961 results about "Recurrent neural network" patented technology

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 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 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.

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 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 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 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.

The invention relates to the field of artificial intelligence, financial science and technology and digital medical treatment, and discloses an image-based video generation method and device, equipment and a storage medium. The method comprises the following steps: receiving and preprocessing an input static image to generate a multi-scale feature; based on the multi-scale features, determining an optimal space-time processing path through differentiable search of a space-time architecture generator, and generating output features fused with time sequence dynamic information; generating a video frame based on the time sequence recurrent neural network and the output features; and inputting the video frames into the video frame sequence, and generating a target video through the video frame sequence. According to the method, the optimal space-time processing path can be automatically determined through differential search, manual intervention is avoided, the time sequence coherence and detail authenticity of the video are ensured by dynamically generating the output characteristics of the fusion time sequence information and generating the video, the video generation quality and efficiency are improved, and the video quality is improved. The method is suitable for high-precision video generation such as video synthesis, content creation and virtual reality in the fields of finance and medical treatment.

The invention relates to a meteorological-based dynamic graph network photovoltaic power station group ultra-short-term prediction method, which is characterized in that a dynamic space-time graph network is constructed, photovoltaic power stations in a region are regarded as a complex network system connected by meteorological fluctuations, each power station is taken as a node, and the propagation relationship of the meteorological fluctuations among the power stations is represented by edges of a graph. The weight and time delay of the edge are dynamically adjusted according to real-time meteorological data, and dynamic interaction between power stations, the overall trend under the stable meteorological condition and rapid fluctuation caused by sudden weather events are accurately captured in combination with a mixed model of a graph neural network and a recurrent neural network. Compared with a traditional method, the method has the advantages that the prediction precision, the real-time response capability and the physical interpretability are remarkably improved, scattered photovoltaic power stations are integrated into a mutually associated dynamic system, an efficient and reliable solution is provided for power prediction of a power station group, and the operation stability and the power grid dispatching efficiency of a large-scale photovoltaic system are enhanced.

The invention relates to the field of motor fault diagnosis, and particularly discloses a permanent magnet synchronous motor fault diagnosis method and system, which dynamically update motor physical model parameters through an online identification algorithm so as to reflect the characteristic change of a motor in real time. Thirdly, predicting the theoretical voltage of the healthy motor under the working condition by using the self-adaptive model and the real-time operation data, and calculating a residual sequence subjected to working condition normalization between the theoretical predicted voltage and the actual voltage; the residual signal is essentially free from the influence of working condition change, and only the abnormal characteristics caused by the fault are highlighted. And finally, inputting the high-robustness residual error sequence into a convolutional recurrent neural network, deeply fusing space-time fault features, and realizing accurate judgment on a motor fault type and confidence thereof, thereby effectively improving variable working condition adaptability and weak fault detection capability of diagnosis.

According to the intelligent recommendation method and device based on driving behavior analysis provided by the embodiment of the invention, accurate analysis of user interests is realized by innovatively constructing a multi-dimensional feature fusion mechanism and integrating registration information, video data, driving data and scene perception data. And designing a trajectory prediction model based on a recurrent neural network, and establishing a feature weighted fusion strategy for intelligent matching in combination with a user interest modeling network of an attention mechanism. And an online learning mechanism is introduced, and model parameters are continuously optimized through clicking, staying duration and visit record data, so that dynamic adjustment of personalized recommendation contents is realized. According to the method, the defects of the traditional technology in the aspects of feature extraction, interest modeling, recommendation strategies and the like are effectively overcome, and the recommendation service level and the user experience in the driving scene are remarkably improved.

The invention discloses an electroencephalogram signal recognition method based on a graph recurrent neural network, and the method specifically comprises the steps: carrying out the preprocessing of electroencephalogram signal data, and obtaining a data segment electroencephalogram signal; performing feature extraction and graph embedding processing on the data segment electroencephalogram signals, and dividing the data segment electroencephalogram signals into a training set and a test set; inputting the training set data into a diffusion convolution recurrent neural network for training; and inputting the test set data into the trained diffusion convolution recurrent neural network, if epilepsy detection is carried out, outputting a binary label and probability that whether the fragment contains the attack or not, and if epilepsy classification is carried out, outputting a label and probability distribution that the fragment belongs to four attack types. According to the method, the influence of the multi-channel space relation on prediction and classification of the electroencephalogram signal attack is deeply explored from three dimensions of time domain, frequency domain and space domain by taking the multi-channel space relation as an entry point, the problem of cross-domain feature deficiency in the prior art is effectively solved, the electroencephalogram signal attack mode can be more accurately recognized, and the recognition rate of the electroencephalogram signal is remarkably improved.

The invention discloses a battery state-of-charge estimation method and system based on an ultrasonic transmission signal, and relates to the field of industrial detection.The method comprises the steps that the ultrasonic transmission signal is obtained, segmentation and normalization preprocessing are carried out, and preprocessed input sample data are obtained; constructing an initial battery state-of-charge estimation model used for extracting local features, extracting global features, extracting a time dependency relationship and performing classified output; inputting input sample data into the initial battery charge state estimation model for training, and calculating a loss function; obtaining a battery state-of-charge estimation model in combination with the training iteration number and / or the loss error threshold value; and estimating the state of charge of the battery through the battery state of charge estimation model. The method comprises the following steps: providing a battery charge state estimation model, and capturing a time dependency relationship of a signal by using a recurrent neural network and a time attention mechanism; an ultrasonic transmission signal is used as input data, and the estimation precision is improved by using information contained in the signal.

The invention discloses an equipment health assessment method based on artificial intelligence and electronic equipment, and belongs to the technical field of equipment assessment. The method comprises the steps that firstly, heterogeneous data such as sensor data, historical maintenance records and environmental parameters in the equipment operation process are acquired through a multi-source data acquisition system, and performance index data of equipment manufacturers and user feedback information are integrated; then, a multi-step preprocessing process including data cleaning, feature extraction, normalization processing, feature dimension reduction based on principal component analysis and the like is adopted, and the problems of data redundancy and noise interference are effectively solved; a deep learning model based on a convolutional neural network or a recurrent neural network is constructed, and a dynamic weight adjustment mechanism is introduced, so that the model can adapt to running state changes of different devices; and finally, outputting an evaluation report containing health grade division and future state prediction, and generating corresponding early warning information and maintenance suggestions.

The invention discloses a transformer substation hidden danger comprehensive monitoring method, system and equipment integrating multiple sensors and a medium, and belongs to the technical field of transformer substation hidden danger monitoring. Cleaning and fusing the collected data, and constructing a unified data set; inputting the unified data set into an identification structure constructed by combining a convolutional neural network and a recurrent neural network, respectively processing image type and time sequence type data, and completing hidden danger type identification according to a preset training set proportion and training parameters; carrying out risk value calculation and grade judgment in combination with an identification result and hidden danger related characteristics; and early warning information is generated based on a judgment result, classified pushing and display are performed, early warning processing feedback information is received, and state updating and recording and archiving are completed. Fusion sensing and recognition of multi-source monitoring data are achieved, hidden danger early warning capacity and good environmental adaptability are achieved, and the operation safety and management efficiency of the transformer substation are improved.

The invention discloses a heat exchange station control method and device based on neural network decoupling and a medium, and relates to the field of neural networks, and the method comprises the steps: carrying out the feedforward decoupling through a trained time-delay recurrent neural network, and outputting a decoupling control instruction; performing prediction through the trained prediction model, and outputting a prediction parameter output value at a future moment; outputting a frequency conversion increment through a rolling optimizer according to the predicted parameter output value; determining an output difference value between the second actual parameter output value and the predicted parameter output value; and adjusting model parameters and / or variable frequency increment of the prediction model. Feedforward decoupling is carried out by using a time-delay recurrent neural network, and dynamic characteristics of the system are learned through a data driving mode without depending on an accurate mathematical model. Compared with the traditional feedforward decoupling and frequency conversion decoupling strategies, the coupling relationship between the quality regulation channel and the quantity regulation channel can be processed more thoroughly, and the problem that multi-variable dynamic response is difficult to coordinate by single pressure difference control is solved.