchronization and change management are remarkably improved, so that the overall collaborative efficiency and the management level of a project are enhanced.

The invention relates to the technical field of data processing, in particular to an intelligent document updating processing method and system.The intelligent document updating processing method comprises the steps that part parameters and coding rules are extracted from engineering design file metadata, a part list and a PDM and PLM system, the semantic association relation between parts is analyzed through a knowledge graph, and the mapping relation between logic identifiers and physical files is constructed; generating an initial version file library; and monitoring file names and version changes in real time based on a micro-service architecture, calling a simulation service in combination with a knowledge graph to verify parameter compatibility, screening an optimal version, updating the optimal version to a main version library, optimizing historical conflict data in a block chain evidence storage index by using a genetic algorithm to generate a standardized code, and outputting a cross-platform parameter mapping table. According to the method, the problems of file name change, non-standard coding conflict, version control missing and cross-platform adaptation incoherence are solved, and the data tracing efficiency, the version management reliability and the system compatibility are improved.

The invention discloses a data knowledge-based method based on semantic fusion, and relates to the technical field of computer information processing.The method comprises the steps that a requirement set serves as input, knowledge requirement analysis, concept modeling, relation modeling and constraint declaration are completed, and a semantic model OB is constructed; taking the original data set Sraw as input, completing standardization processing and structure segmentation under the support of a semantic model OB, and forming an entity corpus and a semantic unit set; based on the semantic unit set, structured and unstructured triple extraction, semantic verification and graph loading are executed, and an initial knowledge graph is constructed; performing entity alignment, relationship merging, rule reasoning and versioning release on the initial knowledge graph to generate a graph; introducing a quality evaluation mechanism, and outputting an optimized atlas and an evaluation document; the KG opt deployment is online, and query packaging, visualization, service arrangement and incremental maintenance are completed. The method aims at solving the problems that multi-source heterogeneous data are not uniform in structure and inconsistent in semantics.

The invention provides a shield intelligent auxiliary type selection system and method based on a large language model. The model selection system comprises a data input module, a rule knowledge base module, a large model reasoning module and a result generation module. The integrated decision-making system integrating a rule knowledge base, a deep learning model and expert system logic is constructed for the practical problems of complicated geological conditions, multiple rule constraints, high expert dependency and the like in shield construction, and the system combines a structured model selection rule and historical case data, and has the advantages of intelligence, standardization, self-learning, high efficiency and the like. The problems of low efficiency, high subjectivity, insufficient intelligent degree and the like of the existing shield tunneling machine model selection depending on artificial experience and partial standardized guide are solved, and the transformation of shield construction management from artificial experience to intelligent decision can be promoted.

The invention provides a supply chain collaborative material management system and method, and relates to the technical field of material management, and the method comprises the steps: receiving a multi-enterprise heterogeneous material data flow, and constructing an industry knowledge graph based on the field-level semantic mapping of a dynamic ontology library; a standardized data table is generated; standardized data is injected into a distributed message queue for parallel processing, dynamic cache partitions are created according to material object labels, timestamp indexes are built in the dynamic cache partitions, a sliding window mechanism is adopted to scan data, and multi-strategy resolution is performed on conflict data in a window; performing conjoint analysis on the cleaned data to generate an enhanced analysis report, and activating a case transfer learning mechanism to update a model when the similarity between new data and historical cases exceeds a threshold value; constructing a material state transfer matrix for the abnormal data, simulating a correction operation in a digital twin environment, and triggering automatic correction; the problems of multi-enterprise heterogeneous data integration, dynamic data processing, intelligent analysis and the like are solved, and the collaboration efficiency and the management level of a supply chain are improved.

A computer-implemented method for evaluating and optimizing a large language model (LLM) includes receiving input data for a large language model; transforming the input data into a standardized format compatible with a multi-metric evaluation framework; applying a set of evaluation metrics to the transformed data, wherein the set of metrics comprises a hallucination metric, a groundedness metric, a relevance metric, a recall metric, a precision metric, a consistency metric, and a Coherence metric; generating an error function that quantifies an overall performance of the LLM, wherein the error function takes as input parameters of the LLM and incorporates the set of evaluation metrics to produce an error score; minimizing the error function wherein a lower error score indicates better performance of the LLM; and outputting an improved LLM with improved parameters.

The invention relates to the technical field of power plant management and control, and discloses an intelligent power plant management and control system based on the Internet of Things, and the system comprises the steps: when a plurality of labels related to the same equipment or parameter exist in different subsystems, according to a similarity index between the labels and an equipment association relationship; on the basis of the detected conflict label group, combining equipment historical operation and maintenance data and an upstream and downstream parameter flow relationship, constructing a label semantic evolution graph, and performing reasoning analysis on label equipment through a fusion rule engine and a graph neural network; through mapping knowledge domain fusion and semantic embedding comparison, matching and clustering among conflict labels are completed based on structural similarity and semantic relevancy, and a label alignment rule is constructed; according to a label coordination result, designing a mapping rule of a data field; and performing inter-system synchronous verification on a result after structure conversion and label standardization processing, and writing a standardized label into a unified semantic database. The method has the advantage of improving data semantic consistency.

The invention discloses a multi-source process parameter mapping supervision system and method based on a big data model, and relates to the technical field of process parameter analysis. Initial process parameters in a process production line are collected, the initial process parameters are processed to obtain standardized process parameters, and the process production line is subjected to process stage division; analyzing a stage product deviation degree of the process stage, calculating correlation between the standardized process parameters and the product deviation degree of the process stage, analyzing the stage product deviation degree of the process stage, predicting the product deviation degree of the process stage in production, and predicting a product reject ratio in production based on the product deviation degree of the process stage. According to the method, the product deviation of each stage and the reject ratio of the whole product are continuously predicted, the production state is judged in real time, a dynamic quality control closed loop is constructed, and the pertinence and effectiveness of supervision are improved.

The invention provides a financial data risk analysis method and system based on a large model, and relates to the technical field of intelligent risk control, and the method comprises the steps: obtaining financial data from a participant, carrying out the standardization and aggregation through a federated learning framework, generating a federated feature vector, constructing a bidirectional knowledge distillation model, receiving a vector, and carrying out the risk analysis of the financial data. A teacher model and a student model are deployed, a risk conduction map is constructed, a generative adversarial network is utilized to monitor changes of an enterprise association network, a risk conduction edge weight is adjusted, a financial report time sequence and a transaction event sequence are aligned through a space-time coupling encoder based on an LSTM and Transform architecture, and a multi-modal fusion feature vector is generated and input to a bidirectional knowledge distillation model; and a final risk score is obtained by combining the atlas, a grading early warning mechanism is triggered, and a risk disposal suggestion and a conduction path are generated, so that the data standardization and aggregation efficiency can be improved, and the dynamic coupling capability of a multi-modal fusion mechanism and the adaptability of a risk conduction model can be enhanced.

The invention discloses a fine-grained access control method and system based on risk identification, and belongs to the technical field of information security. According to the method, user subject attributes, behavior attributes and system environment attribute information are collected in real time, a standardized decision matrix is constructed, an interval type-2 fuzzy set (IT2FS) is used for conducting fuzzy modeling on the attributes, and an upper membership matrix and a lower membership matrix are generated. And calculating the dynamic weight of the attribute index in combination with a CRITIC method, introducing a time decay factor to dynamically correct a risk score through an improved TOPSIS method, calculating the Euclidean distance between an access request and a positive / negative ideal solution, and generating a normalized risk closeness degree. And based on the risk score and a preset threshold value, dynamically matching a hierarchical permission strategy, and adopting a static rule and a priority coverage mechanism to eliminate permission conflicts. According to the method, multi-dimensional risk assessment and dynamic weight adjustment are fused, the problems of insufficient real-time performance, subjective weight dependence and weak uncertainty processing capability in a traditional method are solved, the accuracy and security of access control are remarkably improved, and the method is suitable for scenes with high security requirements such as cloud computing and finance.

The present invention relates to the technical field of data processing, and provides a standardized government affairs data construction method and device based on a knowledge graph. The standardized government affairs data construction method based on a knowledge graph of the present invention comprises: on the basis of seed words of a government affairs scenario, using a feature extraction model to recognize a plurality of initial entities in the government affairs scenario; and then using a mutual information value between the adjacent initial entities to obtain a first phrase entity; on the basis of the mutual information value, obtaining a second phrase entity by calculating the left / right entropy, so that the range of the phrase entities is further expanded; and finally obtaining target entities. A phrase entity formed by embedding a plurality of words is extracted, and thus richer and more accurate entities are obtained to construct a knowledge graph in a government affairs scenario.

A surface defect detection method for multi-scale and small target features comprises the following steps: step 1, acquiring a surface defect data set, converting the format of a labeling file into a required format, and dividing an image into a training set, a verification set and a test set; step 2, constructing a DMS-YOL O surface defect detection model; 3, training the constructed DMS-YOLO surface defect detection model, and training the surface defect detection model by using the training set and the verification set to obtain an optimal surface defect detection model; 4, performing evaluation on the test data set through the trained model; 5, fully testing and debugging the model trained in the step 3 to ensure the accuracy and stability of the surface defect detection method in different scenes; and step 6, deploying the trained DMS-YOLO surface defect detection model in an actual industrial production environment, performing defect detection on the surface of a real product, and recording the accuracy rate, the false detection rate and the missing detection rate of a detection result. And 7, summarizing the whole detection process, forming a standardized solution, and popularizing the standardized solution to different industrial fields for application verification to ensure that the method has practicability and stability.

The invention relates to the technical field of project cost management, in particular to a project cost-oriented information management method and system, and the method comprises the following steps: carrying out multi-source data collection and standardization processing, carrying out the automatic calculation, logic verification and correction of a project amount based on an AI calculation amount engine and a rule engine, and generating a precise project amount list; calling a dynamic pricing model, carrying out deviation analysis on the actual cost and the plan cost based on a earned value analysis method and a machine learning model, predicting the cost hyper-branched risk, and carrying out graded early warning; constructing a multi-participant collaborative platform, and supporting change application submission, associated cost influence calculation, online examination and approval, problem tracking and progress synchronization; the whole-cycle data is classified and archived, an enterprise-level cost index library is generated based on historical project data, cost reference is provided for a new project, and the method is suitable for cost information efficient management and cost control of the whole life cycle of constructional engineering, municipal engineering, installation engineering and the like.

The invention discloses an operation and maintenance management method of an integrated management system, and belongs to the technical field of operation and maintenance of systems. The invention discloses an operation and maintenance management method of an integrated management system, and aims to solve the problems of data islands, slow fault positioning, experience dependence on strategies and the like in traditional operation and maintenance. The method comprises the following nine core processes: dynamically accessing multi-source heterogeneous data and carrying out standardization processing; constructing a hierarchical time series data storage structure; generating a modeling dependency and fault path of the equipment knowledge graph; adopting a three-layer anomaly detection model to identify anomaly; fault root causes are positioned through causal reasoning and a Bayesian network; generating an energy efficiency strategy based on reinforcement learning and multi-objective optimization; triggering the self-healing workflow to execute operation; testing the robustness of the system in a sandbox environment; and iteratively updating the knowledge graph and the AI model to form a closed loop. According to the method, automation and intelligentization of the whole operation and maintenance process are realized, and the system availability and the energy efficiency management level are improved.

The invention relates to the technical field of intelligent decision making, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a decision making method, device, equipment and medium based on multi-modal data, comprising: collecting and cleaning multi-modal original data, executing format conversion and desensitization processing, and generating standardized data; multi-modal features are extracted and fused based on the standardized data, and multi-modal feature vectors are generated; performing reasoning on the multi-modal feature vector through a reasoning model to generate reasoning result data; pushing the reasoning result data to a review terminal, and receiving correction feedback data; and updating parameters of the reasoning model based on the corrected feedback data, and generating an updated reasoning model. According to the method, after data cleaning, format conversion and desensitization processing, multi-modal feature extraction and reasoning model reasoning processing are executed, effective fusion and accurate reasoning of data multi-modal features are achieved, and decision accuracy and data safety are improved.

The invention discloses a grassroots society digital governance method and system, and the method comprises the steps: collecting multi-source heterogeneous data in real time through an Internet of Things sensing network disposed in a grassroots community, and generating a standardized multi-mode sensing data flow; based on the multi-modal perception data stream, outputting a time-space associated cleaned data set; inputting the cleaned data set into a multi-scale space-time encoder, and generating a feature tensor containing regional hotspot distribution and a risk propagation path; based on the feature tensor, constructing a dynamic risk knowledge graph, and outputting a decision matrix including a risk level and an optimal intervention path; inputting the decision matrix into a strategy optimization engine to obtain a hierarchical governance instruction set; and on the basis of real-time governance feedback data after the hierarchical governance instruction set is executed, a conflict instruction in the strategy chain is autonomously corrected through a fuzzy reinforcement learning algorithm. By utilizing the embodiment of the invention, an efficient, intelligent and traceable digital governance scheme can be realized, so that the social governance efficiency and the service quality are improved.

The invention discloses an AI and BIM-based project cost control method and system, and the method comprises the steps: obtaining the three-dimensional model data of a target building, analyzing the file structure of the three-dimensional model data, recognizing the type, size parameters and material attributes of a component, building a mapping relation between a component coding system and an attribute tag if the three-dimensional model data passes the integrity inspection, and carrying out the construction cost control of the target building. Generating a standardized component data set; constructing a material price fluctuation prediction model according to the historical price record, intercepting time series data by adopting a sliding window mechanism, and if the price fluctuation amplitude in the material price fluctuation prediction model exceeds a preset threshold, triggering an early warning identifier, and generating cost prediction data with a risk level; and carrying out association mapping on the standardized component data set and a quota library by adopting a coding matching mechanism, and if component attributes are successfully matched with quota entries, converting and generating engineering quantity data based on geometric parameters, and calculating the cost of a single project. The cost prediction accuracy is improved.

The invention relates to the technical field of operation and maintenance management, in particular to an asset operation and maintenance decision management platform and management method based on data fusion, and the method comprises the steps: collecting asset full life cycle data, and constructing a standardized asset data set; utilizing ontology modeling and knowledge extraction technologies to construct a dynamic asset knowledge graph containing asset attributes, topological relations and operation states; training a deep learning model to realize asset fault diagnosis, constructing an asset operation and maintenance risk detection algorithm, performing real-time analysis on asset multi-dimensional data, identifying an asset security risk level, and triggering a corresponding operation and maintenance strategy; and converting the diagnosed asset fault and the generated operation and maintenance strategy into a natural language by using a natural language generation technology, and pushing the natural language to an operation and maintenance manager for execution. According to the method, multi-source heterogeneous data is fused, and intelligent operation and maintenance decision-making of the full life cycle of assets is realized.

The invention discloses a source load storage dynamic strategy verification method based on double-layer reinforcement learning, and the method comprises the following steps: S1, collecting the operation data of a source load storage system, and constructing a standardized operation data set; s2, constructing a double-layer reinforcement learning model, generating a global scheduling strategy by an upper-layer strategy network, and outputting an action decision strategy by a lower-layer strategy network; s3, performing joint training on the double-layer reinforcement learning model by adopting a strategy gradient optimization method, and outputting a scheduling strategy; s4, introducing an integral gradient method to analyze a scheduling strategy, and constructing a key scheduling state node set; s5, optimizing the generation logic of the global scheduling strategy to obtain an optimized double-layer reinforcement learning model; s6, constructing a plurality of source-load-storage system operation scenes to form a typical operation scene set; and S7, deploying the optimized double-layer reinforcement learning model in the typical operation scene set, and outputting a strategy verification result. According to the invention, through combination of double-layer reinforcement learning and an integral gradient method, source-load-storage dynamic strategy verification is realized.

The invention discloses a report generation system and method based on a legal knowledge graph, and relates to the technical field of artificial intelligence and law, and the method comprises the steps: obtaining a legal appeal of a user through a non-standardized domain corpus collection engine, carrying out the multi-dimensional intention deconstruction through a semantic slicing model, and obtaining a judicial element topology network; inputting the judicial element topology network into a law deduction engine of dynamic value perception ability, calculating a decision vector weight through a dynamic value evaluation network, and generating a judicial decision path by using a time sequence feedback mechanism; based on a judicial decision path, through an association rule mining engine of a multi-modal knowledge graph, a dynamic weighting law retrieval confidence matrix is generated in combination with semantic coupling degree analysis and logic path influence evaluation. According to the method, qualitative change of legal consultation from information retrieval to intelligent decision is realized through collaborative innovation of a legal deduction engine with dynamic value perception capability and a legal semantic constraint decoder.

The invention relates to the technical field of energy management, in particular to a building energy-saving analysis system based on big data. According to the method, through time sequence segmentation of the real-time energy consumption data and the historical load records and in combination with Z-score standardization processing, accurate description of the dynamic fluctuation range of the building energy consumption data is achieved, and interference of seasonal changes and equipment operation fluctuation on an analysis result is effectively avoided. According to the outlier detection algorithm constructed based on multi-dimensional features, the fine granularity level of anomaly recognition is improved through composite analysis of curve slope and power fluctuation. A dynamic threshold self-adaptive mechanism is combined with an operation mode and an equipment state, so that the sensitivity and response adaptation capability of energy consumption data monitoring are enhanced. A multi-dimensional parameter fusion method constructed by an association index and a credibility factor is adopted, and the credibility modeling and risk grade division of the energy consumption data are effectively realized by quantifying the association among the fluctuation amplitude, the duration and the data deviation degree.

The invention provides a whole-process engineering consultation management design method, which comprises the following steps of: constructing a unified data exchange standard and protocol, converting heterogeneous data output by design tools of different professions and different units into a standardized data format, realizing seamless joint and real-time sharing of data through data middleware, eliminating data barriers, and improving the data exchange efficiency. The collaborative design efficiency is improved; according to the method, digital technologies such as BIM are adopted, a digital twin model of an engineering project is constructed in a design stage, a design scheme is deepened through virtual construction simulation and optimization, factors such as a construction process, construction procedures and resource allocation are fully considered, and the construction feasibility and economic rationality of design are improved; a collaborative mechanism of a design stage and a construction stage is established, experienced construction managers and technicians are invited to participate in design review, improvement suggestions and suggestions are provided from the construction perspective for a design scheme, a designer is helped to comprehensively understand construction requirements, and the design scheme is optimized.

The invention relates to the technical field of deep learning, and particularly provides a slope deformation real-time early warning algorithm and system based on deep reinforcement learning, and the method comprises the steps: generating a standardized state representation vector, and building a state quality evaluation index; receiving the state representation vector as input, constructing a dual-mode strategy architecture, generating a preliminary decision and confidence evaluation thereof, and forming a quantitative index of decision reliability; forming a final early warning instruction and a strategy updating suggestion, and generating a strategy performance evaluation report; continuously tracking the change trend of state feature distribution, constructing a strategy degradation early warning system, and triggering a model reconstruction mechanism; the updated policy network parameters are fed back to the dual-mode policy architecture, and the optimized feature extraction suggestions are fed back to the establishment of the state quality evaluation indexes. The system comprises an index establishment module, a report generation module and a reconstruction mechanism module. According to the slope deformation real-time early warning algorithm, a monitoring-decision-optimization end-to-end closed-loop control system is realized through a deep reinforcement learning framework.

The invention relates to the technical field of data processing, in particular to a data fusion intelligent equipment linkage management and control system based on a large model, and the system comprises a data collection module which outputs standardized data carrying privacy grading identifiers; the data processing module outputs an optimization algorithm identifier and a collaborative strategy identifier which are synchronously bound; the privacy algorithm fusion scheduling module generates atomization calculation graph data and strategy audit logs fusing privacy operation; the multiple optimization resource module executes calculation graph data and drives a parallel simulation environment rehearsal resource scheme, and the double-closed-loop feedback module is activated when an arbiter continuously detects that deviation exceeds the standard for three times; the safety instruction generation module fuses the output result and the audit log to generate a digital signature equipment control instruction; and the double-closed-loop feedback module updates knowledge base weight parameters through a data feedback loop, and controls the feedback loop to adjust a resource allocation threshold in a grading manner according to simulation confidence, so that dynamic balance between resource efficiency and privacy intensity is realized.

The invention belongs to the field of aircraft simulation, particularly relates to a flight simulator test data state identification method, system and equipment based on multi-modal learning, and aims to solve the problems of low verification efficiency, poor compliance and difficulty in comprehensive identification of equipment risks. The method comprises the following steps: acquiring a time sequence operation curve image, a standard document text and an equipment time sequence, and generating a multi-dimensional equipment state representation vector through a feature extraction network after standardization processing; analyzing a standard document, constructing a structured knowledge base, extracting an initial allowable deviation range, fusing environmental parameters, and calculating a health index by using a health state decision tree to adaptively adjust the upper and lower limits of a deviation interval; and performing intelligent compliance judgment in combination with the compliance probability output by the state classification model and the dynamic deviation range. According to the invention, through collaborative decision-making of multi-modal data analysis, deviation range optimization and equipment health state quantitative evaluation, the accuracy and environmental adaptability of flight simulator test verification are improved.

The invention discloses a code generation system based on a natural language and meta-model drive, and the system comprises an AI layer which comprises a plurality of agents which work cooperatively, and is configured to receive a natural language instruction, and carry out the intention recognition, entity extraction, context analysis and constraint verification through the agents, so as to generate structured metadata; the meta-model engine is configured to be used for constructing and instantiating one or more meta-model objects according to the structured metadata generated by the AI layer; and the code template engine is configured to be used for matching one or more corresponding code templates from the code template library according to the content and the type of the meta-model object, and generating executable codes or configuration files of one or more target languages through rendering. By means of the scheme, the natural language is converted into the meta-model decoupled from the technology through the AI, and then the high-quality standardized code is automatically generated through the template engine, so that the development efficiency is improved, and developers can focus on core business innovation.