1965 results about "Analysis models" patented technology

The invention relates to the technical field of data analysis, in particular to an operation decision intelligent analysis method and system based on cross-domain data fusion. The method comprises the following steps: firstly, based on an enterprise multi-domain ontology knowledge base, performing entity identification and relation mapping on heterogeneous data from different business systems through a semantic mapping-based multi-source heterogeneous data dynamic fusion algorithm, and establishing a unified data model; then, a causal reasoning and deep learning fused hybrid intelligent decision engine is adopted to analyze and process the model; then, a multi-level causal relationship network among business variables is constructed through a causal relationship discovery algorithm by utilizing an analysis result of the hybrid intelligent decision engine, and an adaptive business scene analysis model based on reinforcement learning is used to dynamically adjust an analysis strategy according to business environment changes; generating a Pareto optimal decision scheme set through a multi-objective optimization algorithm, and outputting operation decision suggestions; according to the invention, the comprehensiveness and accuracy of intelligent analysis of enterprise operation decisions are improved.

The invention relates to the technical field of artificial intelligence and health management, in particular to a health management method and system based on multi-dimensional data analysis, and the method comprises the steps: collecting the physiological, behavior, environment and psychological data of a user, and carrying out the modeling through a recurrent neural network, and generating health state data; extracting a key causal relationship of the health risk based on a causal analysis model of a Bayesian network; comprehensively analyzing health changes through the short-term time sequence model and the long-term trend prediction model, and generating health risk data; a personalized health intervention scheme is designed by using a conditional generative adversarial network, and safe sharing and dynamic management of the scheme are realized through a block chain technology. According to the method, the health risk can be accurately identified, scientific intervention measures can be designed, the health management process is dynamically optimized, powerful technical support is provided for an intelligent old-age nursing scene, and the health management efficiency and effect are improved.

The invention discloses an SCL field safety accident analysis and early warning method and system, a medium and a program product, and the method comprises the steps: carrying out the distributed cleaning and feature extraction of multi-source heterogeneous monitoring data, and obtaining a multi-dimensional time sequence feature flow; constructing a dynamic association network of a security state; identifying a key influence factor chain, and establishing a semantic analysis model; constructing a safety threshold system; evaluating the multi-dimensional time sequence characteristic flow by using a safety threshold system; when the multi-dimensional time sequence characteristic flow triggers early warning, performing tracing analysis by using a semantic analysis model to obtain a root event triggering the early warning, and simulating the development trend of the root event based on a preset evolution model; determining a predicted risk range and a risk type according to the tracing cause analysis result and the accident development trend; and the corresponding accident early warning scheme is matched in the preset database according to the risk range and the risk type, and the accident early warning scheme is sent to the monitoring terminal, so that the accuracy of judgment and classified early warning of different types of safety accidents is improved.

The invention relates to the technical field of special equipment intelligent monitoring and maintenance, in particular to a special equipment monitoring and maintenance method and system based on multi-dimensional data fusion, and the method comprises the steps: collecting and processing multi-source heterogeneous sensor data; constructing a dynamic digital twinborn model based on sensor data and a cross-domain term mapping rule; associating the sensor data with the digital twinborn model to establish a lightweight analysis model, performing health state simulation analysis on the edge equipment, and generating an analysis result; the data quality of the edge device is monitored through the analysis model, when the data quality is lower than a threshold value, a co-simulation process is started, a simulation analysis task is transferred to the cloud digital twin platform, and the edge device continues to conduct simulation analysis based on the analysis model; and constructing a simulation design decision rule base, and generating a maintenance suggestion. Through the multi-dimensional data fusion and edge cloud co-simulation architecture, the accuracy, real-time performance and reliability of monitoring and maintenance of the special equipment are improved.

The invention discloses a non-Newtonian fluid dynamic characteristic intelligent simulation and integrated measurement and control method, which comprises the following steps: sequentially reading basic data such as equipment temperature, pressure intensity, vibration, shear rate, stress, strain and the like, and carrying out anomaly detection and dynamic weight local polynomial regression cleaning by adopting a Gaussian mixture probability density model; carrying out standardization processing through a self-adaptive standardization model; performing multi-scale feature extraction and correlation analysis on the standardized data, and establishing a mapping relation between the rheological parameters and the physical quantities; carrying out multivariable coupling analysis and carrying out turbulence correction by adopting an improved intrinsic orthogonal decomposition method; and performing simulation calculation based on a non-Newtonian fluid analysis model, and analyzing and evaluating model performance and optimizing model parameters through multi-dimensional error analysis. According to the method, the dynamic characteristics of the non-Newtonian fluid under the complex working conditions can be accurately described, and the simulation precision and the control effect are improved.

The invention relates to the technical field of three-dimensional model adjustment, in particular to a three-dimensional model adjustment method and system and a medium. The method comprises the following steps: obtaining a multi-angle image of an original model, carrying out multi-view normalization on the multi-angle image, generating a normalized view image set, extracting feature points of the original model, carrying out parallax correction on the feature points, reconstructing a simulation three-dimensional model, collecting basic purpose data of the model, carrying out ideal demand mapping through the data, and carrying out ideal demand mapping. The method comprises the following steps: determining an ideal three-dimensional model structure, carrying out core region segmentation on a reconstruction model according to basic purpose data to obtain key region model slices, carrying out highlight region comparison with the ideal model structure, analyzing model differences, determining a structure adjustment amplitude interval according to a comparison result, and carrying out cyclic fine adjustment correction on the key region model slices to obtain a three-dimensional model. And the three-dimensional model is consistent with the ideal three-dimensional model in structure, so that the optimized three-dimensional model is generated. According to the invention, efficient and accurate three-dimensional model adjustment and optimization are realized.

The invention discloses an internet-based intelligent bid invitation management platform, which comprises a data integration and analysis module configured to establish an intelligent comprehensive economic analysis model by integrating internal data and / or external data of the intelligent bid invitation platform; the standardized bid invitation file module is configured to construct an industry lexical analysis and language model by utilizing natural language processing and a knowledge graph; the intelligent question-and-answer type bidding file filling module is configured to complete compiling of a bidding file by guiding a bidder to adopt an intelligent question-and-answer mode; the intelligent bid evaluation tool set module is configured to assist the bid evaluation process by establishing an intelligent bid evaluation tool; the risk early warning module is configured to construct a knowledge graph of a supplier, a subject matter and a project; and the large model application module is configured to construct an AI bid evaluation model by using a large model technology. Through intelligent bid invitation management, bid evaluation work efficiency is improved, and fairness and accuracy of bid evaluation results are ensured.

The invention provides a multi-axis linkage numerical control machining control method and device, equipment and a storage medium, and the method comprises the steps: carrying out the modeling analysis of the kinematic characteristics and static stiffness characteristics of a multi-axis numerical control machine tool, and generating a kinematic model and a static stiffness analysis model; and performing multi-objective optimization on the attitude of the target workpiece based on the models to obtain an optimal workpiece attitude parameter, and re-planning an original tool path of the machine tool according to the optimal workpiece attitude parameter to generate an optimized tool path. And according to the static stiffness analysis model and the optimized path, a static deformation compensation strategy in the machining process is formulated, and a self-adaptive stiffness compensation scheme is obtained. And real-time control and self-adaptive adjustment are performed on the machining process of the machine tool by optimizing a path and a self-adaptive rigidity compensation scheme, so that closed-loop control is realized. According to the method, the kinematics characteristic and the static rigidity characteristic of the machine tool are considered, the workpiece posture and the tool path are optimized, static deformation in the machining process is compensated in real time, and therefore efficient and high-precision numerical control machining is achieved.

The invention discloses a fault early warning method and system based on an AI large model, and the method comprises the steps: obtaining multi-source heterogeneous data, and carrying out the denoising and standardization processing, and obtaining fusion data; inputting into a feature extraction model, and outputting a feature vector set; identifying the dynamic operation mode based on a K-means algorithm to obtain an operation mode baseline; inputting a feature sequence model, and outputting a precursor feature sequence; calculating an abnormal score according to the precursor feature sequence, marking as abnormal if the score is greater than or equal to a threshold value, otherwise, marking as normal, and obtaining an abnormal detection result; evaluating a risk level according to a detection result; inputting the risk level into a fault analysis model to obtain fault cause distribution; determining optimized operation mode parameters according to the fault cause distribution; and performing deviation analysis on the data and the optimized parameters, and if a deviation value is greater than a threshold value, triggering an early warning signal. The method can solve the problem of insufficient recognition capability in a scene with variable fault types.

The invention discloses a multi-source three-dimensional model lightweight processing and lossless compression supporting method, system and device and a storage medium, and relates to the three-dimensional technical field, and the method comprises the following steps: carrying out format compatibility check on an imported three-dimensional model, and analyzing the geometric structure, texture characteristics and material attributes of the model; the geometric structure of the three-dimensional model is simplified by adopting a quadratic error measurement algorithm and edge folding, the simplified model is optimized through edge segmentation operation, and a lightweight model is obtained; and compressing geometric data of the lightweight model by using a data compression algorithm, and compressing texture data through predictive coding to complete lossless compression of the three-dimensional model. According to the method, the loading speed and the real-time rendering efficiency are remarkably improved by reducing the size of the three-dimensional model file and optimizing the texture compression algorithm. The lossless compression technique ensures that a high-quality visual effect is kept in the lightweight process, the lightweight model reduces the storage space requirement, and the energy consumption of data transmission and processing is reduced.

The invention relates to the field of information management, and discloses a constructional engineering information management system, which comprises a data acquisition module, an intelligent analysis module, a risk prediction module, a resource optimization module and a feedback learning module, the data acquisition module is used for acquiring multi-source data related to constructional engineering; the intelligent analysis module carries out fusion, preprocessing and feature extraction on the collected data; the risk prediction module predicts possible risks in the future by using a statistical analysis model; the resource optimization module dynamically adjusts and optimizes manpower, equipment, materials and process arrangement of the construction site according to the risk prediction result; and the feedback learning module is used for collecting a system execution result, risk prediction accuracy and user feedback, and recording an optimized execution result. The method has the advantage of realizing efficient engineering management and decision support.

The invention relates to a higher education personalized hierarchical teaching method and system based on artificial intelligence, and the method comprises the steps: obtaining multi-dimensional learning data, carrying out the time-space alignment processing, and generating a synchronous multi-dimensional data set; performing spatial-temporal feature fusion and time sequence modeling on the data set by using a deep neural network, and constructing a dynamic student portrait; analyzing knowledge mastery degree features in the portrait through a semantic analysis model, and generating a personalized resource recommendation sequence in combination with the knowledge graph; based on the sequence and the portrait, planning a personalized learning path by using a path reasoning algorithm; and carrying out teaching hierarchy binding on the personalized resource recommendation sequence and the learning path to form a hierarchical teaching scheme. Accurate teaching support is provided for individual differences of students, and the teaching effect and learning experience are effectively improved.

The invention provides a numerical control machining model verification method and system combined with digital twinning, and the method comprises the steps: firstly obtaining a numerical control machining twinning data association set which is correspondingly formed by a physical machining process actual measurement data sequence and a virtual twinning model simulation data sequence according to a machining stage identifier; the actual measurement data sequence and the simulation data sequence both comprise processing action and material response feature sequences, and then carrying out feature space unification processing on the twin data association set to generate a verification feature corresponding set comprising actual measurement comprehensive features and simulation comprehensive features; performing deviation analysis on the verification feature corresponding set by using a pre-trained model deviation analysis model to obtain a feature matching and association influence relationship, and deducing and determining a specific deviation position of the virtual twin model in a processing action and material response modeling link based on an analysis result; and model optimization guidance information for defining the parameter adjustment direction and the structure correction content is generated, so that the precision and the reliability of the virtual twin model are effectively improved.

The invention provides a user behavior data mining method and system applied to digital enterprise management, and the method comprises the steps: collecting the multi-dimensional behavior data of a target user in a business operation interface, carrying out the multi-modal data analysis of the multi-dimensional behavior data, generating a behavior track feature set with time sequence relevance, and carrying out the mining of the behavior track feature set; training an adaptive time sequence analysis model based on the behavior trajectory feature set, capturing a long and short term dependency relationship in a user behavior mode by the time sequence analysis model through a dynamic window division strategy, generating a potential loss risk prediction index, and constructing an interaction process parameter matrix according to the potential loss risk prediction index; and calling the optimized interaction process parameter matrix to drive a service operation interface to reconstruct, generating an interaction interface adaptive to the current user behavior mode, and iteratively updating the time sequence analysis model through an incremental feedback mechanism in a preset verification period. According to the invention, the comprehensiveness and accuracy of user behavior pattern mining can be improved.

The invention relates to the technical field of data analysis, can be applied to business scenes of medical health, financial science and technology, culture research and the like, and discloses a knowledge graph-based model analysis method, which comprises the following steps of: acquiring and processing target domain data, extracting and labeling keywords, constructing a knowledge graph comprising domain entities, a relational network and attribute characteristics, and analyzing the knowledge graph. Establishing a professional corpus and generating an analysis task, constructing a multi-dimensional analysis index system, inputting the analysis task to a to-be-analyzed model, obtaining a task output result, performing knowledge verification according to the knowledge graph, evaluating the knowledge verification result based on a multi-dimensional analysis index, and generating a final analysis result. According to the method, by fusing the knowledge graph and the multi-dimensional analysis indexes, the accuracy and comprehensiveness of evaluation are enhanced, the evaluation effect of the model in the aspects of professional knowledge understanding, reasoning ability and standard integrating degree is improved, the evaluation result is more reliable, and the application ability of the large model in the specific field is optimized.

The invention discloses a multi-modal data-driven metal multi-axial fatigue life prediction method, which takes multi-modal data as a data driving basis, provides a new fatigue life prediction framework by utilizing a fusion machine learning model, and can be used as an alternative model for fatigue life prediction. The method comprises the following steps: firstly, defining multi-precision data: experimental data obtained through an actual experiment and simulation data obtained through physical simulation; the mechanical properties of the material are obtained through preliminary experiments, and a reliable finite element analysis (FEA) model is established according to the properties. Then, a high-reliability data set is generated by using a finite element analysis model. In order to process data with multi-modal features, the method comprises the step of constructing a hybrid prediction model DL-LGBM-DRS based on dynamic regression selection. The model can learn the characteristics of discrete and time sequence samples at the same time with high accuracy, so that the mixed prediction model DL-LGBM-DRS can efficiently and accurately predict the cyclic fatigue life under various multi-axis loading conditions.

The invention relates to the technical field of data processing, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a data processing method, device, equipment and medium based on a context agreement, comprising: receiving a business demand event, extracting a demand text, and disassembling the demand text to generate a plurality of subtasks; obtaining a data source context according to the sub-tasks and task subject information, generating a corresponding data extraction path and an analysis model strategy, connecting a target data source through an access strategy, collecting original data, preprocessing the original data, and dividing the original data into training data and verification data; and training the model based on the analysis model strategy and generating a reasoning result, and completing verification output through a result verification model. According to the method, task disassembly, data access and model generation processes are driven through a model context protocol, and a knowledge graph reasoning and verification mechanism is combined, so that a closed-loop automatic analysis path is formed, and the processing efficiency and intelligent adaptation capability of cross-domain data tasks are effectively improved.

The invention provides an integrated test scene prediction method and system based on multi-dimensional data, and the method comprises the steps: carrying out the multi-source data collection of an integrated test scene based on a dynamic sampling strategy, obtaining integrated test multi-dimensional data, carrying out the multi-dimensional data preprocessing of the integrated test multi-dimensional data, and obtaining a pre-trained multi-dimensional data analysis model; inputting the pre-processed integrated test multi-dimensional data into the multi-dimensional data analysis model to carry out sub-module data analysis, obtaining a pre-trained intelligent regulation and control model, importing a system diagnosis report and a key index deviation degree report into the intelligent regulation and control model as inputs, generating an intelligent regulation and control decision, and carrying out intelligent regulation and control. By means of the method, system faults, performance bottlenecks and potential problems occurring in the integrated test scene can be accurately predicted, intelligent regulation and control decisions are provided for solving the problems, and accurate system performance evaluation and optimization services can be provided for users.

The invention discloses an intelligent multimedia interactive teaching and examination system and method, and relates to the technical field of education informationization, and the method comprises the steps: obtaining user information and a learning state, and generating a learning portrait; based on the learning portrait, utilizing the generative AI to dynamically generate personalized teaching content, and marking the abnormal state of the user in real time; storing an abnormal state marking result of the user to a dynamic feature field of the learning portrait, and dynamically optimizing a learning path; based on the optimized learning path, a personalized assessment task is dynamically generated, and answer data and learning behavior data of the user are recorded; and performing multi-dimensional evaluation on the performance of the user through a semantic analysis method by utilizing the answer data and the learning behavior data. According to the method, through the comprehensive analysis model and the multi-modal feature vector, multi-dimensional evaluation of the performance of the learner is realized, multiple aspects of knowledge mastery, behavior performance, emotional state and the like are covered, and detailed feedback and improvement suggestions are provided for the learner.

The invention discloses an efficient auditing method and system based on an edge computing technology, and relates to the technical field of auditing methods and systems.The efficient auditing method comprises the following steps that S1, data collection and preprocessing are conducted, specifically, business data are collected in real time through various sensors and interfaces at edge nodes, and preprocessing is conducted; s2, edge side data analysis: performing real-time analysis on the preprocessed data by utilizing edge computing resources; s3, feature extraction and model training; s4, constructing a risk assessment and early warning model based on edge analysis and a model training result; s5, edge and cloud collaborative auditing; the edge node compresses and encrypts the processing result and the key feature data and then uploads the data to the cloud; and S6, visualizing an audit result and generating a report. According to the method, data acquisition and preprocessing, analysis and model training are realized through edge calculation, the auditing efficiency and accuracy are improved, a risk assessment and early warning model is constructed, the edge and the cloud are coordinated, and comprehensive, timely and reliable auditing support is provided for enterprises.

Techniques for generating synthetic data for machine learning (ML) models are described. A system includes a language model that processes a task and a corresponding set of example inputs to generate another input, referred to herein as a machine-generated data. The machine-generated data is processed using a ML, model (that data is being generated for) to determine a model output, and the model output is analyzed to determine whether it corresponds to a target output. If the model output corresponds to the target output, then the machine-generated data is added to the set of example inputs and one of the original example inputs is removed to generate an updated set of example inputs. The updated set can be used for various training techniques.

The embodiment of the invention provides a project research and development data key information processing method and device. A research and development content recognition system and a self-adaptive research and development knowledge graph are constructed. Unified processing and time sequence alignment of text, voice and image contents are realized through multi-modal information decomposition and fusion. Semantic completion and error correction are carried out based on research and development of a semantic analysis model, a knowledge graph structure is dynamically constructed and optimized, and a structured document with a traceability relation is generated. The system adopts a deep neural network model to extract research and development key information, constructs a multi-level document framework, and realizes intelligent conversion from research and development data to a project application document. According to the method, the defects of the traditional technology in the aspects of multi-modal information processing and knowledge structure optimization are effectively overcome, and the research and development data management and project declaration efficiency is remarkably improved.

The invention discloses an inertial microsystem thermal coupling analysis method based on a physical information neural network, and the method comprises the steps: S1, setting material parameters and boundary conditions of an inertial microsystem, and building a thermal coupling analysis model; s2, performing electrothermal coupling analysis to obtain temperature distribution of the inertial microsystem; s3, thermal stress distribution of the microsystem is obtained through thermal-mechanical coupling simulation analysis; s4, predicting a temperature field of the microsystem through the physical information neural network; s5, taking the temperature field as a boundary condition of mechanical simulation of the microsystem to obtain mechanical properties such as stress and strain of the microsystem; and S6, performing mechanical-electric coupling simulation analysis, and analyzing the influence of structural deformation on each parameter in electrical performance. According to the method, the neural network solving precision is improved through the improved adaptive weight strategy, the complete polynomial basis function is combined with the neural network, the state dimension is reduced by introducing the expansion basis function, the calculation cost and time are reduced, accurate prediction of the micro-system multi-moment temperature field is achieved, and the micro-system performance under electric heating power multi-physics field coupling is calculated.

The invention discloses a rolling bearing fault diagnosis method based on an SE-ResNeXt and Informer hybrid network model, and relates to the field of industrial process control, spatial features of time series data are extracted through a plurality of convolution modules in the SE-ResNeXt, an SE module is added for adaptively adjusting the response intensity of each channel, and a fault diagnosis result is obtained. The design of a residual network is combined, so that the feature extraction process is more efficient, and fault diagnosis analysis is performed through an Informer model. In the training process of the model, cross entropy is adopted as a loss function, and optimization is carried out through an Adam optimizer, so that the prediction accuracy and stability are improved. Experimental results show that the method shows excellent fault diagnosis capability on a public data set CWRU, has good robustness and prediction precision, is significantly superior to a traditional deep learning model, and provides a new thought for state monitoring and fault prediction under complex working conditions.

The invention discloses a bridge structure optimization method based on BIM and a finite element method, and relates to the technical field of bridge structure optimization. According to the method, the finite element analysis model can be more accurately established, and the design precision is improved. Through finite element analysis and a digital simulation technology, structure response data is rapidly obtained, and design optimization is accelerated; the optimization of the final scheme is ensured by a multi-iteration optimization strategy, the extreme load response is predicted by introducing a machine learning algorithm, a high-risk area is identified in advance, and the overall performance is improved; based on a multi-scale analysis method, the durability and safety of the structure are comprehensively evaluated, weak links are identified, and long-term performance is guaranteed; meanwhile, the influence of environmental factors on material characteristics is considered, adjustment is performed according to climate data, the design scheme is closer to the actual environment, and durability is enhanced.

The invention provides an urban inland inundation risk management method and system based on multi-model coupling, and the method comprises the steps: constructing a digital twin holographic base, constructing an urban rainfall flood analysis model through the deep coupling of a ponding recognition intelligent model and a physical mechanism drive type rainfall flood simulation engine, and carrying out the construction of an urban inland inundation risk management model. A high-precision waterlogging risk thermodynamic diagram and a dynamic forecast threshold value are generated, and the risk situation is broadcasted in real time through a voice interaction module; constructing a rainstorm-runoff-pipe network three-dimensional coupling simulation system by means of a digital twin holographic base, updating a deduction result at a minute-level frequency, and transmitting rainfall space-time distribution data to an urban rainfall flood analysis model in real time; and establishing a multi-target collaborative decision engine, inputting the accumulated water diffusion rate to the multi-target collaborative decision engine, and generating a pump station scheduling scheme, a traffic control strategy and a material delivery path. The system comprises a model construction module, a data analysis module and a collaborative decision module. According to the invention, a complete urban inland inundation risk management system is constructed.

The invention discloses a grouting method and system for enhancing strip mine slope stability, and belongs to the technical field of slope protection. The grouting method comprises the steps that early-stage data are pre-collected, and an analysis model is established; grouting is designed according to a model analysis result; a pressure-resistant grouting pipe is installed and connected with a grouting pump; grouting parameters are adjusted in real time according to feedback of the sensor; after grouting, all data in the grouting holes are analyzed; outputting a slurry permeation three-dimensional cloud picture, a stress change curve and a stability evaluation index; and storing all monitoring data and model parameters. A cyclic self-adaptive system is formed, it is ensured that the grouting process is efficient and accurate, the overall stability is improved for heterogeneous rock mass, dependence on a static geological report is reduced, and therefore the problem that grouting parameters depend on engineering experience or conservative of the static report is avoided.

The invention relates to the technical field of data processing, in particular to a backtracking analysis model construction method based on an attack chain, which comprises the following steps that: a kernel layer security agent acquires process, file and network behavior characteristics in a hardware isolation environment, and generates an event tuple; the tensor network pipeline performs three-dimensional decoupling mapping on the tuple into a behavior fingerprint vector, an orthogonalization noise feature and an asymmetric adjacent tensor, and compresses the behavior fingerprint vector, the orthogonalization noise feature and the asymmetric adjacent tensor into a space-time topology tensor block; the reinforcement learning controller constructs a directed acyclic graph based on the tensor blocks, calculates connectivity loss and outputs an event risk score; the dynamic routing engine constructs a decision tree model according to the risk mark, the burst frequency and the correlation entropy, and implements three-level shunting and a multiple simulation system to generate an anti-interference index; and when the deviation between the physical trajectory and the digital model exceeds the tolerance, the closed-loop feedback weight coefficient updates the loss function parameter and adjusts the channel resource weight. And the problem of threat discovery delay caused by attack chain breakage under massive events is solved.

The invention relates to an immersive VR psychological detection system and method based on multi-modal AI. The system comprises a data acquisition and processing module which is used for acquiring a multi-modal data set of a user in a virtual reality scene based on unified clock synchronization, performing time-space alignment and noise reduction standardization processing on the multi-modal data set, and extracting key biological characteristics. And the correlation model construction module performs space-time correlation mapping through a spatial transformation network, constructs a three-dimensional space attention model, and generates a real-time fluctuation curve after inputting the key biological characteristics into the trained model. And the state report generation module identifies a real-time fluctuation curve by using a time sequence analysis model, performs backtracking analysis in combination with the psychological state conversion node and a multi-modal cross validation result, and finally generates a three-dimensional interactive report. By adopting the method, multi-modal data fusion can be realized, the dynamic change of the psychological state of the user can be effectively captured, the psychological state of the user can be comprehensively and deeply analyzed, and a scientific basis is provided for psychological health assessment and intervention.

The invention relates to the technical field of artificial intelligence, and discloses a financial report analysis method, and the method comprises the steps: carrying out the data preprocessing of historical financial association data and historical business operation data, and obtaining to-be-analyzed historical data; performing multi-order feature engineering processing on the to-be-analyzed historical data to obtain historical feature vector data; constructing an initial financial analysis large model, and training the initial financial analysis large model by using the historical feature vector data to obtain a trained financial analysis large model; accessing the trained large financial analysis model into a target system, and triggering the large financial analysis model to operate; and driving the large financial analysis model to perform multi-dimensional semantic analysis and quantitative reasoning on the to-be-analyzed financial report data to obtain visual financial report analysis result data. The method can be applied to internal financial statements of enterprises with businesses of science and technology finance, medical health, old-age care and the like, and the financial statement analysis efficiency and comprehensiveness can be improved through the multi-modal data fusion and knowledge enhancement large model architecture technology.