772 results about "Analysis models" patented technology

The embodiment of the invention relates to the technical field of data modeling analysis, and discloses a tunnel rock stratum large deformation analysis method and system based on three-dimensional modeling, and the method comprises the steps: collecting initial three-dimensional point cloud data through three-dimensional laser scanning; constructing an initial three-dimensional geologic model containing spatial morphological characteristics and a rock stratum interface geometric topological relation through space coordinate matching and curved surface reconstruction; finite element unstructured mesh generation is carried out on the model, and a rock stratum mechanical analysis model is established in combination with physical and mechanical parameters; inputting construction parameters and process information, activating unit nodes in sequence, and solving a force balance equation to simulate stress distribution; identifying a potential deformation area through iterative calculation, and adjusting boundary conditions to predict a deformation trend; the three-dimensional space distribution cloud atlas, the time evolution curve and the position marks are fused to generate multi-view deformation early warning information, the accuracy and the visualization degree of tunnel rock stratum deformation analysis are improved, and support is provided for construction safety.

The invention relates to an enterprise multi-source data intelligent association analysis method based on artificial intelligence and a large model, and the method comprises the steps: introducing time sequence dynamic analysis, a business rule base and statistical correlation test, carrying out the multi-dimensional and automatic cross verification and consistency test of an association pair outputted by a semantic association engine, and carrying out the analysis of the association pair. Screening out a high-confidence correlation set conforming to the business logic, the time sequence evolution rule and the statistical significance; and packaging to form a reusable business insight analysis model based on the enterprise data knowledge graph, receiving a business query request by the model, automatically generating a deep analysis report for business process optimization and potential risk early warning through graph reasoning, path discovery or an abnormal sub-graph detection algorithm, and pushing a result to a decision support system.

The invention discloses a submarine pipeline state early warning method and system based on scour-vibration coupling sensing, and the method comprises the steps: obtaining the historical marine environment information of a target submarine pipeline region, laying a monitoring sensor array according to the historical marine environment information, collecting multi-source monitoring data, and carrying out the preprocessing of the multi-source monitoring data, thereby forming a multi-source monitoring data set. And constructing a multi-physical field coupling analysis model, inputting the data set into the model to analyze the response condition of the pipeline in the current seabed environment, and obtaining pipeline response simulation information. And analyzing resonance influence caused by environmental scouring in different sections, generating resonance derivative characteristics, and constructing pipeline multi-modal fusion characteristics in combination with monitoring data. Predicting the future response state of the pipeline through the submarine pipeline state analysis model, and carrying out risk early warning prompt; meanwhile, the fatigue life of each section is analyzed based on resonance derivative characteristics, fatigue-prone sections are identified, and maintenance suggestions are generated and pushed. Therefore, the accuracy and timeliness of sensing the state of the submarine pipeline are improved, and the early warning intelligence of the submarine pipeline is effectively improved.

The invention discloses a local legislation compliance intelligent detection system and method based on deep semantic analysis and a multi-modal legal knowledge graph, and relates to the field of computer technology and law crossing technology, the method comprises the following steps: constructing a legal knowledge graph; designing a multi-dimensional rule sub-library with a dynamic weight adjustment mechanism; a large language model based on Deepseek is utilized to construct legal provisions to perform a deep semantic analysis model, so that the intelligence of generation of triads of laws and regulations in the national field is realized, and the field adaptability of triad generation is improved; executing multi-dimensional conflict detection based on the knowledge graph, a conflict detection rule base and a semantic analysis result; and a multi-dimensional law conflict report is automatically generated. According to the method, a legal knowledge graph is constructed, multi-source legal data is integrated, a multi-dimensional rule sub-library is combined, a Deepseek-based special model is used for carrying out deep semantic analysis on legal provisions, and explicit and implicit conflict judgment is carried out on contradictory point locations and contexts.

The invention relates to the technical field of equipment management, in particular to an equipment full-life-cycle digital twinborn decision-making system enabling a large model. Comprising a digital twin modeling unit; a large model enabling analysis unit, wherein the large model enabling analysis unit adopts a hydroelectric equipment multi-modal causal constraint analysis model; a whole-process closed-loop management and control unit; and an intelligent decision output unit. According to the method, the multi-modal causal constraint analysis model adaptive to the working condition of the hydroelectric equipment is constructed, and a causal chain verification backtracking mechanism is introduced, so that the problem that the reasoning result lacks logic verification is effectively solved, the logic consistency of a fault reasoning conclusion is guaranteed, and the reliability of decision output is improved; through a scene adaptation mode of'pre-training + fine tuning 'of a large model, multi-modal feature fusion processing and deep linkage of a workflow engine and a digital twinborn body, full-life-cycle management requirements of equipment are fully covered, and the refinement and intelligence level of hydroelectric equipment management is further improved.

The invention belongs to the technical field of simulation analysis, and particularly relates to a medium-high voltage switch cabinet internal humidity inversion calculation method based on simulation analysis, which comprises the following steps: establishing a geometric model of a switch cabinet, importing the geometric model into finite element simulation software to obtain a simulation analysis model, adding each physical field and computational domain and boundary conditions thereof, and calculating the internal humidity of a medium-high voltage switch cabinet. All the physical fields are coupled, and mesh generation is carried out; performing numerical solution on the simulation analysis model by using a transient solver to obtain a temperature and humidity distribution condition in the switch cabinet; the simulation analysis model is optimized, a simulation data set is constructed, and a PINN is trained; based on the environment temperature and humidity and the operation current collected in the actual operation of the switch cabinet, the trained PINN is used for prediction, and the steady-state relative humidity of the current transformer is obtained. According to the invention, based on the limited boundary monitoring data, efficient and accurate reconstruction of the humidity field of the key area in the switch cabinet is realized, and a feasible path is provided for equipment state sensing and condensation risk early warning.

The invention relates to the technical field of tunnel engineering safety, in particular to a tunnel fire multi-stage damage collaborative prediction method and system, and the method comprises the steps: constructing a data simulation fusion model, and carrying out the processing of fire and tunnel structure parameter data, and obtaining a multi-working-condition parameter database; constructing a heat conduction equation, a fire plume and wall surface heat exchange relational expression, a boundary layer correction model and a comprehensive damage evaluation model, and analyzing the equation, the relational expression and the model by utilizing a multi-physical field coupling unified boundary condition to obtain a multi-physical boundary constraint condition; obtaining a multi-physical constraint loss function based on a network architecture and a multi-physical boundary constraint condition, and further obtaining a comprehensive constraint loss function of a temperature field-stress field-damage index; constructing a PINN-LSTM network architecture and a damage evolution analysis model based on the fire ending initial state coding vector, the fire course feature vector and the cooling process feature vector; and performing linkage analysis on the function, the frame and the model to predict and monitor the transient damage and the long-term creep damage of the tunnel.

Disclosed in the present invention are a model training method and apparatus based on federated learning, and a device and a storage medium. The method comprises: training a local model on the basis of power data, so as to generate a fault analysis model used for fault analysis of a power device; and uploading local model parameters to a cloud server, such that the cloud server aggregates the local model parameters, then updates a corresponding global model, and issues updated global model parameters to edge nodes, wherein when aggregating the local model parameters, the cloud server marks the edge node corresponding to an abnormal local model parameter as a suspected abnormal node, monitors the marked suspected abnormal node, and removes, when it is detected that the suspected abnormal node is abnormal, the local model parameters uploaded by the suspected abnormal node. By means of the present invention, it can be ensured that the performance of a fault analysis model obtained by training a local model is not degraded.

The invention relates to a big language model enhanced knowledge adaptive learning planning system, and belongs to the field of intelligent education. The system comprises a knowledge center module, a learner portrait module, a path planning module and an intelligent learning guiding module, and the knowledge center module extracts entities and relationships from a multi-modal data source by using a large language model to construct a knowledge graph; the learner portrait module collects multi-dimensional learning data of the user and maps the multi-dimensional learning data to corresponding nodes of a knowledge graph, and dynamically deduces a learner portrait through a Bayesian knowledge tracking model; the path planning module generates an initial learning path based on the knowledge graph and the learner portrait, establishes a collaborative filtering analysis model, predicts and optimizes the expected effect of the current learner following the initial learning path in combination with a Bayesian knowledge tracking model, and finally generates a target learning path; and the intelligent learning guiding module generates a standardized knowledge card for each knowledge node on the target learning path through a security retrieval enhancement generation technology.

The invention discloses a financial knowledge graph construction method and system based on artificial intelligence, and relates to the field of artificial intelligence data processing. The method comprises the following steps: performing multi-dimensional semantic analysis on a heterogeneous financial data source, and extracting a structured semantic fragment; constructing a financial entity perception unit, identifying a multi-granularity entity and generating a unique code; generating a preliminary relation graph based on the event cascade relation and the attachment structure, and injecting a semantic translation label; normalizing the atlas relationship through semantic separation and a label reconstruction mechanism to form a financial relationship network with consistent semantics; executing evolution increment iteration in combination with the newly added corpus, and dynamically updating nodes and edge sets; and performing semantic consistency and structural integrity evaluation on an iteration result, and outputting a stable financial knowledge graph structural body. By introducing a multi-factor semantic analysis model, a causal relationship modeling mechanism and a graph evolution iteration strategy, systematic improvement of the financial knowledge graph in the aspects of structural expression precision, semantic reasoning ability and dynamic adaptability is achieved.

The invention provides a multi-modal sentiment analysis method and system based on main modal two-stage guidance, and relates to the technical field of sentiment analysis. Inputting the multi-modal data into a multi-modal sentiment analysis model, and extracting language, visual and acoustic features from the multi-modal data through a feature extraction module; semantically decoupling the multi-modal features into modal invariant features and modal unique features through a feature space distribution alignment module, and realizing feature distribution alignment dominated by language modals through alignment reconstruction constraints; performing self-attention modeling on the modal invariant feature through an attention enhancement module to obtain a first enhanced feature, and adaptively enhancing the visual and acoustic unique features through a cross-modal attention mechanism by taking the language unique feature as a dominant feature to obtain a second enhanced feature; the first enhanced feature and the second enhanced feature are fused through the emotion prediction module, an emotion intensity prediction result is obtained through regression prediction, and the accuracy and robustness of emotion analysis in a complex scene are improved.

The invention discloses a side slope anchor rod grouting amount three-dimensional visual monitoring method, and relates to the technical field of anchor rod grouting quality monitoring, and the method comprises the steps: collecting real-time grouting parameter data based on a grouting parameter sensor arranged in a side slope anchor rod construction area; the method comprises the following steps: constructing a three-dimensional geometric model of a side slope anchor rod, generating three-dimensional grouting amount distribution data, and generating a real-time dynamic side slope anchor rod grouting amount three-dimensional visual monitoring image by adopting a three-dimensional visual rendering technology; based on a trained image anomaly judgment model, analyzing the three-dimensional visual monitoring image, judging whether an anomaly occurs or not, and outputting abnormal data; and outputting abnormal data based on the image anomaly judgment model, and outputting an anomaly type and anomaly prediction time by using the trained abnormal data analysis model. The slope anchor rod grouting amount full-process intelligent monitoring system is constructed, and the limitation of a traditional monitoring method on data precision, space presentation and abnormal response is broken through.

The invention discloses an AI big data real-time processing and analysis method, and solves the problems of insufficient real-time performance and resource waste in traditional processing. The method comprises the steps of collecting heterogeneous data from multiple sources, dividing priorities through feature vector construction and a dynamic evaluation model, and shunting to an edge rapid processing channel, an edge-cloud collaboration channel and a cloud batch processing channel. The edge node preprocesses the high / medium priority data and compresses an analysis result in a layered manner; and the cloud receives the compression result, the middle-priority residual data and the low-priority data, a unified view is established through fusion, and the edge analysis model parameters are iteratively optimized in real time. And finally feeding back the edge preliminary analysis and the cloud depth result to the terminal. According to the method, through dynamic distribution, cooperative processing, differential compression and model iteration, real-time response of high-priority data, efficient resource allocation and continuous improvement of analysis precision are achieved, and the method is suitable for multi-scene heterogeneous data processing.

The invention relates to a Bayesian KAN fused aircraft rapid multidisciplinary uncertainty analysis method, and belongs to the technical field of spacecraft manufacturing and application. According to the method, Bayesian learning is combined with a Colmogorov-Arnodel network, a Bayesian KAN neural network which can cope with high-dimensional input uncertainty, can quantify model cognitive uncertainty and is higher in fitting capacity is provided, a BKAN network agent model is constructed for a subsubject analysis model, time-consuming numerical simulation of each subsubject is replaced, and the calculation amount is reduced; with reference to a basic framework of a global sensitivity equation GSE of a deterministic field coupling multidisciplinary system, input random input and model cognition uncertainty are introduced, and expressions of mean values and covariances of disciplinary coupling variables and system responses are deduced on the GSE framework; a rapid semi-analytical multidisciplinary random and model cognition hybrid uncertainty propagation method is established, efficient multidisciplinary uncertainty analysis is achieved, and the problems that an existing method is poor in precision and large in calculation amount are solved.

The invention relates to the technical field of coffee latte robots, in particular to a robot task processing method and system for coffee latte. The method comprises the following steps: obtaining a target garland pattern instruction, and carrying out feature extraction on an input instruction through an image recognition and semantic analysis model; constructing a cup body coordinate system, and performing adaptive modeling on different apertures, depths and cup-shaped structures based on the cup body coordinate system and the collected features to generate a cup body space attitude parameter set; generating a time sequence track set of the robot end effector based on the corrected time sequence stable track set in combination with the state vector; and matching with a comparison target structured representation matrix, optimizing a trajectory control parameter and a fluid jet parameter, and realizing real-time adaptive adjustment and stable output of a garland result. By dynamically adjusting the noise covariance matrix, track deviation caused by liquid level disturbance and cup body shaking is corrected in real time, and the problems of garland stroke breakage and deviation caused by external disturbance are solved.

The invention discloses a gear machining multi-source thermal error compensation system based on tensor compression edge deployment, which fuses physically guided multi-source error modeling, tensor-based model compression, edge deployment and bandwidth sensing signal scheduling to realize low-delay and high-precision error compensation. The method comprises the following steps: firstly, establishing a three-layer system architecture comprising a cloud layer, an edge layer and a sensing layer, supporting distributed model training, edge reasoning and error compensation so as to support closed-loop operation for realizing adaptive scheduling with extremely low delay, and mainly comprising: (1) a multi-source error model, thermal and geometric errors are fused into coupling high-order representation for capturing space-time interaction through an analytical method; and (2) establishing a sensitivity analysis model based on a physical guidance tensor mode decomposition method (PG-TMDM), identifying a key error source through a three-order tensor structure, and calculating a physical perception contribution rate to retain physical interpretability. And in order to realize real-time deployment, deploying to the edge layer through model compression, model packaging and an edge deployment strategy.

The invention relates to a wind power cluster short-term power prediction method and device of a space-time diagram neural network fused with physical information and computer equipment, and the method comprises the steps: obtaining related information data of each wind power plant in a wind power cluster, and carrying out the preprocessing; forming a physical prior data set through an engineering analysis model fusing the wake flow analysis model and the blocking effect model; taking each wind power plant as a node of the graph, constructing graph structure data for predicting the power of the wind power plant, and forming a dynamic adjacent matrix; constructing a space-time diagram neural network WB-STGNN model architecture comprising a diagram convolutional neural network module, a gating time convolutional network and a multi-layer perceptron; the method comprises the following steps: pre-training by using a physical prior data set, and then performing formal training based on historical power data and a dynamic adjacency matrix to obtain a space-time diagram neural network WB-STGNN model; inputting the wind speed of the prediction day, and predicting the active power of the whole wind power cluster in 24 hours of the prediction day. By adopting the method, the precision and efficiency of wind power cluster power prediction can be effectively improved.

The invention discloses an AI large model-based arrival person screening method and device, a medium and equipment, and belongs to the field of screening, and the method comprises the steps of firstly obtaining screening conditions input by a user, including text content, image data and qualification information requirements, and to-be-screened arrival person data; next, performing word segmentation, keyword extraction and semantic matching on the text content of the person arrival data by utilizing a preset semantic analysis model in combination with BiLSTM, CRF and LDA technologies, and outputting a semantic score; meanwhile, note styles are recognized through the text classification model, logic judgment is conducted, and styles and logic verification scores are obtained. In addition, element detection and style verification are carried out on the image data through the multi-modal recognition model, and image matching scores are output; the qualification scoring module calculates qualification scores according to a preset weight formula. And finally, fusing the multi-dimensional scores to generate a comprehensive score, and carrying out accurate screening on the arriving persons according to the comprehensive score.

The invention relates to an iron tower foundation safety analysis method considering the dry-wet cycle effect. The method comprises the following steps that 1, a soil body strength degradation constitutive model is established; 2, determining iron tower foundation structure parameters and soil body parameters; 3, establishing an iron tower foundation-soil body three-dimensional geometric model by utilizing three-dimensional modeling software and rock-soil finite element simulation software; 4, applying a load transmitted to the iron tower foundation by the power transmission iron tower in the iron tower foundation-soil body three-dimensional geometric model, defining initial parameters, and establishing a rainwater seepage and dry-wet cycle coupling effect analysis model; 5, analyzing and predicting the cycle index when the safety coefficient of the iron tower foundation is reduced to a threshold value from the two aspects of tower foundation ultimate bearing performance and iron tower foundation-soil body interaction on the basis of a rainwater seepage and dry-wet cycle coupling effect analysis model; according to the method, an efficient and accurate numerical analysis means can be provided for safety assessment of the power transmission tower foundation in the collapsible loess area.

The invention provides a kernel function trajectory-based deep learning inference service performance analysis method, which comprises the following steps of: based on service indexes and hardware theoretical computing power acquired from a production cluster, defining floating point operation times per request (FPR) index to quantify service resource efficiency, and identifying high FPR hotspot services; positioning a reasoning iteration candidate boundary based on a GPU kernel function trajectory, verifying iteration integrity through fingerprint matching and chi-square test, and calculating a second reasoning iteration number IIPS and a model reasoning efficiency MIE; aiming at calculation-intensive operators on the key path, combining a dynamic Roofline model to estimate an operator theoretical performance upper limit, and based on actual execution time, calculating efficiency and a BottleScore index to identify a key bottleneck operator; and outputting targeted optimization suggestions according to analysis results of service efficiency analysis, model efficiency analysis and operator efficiency analysis. According to the method, the inference behavior pattern can be automatically identified from massive kernel trajectories, and the efficiency loss of each level is quantified.

The invention discloses a multi-model inference chain data analysis method and device based on natural language input, a medium and a program product. The method comprises the steps that an analysis problem text in a natural language form is analyzed into a semantic task structure, and the semantic task structure is mapped into a structured analysis model conforming to a preset specification; registering the structured analysis model as an instance to an analysis model library, allocating a unique identifier ID to the structured analysis model during registration, and writing attribution relation information of the structured analysis model; an upstream model ID and a downstream model ID in the attribution relation information of the instance are read, the attribution relation information of the upstream model and the downstream model is analyzed, layer-by-layer expansion is carried out in the mode, and a multi-model reasoning chain of the instance is constructed according to the dependency relation between the models; and according to a sequence determined by the multi-model reasoning chain, executing each model instance in sequence or in parallel, and integrating the obtained final output into a comprehensive analysis report. According to the invention, automatic execution from a natural language to full-process data analysis can be realized.

The invention discloses an event analysis model training method and system based on progressive multi-view exploration, and the method comprises the steps: constructing a multi-view sample generation engine, and generating a multi-view answer set for the same event sample through a differential prompt template by using a heterogeneous knowledge base and a multi-source event corpus; performing semantic deduplication and consistency verification on the multi-view answer set, calculating semantic similarity between answers based on a pre-training language model, performing clustering deduplication on the answers with the similarity exceeding a threshold value, and verifying effectiveness and timeliness of reference information through a knowledge graph; establishing a difficulty quantitative evaluation matrix, performing objective and subjective fusion difficulty grading on the training samples, and calculating a comprehensive difficulty score by combining the two results; and dividing the training sample into a plurality of difficulty grades according to the comprehensive difficulty score, and executing progressive staged training based on a grading result, so that the model is gradually transited from a low-difficulty sample to a high-difficulty sample, and multi-view fusion learning and progressive ability optimization are completed.

The invention discloses a network attack path automatic generation and defense strategy optimization method, system and device and a medium, and relates to the technical field of network security, and the method comprises the steps: constructing a topology mapping model, obtaining a network architecture, an asset list and a dependency relationship, constructing a visual topology chart, building a vulnerability association analysis model, and combining a vulnerability scanning result. The method comprises the steps of obtaining a vulnerability knowledge graph, optimizing a path calculation process based on the vulnerability knowledge graph, establishing a path derivation model, generating an attack chain by applying a path derivation algorithm, optimizing defense logic, formulating an optimization step model, and establishing a real-time simulation feedback model based on the generated attack chain. And performing simulation implementation on the defense strategy through a multi-level simulation training scheme, and dynamically optimizing the defense strategy according to feedback data. According to the method, the crossing from passive protection to active prediction and from single-point defense to global optimization is realized, and the accuracy and adaptive capacity of network defense are remarkably improved.

The invention discloses an unplanned secondary operation decision support method and system based on EMR analysis. According to the method, firstly, multi-source EMR data are collected in real time through an HL7 FHIR standardized interface, differential analysis is conducted through a time sequence analysis algorithm and a BioBERT model fused with a medical knowledge graph, and standardized clinical indexes are generated; secondly, the indexes are matched with an unplanned secondary surgery thematic knowledge graph in real time, multi-path probabilistic reasoning is carried out based on a Bayesian network, and a risk conclusion with confidence and a reasoning chain are output; and generating a structured report including risk early warning, core basis, possible reasons and processing suggestions, and pushing the structured report in multiple channels. And finally, collecting clinical feedback, optimizing the knowledge graph and the analytical model, and forming a closed loop. According to the invention, dependence on experience of doctors is reduced, and accuracy and timeliness of risk research and judgment are improved.

The invention discloses a thinking data-based large model reasoning ability evaluation and analysis method and device. According to the method, a data flow diagram is constructed through a variable use-definition chain during running of a specific language LMCL in the dynamic monitoring field, logic variable LVAR nodes are extracted, redundant edges are eliminated, and a thinking map with a direct dependency relationship is generated. Based on the thinking map, a five-dimensional evaluation system including reasoning efficiency, key node recognition capability, reasoning generality, multi-path reliability and accumulative hierarchical reasoning is designed, and an internal mechanism of a model reasoning process is quantitatively analyzed. A semantic rule is extracted through a frequent mode of mining successful and failed thinking data, reasoning path probability distribution is integrated in combination with a path aggregation strategy, an interpretable cue word optimization strategy is generated, and a large model reasoning process is dynamically injected. Through the rule guidance and path equalization strategy, the model reasoning accuracy is improved, and the problems of thinking data isomerism, single evaluation and black box enhancement in the traditional technology are solved.

The invention relates to a common-situation interaction-oriented adaptive emotion adjustment method in a virtual reality environment, and the method comprises the steps: S1, obtaining a user dialogue text, and calculating an emotion score of the dialogue text through a pre-training emotion analysis model; s2, acquiring behavior data of a user for performing an emotion interaction task, and calculating a behavior emotion score; s3, calculating a comprehensive emotion score capable of representing the emotion state of the user based on the emotion score and the behavior emotion score; s4, on the basis of obtaining the comprehensive emotion score through calculation, dynamically generating an optimal emotion adjustment strategy through a heterogeneous graph modeling technology; and S5, based on the generated optimal emotion adjustment strategy, generating a co-emotional dialogue which is consistent with the user emotion demand and is used for guiding and generating text interaction, and generating an interaction task which is used for guiding the user to explore and adjust the emotion and comprises a color scene. The self-adaptive emotion adjusting method can sense the emotion of the user in real time, dynamically follow and adjust the emotion of the user, and improve the accuracy and flexibility of emotion adjustment.

The invention discloses a model-based cardiovascular image analysis, comparison and recognition system, which relates to the technical field of intelligent diagnosis and comprises an artifact preliminary detection module, an artifact recognition module, a boundary reconstruction module, an authenticity verification module, an interference learning module and a closed-loop regulation and control module, based on frequency domain analysis and spatial feature deconstruction, a multi-scale artifact analysis model is constructed, and preliminary positioning and signal separation of an artifact region are realized; and the artifact identification module is used for executing gray gradient aggregation and structural symmetry comparison based on a positioning result of the artifact analysis model, and generating an artifact mode label containing interference intensity, spatial distribution and morphological difference information. According to the invention, accurate separation of artifacts and real tissues and dual verification of lesions are realized through cooperation of multiple modules, a feedback optimization mechanism is constructed to improve recognition stability, misdiagnosis and excessive intervention risks are effectively reduced, and intelligent recognition capability and application security in clinical images are enhanced.

The invention discloses a time sequence production simulation multi-dimensional risk analysis method and system based on deep learning, and the method comprises the steps: constructing a time sequence production simulation model, and carrying out the calculation to obtain system operation state data; constructing a multi-dimensional risk index analysis system, and calculating an initial weight of each risk index; introducing a real-time state quantity corresponding to each risk index as a driving factor to correct the initial weight of each risk index to obtain a corrected dynamic weight; carrying out weighted fusion on the risk index values based on the dynamic weight to obtain a comprehensive risk value; forming a feature vector by the product of the drive factor normalized value corresponding to each risk index and the dynamic weight, taking the feature vector as input, taking the corresponding comprehensive risk value as output, training the GRU model, and taking the trained GRU model as a risk analysis model; and processing the operation data of the target system according to the steps to obtain a corresponding feature vector, and inputting the feature vector into the analysis model to obtain a comprehensive risk value of the target system. According to the invention, the sensitivity and accuracy of risk identification are improved.

The invention discloses a full-channel consumer behavior tracking and market investigation integrated platform, which adopts federated learning and differential privacy technologies to construct a privacy-friendly multi-feature identity analysis model, and realizes high-precision matching of cross-channel users. The platform uses a graph attention neural network to represent consumer omni-channel behaviors as a heterogeneous directed graph, and proposes a new structural friction index to identify conversion obstacle nodes in a decision trip. Dominant survey data and recessive behavior data are jointly embedded by using an auto-encoder, a causal forest model is introduced to estimate the causal effect of attitude factors on purchase behaviors, and a comprehensive value function is constructed to fuse behavior preference, attitude tendency, causal effect and journey resistance. And the decision support layer automatically triggers personalized marketing intervention according to the friction index and the value score, and feeds back a dynamic optimization model in combination with an A / B test. According to the invention, balance between privacy protection and accurate identification is realized, consumer decision-making journey can be deeply observed, and causal-driven value prediction is provided.

The invention discloses a multi-model reasoning method and device based on a graph structure, a medium and a program product, and the method comprises the steps: querying a task target node in a graph database, starting from the task target node, traversing the graph database according to a corresponding driving model, and carrying out reasoning path extension, each directed acyclic graph is obtained by mapping metadata of the registered analysis model; generating a task topology execution graph according to the model nodes involved in the expanded reasoning path and the corresponding driving model; and according to the task topology execution graph, actual execution of each model node is scheduled in sequence, and an interpretation result corresponding to the task target node is obtained. According to the method, the model attribution relation is explicitly analyzed, and the reasoning path is optimized by utilizing graph calculation, so that the reasoning execution efficiency is improved, and the intelligence, performance and interpretability of multi-model collaborative analysis are improved.