15106 results about "Convolutional neural network" patented technology

The present invention provides a facial recognition system using deep learning methodologies while integrating privacy-preserving capabilities. This system employs convolutional neural networks (CNNs) to extract and classify facial features, ensuring high accuracy in recognition tasks. Moreover, the system addresses privacy concerns by incorporating techniques such as facial feature encryption and anonymization, thereby enhancing user privacy and data security. This invention is applicable across various domains, including security, surveillance, access control, and personalized services, where facial recognition is utilized while preserving individual privacy.

The invention discloses an industrial robot real-time adaptive control method and system based on digital twinning, and relates to the technical field of industrial robots. The digital twin engine module runs a high-fidelity dynamics simulation model and an environment interaction model, performs real-time state estimation, abnormal working condition recognition and twin parameter dynamic updating, is seamlessly integrated with the control execution module, and provides decision support with high robustness and high adaptability for an industrial scene; the adaptive control module performs online rolling optimization on a control strategy based on a deep reinforcement learning algorithm, generates joint space trajectory correction, tail end precision compensation and dynamic load adaptability optimal instructions, and realizes parameter adaptive setting through fuzzy logic or a neural network; and the fault diagnosis module performs multi-scale time sequence analysis by using an LSTM and convolutional neural network fusion model, detects position offset, moment sudden change or temperature overrun and other abnormalities, and triggers emergency shutdown, sound-light alarm and an adaptive recovery strategy.

The invention relates to the field of fault early warning, in particular to a wind power fault dynamic early warning method and system based on multi-source heterogeneous data fusion. According to the method, multi-source data such as SCADA operation data, CMS vibration monitoring data and meteorological environment data of a wind turbine generator are collected in real time, standardization processing is carried out, and a multi-dimensional feature vector is constructed. And generating a fusion data set by using an adaptive weighted fusion algorithm, constructing a fault prediction model based on a deep convolutional neural network, and outputting a health state assessment value and a fault risk level in real time after historical fault sample supervised training. And when the risk level exceeds a threshold value, generating an early warning signal containing a fault type and a positioning and repairing suggestion, dynamically adjusting a monitoring parameter weight, iteratively updating a model, and realizing adaptive optimization of an early warning strategy. The problem that an existing method depends on single data source and multi-source data fusion is solved, and accurate dynamic early warning is achieved.

A method is provided for improving image classification accuracy in few-shot learning scenarios, where only a limited number of training examples are available. The method combines the use of Gabor filters and convolutional neural networks (CNNs) to extract detailed texture and orientation features from images. These features are then enhanced through global average pooling, aggregated into comprehensive feature vectors, and refined using an attention mechanism that identifies and emphasizes the most relevant features for classification. Masks generated from this attention process selectively enhance critical features, which, after optional re-encoding, are used to train a classifier via a metric learning approach. This method aims to increase feature separability and classification performance, facilitating more accurate classification of new images with minimal training data.

The invention relates to the technical field of fault prediction, and provides a fault prediction method for a multi-modal cross-attention enhancement graph neural network, and the method comprises the steps: collecting the data of equipment; performing adaptive enhancement and normalization processing on the image data, performing sliding window segmentation, standardization and noise suppression on a time sequence numerical signal, and performing semantic vectorization coding on a maintenance log text; extracting low-dimensional spatial features of image data by using the pruned lightweight convolutional neural network, connecting time sequence features of modeling time sequence numerical signals in series, extracting context semantic expressions of maintenance log texts, integrating the features into multi-modal data, alternately taking each modal feature as Query and the other modal features as Key and Value, and obtaining multi-modal data; calculating attention weight and performing weighted fusion; constructing a modal node weighted graph, and performing inter-node feature propagation through a multi-layer graph attention network; and a residual service life regression prediction module and a degradation level classification module are deployed in parallel, and fault early warning is completed through multi-task joint optimization.

The invention discloses a mechanical equipment state monitoring method and system based on multiple sensors, and the method comprises the five core steps: multi-modal data collection and preprocessing, dynamic feature fusion, adaptive threshold diagnosis, digital twin fault tracing and predictive maintenance decision. All-domain coverage of equipment is realized through a three-layer sensor network architecture, the problems of data synchronization and interference resistance are solved by utilizing a temperature and vibration integrated sensor, deep fusion and anomaly detection of multi-source data are realized in combination with an attention mechanism, a Gaussian mixture model, a three-dimensional convolutional neural network and the like, and finally a precise maintenance strategy is generated through digital twinning and reinforcement learning. The multi-sensor-based mechanical equipment state monitoring system comprises a sensor network layer, an edge computing layer, a cloud platform layer and a man-machine interaction layer, supports federated learning to protect data privacy, improves real-time diagnosis capability through edge-cloud collaboration, and enhances a reality interface to realize intelligent operation and maintenance interaction.

The invention relates to the technical field of intelligent detection in the electronic manufacturing industry, in particular to a PCB warping degree intelligent evaluation system integrating visual positioning and multi-modal sensing, which comprises a multi-modal sensing unit, a visual positioning unit, an intelligent evaluation engine and a closed-loop control unit, the multi-modal sensing unit integrates laser displacement, infrared thermal imaging and strain sensors to acquire three-dimensional deformation, temperature and stress data; the visual positioning unit realizes sub-pixel-level positioning by using a high-resolution industrial camera and a feature point matching algorithm, and compensates vibration errors; the intelligent evaluation engine fuses data based on a time-space synchronization protocol, predicts a thermal deformation trend through an improved multi-modal convolutional neural network, and dynamically adjusts a qualified threshold value; and the closed-loop control unit executes sorting and rechecking according to an evaluation result, and optimizes warping and leveling parameters. According to the system, multi-dimensional accurate detection and intelligent control are realized, the PCB warping degree detection accuracy is effectively improved, the process can be dynamically optimized according to the production working condition, and the equipment fault risk is reduced.

The invention provides a color steel plate coating flatness evaluation method and system based on artificial intelligence. According to the method, the three-dimensional point cloud data is generated by collecting the interference fringe image, and the surface fluctuation characteristics are quantified; capturing a multi-dimensional vibration spectrum of the transmission roller shaft, generating a servo motor compensation control signal, driving a multispectral scanning head to perform reverse displacement compensation, and generating real-time compensation data; inputting the surface topography features in the three-dimensional point cloud and the real-time compensation data into a lightweight convolutional neural network, and outputting fusion features; and dynamically classifying and identifying surface defects and uneven areas based on the fusion result, adjusting a classification threshold in combination with the speed of the production line, outputting a flatness evaluation result, and synchronizing the flatness evaluation result to a speed regulation system of the production line to realize closed-loop optimization. According to the method, laser interference, vibration compensation and lightweight AI technologies are fused, dynamic high-precision evaluation of the surface flatness of the color steel plate of the high-speed production line is achieved, and the problems of defect misjudgment and measurement distortion caused by vibration interference are solved.

The invention relates to the technical field of image recognition, in particular to an image classification system and method based on the image recognition technology, and the system comprises an image collection module which is used for obtaining original image data to be classified; the preprocessing module is used for carrying out denoising, normalization and size standardization processing on the image; the feature extraction module is used for extracting multi-level features of the image by adopting a deep convolutional neural network; the classification decision module is used for weighting fusion features based on an attention mechanism and outputting a classification result; the output module is used for displaying the classification labels and confidence scores; according to the method, the input quality is optimized by dynamically selecting a preprocessing strategy, the multi-scale representation capability is enhanced by adopting a parallel convolution path and a feature pyramid structure, the robustness of the system is improved by integrating an adversarial sample detection and defense mechanism, and the dynamic scheduling and mixing precision acceleration of computing resources are realized by introducing an edge computing optimization technology. And the operation efficiency is obviously improved on the premise of ensuring the classification precision.

The invention belongs to the technical field of bridge monitoring, and relates to a system and a method for fusing multi-source data of a bridge structure. Comprising a heterogeneous topological graph construction and manifold embedding technology module, a multi-scale space-time cognitive convolutional neural network module, a continuous manifold space-time alignment and Bayesian fusion module and a structure health index calculation and state evaluation module. The heterogeneous topological graph construction and manifold embedding technology module is used for obtaining a heterogeneous topological graph, a node embedding vector and a manifold model parameter; the multi-scale space-time cognitive convolutional neural network module is used for performing deep feature extraction on the heterogeneous topological graph to obtain multi-scale fusion features; the continuous manifold space-time alignment and Bayesian fusion module is used for obtaining space-time alignment parameters and fusion state vectors; the structure health index calculation and state evaluation module is used for carrying out structure health monitoring and state evaluation on the bridge to obtain a final health evaluation result; therefore, the intelligence, automation and reliability levels of the structure monitoring system are improved.

The invention relates to the technical field of hydraulic engineering safety monitoring, and particularly discloses a hydraulic engineering potential safety hazard assessment and prediction system and method based on image recognition. A multi-scale convolutional neural network is combined with a three-dimensional point cloud registration technology to extract surface visual feature parameters, and adaptive time-frequency analysis and a wavelet packet reconstruction algorithm are used to extract physical feature parameters of internal concealment defects; constructing a dual machine learning framework, eliminating environmental interference through a deep residual network, analyzing a causal relationship between features based on a gating cycle unit, and screening a key feature parameter set; a Gaussian process regression model of an adaptive kernel function is used for dynamic risk prediction, risk abrupt change points are identified in combination with multi-scale wavelet transform, and finally a safety state score and a grading early warning signal are generated through a fuzzy comprehensive evaluation algorithm.

The invention provides a road bridge crack detection method and system, and the method comprises the steps: collecting a bridge surface multi-view image, and constructing a training data set containing crack feature labeling through quality screening and standardized labeling; preprocessing the image by using a multi-scale feature fused deep convolutional neural network and carrying out semantic segmentation, initially identifying a suspected crack region and generating a segmentation mask; and constructing a BeNNS proxy model based on the mask, and establishing a mapping relationship between the detection result and the bridge structure topology, the stress flow field and the service function chain so as to evaluate the result reliability. And inputting an evaluation result into a hybrid evaluation mechanism, performing online real-time detection and offline batch verification to optimize precision, and outputting a verified crack region. Finally, morphological analysis is conducted on the area, geometric parameters and danger levels of cracks are extracted and integrated to a bridge health monitoring system, a crack evolution tracking algorithm and an early warning mechanism are established, and dynamic tracking early warning is achieved. The problem of low detection precision in a complex environment can be solved.

The invention provides an intelligent station multi-source heterogeneous data acquisition and remote collaborative analysis method and system, and relates to the technical field of heterogeneous data, and the method comprises the steps: collecting multi-source heterogeneous data, carrying out the standardization processing, constructing a station real-time adaptive three-dimensional model through a self-calibration laser scanning technology, and carrying out the semantic mapping of the model and a BIM model; constructing a self-evolution feature library based on a digital twinborn model, and carrying out space-time correlation analysis by adopting a graph convolutional neural network; a remote cooperation platform is established to realize real-time cooperation of multiple experts, and a diagnosis process is recorded through a block chain technology. According to the invention, efficient acquisition, analysis and remote collaborative diagnosis of the data of the smart station are realized, and the accuracy and efficiency of fault diagnosis are improved.

The invention discloses a geological disaster early warning method and a precise early warning system based on multi-source data fusion, and relates to the technical field of geological disaster early warning. According to the method, multi-source heterogeneous data such as remote sensing, meteorological and geological monitoring are fused, a standardized protocol is utilized to unify a data format and temporal-spatial resolution, a standardized data set is formed, key features are extracted by adopting principal component analysis and a recursive feature elimination algorithm, and a long-short-term memory network and a convolutional neural network model are combined, so that the real-time performance of the system is improved. According to the method, the disaster risk is accurately predicted, the space risk distribution diagram is generated, in addition, through application of the real-time stream processing framework and the self-adaptive learning algorithm, rapid distribution of early warning signals and dynamic optimization of model parameters are achieved, the accuracy and timeliness of an early warning system are remarkably improved, and the geological disaster risk is effectively reduced.

The invention provides a crop monitoring system and method based on multispectral remote sensing and deep learning, and the system comprises a data preprocessing module which is used for carrying out the data preprocessing of a multispectral remote sensing image, and generating a standard reflectivity data set; the feature extraction module is used for extracting a high-dimensional spectral feature vector from the standard reflectivity data set through a multi-scale convolutional neural network; the time sequence dynamic analysis module is used for performing time sequence correlation analysis on the high-dimensional spectral feature vector through a long short-term memory network to generate a weighted time sequence feature vector; the physiological parameter quantification module is used for mapping the weighted time sequence feature vectors into quantitative indexes of crop physiological parameters; and the monitoring result generation module is used for performing dynamic deduction according to the quantitative index and generating dynamic trend prediction data of the crop growth state. The system can dynamically sense the growth stage characteristics of crops and adaptively adjust the spectral feature extraction strategy, thereby improving the crop monitoring precision in a complex agricultural environment.

The invention relates to a multimedia equipment control method and system based on adaptive protocol matching. The method comprises the following steps: firstly, collecting communication protocol data and extracting features to form a protocol feature data set; secondly, performing confidence coefficient verification on a data set type identification result, and if the confidence coefficient is lower than a threshold value, extracting a depth feature through a convolutional neural network; and calling a national standard protocol adaptation conversion engine based on a classification result, converting the heterogeneous protocol into data in a unified format, and generating a secure communication data frame through national secret algorithm encryption and bidirectional identity verification. And finally, performing hash check and structured analysis on the data frame, constructing an interaction information packet in combination with user behavior characteristics, and dynamically optimizing a control strategy through real-time feedback data to generate a self-adaptive interaction control scheme. According to the method, through a technical closed loop of intelligent analysis-standard conversion-security reinforcement-closed loop optimization, the high efficiency, the intelligent level and the security performance of multimedia equipment management are remarkably improved.

The invention discloses a data acquisition method and system and a storage medium, and relates to the technical field of data acquisition and processing.According to the technical scheme, multiple sensor devices and data interfaces are integrated, multi-source data are acquired in real time, format standardization processing, time synchronization correction and spatial information alignment are carried out through a multi-mode fusion module, and the data acquisition efficiency is improved. Calculating to obtain a data consistency factor Tyhz and evaluating the data consistency factor Tyhz; when the data consistency factor Tyhz does not reach the standard, a data optimization module performs noise filtering and abnormal value elimination on a multi-source data set, and an intelligent analysis module calculates a risk prediction parameter Fcyz by using a convolutional neural network; the early warning evaluation module compares the Fcyz with a risk evaluation threshold Fth, calculates a risk early warning index Gyzs, compares the risk early warning index Gyzs with a risk early warning threshold E, and dynamically generates an early warning execution scheme, so that information pushing and emergency resource scheduling are realized, the problems of low multi-source data fusion efficiency and insufficient early warning precision are solved, and the safety of the system is improved. And the risk identification and emergency response capabilities of the urban emergency management system are effectively improved.

The invention discloses a deep learning-combined power transformer partial discharge signal extraction and diagnosis method. The method comprises the following steps of S1, setting a multi-channel synchronous acquisition system in a power transformer body area to acquire a multi-dimensional original partial discharge data set; s2, preprocessing the acquired multi-dimensional original partial discharge data set; s3, performing time alignment and amplitude matching on the processed signal, and dividing the processed signal into a sliding time window to construct a standard input tensor; s4, constructing an attention enhancement model fused by the convolutional neural network and the bidirectional gating circulation unit; s5, performing supervised training on the attention enhancement model by using the labeled sample; s6, inputting the real-time signal into the training model, and outputting a discharge type label; s7, risk grade evaluation is carried out in combination with statistical characteristics; and S8, generating a structured diagnosis report and uploading the structured diagnosis report to a monitoring platform. According to the invention, multi-source signals and a depth model are fused, and intelligent diagnosis and risk assessment of transformer partial discharge are realized.

The invention discloses a method for realizing data communication by a multi-band adaptive antenna based on 5G communication. The method comprises the steps of dynamic sensing and multi-mode signal fusion, multi-band intelligent analysis, hybrid beam forming and dynamic reconstruction, cross-layer parameter joint tuning, real-time monitoring and self-learning compensation, multi-band adaptive switching and fault diagnosis and error calculation. The problems that in traditional 5G communication, the antenna frequency band is fixed, the beam adjusting capacity is limited, the anti-interference performance is poor, and the communication performance is unstable in a complex environment are solved. According to the method, the channel state is accurately evaluated and predicted through fusion of the extended Kalman filtering algorithm and the convolutional neural network, and frequency band resources are intelligently allocated and optimized through the deep Q network DQN and the non-orthogonal multiple access NOMA principle, so that omnibearing optimization and management of the antenna are realized, the communication efficiency and stability are improved, and the method is suitable for large-scale popularization and application. And the efficient and stable operation of the communication system in various complex environments is ensured.

The invention relates to the technical field of intelligent water conservancy, and discloses a cross-regional water transfer project intelligent scheduling method and system, and the method comprises the steps: building a digital twin system based on a geographic information system, hydrological monitoring data and a spatial topological structure, and integrating a meteorological evolution prediction model, a basin hydrological response model and a water demand prediction model; predicting a water demand and an adjustable water amount by using a space-time convolutional neural network and a gating circulation unit; establishing a multi-objective optimization model taking water supply benefit, ecological influence and energy consumption cost as optimization objectives; an optimal water transfer scheme is generated through a Markov decision process and multi-agent cooperation; and carrying out robustness evaluation on the scheme and generating an emergency scheduling plan. According to the method, the scheduling efficiency and adaptability of the water transfer project are remarkably improved, and efficient configuration of water resources and quick response under extreme situations are achieved.

The invention discloses an artificial board surface defect intelligent detection method and system based on machine vision, and particularly relates to the technical field of artificial board surface defect detection. An artificial board surface image is collected, image preprocessing, feature extraction, defect segmentation and classification recognition are carried out through a deep learning algorithm, a multi-scale convolutional neural network is adopted to carry out feature extraction on the image, a shallow convolutional layer captures small defect details, a deep convolutional layer recognizes global features of large defects, and a multi-scale convolutional neural network is adopted to carry out feature extraction on the image. Accurate defect segmentation is carried out through a Mask R-CNN model, a redundant frame is removed in combination with a non-maximum suppression algorithm, the classification problem of adjacent defects is corrected by using an error correction algorithm in combination with the spatial relationship and morphological characteristics of the defects, and defect information is fed back to a production line control system in real time; and defective products are automatically removed or production process parameters are automatically adjusted, so that the automation level of a production line is effectively improved, the product quality is optimized, and human intervention and production cost are reduced.

The invention relates to the technical field of online teaching management, and discloses an online teaching optimization method and system based on emotion recognition, and the method comprises the steps: collecting the real-time emotion data of a learner through a multi-modal sensor, carrying out the fusion of a graph convolutional neural network to generate a feature vector, and optimizing a teaching strategy parameter through a meta-reinforcement learning model, the dynamic course generation model combines an improved genetic algorithm and a knowledge graph to optimize a teaching content sequence, and the hierarchical teaching control model realizes knowledge path planning, interactive adjustment and learning state evaluation, and generates a teaching control instruction. According to the method, personalized online teaching optimization is realized, the state of a learner can be accurately grasped, the teaching interaction mode is optimized, the course content is dynamically adjusted, the learning effect is comprehensively and accurately evaluated, the problems that traditional online teaching lacks personalization, the state of the learner is difficult to grasp and the like are effectively solved, the online teaching quality and the learning experience are improved, and the learning experience is improved. And the online education development is promoted.

The invention provides an engineering construction defect automatic detection and classification method based on deep learning, and the method comprises the steps: obtaining a welding seam surface image through the shooting of an unmanned plane, and carrying out the denoising and illumination normalization processing of the welding seam surface image, and obtaining a standardized image; welding seam surface texture features are extracted from the standardized image, a convolutional neural network is adopted to analyze the spatial distribution characteristics of textures, and vectorization processing is carried out to obtain texture feature vectors; segmenting a weld surface corresponding to abnormal region distribution by adopting a region growing algorithm, and analyzing pore and weld discontinuity in combination with the texture feature vector to obtain a defect candidate region; performing threshold division on the sizes and the numbers of the defects according to the defect types and the feature vectors of the candidate regions to obtain a severity grading result of each type of defects; and severity features are extracted from a grading result, and a Bayesian network is adopted to fuse texture feature vectors and defect type labels to obtain a welding quality evaluation score.

The invention relates to the field of wind power plants, in particular to a wind power plant edge calculation data cleaning and real-time transmission optimization method and system. The method comprises the following steps: forming a multi-source heterogeneous data stream by collecting SCADA operation data, CMS vibration monitoring data and meteorological environment data of a wind turbine generator in real time; performing standardization processing on the data at an edge computing node, constructing a multi-dimensional feature vector, and generating a fusion data set by adopting a self-adaptive fusion algorithm based on a dynamic weight; and constructing a fault prediction model based on the deep dual-channel convolutional neural network, and outputting a health state evaluation value and a fault risk level in real time. And when the risk level exceeds a threshold value, generating an early warning signal. The system also performs data compression transmission optimization according to the real-time bandwidth state, and dynamically adjusts the weight of the monitoring parameter to realize the closed-loop optimization of the preventive maintenance strategy.

The invention discloses a tunnel blasting quality evaluation and optimization method based on multi-source data fusion, and belongs to the field of tunnel blasting quality evaluation, and the method comprises the steps: collecting and preprocessing multi-source data of a tunnel blasting region; based on the preprocessed multi-source data, performing blasting quality evaluation according to local back break, a blasting contour line, average linear back break and point cloud extraction to obtain a blasting quality evaluation result; according to the blasting quality evaluation result, the blasting quality is graded, and a comprehensive blasting quality score is calculated and graded; and establishing a database containing geological parameters, surrounding rock response parameters and blasting process parameters, training through a convolutional neural network model to generate a blasting parameter optimization scheme, and dynamically adjusting blasting parameters of the next cycle according to the comprehensive blasting quality score. According to the method, the blasting parameter optimization and the quality evaluation process are closely combined to form a closed-loop system, the specific situation in the construction can be reflected in real time, and the accuracy of the blasting effect is ensured.

The invention relates to the technical field of machine tool casting detection, and discloses an automatic detection system for surface defects of machine tool precision castings. The system comprises a surface information acquisition core module, a first defect identification core module, a second defect identification core module and a defect type fusion core module. The surface information acquisition module is used for synchronously acquiring real-time optical images and process parameter data in production aiming at the surface of the casting part, and constructing a defect diagnosis characteristic spectrum and an auxiliary text according to the real-time optical images and the process parameter data; the first defect recognition module inputs the atlas and the auxiliary text into a pre-training double-flow convolutional neural network to generate a first classification result of defect types; a second defect identification module extracts defect mechanism characteristic values from the atlas and matches the defect mechanism characteristic values with a pre-stored defect mechanism knowledge base to obtain a second classification result; and the defect type fusion module fuses the two types of results to determine a target defect type. The system solves the problems of single detection information and identification deviation in the prior art, improves the detection accuracy and real-time performance, and meets the requirements of different production scenes.

The invention belongs to the technical field of concrete crack detection, and particularly discloses and provides a high-strength concrete construction crack detection system and method.The system comprises a multi-parameter cooperative sensing module, a construction crack recognition module, a crack risk analysis module and a crack early warning processing module. According to the invention, the temperature, humidity, strain and multispectral image data from a plasticity stage to a hardening stage are collected in real time through the multi-parameter cooperative sensing module, and crack characteristics are extracted by combining staged differential signal processing and a deep convolutional neural network, so that the detection precision of micro cracks and internal defects is remarkably improved. Meanwhile, multi-physics field data are fused, a crack evolution risk rate model is innovatively constructed, dynamic probability prediction before crack initiation is achieved, a grading disposal scheme is automatically triggered through risk grade division and a progressive early warning mechanism, drying shrinkage and temperature crack causes are effectively distinguished, the prevention and control efficiency is remarkably improved, and the method is suitable for large-scale popularization and application. And full-life-cycle accurate monitoring of the high-strength concrete is supported.

The invention relates to the technical field of remote sensing monitoring, in particular to a remote sensing target detection method and system for a low-visibility image. The method comprises the following steps: acquiring multi-modal remote sensing image data; carrying out defogging enhancement processing on the low-visibility input image; normalizing the defogged RGB image and the defogged IR image, and then splicing and fusing the RGB image and the IR image; carrying out layer-by-layer coding on the multi-modal fusion image by adopting a mixed trunk structure fusing Transform, Mamba and CNN (Convolutional Neural Network); performing frequency domain decomposition on the trunk output features based on two-dimensional wavelet transform; generating an HR feature map by adaptively selecting a key region; and carrying out cross-scale aggregation on the HR feature map to obtain a detection target frame. Through the multi-modal image defogging enhancement and feature distillation mechanism, the definition and contrast of the remote sensing image in severe weather such as haze and rainy days are effectively enhanced, the shielding interference of environmental degradation on small target detection is weakened, and the stability and adaptability of the model in complex weather scenes are enhanced.

The invention relates to the technical field of water pollution traceability, and particularly discloses a river pollutant traceability system and method based on digital twinning, and the system comprises a water body sampling module which is used for obtaining the current water quality data of a preset region of a target river; the model construction module is used for constructing a digital twinborn model according to the river topographic data and the current water quality data and by fusing the historical hydrological data, the real-time meteorological data and the drain outlet distribution topological graph; the data analysis module is used for performing spatial-temporal feature extraction on a pollutant diffusion path in the digital twinborn model by utilizing a graph convolutional neural network, applying dynamic correction pollution source position probability distribution in combination with Bayesian inference, and generating a traceability path thermodynamic diagram; and the pollution traceability module is used for obtaining a pollutant traceability result according to the confidence coefficient threshold of the traceability path thermodynamic diagram. According to the method, the efficiency and the accuracy of tracing the river pollutants in the complex dynamic environment can be remarkably improved, and technical support is provided for ecological safety and precise treatment of a drainage basin.

The invention relates to the technical field of biomedical engineering, in particular to a multi-mode brain anomaly detection method and system based on machine learning. The method comprises the following steps: acquiring brain medical image data of different modalities, and realizing spatial registration and alignment through a multi-modal registration algorithm based on mutual information; a multi-branch feature extraction model including a convolutional neural network, a converter and a state space model is utilized to perform feature embedding on the original image of each modal; performing frequency decoupling on the features of each mode through adaptive approximate wavelet transform, and decomposing the features into high-frequency detail information and low-frequency global information; a frequency band fusion strategy based on an attention mechanism is implemented on high and low frequency features of different modal images, and fused frequency sub-band features are input into a space-frequency Mama module. Through the adaptive frequency domain decomposition and cross-modal fusion mechanism, the multi-modal brain image information is effectively integrated, and the accuracy and robustness of brain anomaly detection are remarkably improved.