1122 results about "Recurrent neural network" patented technology

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

An AI-driven financial planning system for real-time market adjustment, consisting of: a neural inference coprocessor configured to execute deep financial forecasting models, including recurrent neural networks and attention-based encoders, on the device, and wherein the processor dynamically updates portfolio parameters in response to market signals exhibiting volatility differences above a statistical threshold calculated using an exponentially weighted moving standard deviation; a financial data acquisition module configured to continuously receive and analyze heterogeneous data streams, including market indices, interest rates, stock and bond price fluctuations, economic indicators, regulatory updates, and financial news sentiment feeds; a behavioral analytics engine configured to create a dynamically evolving user-specific financial behavior profile based on real-time analysis of transaction history, income-expenditure cycles, psychometric test results, and temporal lifestyle patterns using supervised and unsupervised machine learning algorithms; A goal optimization module configured to transform high-level, user-defined financial goals into quantitatively tracked multi-level goals. It uses a reinforcement learning framework that predicts optimal asset allocations across multiple time horizons. a real-time strategy simulation engine configured to perform Monte Carlo simulations and deep Q-learning-based assessments to simulate the resilience of proposed financial strategies under different macroeconomic regimes and trigger redistribution events based on predefined confidence thresholds; a compliance-aware execution interface configured to interact with financial institutions through encrypted API channels, ensuring policy enforcement using a smart contract validator and a hardware-enabled secure transaction signing unit; and a recommendation display unit configured to render dynamic dashboards for visualizing investments, reallocation warnings, confidence intervals, and sensitivity sliders, and where user interaction with the unit flows back into the behavioral model for real-time learning.

A system for detecting service interruptions in a cable broadband network using telemetry-based anomaly detection, wherein the system comprises the following: a telemetry acquisition unit configured to acquire multi-parameter telemetry data from heterogeneous broadband infrastructure elements, including cable modems, amplifiers, optical nodes and cable modem termination systems (CMTS), wherein the telemetry data includes the signal-to-noise ratio, modulation error ratio, forward error correction counter, power levels and latency statistics; a preprocessing and harmonization module that is operationally coupled with the telemetry acquisition unit, wherein the module is configured to normalize heterogeneous telemetry streams by adjusting sampling rates, synchronizing timestamps, interpolating missing data, and filtering out false outliers; an anomaly detection unit that is communicatively connected to the preprocessing and harmonization module, wherein the unit comprises a hybrid detection framework with statistical prediction models and machine learning models, wherein the statistical prediction models include ARIMA or Holt-Winters models to predict the expected telemetry behavior and the machine learning models include recurrent neural networks and autoencoders trained on historical telemetry; an ensemble evaluation subsystem within the anomaly detection unit, configured to combine the outputs of the statistical prediction models and the machine learning models to generate anomaly probability evaluations with adaptive confidence intervals; an interruption classification module configured to receive anomaly probability values ​​and correlate anomalies across multiple devices, geographic clusters, and time windows, wherein the interruption classification module differentiates between transient anomalies and service-impairing interruptions based on a multidimensional correlation; and an alerting interface configured to transmit outage alerts with severity, root cause metadata, and geolocation to a network management system so that the operator can intervene.

The invention relates to the technical field of foundation pit detection, and discloses a deep foundation pit deformation prediction method and system based on neural network and rough set classification, and the method comprises the following steps: data collection and preprocessing; carrying out attribute reduction based on a rough set theory; constructing and training a model based on an improved attention mechanism recurrent neural network (A-RNN); carrying out credibility verification and dynamic feedback adjustment; and outputting a result and an early warning response. By integrating multi-parameter monitoring data of displacement, soil pressure, underground water level, support stress and the like, driving factors of deformation of the deep foundation pit are comprehensively captured, misjudgment caused by data deviation of a single sensor is reduced, the combination of bidirectional LSTM and an attention mechanism effectively captures local features of long-term dependence and key time points in time sequence data, and the accuracy of deep foundation pit deformation detection is improved. And the accuracy of deformation prediction is obviously improved.

A system for real-time analysis of emotional feedback during motivational speeches, consisting of: a series of multimodal sensors, including at least one visual sensor configured to capture facial expressions of spectators, at least one directional microphone configured to capture the audio responses of the audience, and optionally one or more physiological sensors configured to capture biometric signals from spectators; an edge-based processing unit that is communicatively coupled to the arrangement of multimodal sensors, wherein the edge-based processing unit comprises the following: (a) a feature extraction module configured to extract visual features from captured facial images, acoustic features from voice responses, and physiological features from biometric signals; (b) an emotion inference machine configured to process the features using a deep learning-based emotion recognition model comprising a convolutional neural network (CNN) for classifying facial expressions, a recurrent neural network (RNN) for classifying voice emotions, and a multimodal late fusion layer configured to compute a composite emotion state vector representing the aggregated emotions of the audience; (c) a timestamp and speech alignment module configured to correlate the calculated composite emotion state vector with segmented portions of a live motivational speech based on real-time speech-to-text transcription and semantic analysis; and (d) a session-based storage unit configured to log time-indexed emotional state vectors and corresponding speech segments for post-event analysis; A speaker feedback interface comprising a portable display or a podium-mounted visualization panel, wherein the interface is configured to display visual indicators of emotional feedback in real time, the indicators being derived from the emotional state vector and including at least emotional trend graphs, threshold alerts, or engagement indices.

A system, a method and a processor are for AI-powered generation and delivery of video clips. The processor is configured to: load a first video file of a first video content item, the first video file comprising video frames associated with timestamps; load a first subtitle file of the first video content item, the first subtitle file comprising subtitle text associated with the timestamps; execute a natural language processing (NLP) model with the subtitle text as input, the NLP model including language pre-processing steps for classifying words, names or phrases in the subtitle text and associating initial classifiers with the subtitle text, the NLP model including one or more of a recurrent neural network (RNN), a Bidirectional Encoder Representations from Transformers (BERT) model, or a generative pre-trained transformer (GPT) model for a dialogue analysis comprising processing sequences of dialogue in the subtitle text in view of the initial classifiers to associate one or more portions of the dialogue with one or more first classifiers of first narrative elements; execute an image recognition model with at least some of the video frames as input, the image recognition model including a convolutional neural network (CNN) for an object detection analysis and a facial recognition analysis comprising processing video sequences to associate one or more of the video frames with one or more second classifiers of second narrative elements; generate a narrative map of the first video content item by temporally aligning the first narrative elements with the second narrative elements based on the timestamps associated with the video frames and the first subtitle file; and generate a video clip including at least one segment of the first video content item, the at least one segment including selected video frames associated with at least one of the first or second narrative elements identified from the narrative map and selected for inclusion in the video clip.

The invention discloses an intelligent charging and discharging management method and system for a lithium battery, and the method comprises the steps: collecting multi-dimensional parameter data of the lithium battery, and generating a data set with a timestamp; and analyzing the data features based on the battery type classification model, and determining a battery type identifier. And utilizing the recurrent neural network to model the relevance between the capacity attenuation and the health state, and predicting the residual capacity and the health score. If the capacity or health score is lower than the threshold value, extracting the environment temperature and the load demand to generate a temperature-load feature vector; and matching the candidate strategy set from the pre-established index database through the hash table. And distributing weights according to health scores and load demands, sorting strategy efficiency and life influences by adopting a linear regression model, and screening an optimal strategy. And if the strategy calculation complexity exceeds the equipment capability, iteratively optimizing parameters by utilizing a genetic algorithm, simplifying a strategy instruction, generating a charging current, a voltage curve and a discharging rate control instruction, and executing the charging current, the voltage curve and the discharging rate control instruction in real time by a battery management system. The method prolongs the service life of the battery and improves the charge-discharge efficiency.

The invention relates to the technical field of typhoon prediction, and discloses a typhoon rapid enhancement prediction method based on time-space sequence and multi-modal feature fusion, and the method comprises the steps: constructing a multi-modal time-space sequence data set and an auxiliary data set based on typhoon optimal path data and multi-source satellite observation data; a unified manifold approximation and projection method is adopted to carry out dimension reduction preprocessing on the high-dimensional multi-modal space-time sequence data, and one-dimensional time sequence embedding representation of the typhoon observation sequence is generated; taking the one-dimensional time sequence embedded representation and the auxiliary data as independent input channels, and inputting a trained typhoon observation network model to predict a typhoon rapid enhancement probability; wherein the typhoon observation network model is a multi-mode time-space fusion deep learning architecture, the core of the typhoon observation network model is composed of a variational attention recurrent neural network, and hyper-parameter optimization is carried out through an improved Harris eagle optimization algorithm. According to the invention, accurate and robust identification of the typhoon rapid enhancement process is realized.

The invention belongs to the technical field of all-dielectric metamaterial optical devices and machine learning, and discloses an all-dielectric metasurface target spectral response reverse design method based on deep learning. And realizing efficient prediction of the transmission spectrum by using a convolutional neural network-recurrent neural network-residual network architecture. A fitness function is designed, and a machinable structure is generated by aiming at single-peak and multi-peak target wavelength optimization and combining a linear and shape optimization strategy. The method breaks through the limitation of spectrum dependence and fixed structure type of the traditional reverse design, realizes on-demand design, and remarkably improves the design efficiency and processing compatibility of the integrated photonic device.

The invention provides a medicinal and edible product screening method and system based on artificial intelligence, and the method comprises the steps: building a user physique and demand portrait through collecting user health condition data and food preference data; acquiring medicinal and edible raw material information and nutriology data based on the portrait, and constructing a medicinal and edible knowledge graph; analyzing market product information based on the knowledge graph, and generating a market insight report; generating an initial product formula scheme by using a large language model and a binary activation recurrent neural network; and outputting a mature product formula through trial production, user feedback and iterative optimization. According to the method, the problems that traditional medicinal and edible product development depends on expert experience and is lack of personalized and scientific basis are solved, intelligentization and data driving of the whole process of product development are realized, and the success rate and market adaptability of product development are improved.

The invention relates to the technical field of industrial equipment fault diagnosis, in particular to a gas turbine fault diagnosis method, system and equipment based on deep learning multi-mode fusion and a medium. The method comprises the following steps: acquiring temperature, vibration, pressure and flow data during operation of a gas turbine, performing time alignment on the acquired multi-source data, and dividing according to windows to generate a structured data set; extracting spatial features of the vibration data by using a convolutional neural network, extracting sequence dependence features of the time sequence data by using a recurrent neural network, extracting nonlinear features of the structured data by using a deep neural network, and respectively obtaining feature vectors; performing weighted splicing fusion on the plurality of feature vectors to generate a multi-source fusion feature vector; using a particle swarm optimization algorithm to optimize a neural network weight parameter; and performing fault diagnosis on the multi-source fusion feature vector based on the trained neural network, and outputting a fault category. The method solves the problems that a traditional single data source diagnosis method is incomplete in information and insufficient in feature extraction.

The invention discloses a cable optical fiber temperature measurement monitoring system based on big data AI fusion, particularly relates to the technical field of cable optical fiber temperature measurement monitoring, and aims to realize thermal-electric dynamic coupling monitoring through high-density distributed optical fiber temperature measurement and big data AI fusion modeling and introduction of current load synchronous acquisition, and realize high-density distributed optical fiber temperature measurement and big data AI fusion by means of time synchronization and space relocation processing. The method comprises the following steps: identifying a weak response signal of a thermal inertia hysteresis characteristic and an initial temperature rise stage, constructing a cross-scale time sequence recurrent neural network through multi-dimensional enhancement and vector fusion, outputting a continuous scoring sequence reflecting abnormal credibility, and constructing a risk factor group reflecting a fault evolution path in combination with a scoring fluctuation amplitude, a continuous period and a response delay characteristic, and carrying out positioning offset correction through correlation mapping of risk factors and along-cable temperature distribution and by utilizing a topological relation between optical fiber path coordinates and a cable space, and realizing space reverse projection of an abnormal peak value, thereby recovering an accurate position of a fault heat source and a latent hidden danger section.

The invention relates to a cross-attention mechanism optimization method and a cross-attention mechanism optimization system for multi-modal feature fine-grained alignment. The method comprises the following steps: constructing a space-time topological graph of a dynamic scene, and representing a space-time relationship among intelligent agents through nodes and various connecting edges; coding the heterogeneous spatio-temporal information by using a plurality of groups of parallel recurrent neural networks, and converting the heterogeneous spatio-temporal information into uniform dimension feature representation; establishing a multi-modal cross attention alignment component, and quantifying feature quality from multiple dimensions through a multi-criterion evaluation unit; an adaptive weight fusion system is adopted to dynamically integrate evaluation results, and a unified quality score is generated; constructing a progressive optimization architecture based on the quality score, and performing multi-task cooperative training on a double-layer attention mechanism by combining real-time sampling and a directional optimization strategy; and finally generating a movement decision. According to the method, fine-grained alignment and optimization of multi-modal features are realized, and the accuracy and adaptability of intelligent agent navigation and trajectory prediction in a complex dynamic environment are remarkably improved.

The invention belongs to the crossing field of artificial intelligence and process control, particularly relates to an intelligent coupling control strategy for chlorine production through seawater electrolysis based on digital twinning, and aims to solve the problems of response lag, weak disturbance suppression and lack of electrode aging early warning in a traditional control method. According to the strategy, a high-fidelity digital twin fusing electrochemistry, fluid and thermodynamics is constructed, characteristics of current, temperature, impedance spectroscopy and the like are collected and fused at high frequency through a multi-source sensor, a state space model driven by a deep recurrent neural network is established, and current efficiency and electrode aging coupling evolution recognition is achieved; when salinization or flow mutation is detected, the twinborn body predicts and dynamically calibrates the current density and the voltage set value in advance, and the safety process window is synchronously scaled. According to the strategy, the chlorine gas yield fluctuation is reduced by 60%, the energy consumption standard deviation is reduced by 45%, the fault early warning is advanced by 2-5 hours, the fault-free operation time of the system is prolonged by 50%, and high-precision, self-adaptive and preventive intelligent control of the electrolysis process is realized.

A system for real-time creditworthiness assessment of small and medium-sized enterprises (SMEs) based on agent-based artificial intelligence (AI), the system includes: a large number of autonomous agents connected to each other via secure communication protocols; a data ingestion processing unit configured to capture and preprocess heterogeneous data streams from structured and unstructured sources, including APIs for financial transactions, tax databases, e-invoicing registers, inventory management systems, social sentiment feeds, and IoT-enabled devices, with preprocessing including schema harmonization, anomaly detection, and encryption-based integrity preservation; an ensemble of scoring control units configured to generate dynamic credit scores, wherein the ensemble includes machine learning models, including gradient-enhanced decision trees, recurrent neural networks, and graph neural networks, the graph neural networks being configured to assess relational dependencies between SMEs, suppliers, and customer nodes in a business ecosystem; an audit controller configured to generate regulatory-compliant justification paths for each credit score; a feedback processing unit configured to update the evaluation control unit ensemble using reinforcement learning signals derived from observed repayment behavior, payment default trends, and fraud detection markers; and a user interface configured to display timestamped credit histories, confidence intervals and explanatory justifications, with the entire system running on a containerized cloud-native infrastructure with blockchain-based ledgers for version control of each point update.

The invention discloses a settlement amount time sequence prediction method and system for deep foundation pit excavation adjacent buildings. The method comprises the steps that original monitoring data of on-site building settlement are acquired; performing data preprocessing on the obtained original monitoring data; constructing a recurrent neural network; model input and output parameters are determined through principal component analysis; optimizing the recurrent neural network based on an optimizer; performing hyper-parameter optimization based on an optimization result; performing settlement time sequence prediction by using the optimized recurrent neural network; the system comprises a data acquisition module, a preprocessing module, a model construction module, an analysis module, an optimization module, a parameter optimization module and a prediction module. By constructing the settlement prediction model, dynamic modeling and accurate prediction of the settlement trend of the building in the excavation process of each stage of the foundation pit are realized; historical settlement monitoring data and multi-layer soil body excavation information are combined, and multi-source input parameters are introduced, so that the adaptability of the model to complex working conditions is enhanced.

The invention discloses a microseismic source positioning method, device and system, and a storage medium. The method comprises the following steps: dividing a microseismic data sample data set into a training set and a test set; according to the training set, constructing a microseism inversion subnet containing a Swin Transform encoder, and according to the training set, constructing a microseism inversion subnet containing a Swin Transform encoder; the micro-seismic forward modeling subnet realizes seismic wave field continuation by inputting a micro-seismic source position and a speed model and utilizing a recurrent neural network structure and a convolution operator, and establishes a forward modeling subnet based on a wave equation; constructing an inversion-forward modeling closed-loop neural network according to the micro-seismic inversion subnet and the forward modeling subnet based on the wave equation; and inputting the test set into the inversion-forward closed-loop neural network to carry out micro-seismic source positioning. By adopting the technical scheme of the invention, the limitations on physical constraint, feature modeling and anti-noise capability in the prior art are overcome.

The invention provides a coal mine roof dynamic monitoring and early warning method and system based on artificial intelligence, and relates to the field of coal mine safety, and the method comprises the steps: collecting a stress waveform and a displacement waveform of a coal mine roof, extracting stress loosening characteristics, and constructing a three-dimensional stress conduction strength field; identifying a stress sudden change transition point by using a recurrent neural network to form a stress conduction chain; analyzing a regional topology migration rule and extracting a stress field instability path; matching with historical data to determine a prediction window; and continuously verifying the instability path and generating a support reinforcement scheme. The coal mine roof instability risk can be predicted in advance, and the early warning accuracy and timeliness are improved.

The invention discloses a model-based adaptive optical dynamic modeling and control method and device, and belongs to the cross field of adaptive optics and model-based reinforcement learning, and the method comprises the steps: defining the state of an AO system as a combination of multi-mode observation, and carrying out the corresponding coding; fusing the multi-modal observation data through a cross attention mechanism to form a unified observation potential state rich in dynamic information; a deep dynamic world model based on the recurrent neural network is constructed, the model maintains a hidden state and deduces a discrete potential state, and the hidden state and the discrete potential state jointly form complete potential state representation of the world model; rebuilding original observation and predictive rewards according to the complete potential state representation of the world model; and training a reinforcement learning agent in the learned world model, learning an optimal strategy through an imaginary trajectory, and outputting key parameters of the AO system in real time. According to the method, the adaptability, the control precision and the robustness of the AO system in a complex dynamic environment are improved.

The invention relates to the technical field of intelligent gateways, and discloses a multi-mode industrial Internet of Things intelligent gateway based on edge computing and an implementation method thereof, and the method comprises the steps: obtaining sensor data streams of a door magnetic sensor, a human body sensor, a temperature and humidity sensor and a smoke detector in an intelligent region; constructing a space-time fusion data matrix based on the sensor data stream; inputting the space-time fusion data matrix into an edge layer quantization neural network, a fog layer recurrent neural network and a cloud layer large model for distributed reasoning to obtain a reasoning result set; and performing priority queue scheduling and zero-copy transmission on emergency events, important events and conventional events in combination with the reasoning result set to generate a control instruction sequence, so that parallel operation of data acquisition and processing is realized, and the intelligent level, the response performance and the operation reliability of an intelligent regional Internet of Things system are improved.

The invention relates to the technical field of landslide monitoring, in particular to a landslide monitoring data prediction method fusing time-lag reconstruction and an attention mechanism, which takes landslide displacement historical monitoring data and external induction factors as modeling objects, and aims at nonlinear and time-lag characteristics of a landslide deformation process. Constructing a time-delay driving feature set through time-delay cross correlation analysis, and identifying a guiding-following causal relationship between landslide displacement and an induction factor; and a multi-source time-lag feature expression fused with an attention mechanism is constructed, and dynamic prediction modeling is performed on the landslide displacement through a gating recurrent neural network, so that accurate prediction of the landslide displacement trend and periodic change is realized. Experimental data prove that the method is superior to an existing traditional method in the aspects of landslide deformation prediction precision and robustness, the prediction precision, the feature utilization rate and the model interpretability are effectively improved, and the method has good engineering practicability and popularization prospects.

A VEM-Token emotion synchronization function hierarchical fusion method is different from a traditional NLP-Token method, an emotion synchronization function VEM-sync is innovated for the first time, the emotion synchronization function VEM-sync is synchronized with a VEM-Token sequence of music beats, multiple high-dimensional emotions are directly described by adopting mathematical languages, and therefore deviation of discretized natural language characters on description of a high-dimensional emotion analog quantity function is avoided, and the emotion synchronization effect is improved. The method comprises the steps of defining VEM-sync and synchronous content, defining rhythm attributes, emotion attributes and emotion functions, adopting one or combination of multi-layer weighted scanning, a recurrent neural network, a long and short-term memory network, a self-attention mechanism and an RAG network generated by retrieval enhancement, and performing hierarchical fusion calculation to output a time emotion function of a vocal music file. According to the phonetic function or the emotional function, the effects of emotional texts, emotional expressions, emotional languages and emotional multi-dimensional animations are directly driven by crossing discrete text tokens, a model context protocol (MCP) and a function calling function are supported, and copyright management and encryption and decryption or interfaces are provided.

The invention discloses an abnormal sleep signal identification method for an intelligent sleep monitoring pillow, and relates to the technical field of abnormal sleep signal identification. Comprising the following steps of collecting multi-modal sleep physiological data, preprocessing the multi-modal sleep physiological data, extracting multi-dimensional features from the preprocessed data, constructing a fusion analysis model to judge abnormal sleep signals, triggering a hierarchical intervention mechanism and outputting associated intervention suggestions. According to the method, the time dimension alignment technology and the sliding window matching technology are adopted, the problem of data synchronization caused by multi-sensor sampling frequency difference in a traditional abnormal sleep signal recognition technology is solved, multi-source features are fused through deep learning, the accuracy of abnormal sleep signal recognition is remarkably improved, and the recognition accuracy of abnormal sleep signals is improved. An intelligent analysis model based on deep learning multi-network fusion is constructed, deep extraction and fusion judgment of complex sleep features are realized by utilizing cooperative processing of a convolutional neural network, a recurrent neural network and an attention mechanism, and the reliability of abnormal sleep signal recognition is improved.

The invention provides a bidirectional recurrent neural network acoustic logging curve reconstruction method. The reconstruction method comprises the steps of S1, acquiring data and performing sequence segmentation; s2, carrying out normalization processing on the logging curve; s3, carrying out superposition networking and training on the bidirectional recurrent neural network structure block; s4, model testing and parameter storage; and S5, reading the model parameter file to reload the model, importing the to-be-reconstructed logging data, generating a prediction curve, and storing and exporting a result. A bidirectional recurrent neural network algorithm of artificial intelligence deep learning is adopted, bidirectional depth sequence information contained in a logging curve is effectively captured, the curve reconstruction accuracy is improved, unmeasured, missing and low-quality curves are reconstructed, multi-curve information is fused, and the sensitivity of a reconstructed acoustic curve is improved. The system has the advantages of being low in cost, high in efficiency and high in precision.

The invention discloses a key parameter long-time-sequence prediction method for a complex process industry, and the method comprises the steps: collecting multivariable sensor data in the process industry, and constructing a high-dimensional long-time-sequence prediction data set; constructing a PatchConvRNN prediction model by combining time slice embedding, dimension decoupling convolution, depth separable convolution and a recurrent neural network based on a sequence-to-sequence normal form; a point value-statistical mixed loss function is adopted, the point prediction precision, the sequence mean value and the standard deviation consistency are optimized at the same time, a prediction model is trained in combination with an optimization algorithm, and network model parameters are adjusted; and comprehensively evaluating the prediction model through a root-mean-square error, an average absolute percentage error and a standard deviation average absolute error. According to the method, high-precision prediction and fluctuation maintenance of the key time sequence variables under the complex working condition of the industrial process are achieved, and powerful support is provided for quality control and predictive maintenance of the production process.

The invention provides a social network key node identification method and system fusing propagation characteristics, and relates to the technical field of node identification, and the method comprises the steps: collecting various data in real time; the method comprises the following steps: identifying network propagation data to obtain key information data, constructing an enhanced recurrent neural network optimized based on a sodat swarm optimization algorithm, inputting the key information data, outputting to obtain propagation network characteristics, and analyzing the key information data to obtain node propagation performance data. And extracting a multi-factor influence coefficient from the external factor data, constructing a node evaluation system, and screening the preliminary nodes according to the multi-factor influence coefficient to obtain key nodes. According to the method, the recurrent neural network is enhanced, the propagation network features are extracted, the node evaluation system is constructed, the topological structure data are classified, and the key nodes are determined in combination with the multi-factor influence coefficient, so that the understanding and prediction capabilities of the propagation process are improved, and the key nodes are identified more accurately.

The invention discloses a digital-intelligence tumor screening platform and method, and relates to the technical field of digital-intelligence tumor screening, and the method comprises the steps: constructing a multi-modal fusion model of a fusion convolutional neural network and a recurrent neural network based on a deep learning framework, and outputting tumor comprehensive correlation features; a machine learning algorithm is utilized, large-scale tumor data are combined for training to construct a tumor risk prediction model, tumor comprehensive association features are input, and different tumor incidence probabilities are output. By means of the multi-modal fusion model, multi-source data such as images, clinic, genes and living habits are integrated, tumor characteristics are comprehensively captured, and the early-stage missed diagnosis rate of common tumors can be greatly reduced. Multi-source data are accessed in real time through a standardized interface, the multi-modal model completes feature extraction and risk prediction in a short time, and the time of the whole screening process is greatly shortened. The result of the visual report is presented by a visual chart, and by matching with popular character interpretation, the understanding degree of the patient on the screening result is improved, and the doctor-patient communication efficiency is remarkably improved.

The invention relates to the technical field of wireless charging, and discloses a wireless charging power adaptive adjustment method and system based on machine learning, and the method comprises the steps: collecting and preprocessing multi-dimensional parameter data of a charging environment; learning the data by using a recurrent neural network, and establishing a correlation model of environmental factors and transmission loss; modeling a charging environment based on a graph structure, and extracting spatial relationship characteristics by using a graph neural network; a neighborhood sampling and dynamic pruning technology optimization model is adopted, and a hierarchical power decision system is constructed; adjusting the charging power by adopting feed-forward control according to the output of the decision-making system; according to the invention, by introducing the deep learning technology, accurate prediction and dynamic adjustment of the charging power are realized, and the charging efficiency and stability are improved; collaborative optimization of multiple charging points is realized by adopting graph structure modeling; and the calculation complexity is reduced through an optimization technology, so that the system is suitable for a large-scale application scene.

The invention discloses a depth time sequence prediction method and system based on an event gating mechanism, and belongs to the technical field of artificial intelligence, and the method comprises the steps: obtaining time sequence data and corresponding event sequence data; mapping the time sequence data and the corresponding event sequence data to a low-dimensional potential vector space to obtain a time sequence feature representation and an event sequence feature representation; according to the time step sequence, inputting the time sequence feature representation and the event sequence feature representation into an improved recurrent neural network for processing to obtain a hidden state sequence; performing attention calculation based on the hidden state sequence to obtain final vector representation; and obtaining a prediction result according to the final vector representation. According to the invention, the prediction capability of the time sequence containing the non-periodic event interference can be improved.

The invention relates to the technical field of metallurgical industry simulation and intelligent control, and discloses an intelligent metallurgical process virtual simulation method and system based on digital twinning. The method comprises the following steps: acquiring multi-modal high-dimensional data in a metallurgical process and reducing dimensions to obtain a feature vector set of a hidden layer space; performing disturbance injection simulation by using the feature vector set of the hidden layer space to obtain a multi-path state sequence with time dependence; predicting the abnormal path by using a recurrent neural network to obtain a predicted state evolution trajectory; utilizing a preset inverse mapping function to obtain multi-path state representation in the physical space; screening to obtain a risk path set; key evolution nodes are extracted from the set to be processed, and a virtual simulation scene is obtained; and performing optimization simulation on preset process adjustment parameters according to the virtual simulation scene, and determining optimized parameter configuration. The method can solve the problem that it is difficult to construct a comprehensive virtual simulation scene which truly restores the physical production rule.