391 results about "Dynamic Bayesian network" patented technology

The invention provides a network attack link tracking and threat situation reasoning method based on a knowledge graph, and relates to the technical field of network security, and the method comprises the steps: collecting and processing original data; extracting features and constructing a network attack knowledge graph; performing representation learning by adopting a graph neural network; identifying a time sequence dependency relationship; generating and analyzing candidate attack links; constructing a dynamic Bayesian network model; performing parameter learning by adopting a variational reasoning algorithm; future attack targets and means are predicted. According to the method, the network attack link can be effectively tracked, the future threat situation is accurately predicted, and the network security defense capability is improved.

The invention discloses a turbofan engine operation monitoring method and system based on digital twinning, belongs to the technical field of turbofan engine monitoring, and aims to solve the problems that weak fault signals such as early cracks and abrasion are difficult to extract and the prediction precision of a multi-source fault propagation path is low under a strong noise background. An original operation signal is collected through a sensing array, and is processed by an adaptive resonance demodulation chain to generate a demodulation signal. The method comprises the following steps: carrying out time-frequency transformation on a demodulation signal, constructing an initial candidate feature set by combining feature frequency prior matching actual measurement and theoretical feature frequency, and generating an independent feature set by fusing multi-scale decoupling network separation features of digital twin constraints; for independent features, effective causal pairs are screened by adopting a physical coupling relationship combining Granger causal analysis and digital twinborn simulation, a dynamic Bayesian network is constructed to simulate fault propagation, a posterior probability is calculated through digital twinborn verification and Monte Carlo simulation, early warning is triggered, and a maintenance decision is generated. And weak signal extraction and accurate fault prediction under strong noise are realized.

The invention relates to the technical field of unmanned aerial vehicle control, in particular to an unmanned aerial vehicle autonomous obstacle avoidance decision-making method and system based on multi-source sensor fusion, and the method comprises the steps: achieving the time-space synchronization of a laser radar, a visual camera and a millimeter-wave radar through timestamp alignment and coordinate mapping, and constructing a dynamic obstacle grid map; fusing multi-source data based on a dynamic Bayesian network, and dynamically adjusting the confidence coefficient weight of the sensor in combination with the environment illumination intensity and the barrier surface material; and adopting a reinforcement learning model to generate an incremental obstacle avoidance strategy, and triggering a grading response instruction according to the risk assessment grade. The problem of fusion errors caused by spatial-temporal asynchronization of multi-source sensor data is solved, and the real-time obstacle avoidance success rate of dynamic obstacles is increased.

The invention discloses a concrete surface intelligent construction method and system based on multi-modal sensing. The method comprises the following steps: firstly, receiving multi-source sensing data, and generating standardized feature data and defect feature vectors through standardization processing and feature extraction; performing time sequence alignment processing on the sensing data, and generating time sequence alignment data in combination with multi-scale analysis; constructing a state evaluation model based on the dynamic Bayesian network, and generating an environment state vector and a construction difficulty graph; carrying out region segmentation and optimizing task allocation by adopting a spectral clustering algorithm, and generating a task allocation scheme and a collaboration strategy; then, construction parameters are determined through multi-objective optimization, tool configuration is planned, and an optimization parameter set and a tool configuration scheme are generated; and finally, trajectory planning is carried out based on an improved A * algorithm, and a multi-robot cooperative construction trajectory is generated through comprehensive optimization. Full-process control of intelligent construction of the concrete surface is achieved, and the construction quality and efficiency are improved.

The invention relates to the technical field of enterprise finance and taxation risk management and control, and discloses an enterprise finance and taxation integrated risk management and control platform based on multi-source data fusion. The platform collects multi-source heterogeneous finance and taxation data in real time through a finance and taxation data collection module, and a data fusion preprocessing module generates a fusion data cube by using federal learning and a cross-domain data alignment algorithm. The risk feature modeling module constructs a multi-dimensional risk feature map based on a graph convolutional network and a dynamic Bayesian network, and the risk dynamic assessment module assesses risks in real time through an adaptive weighted ensemble learning algorithm and a risk conduction model. And the risk management and control decision module generates a management and control scheme by adopting a multi-objective optimization algorithm and a game theory strategy. In addition, the finance and tax data security storage module ensures data security. According to the platform, multi-source data fusion and efficient risk management and control are realized, risks can be accurately evaluated, scientific decisions are provided, enterprise finance and taxation data security is guaranteed, and enterprise finance and taxation management level is improved.

The invention provides a circuit board production yield root cause tracing method, which comprises the following steps of: acquiring process parameters, equipment states, environment variables and quality detection results of a whole production process, and constructing a multi-dimensional time sequence database; extracting a typical manufacturing process modeling unit through a sliding time window and dynamic time warping; establishing a cross-process dynamic causal relationship graph in combination with nonlinear Granger causal test, a structural equation model and a dynamic Bayesian network; an intervention and anti-factual reasoning method is applied, the causal effect and path stability under parameter disturbance of each process are evaluated, and the influence of a key causal path is quantified; according to the method, the accuracy of defect rate root cause positioning can be improved, and powerful support is provided for circuit board production process optimization and quality improvement.

The invention discloses an electromechanical engineering construction information management method and system and a medium, and relates to the field of electromechanical engineering, and the method comprises the steps: collecting an electromechanical engineering construction site image set; performing feature recognition on the image set; according to an identification result, dividing construction personnel behavior risk levels and marking material attributes; the method comprises the following steps: dividing a construction process into a plurality of stages, constructing a Bayesian network model of a time slice in each stage, adding a time sequence dependency relationship between the Bayesian network models of adjacent time slices, and establishing dynamic Bayesian network risk evolution models of different stages of the construction process; predicting a risk distribution thermodynamic diagram of each construction stage in a future period of time by using a dynamic Bayesian network risk evolution model; and performing construction management according to the risk distribution thermodynamic diagram. For static risk assessment by adopting traditional Bayesian reasoning in the prior art, the accuracy and real-time performance of electromechanical engineering construction risk prediction are improved.

The invention relates to the technical field of medical auxiliary evaluation and human-computer interaction, in particular to a cognitive level evaluation system and method applied to a caregiver of a cerebral infarction patient. Generating task parameter data based on the multi-modal feature data through a task generation module; the cognitive state decoding module decodes a comprehensive cognitive state by using a double-flow deep learning network to generate a cognitive load, an attention trend and a task response score; the feedback optimization module predicts the change trend of the cognitive ability in combination with a dynamic Bayesian network, and dynamically adjusts task parameters through a closed-loop feedback mechanism; according to the method, the dynamic adaptation of the evaluation task and the design of the intervention task are realized, and the accuracy, interestingness and applicability of evaluation are effectively improved.

The invention relates to the technical field of robot decision making, and provides a robot multi-modal fusion autonomous decision making method and system based on a large language model.The method comprises the steps that a robot obtains multi-modal environment information through a visual sensor, a touch sensor, an auditory sensor and a laser radar which are carried by the robot; performing preliminary filtering and noise reduction processing on the original sensor data, and synchronously recording all the sensor data by timestamps; performing space-time semantic alignment on the preprocessed multi-modal data, mapping pixel coordinates of a target in a visual target coordinate quantization original image to a robot coordinate system, performing uncertainty evaluation on a multi-modal signal through a dynamic Bayesian network, and taking entropy or variance as an uncertainty quantitative evaluation index. According to the method, the information quality is improved from a data fusion source, accurate and reliable basic support is provided for subsequent decision making, and decision making errors caused by data deviation are greatly reduced.

The invention relates to the field of power system monitoring and fault prediction, in particular to a lightning arrester fault type prediction method and system based on an extremely cold environment working condition. Probabilistic prediction of future key indexes of the lightning arrester is realized, and physical state data is generated through real-time simulation of a digital twin platform; through weighted least square method fusion correction and fuzzy logic, an integrated decision model constructed by a dynamic Bayesian network, system output fault mode classification and early warning decision, the effects of early warning of fault risks in advance in an extremely cold environment and improvement of the safety of a power system are achieved.

The invention relates to the technical field of industrial equipment health monitoring and fault diagnosis, and discloses a centrifugal pump intelligent detection method and system based on multi-source data. The centrifugal pump intelligent detection method based on the multi-source data comprises the steps that centrifugal pump multi-source heterogeneous data are collected and preprocessed; extracting multi-scale features and constructing an optimal feature subset through hierarchical feature selection; constructing a multi-level dynamic Bayesian network to establish a probabilistic reasoning framework; calculating a feature credibility weight based on the signal quality, the feature stability and the diagnosis correlation; executing multi-source evidence fusion and Bayesian state reasoning; confidence self-calibration and parameter self-adaptive adjustment are realized through historical diagnosis result feedback; the method can effectively cope with the complex and changeable environment of an industrial site, can still keep stable diagnosis performance under the condition that the sensor part loses efficacy or the data quality is uneven, improves the accuracy and reliability of fault diagnosis, and provides powerful technical support for predictive maintenance of industrial equipment.

The invention provides a hydrological trend prediction method based on big data analysis, and the method comprises the steps: building an initial graph structure which takes a monitoring station as a node and geographic distance and water system connectivity as an edge weight, carrying out the iterative aggregation of node features through a message passing mechanism, and capturing the space interaction of hydrological variables between stations; dynamically adjusting a water system connectivity parameter and an edge weight based on rainfall change, and generating an adaptive graph structure; in combination with the graph neural network, the dynamic Bayesian network and the space-time collaborative Kriging interpolation algorithm, hour-level hydrological dynamic transmission features are extracted, and a space-time coupling prediction model is formed; model parameter optimization and message passing mechanism adjustment are triggered through a prediction deviation threshold value, and a self-adaptive prediction process of'dynamic modeling-feature fusion-closed loop optimization 'is realized. According to the method, hydrological element space-time correlation can be accurately described, the analysis precision of a complex hydrological process is improved, and the adaptability to sudden hydrological events and basin environment changes is enhanced.

The invention provides a power transmission corridor forest fire spreading prediction and risk assessment method based on a satellite internet and a related device, and relates to the technical field of disaster prevention and early warning of a power system. Building a fire risk factor database based on multi-modal data of a satellite-ground fusion network mode; applying a random forest algorithm to screen out key factors and determining fire risk contribution degrees corresponding to the key factors; based on the power forest fire hidden danger factors, similar features of a power transmission corridor neighborhood are extracted, and sub-regions are divided by adopting a clustering algorithm; constructing a fire risk weighted scoring model to identify high, medium and low risk areas; a mountain fire spreading model is constructed based on meteorological observation data driving, a fire risk state transition matrix is established, and a fire spreading path is predicted in real time in combination with a dynamic Bayesian network; evaluating the damage risk of the forest fire to the power transmission line, and generating a damage risk grading graph of the power transmission corridor. The technical effects of dynamic assessment of the forest fire risk and accurate prediction of the spreading trend are achieved.

The invention belongs to the technical field of industrial control, and discloses a powder mixing processing control method, device and equipment and a storage medium, and the method comprises the steps: collecting target data in a powder mixing processing process in real time; inputting the target data into a prediction model to obtain a probability distribution prediction result of the state of the mixed production line in a plurality of time steps in the future, the prediction model being constructed by adopting a dynamic Bayesian network in advance based on the mixed production line; based on the probability distribution prediction result and the target data, a model prediction control algorithm is adopted to optimize a real-time control strategy, and a control instruction is obtained; and controlling the mixed production line according to the control instruction. The powder is uniformly mixed, and the dynamic adaptability is high.

The invention discloses a railway catenary reliability evaluation method based on a Markov chain and a dynamic Bayesian network, and the method achieves the systematic monitoring and fault early warning of the operation state of catenary equipment through the construction of a catenary monitoring data collection system, and the utilization of multi-source data integration, multi-node multivariable modeling and hierarchical time scale monitoring. According to the scheme, comprehensive acquisition and analysis of key parameters of a contact network system are ensured by integrating various factors such as meteorology, load and equipment state. Through combined application of the dynamic Bayesian network and the Markov chain, multi-node and multi-variable modeling is carried out, and the dynamic influence of factors such as equipment aging, load fluctuation and external environment change on the system reliability can be accurately captured. In addition, by refining the time scale, performing high-frequency sampling on short-term variables such as weather and load fluctuation, and performing low-frequency monitoring on long-term factors such as equipment aging and corrosion, the state of the contact network is finely evaluated on different time scales.

The invention relates to the technical field of computer systems based on specific calculation models, and discloses a blood oxygen change monitoring algorithm fused with dynamic Bayesian modeling, which comprises the following steps: quantizing signal uncertainty by calculating entropy of pulse waveform harmonic energy distribution; and on the basis of the entropy value, driving a dynamic Bayesian network to carry out confidence coefficient evaluation and weighting processing on the blood oxygen estimation model, and finally outputting a decision pair containing a blood oxygen estimation value and the confidence coefficient thereof. According to the method, the signal uncertainty is converted into a computable entropy index, so that the system can autonomously distinguish real physiological changes and measurement noise, the problem of misjudgment caused by motion interference in traditional blood oxygen monitoring is avoided, meanwhile, non-inductive personalized calibration is achieved by utilizing the continuous learning ability of the Bayesian network, and the accuracy of the system is improved. And the reliability and the practicability of the medical wearable equipment are remarkably improved.

The invention discloses a geological disaster monitoring and early warning method based on multi-factor dynamic coupling, belongs to the technical field of geological disaster monitoring and early warning, and aims to solve the problems that a traditional method cannot fuse multi-source factors in real time, is low in early warning precision, lags in response and the like. A geological environment static background factor is combined to construct a susceptibility evaluation model, a dynamic weight is analyzed and calculated by adopting a time sequence, a dynamic Bayesian network is utilized to carry out coupling analysis, and a geological disaster risk probability value is output in real time, so that a corresponding early warning level and an emergency response are triggered. The method is mainly used for real-time monitoring, accurate risk assessment and timely early warning of geological disasters.

The invention discloses a ship desulfurization system reliability evaluation method and system based on multi-source information fusion, and the method comprises the steps: collecting multi-source information data of a hybrid desulfurization system, and carrying out the self-adaptive preprocessing; generating a fusion feature vector; constructing a dynamic Bayesian network based on multi-source fusion features, performing real-time reasoning by adopting data-driven transition probability learning and particle filtering, describing transient behaviors of system state evolution and mode switching, and performing dynamic multi-state reliability modeling; a fault mode is automatically extracted, and data-driven systematic risks are identified and quantitatively analyzed; a multi-resolution digital twinborn architecture is constructed, dynamic simulation prediction is carried out, a self-adaptive updating mechanism is adopted to keep the model synchronous with a physical system, and a virtual verification environment for reliability evaluation is provided; according to the method, an intelligent decision optimization system is constructed, self-adaptive generation and dynamic adjustment of a maintenance strategy are realized, closed-loop feedback is carried out, and the accuracy of reliability evaluation of the hybrid desulfurization system is improved.

Disclosed in the present invention are a low-altitude air route risk map construction method and system based on hexagonal grid cells. The method comprises: on the basis of management and control requirements for different types of airspace, forming low-altitude airspace three-dimensional hexagonal hierarchical grids by means of multi-scale partitioning of an airspace horizontal plane and multi-scale partitioning of an airspace vertical plane; constructing an unmanned aerial vehicle flight risk assessment indicator system for the low-altitude airspace three-dimensional hexagonal hierarchical grids, and after optimization, constructing an unmanned aerial vehicle operational risk assessment model based on a dynamic Bayesian network, so as to generate multi-scale grid risk values; and combining the multi-scale grid risk values with geographic location information of a region, designing a hexagonal grid code index, and generating a dynamic multi-scale low-altitude air route three-dimensional hexagonal risk map. The present invention can improve the accuracy and real-time performance of unmanned aerial vehicle flight risk assessment of grids, quickly obtain low-risk grids of an unmanned aerial vehicle in a target region, and improve the safety of air route planning.

The invention discloses a soft start control method for intelligent temperature control of a glue injection mold, and particularly relates to the technical field of glue injection molds. Initial temperatures of a plurality of heating areas of the mold are collected, and a temperature distribution model is constructed; calculating a thermal inertia score value based on the thermal response characteristic and the thermal capacity parameter of each region; predicting temperature rise rates of different areas by using the thermal inertia score value, the thermal coupling degree abnormal value and the historical control deviation frequency, and setting differentiated soft start amplitude limiting parameters; temperature feedback is collected in real time in the heating process, a temperature rise rate error sequence is constructed, and the future deviation risk is predicted through the dynamic Bayesian network; when the risk value exceeds the limit, the PWM duty ratio or the conduction angle of the heating area is dynamically corrected, and power output self-adaptive adjustment is achieved; the method has the advantages of being high in predictability, accurate in response and intelligent in control, and is suitable for high-precision heating control application of the complex glue injection mold.

The invention discloses a power monitoring system host security micro-isolation method and system, particularly relates to the technical field of power terminal security isolation, and aims to monitor electromagnetic interference and quantum state changes in real time and improve the stability and security of a quantum key distribution process in combination with quantum error correction and dynamic prediction technologies. A high-confidentiality encryption key is generated through the randomness of a quantum key generator, the interference level is monitored in real time in a high electromagnetic interference environment, a security protocol is automatically triggered, and parameters of a quantum system are adjusted through an active feedback mechanism; a dynamic Bayesian network is utilized to predict entanglement attenuation of a quantum state, an information exchange behavior of a quantum system is analyzed through quantum entanglement entropy, the attenuation degree and interaction intensity of the quantum state are comprehensively analyzed, and a dynamic micro-isolation strategy is implemented according to an analysis result; the reliability of quantum key distribution is improved, and the anti-interference capability of a power monitoring system in a complex electromagnetic environment is enhanced.

The invention provides a highway tunnel decision-making maintenance system and a maintenance method thereof. The method comprises the steps of obtaining tunnel static and dynamic data and tunnel surrounding geological environment data; according to the geological environment data, carrying out geological risk analysis by adopting a geological statistical simulation algorithm and a time sequence decomposition algorithm to obtain a safety risk assessment result of the tunnel structure influenced by geological factors and an underground water dynamic change trend prediction result; according to the geological risk analysis result, fusing the tunnel technical condition data by adopting a heterogeneous integration model of a dynamic Bayesian network and an extreme gradient boosting tree, predicting the tunnel technical condition, and generating maintenance decision parameters; optimizing the maintenance decision parameters by adopting a cuckoo search algorithm so as to minimize the maintenance cost under the influence of the geological risk; and outputting decision results including the total maintenance cost distribution based on the geological risk, the technical condition improvement expectation, the rock stratum stability safety factor prediction value and the underground water seepage risk early warning level.

The invention provides an intelligent decision-making method based on multi-dimensional risk coupling modeling of new energy ships and related equipment. The method comprises the following steps: fusing multi-modal data of each new energy ship to obtain a target multi-modal feature vector; mining risk implicit association strength by using a large language model, and constructing a risk knowledge graph; performing risk time sequence evolution prediction by adopting a dynamic Bayesian network based on the atlas to obtain a single-ship risk prediction result; constructing a graph structure according to the single ship risk and the operation parameters, and carrying out space coupling modeling by adopting a graph convolutional network to obtain a cluster risk prediction result; and generating an optimal operation and maintenance strategy by adopting reinforcement learning based on a cluster risk prediction result and a reward function by taking a high-fidelity digital twinborn body as a virtual environment. Therefore, according to the method, the problem of closed-loop adaptive control from risk deduction to decision response in a complex navigation scene is effectively solved by constructing a unified modeling mechanism of multi-dimensional risk coupling and linking real-time control strategy generation.

The invention discloses a transmission path planning method and system for a box-type logistics system, and the method comprises the steps: constructing a four-dimensional space-time transmission network model which comprises the three-dimensional space coordinates of the box-type logistics system and department operation time window constraints, and quantifying the node traffic capacity; a hierarchical planning architecture is adopted, and a dynamic Bayesian network is introduced to predict the node congestion probability; a multi-agent cooperation mechanism is established, an online layer adjusts a path weight in real time by improving an ant colony optimization algorithm, and an offline layer optimizes global strategy parameters by using deep reinforcement learning and constructs a knowledge distillation channel to realize cross-period experience migration; and a multi-objective evaluation function is applied, transmission time cost, system energy consumption indexes and path load balance degree are synchronously optimized, a conflict resolution mechanism based on the game theory is designed, and transmission path planning of each box body is realized. According to the invention, through dynamic path planning and real-time flow monitoring, materials can be rapidly and accurately delivered to a destination, and the overall efficiency of a logistics system is improved.

The invention relates to the technical field of illumination control, in particular to an LED illumination efficiency optimization method and system, and the method comprises the following steps: recognizing an illumination use mode and a change trend of an environment condition based on real-time external environment data and historical LED illumination operation data, and obtaining an environment and operation data analysis result. According to the invention, prediction analysis is carried out on the LED illumination demand through the Gaussian process regression model, accurate prediction of the illumination demand under differentiated environmental conditions and time periods is realized, the responsiveness and adaptability of the illumination equipment are improved, the illumination control decision process is optimized by comprehensively considering the real-time external environment data and the historical illumination operation data, and the accuracy of the illumination control decision process is improved. The introduction of the hybrid dynamic Bayesian network enhances the real-time evaluation capability of the energy efficiency state of the LED lighting equipment, so that the energy efficiency management is more precise and efficient, and the use of a multi-objective optimization strategy provides a more comprehensive solution for balancing the energy efficiency, the illumination quality and the user comfort.

The invention provides a chemical potential safety hazard intelligent identification and early warning method. Relates to the field of chemical safety, and discloses a chemical potential safety hazard intelligent identification and early warning method comprising the following steps: S1, preprocessing multi-modal data through a multi-modal feature self-calibration fusion algorithm; s2, constructing a dynamic Bayesian network time-varying coupling evaluation model based on a protection layer theory; s3, related knowledge documents are retrieved by adopting an enhanced RAG technology; s4, carrying out hidden danger identification based on the knowledge-enhanced large model; s5, four-level intelligent early warning is generated based on the risk value calculation model; and S6, optimizing model parameters through deep reinforcement learning. The chemical potential safety hazard intelligent identification and early warning method based on the protective layer theory and the large model technology has the advantages that dynamic risk perception can be realized, cross-modal potential hazards can be accurately identified, an early warning scheme can be quickly generated, and the initiative and scientificity of chemical safety management can be improved.

The invention relates to the technical field of bridge earthquake damage performance evaluation, and particularly discloses a high-speed railway bridge earthquake damage performance rapid evaluation method based on machine learning, and the method comprises the steps: collecting multi-source data of a bridge structure of a high-speed railway, a historical earthquake database and a BIM model of the bridge structure in real time through a heterogeneous data collection system; constructing a multi-modal feature space containing partial features of time-frequency domain features, spatial features and probability features; constructing a hierarchical machine learning framework based on a dynamic Bayesian network damage propagation model, a mixed model of a multi-scale convolutional neural network and a long-short term memory network and an uncertainty quantization and dynamic updating mechanism, and establishing an earthquake damage performance evaluation model based on the hierarchical machine learning framework; carrying out seismic damage performance evaluation on the multi-modal feature space by adopting a seismic damage performance evaluation model, and generating damage probability distribution; potential earthquake damage related information behind the data is effectively mined, and the earthquake damage condition of the bridge in an earthquake can be more accurately simulated and predicted.

The invention provides an underground tunnel driving risk evaluation method based on a dynamic Bayesian network. The method comprises the following steps: constructing an underground tunnel driving risk evaluation index system; determining Bayesian network nodes according to the underground tunnel driving risk evaluation index system, and constructing an underground tunnel driving risk dynamic Bayesian network model; obtaining a driving risk assessment result according to the dynamic Bayesian network model of the driving risk of the underground tunnel; and performing driving improvement according to the driving risk assessment result. The method can effectively identify and predict the driving risk probability of different road sections of the underground tunnel, provides risk early warning for drivers, and provides data support for a traffic management department to optimize an induction system and an accident prevention strategy, thereby improving the driving safety and traffic efficiency in the tunnel.

The embodiment of the invention provides a dynamic risk assessment management method and system based on business risk control, and belongs to the technical field of business risk monitoring. Comprising the following steps: acquiring behavior data of a user, and performing statistical feature extraction on the behavior data; constructing a dynamic Bayesian network according to the statistical characteristics of the behavior data; performing incremental updating on a conditional probability table of a dynamic Bayesian network according to the real-time behavior data; constructing and training an LSTM model and a random forest model by adopting the behavior data; acquiring a current comprehensive risk assessment result according to the real-time behavior data, the dynamic Bayesian network, the LSTM model and the random forest model; a multi-model fusion mode is adopted, so that the risk assessment precision can be effectively improved; and finally, an adversarial sample is constructed, and the dynamic Bayesian network and the LSTM model are trained offline according to the adversarial sample, so that the generalization ability for an unknown attack mode can be improved, and the security is higher.

The invention, which relates to the technical field of artificial intelligence, discloses an artificial intelligence-based education evaluation and feedback system comprising a data acquisition module, a vector generation module, a prediction module, an error region positioning module and a feedback module. The system constructs a unified high-dimensional cognitive state vector by collecting answering behaviors, eye movement tracks, facial micro-expressions, voices and intonations and electroencephalogram signals of students; generating a learning evolution path map based on a dynamic Bayesian network and a causal reasoning mechanism, and predicting future learning bottleneck nodes; an error region is recognized through semantic deconstruction and graph matching, and context-associated personalized feedback content is generated in combination with a generative language model; according to the system, an evaluation feedback closed loop of cognitive state modeling, accurate identification of an erroneous region and intelligent feedback pushing is realized, and the accuracy of education evaluation and the effectiveness of intervention are improved.