745 results about "Event recognition" patented technology

The invention discloses a security event log real-time monitoring and analyzing system and method for a power grid communication network, and relates to the technical field of network security management. The causal relationship graph building module is used for building a causal relationship graph of the target power grid communication network; the multi-dimensional correlation analysis module is used for collecting and analyzing multi-source heterogeneous log data in real time; the abnormal security event identification module is used for identifying an abnormal security event according to the causal relationship graph and the multi-dimensional correlation analysis result; and the attack chain tracking response module is used for tracking the attack chain. According to the method, the technical problem that the existing power grid communication network security monitoring lacks tracking of abnormal event evolution from the time dimension and cannot accurately identify and track a multi-stage attack chain is solved, and the effects of dynamically tracking the evolution process of the abnormal event and identifying a potential attack chain by establishing a time causal chain graph are achieved. And the detection precision and the response speed of the attack behavior are improved.

The invention discloses a traffic large model construction and decision-making method and device based on multi-modal two-way map reasoning, and the method comprises the steps: constructing a multi-modal data set of a text, an image and a track, generating fusion features through spatial-temporal clustering and cross-modal Transform coding, carrying out the two-way map reasoning in combination with a traffic knowledge map, and carrying out the decision-making of the traffic large model. The method comprises the following steps: generating an embedded representation through a forward graph neural network, reversely mapping a decision scheme generated by a language model to a graph to verify consistency, outputting knowledge to enhance embedding, fusing multi-modal features and knowledge embedding by adopting an LoRA multi-task joint fine tuning technology, adapting to traffic field tasks, deploying a real-time inference engine, and carrying out real-time inference on the traffic field. And processing the dynamic data flow through an aging perception attention mechanism, and outputting traffic event identification, path planning and scene question and answer results in parallel. Compared with the prior art, the method has the advantages that the problems of insufficient multi-source heterogeneous data fusion, low knowledge utilization efficiency and poor real-time decision consistency can be solved, and the semantic understanding and decision accuracy of the traffic large model is effectively improved.

The invention discloses a photoelectric event identification optimization method and system for a single photoelectron detector, and the method comprises the steps: collecting environment noise data, carrying out the modeling of environment noise, and generating a noise processing model; real-time operation monitoring data of the target single photoelectron detector are acquired, noise processing is carried out, and time-frequency domain waveform decoupling is carried out after processing is completed; performing pulse accumulation detection through a time-frequency domain decoupling result, marking an accumulation event, and separating accumulated pulses by using a pre-trained pulse separation network to obtain pulse separation information; generating a floating judgment boundary parameter according to the pulse separation information, and carrying out photoelectric event judgment to obtain a photoelectric event judgment list; and obtaining a historical photoelectric event discrimination list of the target single photoelectron detector, extracting an abnormal discrimination event, analyzing whether maintenance is needed, generating a maintenance self-inspection report, and pushing the maintenance self-inspection report. The detector keeps high robustness and anti-interference capability in the whole life cycle, and the identification error rate improvement caused by aging change is avoided.

The invention provides an intelligent event identification method and system based on a high-speed camera, and the method comprises the steps: setting the collection parameters of the high-speed camera, and triggering the camera to collect a target scene video stream. And hardware acceleration decoding processing is carried out on the collected original video data stream, real-time environment illumination information of the environment illumination sensor is obtained, and dynamic brightness equalization processing is executed. And performing motion adaptive denoising processing on the video sequence. Geometric distortion correction is carried out on the image sequence through camera calibration parameters, sub-pixel-level displacement vectors and dense optical flow field data of a moving object are extracted, and multi-scale morphological features are extracted. And the features are fused to generate motion feature data, the data are processed through a spatio-temporal joint event classification model, an event identification result is output, the result is bound with a high-precision timestamp, and event identification information is output to an industrial control system display device in real time. According to the invention, the accuracy and real-time performance of event identification can be improved.

The invention relates to the technical field of error correction, in particular to a water conservancy facility environment interference error correction method and device based on multi-dimensional data. The method comprises the following steps: collecting historical monitoring logs of water conservancy facilities, carrying out environmental disturbance event identification and multi-scale disturbance relevance mining, and constructing an environmental disturbance space-time relevance map; monitoring surrounding environment water monitoring parameters of the water conservancy facility, performing sequential response trend change analysis and dynamic response characteristic modeling, and constructing a facility environment personalized change response portrait; identifying a plurality of water body position monitoring data according to the surrounding environment water body monitoring parameters, and constructing a hydrodynamic full-field sensing graph; and performing nonlinear correlation analysis based on the hydrodynamic full-field perception graph and the personalized change response portrait of the facility environment, and constructing a digital twinborn model of the water conservancy facility under the water flowing condition. According to the invention, through dynamic error trend analysis and correction, the monitoring precision and monitoring stability of the water conservancy facility are improved.

The invention discloses an urban traffic event semantic recognition method based on a knowledge graph, and relates to the technical field of intelligent traffic and artificial intelligence, and the method comprises the steps: obtaining the multi-modal traffic data of urban traffic, and constructing a knowledge graph model; preprocessing and feature extraction are carried out on the multi-modal traffic data, the extracted multi-modal features are mapped to entity nodes of a knowledge graph model, a fusion feature vector is generated, and semantic embedding coding is carried out on the fusion feature vector through a graph neural network; constructing an event inference rule base based on a semantic embedding coding result, and performing multi-layer inference calculation on the fusion feature vector by using a graph convolutional neural network to obtain a matching strength score of the candidate traffic event and a standard event mode in the knowledge graph; and in combination with the event space-time constraint condition and the historical event mode, outputting a traffic event recognition result, confidence evaluation and disposal suggestions. According to the invention, the accuracy and practicability of urban traffic event identification are improved.

The invention belongs to the technical field of meteorological safety, and discloses a circum-Bohai sea low-altitude meteorological safety engine artificial intelligence algorithm based on historical reanalysis and multi-mode data. The low-altitude meteorological safety engine artificial intelligence algorithm comprises the following modules: a meteorological variable reconstruction and extraction module, an extreme event identification module, a model training module, a mode fusion module and a safety engine construction module. The low-altitude meteorological safety engine artificial intelligence algorithm of the Bohai Sea has the following advantages: (1) data fusion and timeliness improvement are realized; (2) the low-altitude small-scale extreme weather identification precision is improved; (3) performing multi-dimensional meteorological risk assessment system and standardized grading; and (4) performing dynamic adaptive optimization and real-time adjustment.

The invention provides a hydro meteorology early warning method for an offshore oil and gas platform, and belongs to the technical field of offshore hydro meteorology. Extreme weather events are identified by adopting minimum probability abnormal event identification vectors to match abnormal characteristic parameters, and abnormal signal characteristic parameters are input into an ocean dynamics prediction model to calculate real-time sea condition parameters; calling a multi-temporal-spatial-scale early warning fusion matrix to combine with a wavelet decomposition technology and a recurrent neural network to realize multi-scale information integration, analyzing an environmental parameter change trend through a sea condition jump identification model and triggering an emergency response, dynamically adjusting system parameters according to a stability evaluation index vector, and optimizing prediction precision by adopting an early warning residual value compensation matrix. And finally, multi-level early warning information is generated and a real-time early warning notification is sent to an operator, so that the technical problem of insufficient early warning precision of an offshore oil and gas platform hydro meteorology early warning system in multi-spatio-temporal scale data fusion processing is solved.

The invention provides a visual early warning method and system for a network security event in the cross technical field of network security and machine learning, and the method comprises the steps: S1, creating a security event recognition model, and setting a loss function of the security event recognition model; s2, acquiring historical monitoring data to construct a data set; s3, the data set is divided into a training set, a verification set and a test set, and the training set, the verification set and the test set are respectively utilized to train, verify and test the security event identification model; s4, performing knowledge distillation and deployment on the security event recognition model passing the test; s5, collecting real-time monitoring data, and inputting the data into the security event recognition model to obtain a security event recognition report; and S6, displaying the security event identification report and statistical data of the security event identification report and the real-time monitoring data in real time through a visual interface. The method has the advantage that the accuracy and the timeliness of early warning of the network security event are greatly improved.

The invention provides a multi-dimensional data fusion system for operating truck risk rating, and relates to the technical field of data processing, and the system comprises a data collection module which is used for obtaining the dynamic operation data and historical static information of a target operating truck; the data preprocessing module is used for performing field alignment, abnormal value elimination and format standardization; the feature analysis module is used for extracting dynamic behavior features of vehicle operation and performing statistical calculation in a preset time window; the event identification and risk assignment module is used for identifying a single event or a combined event, assigning an event risk weight to the single event or the combined event, and determining a corresponding road condition risk level in combination with the vehicle position information; the multi-dimensional fusion module is used for fusing features and generating time serialized multi-dimensional risk feature vectors; the risk rating module is used for scoring the multi-dimensional risk feature vector and outputting a corresponding risk rating result; according to the invention, the accuracy of the multi-dimensional data fusion system is improved.

The invention provides a power load prediction method and device, and belongs to the technical field of power load prediction.The method comprises the steps that current waveform data are obtained, and fundamental wave and harmonic components in the current waveform data are extracted; carrying out waveform spatial form geometric analysis to obtain a real-time load characteristic sequence, and then carrying out segmentation processing; the current effective value sequence of each time window is converted into a time-frequency domain energy distribution vector, and then a three-level feature library is constructed; constructing a three-dimensional tensor model through equipment start-stop event identification, inputting the three-dimensional tensor model into a multi-target optimizer to evolve feature weights, and filtering abnormal samples to obtain a feature cluster; performing random masking processing on the time sequence data of the feature cluster to generate a mask sequence, inputting the mask sequence into an encoder to reconstruct masking data, comparing, learning and judging abnormal output correction data, and inputting the corrected data into a prediction network to generate a feedback signal flow; and analyzing the feedback signal flow to update the prediction network weight. Based on the method, the invention also provides power load prediction equipment. According to the invention, the precision of power load prediction is obviously improved.

Disclosed is a fraud-related gang incident recognition system and method based on intelligence sharing and graph calculation, and a related device. The solution mainly comprises an intelligence sharing platform and an incident model based on graph calculation. The intelligence sharing platform integrates a plurality of trained recognition models, establishes a model intelligence sharing system, and summarizes multivariate information of each model in real time. The incident model based on graph calculation is configured to perform data exchange with the intelligence sharing platform. The incident model based on graph calculation is configured to classify nodes based on a graph neural network, and perform analysis on same on the basis of economic characteristics and social characteristics, to construct a social network graph of the incident, further identify overlapping gang structures on the basis of the social network graph, and transmit the overlapping gang structures to the intelligence sharing platform for recognition processing. In the solution of the present invention, gang organizations in social networks can be effectively discovered and identified by combining a graph calculation method of node attributes and a network topology structure and by using an intelligence sharing platform.

The invention discloses a double-branch sound-vibration fusion event recognition and positioning method based on DAS and AI, and the method comprises the steps: synchronously collecting sound and vibration data, and constructing a sound recognition branch and a vibration positioning branch; the sound branches extract multi-scale joint features through time-frequency domain feature fusion, weights of CNN and BiLSTM are dynamically adjusted to realize adaptive fusion, and event types and confidence coefficients are output; and the vibration branch calculates an initial coordinate by using a four-point space-time weighted optimization method, performs position fine tuning in combination with an ASTCN network, and outputs a final event position. Performing probability distribution verification by fusing double branch results and adopting a dynamic likelihood ratio evaluation mechanism, and outputting a final judgment result; intelligent event classification is achieved through the voice recognition branch, high-precision event space-time positioning is achieved through the vibration positioning branch, intelligent cooperation and system optimization of multi-modal data are achieved through the fusion and verification module, and the bottlenecks of the traditional technology in the aspects of recognition precision, positioning errors and system robustness are effectively broken through.

The invention discloses an adverse drug reaction event recognition method and system based on multivariate knowledge mixed retrieval enhancement, and the method comprises the steps: extracting drug entities from a to-be-recognized clinical disease course record, segmenting the disease course record, and obtaining a drug entity set and a sentence set; for each extracted drug entity, retrieving drug concept knowledge having a hyponymy relationship with the drug entity and drug adverse reaction knowledge having an adverse reaction relationship with the drug entity in a pre-constructed multivariate knowledge base; for each sentence obtained through segmentation, searching suspected adverse drug reaction events meeting a first similarity requirement and drug field text knowledge meeting a second similarity requirement in a pre-constructed multivariate knowledge base; and finally, calling a large language model, taking all the retrieved knowledge as reference knowledge, and identifying the adverse drug reaction event from the to-be-identified clinical disease course record. According to the invention, the accuracy and reliability of adverse drug reaction event identification can be improved.

The invention relates to a method and system for identifying a highway event by using a video large model, and the method comprises the steps: employing a three-stage processing architecture, firstly carrying out the real-time target detection and preliminary event judgment of a highway monitoring video stream through employing a YOLO algorithm, and generating an event candidate set; inputting the candidate events and the video clips thereof into a specially trained visual large model for deep semantic analysis and secondary reasoning; and finally, a reasoning result is rechecked through a rule engine, and false alarms are filtered by applying illusion suppression and a space-time association rule. According to the method, the real-time performance of traditional target detection and the deep reasoning capability of a visual large model are fused, so that the problems of high false alarm rate and high missing report rate of a traditional method are effectively solved, the accuracy and reliability of event identification in a complex traffic scene are remarkably improved, and meanwhile, the real-time processing capability of a system on multiple paths of high-definition video streams is ensured.

The invention relates to the technical field of fault diagnosis, in particular to an SMT production line equipment fault diagnosis method and system based on the Internet of Things, and the method comprises the following steps: obtaining multiple types of signals, aligning a time window, judging that the trend is consistent and fluctuation is synchronous to generate a sudden change event, recognizing an intersection point, extracting a key response position, and outputting an inflection point node number. And constructing a response time sequence and a propagation path, identifying abnormal nodes, and generating a fault traceability result. According to the invention, through time alignment and trend linkage identification of current, temperature and acceleration signals, fusion of trend direction judgment and fluctuation synchronization relation, a cross-equipment linkage sudden change identification mode is established, key response points are extracted through cross positioning of a jump terminal point and a vibration peak value, and a dynamic judgment mechanism of inflection point nodes is formed. In combination with a response sequential sequence and a chain path structure, local signal fluctuation analysis is converted into perception, and the judgment accuracy and traceability of abrupt change abnormity are effectively improved.

The invention provides an emergency event identification method based on a neural network model and computer equipment, and the method comprises the steps: obtaining a multi-source perception data set covering a target region, carrying out the feature coding processing of the multi-source perception data set, obtaining a multi-dimensional feature representation set of perception data units, and carrying out the recognition of an emergency event. Calling a pre-trained emergency event recognition neural network model to perform event recognition processing on the multi-dimensional feature representation set, generating an event recognition result of the sensing data unit, and determining the type of an emergency event existing in the target area and spatial-temporal feature information of the emergency event in the sensing data unit according to the event recognition result; and based on the emergency event type and the time-space feature information, generating an emergency early warning instruction containing an event positioning identifier, and sending the emergency early warning instruction to the target emergency response terminal to start a response operation. According to the invention, the comprehensiveness and accuracy of emergency event identification are improved, and the conversion efficiency of the identification result to the actual response is enhanced, so that the timely early warning of the emergency event is effectively supported.

The invention discloses a method, a system and equipment for voice interaction inside and outside a vehicle and a storage medium, and relates to the technical field of intelligent cabins and human-computer interaction. Identifying an interaction trigger type (in-vehicle active request, out-of-vehicle passive request, or system active trigger) based on the perceived data and / or the vehicle event; determining a corresponding response permission strategy according to the trigger type, and determining whether to allow the system to respond based on the response permission strategy; and if so, calling a large language model to generate a voice text, synthesizing voice by combining external perception characteristics, and broadcasting the voice through a loudspeaker outside the vehicle or a sound box in the vehicle. According to the scheme, three types of interaction intentions including the in-vehicle active request, the out-vehicle passive request or the system active triggering are recognized, the large language model is called to generate the scene-adaptive voice text, and personalized voice synthesis and broadcasting are performed in combination with the external perception characteristics, so that the response efficiency, the expression naturalness and the object adaptability of the in-vehicle and out-vehicle voice interaction are improved.

The invention provides a wireless sensing platform with an edge computing function, and relates to the technical field of wireless sensors. In order to solve the problems that in the prior art, a wireless sensor is large in data redundancy, high in transmission delay and weak in emergency recognition and response capacity in a complex scene, a joint processing mechanism combining a causal event recognition assembly, a causal data fusion assembly, an event separation assembly and a data distribution assembly is adopted. By detecting a causal change event in an environment and dynamically extracting and fusing sensor data with a strong causal relationship, redundant data transmission caused by emergencies is reduced, transmission delay is reduced, the robustness of causal data fusion in a concurrent event scene is improved, and the reliability of data fusion is improved. According to the invention, efficient and accurate sensing and response to emergencies in a complex environment are realized.

The embodiment of the invention relates to the technical field of artificial intelligence, in particular to a security behavior event recognition method and system based on multi-modal analysis, and the method comprises the steps: collecting a multi-modal monitoring data set of a target monitoring scene, the multi-modal monitoring data set comprising a visual imaging data stream and an environment sensing data stream; performing spatial feature extraction processing on the visual imaging data stream to obtain a motion state feature set of the target object; time sequence feature extraction processing is carried out on the environment sensing data stream to obtain an environment state feature set, and the environment state feature set comprises a sound field distribution feature, a temperature gradient feature and an illumination intensity feature; and carrying out space-time fusion processing on the motion state feature set and the environment state feature set to generate a fusion monitoring feature vector, calling an AI security behavior recognition model to carry out event classification processing on the fusion monitoring feature vector, and outputting a security behavior event recognition result of the target monitoring scene.

Examples of the present disclosure describe systems and methods for automating the identification of events in a text file. In examples, a computing system identifies a subset of a text file that comprises an unknown event using a set of rules. Each rule of the set of rules specifying a first pattern of characters is compared to the subset of the first text file. When the set of rules does not identify the unknown event, the subset of the text file is provided to a language model to generate a new rule with a second pattern of characters and an identifier of the new rule. The system then generates an updated set of rules by adding the new rule to the set of rules.

The invention relates to the technical field of sound recognition, in particular to an optical fiber sensing voiceprint feature analysis model construction method of a composite neural network, and the method comprises the steps: collecting a sound signal through distributed optical fiber sensing, setting a sound frequency amplitude threshold value, and extracting a sound signal of an abnormal interval; carrying out noise reduction processing on the collected signals and extracting voiceprint features; constructing an unsupervised model to calculate an outlier score value, and combining a threshold value to judge whether the signal is an abnormal signal; carrying out principal component analysis on the abnormal signal, and carrying out feature compression and mapping; a composite neural network model is trained based on the extracted features, and multi-class voiceprints are recognized; and an uncertainty evaluation mechanism is introduced, a classification result is dynamically adjusted according to confidence, low-confidence data is marked as unidentified and collected and classified again, and self-learning and iterative optimization of voiceprint recognition are realized. The method provided by the invention has high precision, high robustness and strong generalization ability, and is suitable for optical fiber voiceprint event recognition in a complex environment.

The invention relates to the technical field of traffic event identification, and discloses an abnormal traffic event identification method and system based on a traffic large model, and the method comprises the steps: obtaining a traffic data flow, extracting an abnormal feature vector, and obtaining an abnormal signal candidate set; grouping the candidate sets and calculating a deviation degree, and if the deviation degree exceeds a threshold value, taking the deviation degree as a risk signal to form an input subset; environment variables are extracted from the subsets, a mapping relation is established, and anomaly recognition embedding representation is obtained; classifying the embedded representation, judging a congestion precursor and generating an early warning signal to obtain an early warning signal sequence; matching the sequence to obtain an abnormal event chain; if the integrity is higher than a threshold value, analyzing the type to obtain an abnormal event type; extracting a correlation feature vector from the type, pushing the correlation feature vector to a traffic management platform to obtain an instruction, and obtaining an emergency response trigger instruction sequence; and executing the instruction sequence to extract a feedback data stream, inputting the traffic large model to judge the accuracy rate, and if the judgment accuracy rate is met, determining an optimized anomaly recognition framework. The method can solve the problem of insufficient early warning capability.

The invention relates to the technical field of meteorological data processing and artificial intelligence prediction, and discloses an extreme weather event prediction method based on an intelligent model. The method comprises the steps of collecting multi-source meteorological data, and performing preprocessing to unify time and space references of the data; extracting spatio-temporal features reflecting meteorological element change rules based on a preprocessing result, and forming feature vectors for training a deep learning model; inputting fusion data in the real-time platform according to a sliding time window for prediction, generating early warning information according to a prediction result and a set threshold value, and issuing the early warning information through multiple channels; and early warning and monitoring results are fed back to a feature extraction and model updating link to realize online updating and closed-loop processing. According to the method, the timeliness and accuracy of extreme weather event recognition can be improved on the basis of ensuring data consistency through multi-source meteorological data fusion and spatial-temporal feature modeling in combination with conjoint analysis of time and spatial information by deep learning.

The invention discloses an expressway tunnel abnormal event recognition method based on multi-scale polarization fusion, relates to the technical field of intelligent recognition of road traffic videos, and is used for solving the problem of robust recognition of trailing collision, fire smoke and abnormal static behaviors in an expressway tunnel scene. The method comprises the following steps: acquiring polarization angle and polarization degree information of each pixel point in a vehicle driving process; then, the image is divided into a plurality of partitions according to longitudinal illumination changes, a time sequence locking window is established in each partition, and the difference between traffic flow reflection characteristics and background interference is extracted; and identifying a smoke area by constructing a disturbance graph and a direction texture graph, calibrating a trailing collision risk, and judging an abnormal static behavior. And finally, an event atlas is generated, full-process identification closed loop and response cooperation are ensured through structure fingerprints and version numbers, and a high-robustness identification means is provided for fire, trailing and abnormal static targets in a tunnel scene.

The invention relates to a traffic scene vehicle and event identification method based on cooperation of an edge small model and a cloud large model, and belongs to the technical field of intelligent traffic. Aiming at the problems of low recognition precision, dependence on a large amount of labeled data, incapability of recognizing unknown categories and the like in a complex environment in the prior art, the method provides a multi-modal traffic visual perception coding system, a large model enhanced small sample cooperative training algorithm and a dynamic trigger type double-model reasoning framework. Small target feature expression is enhanced through semantic and visual joint coding, dependence of a small model on annotation data is reduced by using a large model pseudo tag and knowledge distillation, and a cloud large model is dynamically called according to confidence and scene complexity for secondary discrimination. According to the method, the recognition robustness of the system under severe conditions is effectively improved, and the balance between open vocabulary perception and low-resource efficient deployment is realized.

The invention relates to the technical field of disaster recognition, and provides a disaster event recognition method based on a large language model and related equipment, and the method comprises the steps: carrying out the disaster recognition of a target disaster text through a large language model, and obtaining a disaster type tag corresponding to the target disaster text; constructing an adjustable geographic position cue word template and a coordinate cue word template; based on the geographic position cue word template and the coordinate cue word template, reasoning the target disaster text by using a large language model to obtain a final disaster site coordinate corresponding to the target disaster text; and generating a disaster event identification result of the target disaster text according to the disaster type label corresponding to the target disaster text and the final disaster site coordinate. According to the method, the accuracy of disaster event identification can be improved, and accurate arrangement of disaster event information is realized.

The invention provides a multi-camera cooperative intelligent early warning method and system for abnormal events, and belongs to the technical field of intelligent security and big data. The method comprises the following steps: collecting video data through a multi-view and multi-type camera, collecting environmental parameters through a sensor, extracting target appearance, motion features and environmental state features by using deep learning after preprocessing, and fusing to construct a multi-modal feature vector; and analyzing and identifying an abnormal event based on the trained abnormal event identification model, determining event information through multi-camera cooperative feature matching and geometric operation, and finally generating early warning information according to a preset strategy and linking the equipment. The abnormal event recognition accuracy and early warning timeliness are improved, and the method is suitable for the field of intelligent security and protection of complex scenes.

The invention provides an optical cable fault positioning method based on OTDR signal characteristic adaptive denoising and event identification, and the method comprises the steps: classifying modal components after OTDR signal adaptive noise decomposition based on a preset entropy threshold value, and obtaining a noise dominant component, a mixed component and a signal dominant component; singular value difference spectrum abrupt change point detection is carried out on the noise dominant component, and residual useful signals are extracted; verifying the entropy value of the mixed component, and reconstructing the component meeting the entropy threshold value, the signal dominant component and the residual useful signal into a de-noised signal; synchronously optimizing hyper-parameters and feature selection subsets of the support vector machine by adopting a swarm intelligence algorithm, wherein search parameters of the algorithm are dynamically updated according to an exponential decay mechanism; and outputting fault point space position information based on the optimized support vector machine model.

The invention relates to the technical field of medical treatment, and discloses a knee joint knee protection treatment personalized planning system based on multi-modal data fusion. A data acquisition and preprocessing module of the system acquires a multi-modal data stream of the knee joint of a patient in real time and generates a standardized data packet; the treatment event identification module is used for automatically identifying and verifying key treatment events from the standardized data packet and generating a treatment event sequence; the historical treatment path query module queries a distributed database and extracts treatment modes of similar patients; the graph matching and path generation module is used for calculating the similarity between the current treatment event sequence and a historical treatment mode, generating a fusion treatment path when the similarity exceeds a threshold value and storing the fusion treatment path to the block chain network; the abnormity monitoring and analysis module monitors abnormal event points in the fusion treatment path in real time, triggers a path decomposition mechanism and performs multi-dimensional performance analysis; and the personalized scheme generation module is used for dynamically adjusting treatment parameters and generating a personalized treatment planning scheme.