878 results about "Feature coding" patented technology

The invention belongs to the technical field of intelligent monitoring, and particularly relates to an urban underground pipe network real-time monitoring algorithm and system based on multi-source data fusion, and the method comprises the steps: obtaining multi-source heterogeneous monitoring data; performing multi-source data preprocessing; carrying out multi-source heterogeneous feature coding and fusion; carrying out real-time monitoring and anomaly detection on a pipe network state; fault diagnosis and prediction are carried out; and generating decision support information and early warning. The system comprises a data acquisition module, a data preprocessing module, a multi-source heterogeneous feature coding and fusion module, a pipe network state real-time monitoring and anomaly detection module, a fault diagnosis and prediction module and a decision support and early warning module. According to the scheme, multi-source heterogeneous data are integrated, spatial-temporal feature coding and fusion are carried out through deep learning, accurate sensing, early warning and intelligent fault diagnosis of the operation state of the pipe network are achieved, and the safe operation level and maintenance management efficiency of the urban underground pipe network are improved.

The invention provides an oil and gas pipeline defect three-dimensional contour determination method and device. Acquiring a three-axis magnetic flux leakage detection signal of a to-be-detected target oil and gas pipeline and corresponding space coordinate information of the three-axis magnetic flux leakage detection signal; constructing multi-channel input data according to the three-axis magnetic flux leakage detection signal and the space coordinate information; determining a defect contour prediction result of the target oil and gas pipeline according to the multi-channel input data by using a pre-trained defect contour inversion model; wherein the pre-trained defect contour inversion model comprises a multi-axis feature extraction and fusion module, a feature coding module and a multi-task decoding module; the defect contour prediction result is used for indicating whether the target oil and gas pipeline has defects or not, and the defect contour prediction result is further used for indicating the three-dimensional contour shape of the defects under the condition that the target oil and gas pipeline has the defects. Therefore, high-precision visual reconstruction of the defect position and the three-dimensional form of the oil and gas pipeline is realized, and the accuracy of defect identification and the reliability of evaluation are improved.

The invention relates to the technical field of action recognition, in particular to a somatosensory action interaction recognition method and system based on skeleton coordinate points. The method comprises the following steps of collecting real-time skeleton coordinate data of a human body and performing multi-modal feature extraction to obtain a real-time skeleton coordinate sequence; obtaining a standard skeleton posture corresponding to the target interaction action, performing pre-recording and feature coding, and generating a target posture skeleton feature template library; performing skeleton time sequence filtering and joint mapping and joint included angle calculation on the real-time skeleton coordinate sequence, performing similarity measurement and dynamic binding tracking at the same time, and starting a binding recovery mechanism when binding loss is detected so as to guide the user to execute a preset binding posture and re-establish a binding relationship; and mapping the joint included angle time sequence data to a corresponding joint of the virtual human shape interaction model in real time, outputting a somatosensory interaction instruction, and driving to repeat a human body action so as to trigger a somatosensory action interaction event. According to the invention, the stability of somatosensory action interaction recognition can be improved.

The invention discloses a three-dimensional scene reconstruction method based on an intelligent LED street lamp multi-mode sensor. The three-dimensional scene reconstruction method comprises the following steps that RGB images are obtained and preprocessed; the information is input to a visual feature coding module, two-dimensional bounding box information is extracted, and an object segmentation module is guided to output a two-dimensional segmentation mask; obtaining point cloud data, projecting the point cloud data to the standardized RGB image, and screening target points in combination with the two-dimensional segmentation mask; complementing the preliminary segmentation result of the point cloud, mapping the result to a standardized RGB image, and extracting a pixel region; carrying out joint coding, implicit representation and neural decoding processing on the object-level RGB image and the point cloud complete segmentation result; and fusing into an original three-dimensional scene, and completing spatial restoration through point cloud registration, attitude optimization and semantic constraint. The invention provides an efficient three-dimensional scene reconstruction method in combination with a multi-mode sensor of an intelligent LED street lamp, and the method has high precision, real-time performance and dynamic target processing capability.

The invention belongs to the technical field of computers, and particularly relates to a software fault repairing method and system fused with intelligent analysis, which comprises the steps of collecting a multi-level running log and performing structured preprocessing, constructing a dynamic calling graph through a time sequence encoder and a graph neural network, inferring a fault root cause in combination with a Bayesian causal inference model, and repairing a fault fault according to the fault root cause. And matching the repair strategy to generate an atomization instruction sequence, and deploying the atomization instruction sequence to a production system after sandbox environment verification. The system comprises a log acquisition module, a feature coding module, a graph construction module, a causal reasoning module, a strategy matching module, an instruction generation module, a sandbox verification module, a deployment feedback module and the like. Through end-to-end intelligent analysis and a closed loop verification mechanism, the fault positioning precision and the repair safety are remarkably improved, system self-evolution is supported, and operation and maintenance are promoted to be transformed from passive response to active autonomy.

The invention discloses an intention recognition method based on cross attention and multi-scale uncertainty. The intention recognition method comprises the following steps: preprocessing multi-modal data; parallel multi-modal feature coding oriented to intention recognition; the invention relates to multi-scale uncertainty perception decoding. According to the method, a parallelized multi-modal feature extraction path is constructed, and a hierarchical fusion mechanism based on cross attention is designed, so that deep semantic alignment and complementary enhancement of four types of heterogeneous information including the posture, the motion track, the global scene and the local vision of a rider are realized; the problems of incomplete feature representation and insufficient cross-modal correlation modeling caused by dependence on a single information source or adoption of a shallow fusion strategy in a traditional method are solved, so that the accuracy and robustness of intention recognition in a complex traffic scene are remarkably improved. According to the method, a multi-scale uncertainty perception decoding framework is introduced, risk early warning or context auxiliary verification is carried out on a low-confidence identification result, and the reliability of an automatic driving system in a safety critical scene is improved.

The invention relates to the technical field of industrial robot control, and particularly discloses an intelligent foreign matter sorting robot control system based on AI recognition, which comprises a dynamic spatial feature extraction module, a manipulator motion state coding module, a collaborative conflict detection module, a dynamic trajectory optimization module and an execution control adjustment module, constructing a three-dimensional dynamic space model through multi-sensor fusion, and extracting spatial topological features by utilizing continuous coherence analysis; manipulator motion parameters are converted into topological space representation, and a track feature coding matrix is established; detecting interaction conflicts among the manipulators in real time by adopting a multi-scale coherence analysis method, and generating graded early warning signals; a collision avoidance track is optimized based on topological constraints and a virtual rejection field technology; precise execution is achieved through inverse kinematics of the Lie group theory and self-adaptive control.

The invention relates to the technical field of financial risk control, and discloses a telecommunication fraud risk identification method based on a bank card transfer scene. The method comprises the following steps: a basic feature construction stage: acquiring multi-source data of historical victim users, extracting multi-dimensional features, and processing the multi-source data into standardized time sequence data through feature coding and a DTW algorithm; in the multi-modal fusion and adversarial learning stage, a core layer containing a multi-modal analysis engine, a risk reasoning model and an adversarial generation model is constructed, and multi-modal feature fusion, risk probability output under an RLHF framework and simulated fraud feature generation driven by WGAN-GP are achieved; in the strategy output stage, risk scores are mapped through a double-layer scoring system, and three-level interception is triggered; in the model management stage, model iteration is achieved by means of a monitoring instrument panel and a rolling time window, the online effect is guaranteed by combining gray release and A / B testing, the telecommunication fraud in the transfer scene can be accurately recognized, and the risk control efficiency is improved.

The invention discloses a printed circuit board withstand voltage detection and evaluation method, which comprises the following steps: in the whole withstand voltage detection process, aiming at different process batches and environments, acquiring electrical signals, process parameters, environment variables and multi-modal visual data, and carrying out time sequence synchronization marking and standardized preprocessing; extracting multi-source features by using a feature coding algorithm, and establishing a multi-mode distribution model covering signal mutation, visual defects and working condition anomalies; through cross-modal feature fusion and a time sequence causal inference model, an abnormal event causal relationship graph is automatically constructed, and a voltage withstanding failure root cause chain is identified and positioned; according to the method, the efficient structured management of the detection data, the accurate traceability of the abnormal reasons and the cross-batch analysis are realized, and the capability of diagnosing and preventing the withstand voltage abnormality of the printed circuit board is remarkably improved.

The invention provides an atmospheric precipitable water quantity inversion method and system based on multi-source satellite remote sensing data, and relates to the technical field of remote sensing data. After clear sky pixels are screened, radiation brightness temperature data, digital elevation model data and spatio-temporal feature coding data are processed through three feature extraction branches, obtained feature representations are spliced and input into a feature fusion network, a spatial attention module is introduced in the feature fusion stage, finally, a composite loss function is adopted for training, and the feature fusion network is constructed. And high-precision atmospheric precipitable water quantity inversion is realized. According to the method, the multi-source data characteristics are fully utilized, and the inversion precision and the space continuity are improved.

The invention discloses a crop disease multi-modal diagnosis and classification method and system, and relates to the technical field of data processing, and the method comprises the steps: collecting disease multi-modal data, and carrying out the preprocessing; constructing a disease multi-modal diagnosis model, respectively inputting images and text sequences in the multi-modal data into a visual feature extraction subnet and a text feature coding subnet of the disease multi-modal diagnosis model, and extracting global visual features, sequence state features and global text features; calculating a global matching score based on the global visual features and the global text features, executing fine-grained local interaction on the global visual features and the sequence state features, introducing a category channel attention mechanism to correct the features obtained by interaction, and weighting to generate multi-modal fusion features; and constructing a loss function based on the global visual features, the global text features and the multi-modal fusion features, training a disease multi-modal diagnosis model, inputting to-be-classified data into the disease multi-modal diagnosis model, and outputting a classification result.

The invention discloses a multi-mode large model video content understanding reasoning acceleration method and system, and mainly relates to the technical field of artificial intelligence reasoning acceleration. Comprising the following steps: inputting video data and preprocessing the video data to generate a video frame sequence; performing adaptive video Token compression on the generated video frame sequence, and outputting a compressed visual Token set; performing visual feature coding and Key-Value generation on the compressed visual Token set to obtain visual KV data; performing video KV cache partition management on the visual KV data; executing cross-modal reasoning based on the vLLM framework to generate a video content understanding result; and outputting a video content understanding result, and carrying out post-processing and structured mapping. The method has the beneficial effects that the obvious reasoning acceleration and throughput improvement can be realized on the premise of keeping the precision of the original large model.

The invention belongs to the technical field of software system optimization and intelligent operation and maintenance, and discloses a shared platform autonomous upgrading method based on a graph neural network, and the method comprises the following specific steps: S1, component state perception and feature coding; s2, constructing a heterogeneous dynamic evolution diagram; s3, dynamic graph neural network embedding modeling; s4, state prediction and conflict evaluation; s5, constructing an upgrade game diagram and strategy reasoning; s6, executing an upgrading plan and real-time feedback acquisition; and S7, graph structure reconstruction and strategy closed-loop optimization. According to the method, the heterogeneous dynamic evolution diagram is constructed, the time sequence label and the state quantification model are introduced, real-time sensing and standardized modeling can be carried out on the running state of each service component in the sharing platform, and compared with a traditional upgrading method depending on static configuration and a predefined template, the upgrading efficiency is greatly improved. According to the mechanism, inductive expression of multi-dimensional features such as node resource occupation, access frequency and response fluctuation is completed in the stage before upgrading.

The invention relates to the technical field of computers, and discloses an interaction control method of intelligent glasses. The method comprises the following steps: synchronously acquiring multi-modal data such as eye movement, voice, gestures and head postures and environment and application context information; carrying out independent time sequence feature coding on each modal data; generating a modulation vector in combination with the context, and outputting probability distribution of user intentions through a cross-modal attention fusion network; and a unique execution instruction is determined through an instruction arbitration module based on rules and a state machine. The system comprises corresponding function modules. According to the method, the accuracy, robustness and naturalness of interaction are improved through multi-modal synchronous fusion and a context self-adaption mechanism.

The invention discloses a three-dimensional scene reconstruction method and system based on monocular depth estimation, and belongs to the technical field of computer vision and three-dimensional reconstruction, and the method comprises the steps: carrying out the multi-scale feature coding of a monocular RGB image through a mixed attention depth coding module, and obtaining the hierarchical depth feature representation; carrying out autoregression depth decoding through a self-adaptive edge perception depth decoding module to generate an initial depth map; a depth confidence map is calculated through a geometric consistency constraint optimization module and is fed back to a coding module for iterative optimization, and a refined depth map is output; and three-dimensional Gaussian ellipsoid scene representation is constructed through the Gaussian ellipsoid scene reconstruction module. According to the invention, high-precision depth estimation and high-quality three-dimensional reconstruction are realized by constructing a depth-coupled closed-loop cooperative system.

The invention relates to the technical field of environmental monitoring and atmospheric pollution early warning, in particular to a Yangtze River Delta composite extreme weather ozone pollution early warning model construction method, which comprises the following steps of S1, acquiring high-resolution meteorological data and pollutant concentration data of a Yangtze River Delta region to form an original data set; and S2, carrying out missing value interpolation and abnormal value elimination on the meteorological data and the pollutant data, and carrying out grid alignment according to time and space to generate a unified spatial-temporal characteristic matrix. According to the method, by collecting Yangtze Delta high-resolution weather and pollutant data, performing data cleaning, bimodal feature coding and joint representation modeling, predicting the ozone concentration and generating regional early warning through multi-layer Transform self-adaptive attention, the problems that traditional ozone early warning mostly depends on a single-modal prediction model, and the reliability of the ozone early warning is greatly improved are solved. And due to the lack of multi-modal space-time dependent capture, the problem of early warning information lag is caused.

The invention relates to the technical field of computers, and discloses a multi-modal fusion key frame extraction method and device, equipment and a medium, and the method comprises the steps: obtaining multi-modal input data, carrying out the modal feature coding, and obtaining a video modal feature, an audio modal feature and a text modal feature; carrying out attention fusion on the video modal features, the audio modal features and the text modal features to obtain fused cross-modal causal features; analyzing the cross-modal causal features through a causal reinforcement learning decision module in combination with a preset time sequence causal graph to obtain a fusion feature sequence and key frame probability distribution; and carrying out key frame selection operation on the time slice of the fusion feature sequence based on the key frame probability distribution to obtain a key frame set, and generating a space-time thermodynamic diagram and causal relationship visualization result corresponding to the key frame set. The multi-modal fusion key frame extraction method and device can be applied to financial science and technology or medical care service program systems, and the accuracy and interpretability of multi-modal fusion key frame extraction can be improved.

The invention provides a multi-modal neural causal inference micro-service fault positioning method and system, and the method comprises the steps: accessing observability data in a service operation process, and representing the tracking information of each request as a directed acyclic graph of a multi-modal feature; performing multi-modal feature coding and graph self-coding anomaly detection on the calling graph, and identifying an abnormal node through a reconstruction error; based on service topology prior, learning a sparse causal relationship graph between services by adopting a multi-scale neural causal inference method; calculating a node root cause score according to the causal relationship graph and the abnormal score, and executing causal path search to generate a fault propagation path; and marking the potential root cause according to the path weight of the propagation graph and the node popularity, and outputting a visual diagnosis result. According to the method, the system operation state is comprehensively described by fusing three kinds of micro-service system multi-modal data of logs, indexes and Trace in the micro-service system, and the structure-perceived causal diagram is constructed, so that accurate and explainable root cause positioning is realized.

The invention discloses a shared unmanned aerial vehicle task scheduling method based on big data analysis, and the method comprises the following steps: collecting multi-source data of a shared unmanned aerial vehicle platform, and carrying out the preprocessing; performing time sequence feature extraction and multi-dimensional feature fusion processing; inputting an improved Crossform model, sequentially carrying out feature coding, time sequence modeling and multi-task prediction processing, and then carrying out feature splicing; task request information is extracted, and feature fusion, correlation analysis and matching score calculation are carried out in combination with a task energy consumption comprehensive result; establishing a multi-objective optimization function to perform state and action modeling and reinforcement learning iterative optimization processing; a task instruction is generated and issued, feedback is collected and executed, an optimal scheduling strategy is updated in real time, and the unmanned aerial vehicle is driven to execute adaptive scheduling. According to the invention, big data analysis and reinforcement learning technologies are fused, intelligent cooperative scheduling of tasks and energy consumption of the shared unmanned aerial vehicle is realized, and the method has the advantages of high efficiency, energy saving and adaptive optimization.

The invention belongs to the technical field of surveying and mapping and geographic information processing, and discloses a geographic entity intelligent identification and reconstruction system based on multi-source surveying and mapping data. The system is composed of a multi-source data acquisition and preprocessing module, a cross-modal feature coding and fusion module, a structural atlas construction and spatial logical reasoning module, a deformable neural modeling module and a physical prior guided collaborative prediction and closed-loop optimization module. According to the method, a cross-modal feature coding and fusion module is arranged, and a modal attention mechanism is introduced to dynamically weight multi-source data, so that heterogeneous information such as a laser point cloud, an inclined image and a multispectral image is fused into a unified coding vector in a high-dimensional space; compared with feature extraction performed by using a static deep network in a comparison file, the method of the invention adopts a minimum residual function to perform modal weight training, has an adaptive feature integration capability, and effectively improves the accuracy of geographic entity recognition and the robustness of boundary segmentation in different scenes.

The invention discloses a self-supervised traffic flow prediction method based on multi-scale space-time-frequency fusion. The method comprises the following steps: acquiring enhanced data; performing multi-scale spatial-temporal feature coding; performing frequency domain residual filtering; generating a traffic flow prediction result; and carrying out joint target optimization. According to the invention, the multi-scale space-time frequency encoder is designed, local and global time features are captured at the same time through the mixed time encoding module in the time dimension, the multi-scale space encoding module aggregates space features under different distances in the space dimension, and the prediction precision is significantly improved. A frequency domain residual filtering module is embedded in the encoder to adaptively purify frequency domain features in an end-to-end mode, enhance key periodic features and suppress irrelevant noise, space-time features are fused through residual connection, space-time-frequency three-dimension collaborative modeling is achieved, meanwhile, frequency domain consistency loss is introduced in the optimization stage, and time-frequency three-dimension collaborative modeling is achieved. And the method is more robust when facing real traffic data containing noise.

The invention relates to an electric power multi-modal total element sample fusion labeling method and system based on Transform, and the method comprises the following steps: S1, obtaining multi-modal original data of a production operation site, and carrying out the preprocessing of the multi-modal original data, and obtaining a preprocessed multi-modal data set; s2, according to the preprocessed multi-modal data, multi-modal feature coding and Transform representation modeling are carried out, and through cross-modal feature fusion and information completion, fusion feature representation is obtained; s3, according to the fusion feature representation and a candidate tag set corresponding to the fusion feature representation, obtaining a multi-modal semantic alignment tag set through semantic consistency detection and a semantic mapping mechanism; s4, aligning the label set according to the multi-modal semantics, and constructing a label system structure tree; and S5, based on the fusion feature representation and the label system structure tree, constructing an automatic labeling module to generate a preliminary label, and directly inferring a label based on similar samples and category probabilities. According to the invention, efficient automatic label generation and sample dynamic classification are realized.

The invention discloses a venous thrombosis risk assessment method based on a large language model, and relates to the technical field of medical artificial intelligence, and the method comprises the steps: collecting thoracic surgery diagnosis and treatment data of a patient, carrying out the space-time alignment, generating a standard diagnosis and treatment data flow, and carrying out the homomorphic encryption of the standard diagnosis and treatment data flow, and forming an encrypted patient data package; inputting the encrypted patient data packet into a multi-task large language model, performing feature extraction and semantic coding by a feature coding layer, performing time sequence modeling and risk probability calculation by a risk quantification layer, and outputting a venous thromboembolism risk level of a patient; and performing feature decoupling and potential space mapping on the encrypted patient data packet to obtain thrombus semantic potential features. Through the multi-task large language model, the dual machine learning algorithm and the homomorphic encryption, the accuracy of venous thrombosis risk early warning is improved, and the safety of the risk assessment process is enhanced.

The invention belongs to the technical field of visual positioning, and relates to a visual positioning method based on semantic comprehension and attribute distinguishing enhancement. The framework mainly comprises a feature coding module, a semantic sensitive data enhancement module, a fine-grained attribute guiding module and a multi-stage cross-modal decoder module. Specifically, the feature coding module performs feature coding on an input graph. The semantic sensitive data enhancement module generates a plurality of queries which are consistent with long text semantics by keeping the consistency of spatial relation words in combination with a large language model, so that a training data set for the long text is expanded. The fine-grained attribute guiding module extracts attribute prior information from a text query in combination with a text graph model and an image encoder, constructs a visual feature representation with higher discrimination by using the information guiding model, and generates a target query with attribute difference at the same time.

The invention relates to the field of intelligent report generation, in particular to an intelligent insight report generation method and system based on an AI large model, and the method comprises the steps: inputting an insight demand, and generating an insight data package comprising insight contents, associated data and industry labels; extracting a basic keyword set of the insight data packet to form a mixed feature code; after mixed feature coding preprocessing, weight distribution is carried out; and after weight distribution of the AI large model, injecting a high-weight feature vector into a semantic understanding core layer, injecting a low-weight feature vector into a logical reasoning layer, and outputting analysis data to form an intelligent insight report. According to the method, word embedding parameters are optimized according to field semantic characteristics, a parameter verification mechanism is introduced, field text characteristics are adapted by adjusting vector dimensions, context windows and low-frequency vocabulary filtering threshold values, window parameter validity is verified through cosine similarity, word frequency threshold value reasonability is verified through standard deviation, and field text characteristic matching is achieved. And it is ensured that the feature vectors can accurately capture domain-specific semantics.

The invention provides an energy storage battery management system and a control method, belongs to the field of energy storage battery management, and is used for solving the problems of low battery state estimation precision, insufficient fault diagnosis sensitivity and poor full life cycle adaptability in related technologies. By combining a neural network-Kalman filtering cascaded state estimation algorithm and a twin structure feature coding fault diagnosis algorithm and matching with an edge-cloud collaborative model self-evolution mechanism, accurate estimation of a battery state and timely diagnosis and positioning of a fault are realized, and the full-working-condition adaptability and operation reliability of a system are improved.

The invention discloses a greenhouse gas concentration time sequence prediction method based on a sudden change perception attention mechanism. The method comprises the steps of data preprocessing, sudden change intensity sequence construction with boundary processing, time sequence feature coding, sudden change perception attention weight calculation, context vector generation and concentration prediction, model training and optimization and model prediction. The method aims to solve the problem that a standard deep learning model is slow in sensing and lagged in prediction for a sudden change event in a concentration sequence, and finally realizes high-precision prediction for future concentration change, especially a sudden concentration peak value by endowing the model with the capability of actively identifying and reinforcing the learning of a historical sudden change mode. The urgent demand for early warning of abnormal emission in practical application is met. The method is particularly suitable for processing foundation observation data with small resolution and even higher resolution, has the core value of improving the prediction capability of concentration dramatic change driven by sudden emission events, and can be widely applied to key scenes such as accurate carbon emission monitoring, environmental pollution early warning and climate model simulation.

The invention discloses a multi-modal robot control method and system based on space-time fusion and a liquid neural network. The method comprises the following steps: collecting asynchronous time sequence data of a multi-source heterogeneous sensor, and establishing a unified reference clock and time grid; through a neural phase-locked loop and soft-DTW, learnable synchronization is realized, and an alignment sequence, confidence and a residual error are output; after modal feature coding, fusion features are formed through confidence-gated layered space-time cross attention; a nominal control instruction is generated by the liquid neural network of the self-adaptive time constant; and the command is optimized and corrected in combination with CLF / CBF and QP, and a safe and feasible control command is output. According to the method, uncertainty caused by asynchronization, shielding and the like is effectively inhibited, the path deviation and the collision rate are remarkably reduced, and high precision, high robustness and provable safety are kept in a dynamic unstructured environment.

The invention discloses an interaction method and system based on multi-modal data, and relates to the technical field of man-machine interaction. The method comprises the steps that initial interaction data of a user is acquired, preprocessing and feature coding are carried out on the initial interaction data, target interaction data are obtained, and the initial interaction data comprise a hand depth image, a voice signal, a through hole center coordinate and a head rotation matrix; determining a target weight of each piece of target interaction data by using a preset weight strategy, performing conflict resolution on each piece of target interaction data based on the target weight of each piece of target interaction data or the timestamp of each piece of target interaction data, and fusing each piece of target interaction data after conflict resolution to obtain a target interaction instruction; and executing the target interaction instruction, and displaying the virtual scene and the operation target after executing the target interaction instruction. Therefore, the problems that the interaction dimension of the current virtual reality technology is single, the multi-modal signal fusion degree is low, a cooperation mechanism is missing, and misjudgment is easily caused are solved.

The invention discloses a cross-coupling reaction site analysis system driven by a multi-modal neural network. The cross-coupling reaction site analysis system comprises a data input module, a multi-modal feature extraction module, a multi-modal neural network reasoning module, an analysis result output module and a model iterative optimization module. The system acquires molecular structure, electronic characteristics, reaction environment and historical experimental data, inputs the data into a neural network for fusion reasoning after multi-modal feature coding, and outputs reaction site recognition, activity quantification and side reaction early warning results. Through multi-modal data fusion and cross-modal feature reasoning, accurate recognition and comprehensive risk assessment of reaction sites are realized, the accuracy and reliability of complex molecule analysis are remarkably improved, the dependence on high-cost calculation and experimental trial and error is greatly reduced, and the method is suitable for large-scale popularization and application. And meanwhile, the universality and the self-adaptive optimization capability of the system in different coupling reaction systems are enhanced.