1312 results about "Pattern identification" patented technology

The invention provides a method and system for preventing identity spoofing during digital authentication processes using artificial intelligence (AI)-driven pattern recognition. The system receives an input data stream from a user attempting to authenticate, which may include biometric data, device behavior data, or user interaction data. An AI-based pattern recognition model processes this data to analyze user behavior patterns and detect any anomalies that may indicate potential spoofing attempts. The system compares the processed data against a pre-established user profile to generate an authentication decision. If anomalies are detected, the system can flag the authentication for further review or trigger additional verification steps, such as multi-factor authentication (MFA) or one-time password (OTP) prompts. The system continuously learns from user interaction data and dynamically updates the user profile to improve the accuracy of identity verification.

The invention provides a power station equipment state real-time monitoring and diagnosing method and system based on cloud edge collaboration, and the method comprises the steps: adjusting a data collection period dynamically determined based on an adaptive sampling frequency adjustment algorithm, and collecting a vibration signal, a temperature signal and a current signal through a multi-source heterogeneous sensor array disposed in a power station equipment body; carrying out preprocessing by utilizing the edge computing node, generating a compressed feature vector, and uploading the compressed feature vector to a cloud end through an MQTT protocol; a multi-modal data fusion analysis module is started through a cloud, a three-dimensional evaluation matrix of the equipment health state is constructed in combination with historical operation data and environmental parameters of the equipment, and a calculation task distribution strategy between an edge calculation node and the cloud is adjusted in real time according to an evaluation result of the three-dimensional evaluation matrix. Abnormal mode recognition based on a deep residual network and fault source tracing double-channel analysis based on a physical model are executed, fault types and fault reasons are diagnosed, and the accuracy and timeliness of fault diagnosis are guaranteed.

The invention provides a pressure-bearing equipment damage mode identification and risk assessment method and system, and relates to the technical field of safety engineering, and the method comprises the steps: collecting multi-source sensor data and image data, inputting the data into a deep neural network after preprocessing and feature extraction, extracting spatial features through a convolutional layer, and extracting time sequence features through a recurrent neural network. And using the attention mechanism to fuse the features to identify an injury pattern. And then, constructing a multi-level evaluation index system, performing combined weighting by adopting an analytic hierarchy process and an entropy weight method, inputting weights into an improved Bayesian network model based on a D-S evidence theory, dynamically updating a conditional probability table by the model by utilizing a deep neural network and a fuzzy inference rule, and finally obtaining a risk evaluation result. According to the invention, the damage mode of the pressure-bearing equipment can be effectively identified, risk assessment is carried out, and assessment precision and reliability are improved.

The invention discloses a slope geological disaster multi-mode early warning method and system, and relates to the technical field of slope monitoring, and the method comprises the steps: laying a distributed sensor network, and collecting slope multi-source monitoring data; the multi-source monitoring data comprises displacement data, stress data and vibration frequency data; feature extraction is performed on the multi-source monitoring data by using a graph neural network, and the feature extraction comprises capturing spatial correlation among sensor nodes and identifying an abnormal mode, identifying a potential instability area of the slope according to the abnormal mode, and outputting features of the potential instability area; and generating a slope stability risk grade assessment report based on the characteristics of the potential instability region and a disaster evolution graph constructed by combining historical disaster data. According to the invention, the comprehensive monitoring of the slope from the outside to the inside and from the static state to the dynamic state can be realized, the abnormal mode and the potential instability area can be accurately identified, and the accurate assessment and timely early warning of the slope risk can be realized based on the historical data.

The invention relates to the technical field of engineering safety monitoring, in particular to a foundation pit deformation intelligent early warning system based on multi-modal fusion and a method thereof.According to the system, quality evaluation and weighting processing are conducted on multi-modal sensor data through a self-adaptive weight dynamic distribution module, and the data credibility is ensured; the multi-modal feature cross extraction module extracts and interacts features by using a specific sub-network and a multi-head attention mechanism, integrates information through a space-time diagram convolutional network, and generates accurate fusion feature representation; the multi-granularity abnormal mode identification module is combined with a mixed density network and time sequence analysis to accurately identify deformation anomalies; the causal reasoning and weight feedback module analyzes deformation reasons through a causal graph model and provides feedback for sensor weight adjustment; according to the system, the precision and reliability of deformation detection are remarkably improved, the detection precision is improved to the millimeter level, the accuracy is improved by 40%, and powerful technical support is provided for engineering safety monitoring.

A behavioral authorship verification system captures and analyzes multi-modal behavioral patterns during content creation to authenticate human authorship. The system comprises a processor executing behavioral analysis modules that generate comprehensive behavioral fingerprints distinguishing genuine human authors from AI-generated content and impostor authorship. A sentence progression mapping module detects sentence boundaries and captures intermediate composition states including additions, deletions, and modifications. A multi-modal input analysis module monitors keystroke dynamics including flight time and dwell time while detecting paste events and input method transitions. A behavioral pattern recognition engine generates user-specific baselines from historical sessions and computes deviation scores using statistical distance metrics. An anomaly correlation module aggregates behavioral deviation signals using weighted fusion algorithms to detect sophisticated mimicry attempts. An authorship scoring engine synthesizes outputs into unified confidence scores while maintaining temporal authorship chains. The system enables real-time authorship verification during content creation rather than post-hoc analysis.

The invention discloses a full-life-cycle auditing tracking system and method, and belongs to the technical field of data tracking backtracking, and the method specifically comprises the steps: carrying out the global unique identifier distribution of personnel configuration data during the first collection, embedding a timestamp, an operation identifier and a link identifier in each life cycle node of the data, constructing a real-time auditing model, and carrying out the real-time auditing of the data; through data change monitoring, behavior pattern recognition and an anomaly detection algorithm, each data operation is recorded in real time and compared with a preset auditing strategy, risk early warning is triggered, block chain evidence storage is established at a key operation node, a multi-block chain link relation network diagram is constructed based on multiple block chains, and when an abnormal node occurs, a data life chain is generated. Predecessor nodes of the abnormal nodes are backtracked, recursive check is carried out on each predecessor node, and an abnormal source and a responsibility subject are positioned; according to the method, the multi-step association risk can be found, the situation that tracking cannot be achieved during tracking and backtracking is prevented, the missed judgment rate is reduced, and the accuracy and efficiency of tracking and backtracking are improved.

The invention provides a mine abnormal event real-time identification method and system based on time sequence characteristics, and relates to the technical field of mode identification, and the method comprises the steps: carrying out the time-space alignment and semantic annotation of multi-modal monitoring data, and constructing a time sequence knowledge graph; calculating a dynamic association weight between entities, and analyzing a risk propagation path; predicting a risk situation based on a historical evolution rule; and dynamically generating a differential early warning strategy and establishing a closed-loop tracking system. According to the invention, early identification, accurate prediction and efficient disposal of mine safety risks can be realized, and the mine safety management level is improved.

A spatiotemporal modeling system for Personal Health Database (PHDB) platforms integrates diverse health data types into a comprehensive 4D model of an individual's health status. By combining genomic, imaging, clinical, and real-time health data, the system creates a dynamic, time-based representation of the user's anatomy and physiology. This model enables real-time analysis, pattern recognition, and predictive forecasting of health outcomes. The system preprocesses and aligns data from various sources, constructs a detailed spatial framework, and continuously updates the model with new inputs. Through interactive visualizations, it provides users and healthcare providers with intuitive, personalized insights for improved health management and decision-making.

The invention relates to the technical field of health information monitoring and management, and discloses a health information monitoring and management system based on multi-source data fusion analysis, which comprises a physiological data acquisition unit, a fusion analysis engine, an intelligent decision management module and the like. The physiological data acquisition unit acquires a multi-source heterogeneous data stream, and a multi-layer fusion topology is constructed through preprocessing; the fusion analysis engine realizes data feature association and anomaly detection through feature association and mode recognition; and the intelligent decision management module generates a health state reference strategy and dynamically allocates data source weights. The real-time calibration module calibrates a signal time domain and adapts to an analysis frequency, the data weight optimization module evaluates an optimization strategy based on credibility, and the fault-tolerant processing module completes data verification and recovery in combination with the distributed cache unit. The system realizes efficient fusion, dynamic decision and reliable management of multi-source data, improves the accuracy of health monitoring and the robustness of the system, and is suitable for intelligent health management scenes.

The invention relates to the technical field of network and information security, and discloses a multi-source security information collaborative analysis method and system fused with an AI intelligent agent, and the method comprises the steps: collecting security related data; cleaning and normalizing the collected data, and extracting target security features from the preprocessed data; constructing a plurality of AI agents for different data sources, and generating a preliminary threat judgment result through semantic understanding, behavior pattern recognition and association rule mining based on target security features; performing time sequence fusion on the preliminary threat judgment result, constructing a dynamic security situation model, capturing a threat evolution trend, and dynamically determining a risk level and a priority processing sequence of an event in combination with threat intelligence; according to the risk level and historical response experience, the AI intelligent agent generates an automatic response suggestion and pushes the automatic response suggestion to operation and maintenance personnel; according to the invention, the threat identification capability is improved.

The invention relates to the technical field of data processing, and discloses an anti-oxidation barrier layer performance degradation prediction method and system based on multi-source data. The method comprises the following steps: collecting and preprocessing multivariate data of a barrier layer environment, and constructing three-dimensional tensor data; indexes such as oxygen blocking efficiency and anti-permeability performance are calculated, and performance time sequence data are obtained; extracting multi-dimensional features and fusing the multi-dimensional features through an automatic encoder; applying a hybrid deep learning model to predict performance degradation; analyzing degradation curve characteristics, and executing mode clustering to obtain a risk matrix; and optimizing a maintenance decision scheme based on risk assessment. According to the invention, the hybrid deep learning model is utilized to capture the dependency relationship of time and space dimensions at the same time, and the prediction precision is significantly improved; based on degradation mode identification and risk level evaluation, accurate multi-scene maintenance decision is realized, environmental risk is reduced, and maintenance cost is optimized.

The invention relates to the technical field of power equipment fault diagnosis, in particular to a power equipment fault intelligent diagnosis method and system based on deep learning. The method comprises the following steps: automatically learning high-dimensional space-time correlation features in original time series data through a deep feature extraction network, and generating feature vectors representing potential abnormal modes of equipment; performing adaptive weight distribution on the high-dimensional space-time correlation features by using an attention enhancement mechanism, and marking a fault sensitive area to form enhanced fault features; inputting the enhanced fault features into a multi-level classifier for joint fault mode recognition and severity evaluation, and outputting a diagnosis result tensor containing a fault type and confidence; and an equipment maintenance decision signal is triggered based on the diagnosis result tensor, and the feature extraction network and classifier parameters are iteratively optimized according to feedback data, so that the intelligent level of operation and maintenance of the power equipment can be comprehensively improved.

The invention provides a production process intelligent monitoring method and system based on an intelligent mine, and the method comprises the steps: collecting a target monitoring data set in the real-time production process of mine equipment, covering equipment vibration time sequence signals, environment temperature and humidity distribution data and an equipment energy consumption fluctuation curve, and carrying out the dynamic standardization processing, the method comprises the following steps: obtaining a standardized monitoring data set matched with an equipment type and a production process stage, calling a pre-trained multi-dimensional feature extraction model to carry out joint feature mapping, generating an equipment operation state, environment association and data abnormal fluctuation features, and carrying out dynamic fusion analysis on the features based on a preset abnormal mode identification model, and finally, according to the risk level, an alarm instruction is triggered, an optimization strategy is fed back to a production control terminal to adjust equipment operation parameters, and intelligent monitoring and optimization of the intelligent mine production process are realized.

The invention belongs to the technical field of bearing fault diagnosis, and discloses a self-aligning roller bearing fault diagnosis method and system, and the method comprises the steps: obtaining operation data and sensor data, carrying out the simulation through a digital twin model, calculating the deviation between model prediction and actual measurement, and carrying out the mode recognition according to the deviation. Digital twin model parameters are dynamically calibrated in a normal mode, fault type identification and bearing positioning are carried out in combination with the calibrated model in an abnormal mode, accurate diagnosis of new installation and bearing faults in a running-in period is realized, dynamic calibration of the digital twin model parameters is realized, normal deviation and abnormal deviation are distinguished, and fault diagnosis accuracy is improved. The accuracy of fault diagnosis in the new installation and running-in period is improved, and fault type recognition and fault bearing positioning are achieved.

The invention relates to the technical field of image processing, in particular to an intelligent fracture diagnosis system based on image recognition, which comprises an image analysis module, a mode recognition module, a form analysis module, a risk assessment module and an auxiliary decision module. According to the method, skeleton gray level distribution and boundary consistency are analyzed through continuous frames of X-ray images, fracture feature extraction precision and time sequence coherence are improved, key point space distribution, symmetry standards and form proportions are fused, fracture area structured quantitative evaluation is achieved, the form change trend and abnormal offset point screening are combined, and the accuracy of fracture feature extraction is improved. The method enhances abnormal trajectory recognition precision, associates bone mineral density and form offset, calibrates high-risk time periods, improves risk assessment perspectiveness and individual adaptability, dynamically corrects output content according to an inter-frame suggestion change trend and extended feedback, enhances timeliness of diagnosis suggestions and closed-loop feedback quality, and improves risk assessment accuracy. Image evolution, structural geometry and physiological data are integrally fused, and multi-dimensional intelligent judgment of fracture recognition and evaluation is achieved.

The invention is suitable for the field of transformer fault analysis, and provides a transformer potential fault mode identification method and device based on reverse derivation, and the method comprises the steps: generating a multi-field coupled feature puzzle model through real-time collection of four-dimensional physical quantity spatio-temporal data of temperature, vibration, oil chromatography, partial discharge and the like, and carrying out the topological matching of the feature puzzle model and a historical health file, and reversely deducing a fault coupling path for the abnormal vacancy region through a constraint satisfaction algorithm, reconstructing a missing feature block, decoupling and extracting interface abnormal features, dynamically optimizing model topology in combination with an equipment aging factor, and finally quantifying the full-life-cycle risk accumulation intensity and positioning a composite fault source. The method breaks through the limitation of a traditional single-parameter threshold value, achieves the early recognition and precise traceability of the multi-field coupling fault through a reverse derivation mechanism of the feature jigsaw blocks, is suitable for the health management of the whole life cycle of the transformer, and can remarkably improve the timeliness and accuracy of potential fault early warning.

The invention relates to a copper-clad plate layering defect diagnosis method based on pattern recognition, and the method comprises the steps: forming a rich input sample set through spatial multi-channel imaging, variable illumination and time sequence synchronous sampling in order to solve the problems that the recognition capability of tiny layering defects is weak, and the recognition is easily influenced by noise and environment interference in the existing detection; according to the method, an attention mechanism and small sample discrimination are combined, high-confidence-coefficient defect identification is realized, region self-adaptive re-detection is further triggered in real time to reduce the missing detection risk, and finally defect distribution mapping is automatically generated and data archiving and traceability analysis are supported.

The invention belongs to the technical field of artificial intelligence, and discloses a PCB use fault early warning system based on artificial intelligence, which comprises a data acquisition module, a data processing module, an intelligent analysis module, a decision level fusion module, a self-adaptive modeling module, a multi-model cooperation module and a fault early warning module. The intelligent analysis module realizes double breakthrough of nonlinear feature capture and adaptive anomaly discrimination ability through a dynamic error threshold mechanism of an LSTM time sequence prediction engine and a depth autoencoder; an XGBoost-1DCNN hybrid classifier is constructed, a gradient boosting tree and multi-scale convolution features are fused in fault mode recognition, and the complex fault classification precision is remarkably improved; and the decision level fusion module constructs a multi-model decision conflict resolution mechanism based on an improved D-S evidence theory, and realizes great optimization of a false alarm rate through a confidence interval dynamic synthesis algorithm, thereby forming a closed-loop system with real-time response, multi-dimensional root cause analysis and intelligent hierarchical early warning.

The invention discloses an AI-based medical and health care integrated data management method and system and a storage medium, and relates to the technical field of artificial intelligence, and the method comprises the steps: carrying out the data preprocessing and data standardization of collected multi-source medical and health care data, and obtaining a standardized medical data set; performing classification and semantic annotation on the data of the standardized medical data set by using a preset medical ontology, and constructing a structured medical knowledge graph; performing virtual mapping based on the structured medical knowledge graph by adopting a digital twinborn model to form a virtual medical twinborn body; based on the virtual medical twins, combining real-time monitoring data and historical patient flow data, through multi-dimensional feature extraction and pension mode recognition, dynamically optimizing and presetting a personalized medical scheme and a resource scheduling strategy, and outputting a medical care service optimization scheme; generating a target service list in combination with the analyzed doctor-patient dialogue text, and distributing the target service list to a preset pension service cooperation terminal; the method has the effect of improving the real-time performance and the accuracy of the old people medical care service strategy.

The Marker Fingerprinting system provides a method for identifying and correlating serial or sequential data patterns across diverse domains such as geological, biological, and financial datasets. This innovation transforms single- or multi-attribute data series into feature matrices, generating unique hash tokens—or fingerprints—that encapsulate specific data patterns. Using advanced signal analysis and spectral transformations, it enables efficient processing and pattern recognition within complex datasets. Fingerprints from reference patterns are matched against target datasets, with quantitative confidence metrics derived from weighted algorithms assessing match accuracy. Iterative data conditioning enhances robustness by addressing noise and inconsistencies, ensuring reliability at scale. The invention improves decision-making by delivering rapid and accurate pattern identification with quantified reliability, making it particularly suited for applications like geological top picking, seismic data analysis, and other fields requiring precise data correlation

The invention belongs to the technical field of power transmission line monitoring, and discloses a power transmission tower power transmission line galloping monitoring system which comprises a data acquisition module, a data processing module, an intelligent prediction module, an early warning response module and a man-machine interaction module. The galloping prediction precision is improved through multi-source data fusion and physical constraint modeling, a mixed architecture of time sequence analysis and dynamic characteristic fusion is adopted, complex correlation characteristics of meteorological parameters and conductor dynamic behaviors are effectively captured, an intelligent prediction model is optimized in combination with physical equation constraints, and the galloping prediction accuracy is improved. The physical rationality and extreme scene adaptability of a prediction result are obviously enhanced; the line state change is adapted in real time based on a dynamic threshold adjustment mechanism, and the early warning sensitivity and reliability are optimized; through spatio-temporal feature alignment and a multi-mode galloping mode identification technology, a composite vibration form is accurately analyzed, medium and long term trend pre-judgment and short-time risk early warning are synchronously realized, and a multi-dimensional decision support is provided for line safety regulation and control.

The invention relates to the technical field of geological disaster monitoring and early warning, and discloses a Beidou-based geological disaster intelligent monitoring and early warning method and system. Beidou high-precision monitoring equipment is deployed by selecting a geological disaster prone area, earth surface displacement, settlement and inclination deformation data are collected in real time, and a multi-modal database is constructed in combination with environmental parameters. And performing alignment and noise correction on the spatio-temporal data by adopting Kalman filtering and a weighted evidence theory, extracting short-term and long-term deformation characteristics by utilizing a DBSCAN spatial clustering algorithm, and realizing multi-scale abnormal change pattern recognition in combination with a GeoHash grid index. Dimensional differences are eliminated through Z-score standardization processing, a geological stability index and change rate model is established, a causal reasoning framework is further constructed based on a Bayesian network, and a risk prediction model is trained in combination with a space-time neural network. The system can dynamically adjust a monitoring period threshold value and automatically trigger graded early warning, and supports hidden danger rectification whole-process tracing and multi-level gridding management. According to the scheme, the limitation of traditional single-source monitoring is broken through, the full-chain prevention and control of geological disasters from deformation feature extraction, causal relationship modeling to dynamic risk prediction is realized, and the early warning timeliness and accuracy are remarkably improved.

The invention provides a data flow monitoring method and system based on a large model, and the method comprises the steps: obtaining a data flow record set generated by a to-be-monitored system in a continuous operation period, carrying out the correlation path construction of the data flow record set, generating a data flow topological graph containing a node interaction relation and a time sequence dependency relation, and carrying out the correlation path construction of the data flow record set; calling a pre-trained circulation behavior analysis large model to perform node sequence pattern recognition on the data circulation topological graph, and generating behavior abnormal confidence and abnormal pattern labels of each node in the data circulation topological graph; and according to the abnormal behavior confidence and the abnormal mode label, screening an abnormal interaction node cluster in the data flow topological graph. According to the method, relevance between abnormal nodes and time sequence relevance are considered, missing detection or false detection is avoided, and the reliability of the monitoring effect is improved.

The invention discloses an urban ecological unit scale water conservation function evolution simulation system, relates to the technical field of ecological hydrological simulation, and is technically characterized in that the system combines an InVEST model, a water balance method, a geographic detector and a Moran index to construct a multi-source data fused water conservation evaluation method. According to the system, key factors are obtained through remote sensing data and geostatistical data, a mapping model between water yield and actually measured runoff depth is established based on a regression fitting relation, and a spatial heterogeneity analysis and factor interaction detection mechanism is introduced, so that time sequence dynamic simulation and spatial pattern recognition of a water conservation function are realized. The system can be widely applied to the fields of urban water resource regulation and control, ecological space planning, hydrological safety evaluation and the like.

The invention discloses a medical health question and answer method and device based on an intelligent agent and a medium, and relates to the technical field of artificial intelligence medical question and answer, and the method comprises the steps: receiving an intention analysis report through a master control intelligent agent, accessing a dynamic dialogue context pool, and generating an intelligent agent cooperation instruction according to the state of the intention analysis report and the state of the dynamic dialogue context pool; performing credibility evaluation on the preliminary medical answer report by using a credibility calibration agent, generating a confidence score and an evidence conflict level, and performing multi-dimensional weighted fusion and risk mode recognition by using a dynamic risk evaluation strategy to generate a diversified disposal instruction; and when the diversified treatment instruction is issuing permission, performing safety compliance check on the preliminary medical answer report to generate compliance medical answers. According to the invention, multi-dimensional control of credibility and security of medical answers is realized, and finally the beneficial effects of providing personalized medical questions and answers and ensuring that information is real, reliable, compliant and safe are achieved.

The invention discloses a specific detection value identification method and device and a computer program product, and the method comprises the steps: S1, collecting the power data of a to-be-detected node of a power system, and transmitting the power data to an edge calculation layer in real time through a distributed operation system; s2, preprocessing the power data at an edge calculation layer to generate standardized time sequence data; s3, extracting time domain statistical features and frequency domain transformation features of the standardized time series data, and fusing high-dimensional features output by a pre-training deep learning model to construct a multi-dimensional feature vector; s4, performing pattern recognition on the multi-dimensional feature vector based on a pre-trained classification or clustering model, and outputting an anomaly detection result; and S5, feeding back an abnormal detection result to the power equipment control unit in real time through the distributed operating system, and triggering an alarm or executing parameter adjustment. According to the invention, efficient distribution of detection results and multi-node parallel processing can be realized, and the system performance is further optimized.

The invention relates to the technical field of large model agent application, and discloses a hierarchical management alarm analysis and prediction implementation method based on a large model agent, and the method comprises the steps: obtaining historical alarm logs and real-time operation parameters of a target system, and constructing a hierarchical management feature library; designing an alarm grading evaluation framework and determining a multi-source data acquisition scheme; collecting multi-dimensional alarm data, and carrying out alarm grade judgment and abnormal mode recognition through a large model agent; and transmitting an analysis and prediction result to a management decision module to optimize a grading strategy. According to the invention, through multi-modal data fusion, dynamic weight distribution and intelligent analysis, the problems of single data processing and rigid grade division in traditional alarm management are solved, accurate classification and prediction of alarms are realized, and the operation and maintenance efficiency and reliability of the system are improved.

The invention discloses an exercise rehabilitation assessment method and system based on limb posture and emotion recognition, and the method comprises the steps: collecting a motion video, recording the age and gender information of a patient, constructing a self-made data set, defining a candidate region containing the rehabilitation motion of the patient, constructing a basic motion posture data set, and carrying out the recognition of the rehabilitation motion of the patient based on a posture estimation algorithm. Personalized limb skeleton key points are extracted, and emotion features are obtained through face key point detection and face action unit analysis; analyzing the motion trail of the knee joint based on the personalized limb skeleton key points, and extracting limb posture information; and carrying out feature fusion on the limb posture information and the emotional features to form a quantitative rehabilitation evaluation result. According to the invention, high-precision capture of the key points of the human skeleton is realized through computer vision and pattern recognition technologies. In addition, in combination with analysis of the emotional state of the patient, the evaluation accuracy is enhanced, and the rehabilitation training effect evaluation is more comprehensive and accurate.

The invention discloses a neurology patient rehabilitation nursing method based on multi-modal data analysis, and relates to the technical field of medical health, and the method comprises the steps: extracting neural function features through a multi-modal data fusion algorithm, and carrying out the calculation through a weighted fusion and statistical analysis method, and obtaining a neuroplasticity index vector; combining the neuroplasticity index vector with the unified multi-modal feature representation, carrying out multi-modal abnormal mode recognition analysis, obtaining a rehabilitation risk early warning signal and a personalized intervention suggestion, and generating a personalized rehabilitation scheme; performing dynamic optimization and self-adaptive adjustment on the individualized rehabilitation scheme by using a feedback loop mechanism to generate an optimized individualized rehabilitation scheme; physiological signals in individualized rehabilitation training based on the optimized individualized rehabilitation scheme are collected in real time and preprocessed, and a standardized physiological response data sequence is generated. According to the invention, the scientificity, timeliness and individual adaptability of regulation and control are improved, so that nerve function remodeling is accelerated and the rehabilitation risk is reduced.