716 results about "Hybrid neural network" patented technology

The invention relates to a regional pollution process real-time monitoring, regulation and control system and method based on artificial intelligence. The system is composed of a data acquisition module, a communication module, an artificial intelligence analysis module, an intelligent regulation and control module, an execution terminal and a user interaction module. The data acquisition module acquires multi-source data such as pollutant concentration and meteorological parameters by means of a multi-modal sensor, the data are processed and transmitted by the data communication module, and the artificial intelligence analysis module realizes pollution source tracing and accurate prediction by using technologies such as a space-time diagram convolutional network and an LSTM-Transform hybrid neural network. The intelligent regulation and control module generates a regulation and control strategy based on an NSGA-II algorithm, and the execution terminal is responsible for implementation. The user interaction module provides a visual interface and a manual intervention channel. The method comprises the steps of data acquisition and processing, model construction and prediction, regulation and control strategy formulation and execution and feedback optimization closed-loop operation. According to the invention, comprehensive real-time monitoring and accurate regulation and control of regional pollution are realized, the prediction accuracy is improved, the environmental, economic and social benefits are balanced, and an efficient technical means is provided for regional pollution treatment.

The invention relates to the technical field of equipment fault diagnosis, in particular to an extruder equipment fault recognition method and system based on artificial intelligence, and the method comprises the following steps: collecting key fault features of an extruder in real time based on a multi-mode sensor network, optimizing the signal quality through data preprocessing and feature decoupling, and obtaining a fault recognition result; and the generalization ability of the model is improved by using cross-device feature mapping and transfer learning, a hybrid neural network is combined, a physical constraint layer is embedded on the basis of a data driving layer, a feature incidence matrix conforming to the dynamic characteristics of the extruder is constructed, and a fault prediction model can be adjusted in real time through a dynamic weight distribution mechanism and dual-target loss optimization, so that the fault prediction efficiency is improved. The method adapts to the change of the operation state of the equipment, and realizes the real-time detection, graded early warning and precise operation and maintenance of faults in combination with an intelligent early warning mechanism and a multi-target optimization decision. According to the invention, the operation stability and maintenance efficiency of the extruder equipment are obviously improved, and the method is suitable for equipment health management in the field of intelligent manufacturing.

The invention discloses a medical whole-course intelligent management system based on a large model, and belongs to the technical field of large models. Comprising a multi-modal data acquisition module, a privacy calculation preprocessing module, a dynamic knowledge enhancement module, a time sequence data analysis module, an intelligent decision engine module, a multidisciplinary collaboration module, a patient interaction platform module, a dynamic intervention feedback module and a system security center module. The cross-mechanism data security sharing is realized, and the compliance of sensitive information processing is also ensured; a two-channel medical knowledge base is constructed, authoritative guidelines can be synchronized, newest clinical research data can be analyzed in real time, the knowledge base is kept in the newest state all the time, and the frontier scientific basis is provided for clinical decisions; dynamic modeling and trend prediction are carried out on long-term monitoring data of a patient by adopting a hybrid neural network model, and potential health risks and development trends can be identified more accurately.

The invention provides a multi-feature fusion rumor detection method, system and device based on knowledge distillation, and mainly solves the problems that an existing model is high in calculation overhead, insufficient in feature fusion and insufficient in emotion utilization. The method comprises the steps of firstly obtaining multi-dimensional data such as social media original texts and comments; extracting deep semantic representation by using a pre-training model, and analyzing comment emotion features in combination with a hybrid neural network; then, features such as semantics, emotions, emoticons and populations are input into a hierarchical gating interactive fusion network (GIFN), and weights are dynamically adjusted to achieve effective fusion of multi-granularity features; in order to reduce complexity, a knowledge distillation framework is designed: a deep GIFN is used as a teacher network to generate a soft label, and a lightweight student network (LSTM) is guided to perform training. According to the trained student model, the parameter quantity is remarkably reduced, meanwhile, good detection performance is kept, the student model can be conveniently deployed in an actual content auditing system or edge equipment, and social content rumors can be efficiently recognized and judged.

The invention provides a thermal power plant thermal instrument fault diagnosis method and system based on vibration analysis, and relates to the technical field of fault diagnosis, and the method comprises the steps: collecting and processing a vibration signal of a thermal instrument, extracting an optimized vibration feature vector, constructing a hybrid neural network model, and training the hybrid neural network model to obtain an optimized fault diagnosis model; performing fault diagnosis on the vibration feature vector, and generating a fault type, possibility and confidence; and performing fault risk assessment and case reasoning, and generating a fault reason analysis report and a maintenance suggestion.

The invention discloses a rockburst risk early warning method based on TBM multi-source data fusion and a hybrid algorithm, and the method comprises the steps: collecting TBM tunneling parameters, geological exploration data and micro-seismic monitoring data of different rockburst levels, achieving feature complementation through multi-source data fusion, improving the completeness and reliability of early warning, constructing a hybrid neural network model based on CNN, LSTM and an attention mechanism, and carrying out the early warning of the rockburst risk. Feature weight distribution is optimized by introducing an attention mechanism, so that the model can adaptively focus key risk signals, and the applicability of model early warning is improved; and in combination with a fuzzy comprehensive evaluation algorithm and a Bayesian probability model, outputting four rockburst grades of no rockburst, slight rockburst, medium rockburst and strong rockburst and occurrence probabilities thereof, and realizing real-time dynamic early warning and probabilistic early warning of the rockburst grades. Compared with an existing method, the method has the advantages that the accuracy, timeliness and engineering applicability of rockburst early warning are remarkably improved, and real-time and accurate early warning of potential rockburst and the grade of the potential rockburst can be achieved.

The invention discloses a multi-domain collaborative flexible load schedulable potential evaluation and energy management method, and relates to the technical field of park energy management, and the method comprises the steps: obtaining space-time multi-source data of a smart park, constructing a graph neural network power consumer clustering model based on iterative self-organization analysis, and generating a power consumption behavior portrait of a power consumer; based on power utilization parameters of building air conditioners, electric vehicles, park ponds and energy storage batteries in the smart park, the schedulable potential of the multi-element flexible resources is evaluated; and based on the hybrid neural network and the Harris eagle optimization algorithm, constructing a load prediction model, and predicting various types of energy loads in a future time period. The invention aims to establish an accurate model and method, accurately evaluate the schedulable potential of different types of flexible loads in different scenes, realize efficient utilization, energy conservation and emission reduction and optimal configuration of park energy, and improve the overall energy management level and operation efficiency of the park.

The invention provides a plateau lake agricultural non-point source pollution treatment method which comprises the following steps: acquiring vegetation indexes and surface temperature field data by using a remote sensing satellite, and generating a pollution source space thermodynamic diagram in combination with water quality and soil parameters of ground sampling points; performing space-time alignment and data fusion on the thermodynamic diagram and real-time runoff and soil permeability data acquired by the hydrological sensor network, and constructing a structured pollution migration database; on the basis of the database, a hybrid neural network model embedded with physical constraints is utilized to predict pollutant concentration distribution within 72 hours in the future; inputting the predicted value into a multi-stage optimization controller, and generating a control parameter set comprising treatment intensity, engineering parameters and a fertilization ratio; generating a treatment strategy map covering the drainage basin through a GIS; and deploying an Internet of Things monitoring node to collect the treated water quality data to form a closed-loop control link. The treatment efficiency and effect can be improved, the treatment cost is reduced, and the negative influence on the ecological environment is reduced.

The invention relates to the technical field of meteorological observation, and provides a ground hail identification method and system based on a hydrogel classification result, and the method comprises the steps: carrying out the time-space correlation of multi-source hail data through a time-space matching algorithm, and obtaining a hail event data set of time-space matching; through a dynamic membership function optimization algorithm, self-adaptive phase state identification is carried out on the dual-polarization radar data, and multi-elevation hail phase state characteristic parameters containing rain-ice mixture categories are obtained; based on the multi-elevation hail phase state characteristic parameters, performing integrated preprocessing on the multi-source meteorological data to obtain standardized multi-dimensional meteorological characteristic data fused with phase state characteristics; performing unsupervised pre-training and supervised fine-tuning training on the DCNN-DBN hybrid neural network through the standardized multi-dimensional meteorological feature data to obtain a ground hail recognition model; and outputting a hail falling area identification result through the ground hail identification model. According to the invention, the distinguishing capability of easily-confused phase states is improved, and the false alarm rate and the missing report rate of hail identification are reduced.

The invention relates to the technical field of collaborative optimization, in particular to a virtual power plant power generation-utilization-price collaborative optimization system based on an AI large model, and the system comprises a load confidence matching module, a resource stability mapping module, a source-load capacity coupling module, an electricity price interval adjustment module and a comprehensive regulation and control linkage module. According to the method, the confidence interval prediction of the load demand is realized based on the hybrid neural network modeling of the load behavior data and the equipment temperature control characteristic sequence, and the scheduling matching confidence is measured according to the boundary overlapping condition of the prediction interval and the power generation response characteristic; a stability screening mechanism for adjusting resources is constructed in combination with the output fluctuation ratio and the equipment inertia characteristic, the controllability of load adjustment and the real-time performance of source side response are improved, the price adjustment rhythm is corrected through an electricity price response delay factor, dynamic closed-loop linkage between load adjustment and price guidance is achieved, and the load adjustment efficiency is improved. The execution priority is dynamically updated under the condition that multiple response conditions are matched, and the certainty of resource scheduling and the sensitivity of response are improved.

The invention provides a wind power mixed tower damage prediction method and system based on digital twinning. The method comprises the steps of obtaining multi-source data of a wind power mixed tower; wherein the multi-source data comprises structure response data, environment load data and historical operation and maintenance data; based on a building information model and finite element model fusion technology, constructing a digital twinborn model of the wind power mixing tower; inputting the multi-source data into a digital twinborn model to realize real-time mapping of the wind power mixed tower and the digital twinborn model, and obtaining real-time structure state data of the wind power mixed tower through the digital twinborn model; inputting the multi-source data and the real-time structure state data into a pre-trained LSTM-GRU hybrid neural network model, and predicting a damage evolution trend of the wind power hybrid tower in combination with an attention mechanism; and on the basis of the damage evolution trend, a maintenance scheme of the wind power mixed tower is generated by using a multi-target particle swarm algorithm, so that balanced optimization of maintenance cost, power generation loss and risk level is realized, and the operation and maintenance efficiency and safety of the wind power mixed tower are integrally improved.

A drifting buoy trajectory prediction method based on a hybrid neural network prediction model, includes: S1, obtaining marine environmental data and historical trajectory data of a drifting buoy; S2, performing preprocessing on the marine environmental data and the historical trajectory data to obtain input data configured to predict northward and an eastward velocities of the drifting buoy; S3, inputting the input data into the hybrid neural network prediction model to obtain predicted values of the northward and eastward velocities; S4, calculating latitude and longitude coordinates of a trajectory point of the drifting buoy based on the predicted values of the eastward and northward velocities; and S5, predicting, by repeating the step S1-S4, latitude and longitude coordinates of trajectory points of the drifting buoy at multiple time points to obtain a sequence of trajectory point coordinates to thereby achieve trajectory prediction of the drifting buoy over a target future period.

The invention discloses a sensing node communication system and method with a self-healing mechanism. The sensing node communication system comprises a node detection module, a node communication module, a preprocessing module, a mixed training module and a self-healing control module. The system realizes periodic acquisition of multi-dimensional environmental parameters by configuring a plurality of sensing nodes, shares state information based on a self-organizing network, and constructs a local network health state map. The preprocessed data is used for training a hybrid neural network, and long-term dependency features and local abnormal fluctuation features of node states are extracted from the data to assess fault risks. When the risk score exceeds a threshold value, the system automatically executes self-healing operations such as dynamic route reconstruction and standby link activation. According to the invention, the stability and the adaptive capability of the underwater sensor network are improved, and the method has wide application value.

The invention provides a cellular network flow space-time prediction method and system based on a hybrid neural network, and belongs to the technical field of intelligent communication. The method adopts a layered deep neural network architecture, and comprises a data embedding layer, a space-time coding layer, a feature fusion layer and an output layer. The data embedding layer maps a historical traffic sequence, cross-domain external data and metadata into high-dimensional features; the space-time coding layer is used for respectively fusing one-dimensional causal convolution and a Mama neural network to extract multi-scale time features and densely connecting convolution and a multi-head attention mechanism to capture multi-scale space features through time and space modeling branches; the feature fusion layer realizes adaptive weighted fusion of spatial-temporal features, cross-domain features and metadata features by using a gating fusion mechanism; and the output layer performs linear transformation on the fusion features to generate a final prediction result. According to the method, the spatial-temporal dynamic capture of the service traffic is accurate, the prediction curve is highly fit with the true value, and the accurate prediction of the multi-service traffic of the cellular network is realized.

The invention discloses a corrugated carton production management system and method, and relates to the technical field of corrugated carton production, the system comprises a production scheduling module, a material tracing module, an equipment cooperation module, a multi-mode detection module and a cost control module, and solves the problem that supply and demand relationships, production efficiency and quality management and control are not coordinated; according to the technical key points, an order ID is coded into an integer chromosome, sequential crossover and reverse mutation operation are applied, a fuzzy comprehensive evaluation model is constructed by combining Hash chain table and local hill climbing method optimization, and factors are quantified through a trapezoidal membership function, so that the order delivery punctuality rate and the equipment utilization rate are improved, and the mold replacement cost is reduced; the real-time production cost is collected and calculated through Kafka and Flink, an operation cost method model is constructed to maintain a standard cost database, abnormity is detected through a CEP engine, the cost hyper-branched risk is predicted through an LSTM-Transform hybrid neural network, a thermodynamic diagram is generated through a cache result to assist decision making, the cost accounting delay time is shortened, and the hyper-branched processing efficiency is improved.

The invention relates to the technical field of chemical safety and intelligent control, in particular to an explosion-proof intelligent temperature control method and system for a hydrogen peroxide storage tank based on multi-modal monitoring, and the method comprises the steps: collecting multi-dimensional data such as temperature, pressure and H2O2 concentration through a sensor array, carrying out the Kalman filtering and wavelet transform denoising, recognizing local abnormal points through an improved WMDS-DBSCAN algorithm, and obtaining the explosion-proof intelligent temperature control of the hydrogen peroxide storage tank based on the WMDS-DBSCAN algorithm. Predicting a hot spot diffusion trend in combination with an unsteady heat conduction equation and an RF-ADI algorithm; weighted Delaunay triangulation is utilized to construct a self-adaptive monitoring grid, risk area probability distribution is output through an SAM-LSTM-CNN hybrid neural network, and a dynamic amplitude limiting fuzzy PID controller adjusts the opening degree of a nozzle to achieve temperature control. According to the method, multi-modal data and an intelligent algorithm are fused, abnormal accurate detection, risk prediction and self-adaptive temperature control are realized, a closed-loop optimization mechanism is formed, and the operation safety and reliability of the hydrogen peroxide storage tank are effectively improved.

The invention provides a power cable comprehensive online monitoring system, and relates to the technical field of data processing, and the system comprises the steps: extracting a partial discharge distribution point set in a multi-dimensional feature fusion data set, and calculating the weight factor of each distribution point; based on the weight factors, a weighted point set concave packet generation algorithm is adopted to construct a partial discharge source probability distribution boundary, and a boundary feature point set is obtained; inputting the boundary feature point set and the multi-dimensional feature fusion data set into a convolution-long and short-term memory hybrid neural network to generate a diagnosis result; and a third-level early warning signal is generated based on a diagnosis result, early warning information is pushed to a remote operation and maintenance terminal and a mobile APP through an MQTT protocol, a digital work order service is automatically triggered to generate a maintenance task, and positioning verification is executed. According to the invention, the accuracy of monitoring and fault diagnosis is improved.

The invention provides a ship main engine power real-time optimization method based on a hybrid network model, and belongs to the technical field of ship energy saving.A hybrid neural network model is constructed, the hybrid neural network model is based on a physical information neural network, a KAN network is introduced to serve as a front-end network structure, and the power of a ship main engine is optimized in real time; the high-dimensional nonlinear mapping module is used for establishing high-dimensional nonlinear mapping from navigational speed, a ship type parameter matrix, propulsive efficiency, fuel conversion efficiency and environmental factors to the minimum power of a main engine; a composite loss function is adopted to train the hybrid neural network model, wherein the composite loss function is formed by weighted summation of mean square error loss, dynamics constraint loss, propulsive efficiency constraint loss and fuel consumption constraint loss; using the trained hybrid neural network model to receive ship operation parameters and environment parameters collected in real time, and outputting a minimum power prediction value of the ship main engine through one-time forward propagation calculation to realize real-time optimization of the main engine power.

The invention provides a power system net load prediction method based on multi-mode decomposition and reconstruction, and aims to solve the problems of violent net load fluctuation, frequent sudden change, difficult prediction and the like under a new energy high-permeability background, and the prediction precision and the trend response capability are improved by fusing a physical mechanism and a deep learning modeling means. Firstly, historical net load data and environmental parameters are collected, and historical wind and light net generating capacity is accurately estimated based on a wind and light generating capacity dynamic coupling calculation method. Afterwards, an improved complete set empirical mode decomposition algorithm is used for conducting multi-mode decomposition on historical net loads, an effective mode function is screened in combination with the energy proportion and correlation, the effective mode function and a residual term jointly reconstruct a net load sequence, and noise interference is eliminated; and after the reconstructed sequence generates a sample through a sliding window, inputting the sample into a hybrid neural network model composed of a convolutional neural network and a long-short-term memory network, and performing training in combination with wind-solar power generation. And finally, multi-step accurate prediction of the future net load is realized.

The invention relates to the technical field of agricultural information, in particular to a tobacco agriculture standard named entity recognition method and system based on a hybrid neural network, and the method comprises the steps: carrying out the embedded representation of an input tobacco agriculture standard text through a BERT pre-training language model, and generating a word vector sequence containing global semantic information; performing local feature extraction on the word vector sequence by using an iterative expansion convolutional neural network to obtain a local feature vector; splicing the global semantic information and the local feature vectors, and inputting the spliced global semantic information and local feature vectors into a bidirectional long-short-term memory network for context feature extraction to generate context enhancement features; weight optimization is carried out on the context enhancement features through a multi-head attention mechanism, and key semantic features are highlighted; and performing label prediction on the optimized feature sequence by adopting a conditional random field decoder, and outputting a standard article element entity identification result. According to the method, high-precision and high-robustness named entity recognition is realized, and the method is particularly suitable for complex semantic and low-resource field scenes.

A system for the real-time delivery of personalized motivational content based on biometric information; the system includes: a biometric acquisition module configured to capture a variety of physiological signals from a user, wherein the physiological signals include at least heart rate variability, electrodermal activity, facial expressions and electroencephalographic (EEG) signals; a preprocessing module that is operationally coupled with the biometric acquisition module, wherein the preprocessing module is configured to remove noise, normalize and extract signal features from the physiological signals in real time; a multimodal biometric fusion engine configured to temporally align and synchronize the extracted features across signal modalities using dynamic time distortion and confidence-weighted interpolation; a motivational state inference model with a hybrid neural architecture comprising a Convolutional Neural Network (CNN) for spatial pattern recognition and a Recurrent Neural Network (RNN) for temporal sequence modeling, wherein the inference model is configured to output a motivational input score and an affective state classification; an engine for recommending motivational content, configured to select and prioritize content from a content repository based on motivational uptake score, user profile metadata, contextual signals including time of day and geolocation, and historical content effectiveness profiles; and a content delivery subsystem comprising one or more output modalities selected from an acoustic actuator, a visual display, a haptic actuator or an environmental controller, wherein the content delivery subsystem is capable of presenting the selected motivational content in a modality that is dynamically adapted to the user's current psychophysiological state.

The invention discloses an AI-based intelligent park source, network, load, storage and charging integrated scheduling method, and relates to the technical field of energy scheduling, and the method comprises the steps: collecting park equipment data in real time, carrying out the preprocessing, and generating a standardized time series data matrix; time sequence information is used for screening and extracting data features of the standardized time sequence data matrix, a hybrid neural network frame is used for constructing a source network load storage and charging optimization model, and an embedded vector is obtained; optimizing the preliminary scheduling strategy by using a game theory, and generating a collaborative scheduling instruction set; converting the collaborative scheduling instruction set into an equipment control instruction by adopting protocol conversion, and executing the equipment control instruction; dynamic threshold value filtering is used for monitoring the equipment operation state, the safety boundary and the economical efficiency threshold value in real time, and online learning is used for optimizing an equipment control instruction. According to the invention, by using the near-end strategy to optimize the cutting algorithm and the Nash equilibrium game, the economical efficiency of the scheduling strategy is improved.

A hybrid neural network architecture is disclosed that integrates matrix multiplication-free (MatMul-free) transformation layers with spiking neural network (SNN) layers for efficient, low-power computation. The system includes an interface module configured to convert intermediate continuous-valued data from MatMul-free layers into a spike-compatible format using encoding techniques such as rate coding, phase coding, or threshold-based conversion. The SNN layers process the spike-encoded data in an event-driven manner, enabling sparse, temporal inference. Training is supported by a hybrid optimization strategy combining backpropagation in MatMul-free components with surrogate gradient descent or spike-timing-dependent plasticity (STDP) in SNN layers. The architecture reduces computational complexity, supports real-time adaptability, and enables deployment in energy-constrained environments such as edge devices and neuromorphic platforms. The system may be implemented in hardware, software, or a co-designed pipeline optimized for dynamic sensor data, control signals, or continuous inference tasks.

The invention relates to the technical field of computer graphics, and provides a rendering method and device for a static scene under a dynamic view angle, equipment and a medium, and the method comprises the steps: obtaining a future view angle probability cloud picture of a preset frame number according to the historical behavior data of a user and a hybrid neural network view angle prediction model; dividing a pre-rendering static scene of at least one target pre-rendering view angle based on a preset dynamic semantic weight matrix to obtain layered baking data; obtaining target optical flow data of a target adjacent view angle adjacent to the current view angle from a preset optical flow mapping table; during view angle switching, dynamically mixing a static scene rendered in real time at the current view angle and a static scene pre-rendered according to a pixel displacement relation based on target optical flow data to obtain scene mixed data; and rendering based on the scene mixed data to obtain a target static scene. The problem of real-time high-quality rendering of the static scene under the dynamic view angle can be solved.

The invention provides an information security risk management method and management system. The system is composed of four core modules: a multi-modal data acquisition layer: probes deployed at a terminal and a server support heterogeneous data acquisition of network traffic, operation logs and an API (Application Program Interface) call chain; according to the block chain log storage layer, a private chain is constructed based on an improved BFT consensus algorithm, and each block comprises a timestamp hash value and a preorder block fingerprint; the dynamic risk assessment engine adopts an LSTM-Transform hybrid neural network, and input dimensions comprise a user behavior baseline, a vulnerability library version and threat intelligence feed; and the intelligent response decision module is used for integrating a reinforcement learning algorithm, automatically generating a disposal strategy and triggering the SDN controller to execute network isolation. According to the method, a local chain-alliance chain-audit chain three-level structure is applied to data island treatment and data integration in organization, the problem of internal data fragmentation is solved through the local chain structure, unified storage and rapid source tracing of heterogeneous logs are achieved, and data authenticity and time sequence integrity are ensured.

The invention provides an artificial intelligence network model-based early warning and monitoring method for the occurrence amount of agricultural pests, and particularly relates to a time sequence modeling method by combining a variable structure bus module VSB with a bidirectional long short-term memory network BiLSTM, which is used for predicting the occurrence dynamic state of important pests in a field and an orchard with high precision. Comprising the following steps: selecting three monitoring sites in a main crop producing area; and establishing a time sequence data set of the corresponding relationship between the average daily temperature, the rainfall and the effective accumulated temperature and the number of pests in ten days. According to the method, a hybrid neural network prediction model is constructed, the model comprises five function modules, and the model can accurately early warn annual dynamic changes of main crop main pest populations and judge peak values, and helps farmers establish efficient pest prevention and control measures.

The invention discloses a ship track prediction method based on a local wandering activity scene, and belongs to the technical field of intelligent maritime affair supervision, and the method comprises the steps: obtaining AIS data, carrying out the preprocessing, and dividing the AIS data into a training set, a verification set and a test set; constructing a hybrid neural network prediction model; inputting the training set into a hybrid neural network prediction model, and carrying out training optimization through MSE and Adam; inputting the test set into the trained hybrid neural network prediction model to obtain a prediction result, dynamically updating by using rolling prediction, and calculating an actual distance error by using a Haversine formula; and performing trajectory prediction and dynamic updating by using the trained trajectory prediction model. Advantages of multiple models are fused, data quality is guaranteed through preprocessing, prediction is accurate, dynamic updating is reliable, and maritime affair supervision efficiency is improved.

The invention discloses a flow control method based on a neural network, and relates to the technical field of network flow control, and the method comprises the steps: collecting network flow metadata, constructing a standardized feature data set, carrying out the flow data classification and prediction based on a hybrid neural network, generating a differential flow control strategy, and carrying out the strategy optimization and dynamic adjustment; statistical features are extracted by using flow metadata acquired in multiple environments, spatial-temporal feature fusion and attention weighting are carried out in combination with a convolutional neural network and a long-short-term memory network, and network state recognition and flow trend analysis are realized; based on an analysis result, generating a control strategy adaptive to different network environments, and performing strategy optimization and real-time adjustment through reinforcement learning; the problems of accurate flow control and dynamic strategy adjustment in a complex network environment are effectively solved, and the accuracy, the adaptivity and the system stability of flow control are improved.

The invention discloses a multivariable predictive control energy consumption adjustment method and system, and belongs to the technical field of automatic control, and the method comprises the steps: collecting a parameter adjustment log in real time through an edge node, carrying out the intervention behavior recognition, and verifying the validity, so as to detect a manual parameter adjustment event; once an artificial parameter adjustment event is detected, multivariable data before and after intervention are extracted, and parameters of the local prediction model are dynamically corrected; performing rehearsal intervention based on the manual intervention parameters, generating space-time coupling constraints, and constructing a hybrid neural network to generate an energy consumption prediction trajectory; dividing types according to operator behavior modes, fusing prediction data to generate a comprehensive state vector, adjusting a reward function, focusing a sensitive variable, and generating a control instruction; and acquiring an actual energy consumption value in real time, comparing the actual energy consumption value with an energy consumption prediction track, calculating an energy consumption deviation, performing secondary optimization, positioning an error root cause through multi-scale decomposition in combination with a semantic tag and a knowledge graph, and performing layered compensation.

The invention belongs to the field of aero-engines and turbines, and provides a quick prediction method, system and device for a two-dimensional viscous compressible flow field of a gas compressor and a medium, and the method comprises the steps: obtaining CFD simulation data of a two-dimensional blade profile of the gas compressor under multiple working conditions, and carrying out the preprocessing to obtain a standardized training data set; on the basis of the data set, constructing a hybrid neural network model which takes a blade profile geometric parameter and an operation condition parameter as input and takes a two-dimensional viscous compressible flow field as output; combining mean square error, gradient loss and physical consistency loss based on RANS equation residual error to construct a total loss function, and performing end-to-end training on the model; during reasoning, to-be-predicted parameters are input into the trained model according to the same preprocessing mode, and then a flow field prediction result can be rapidly output. According to the method, the problems of long consumed time, low precision and poor consistency of traditional CFD simulation can be solved, millisecond-level flow field prediction is achieved, high precision and high physical consistency are achieved, and the method is suitable for efficient optimization design of the blade profile of the gas compressor.