3005 results about "Network architecture" patented technology

The invention relates to the technical field of cable operation state detection, and particularly discloses a multi-source data driven cable operation state comprehensive evaluation method, which comprises the following steps of S1, adopting a layered distributed sensing network architecture, and deploying three types of core sensors at key nodes of a cable, through space-time calibration of the multi-source heterogeneous sensor, data consistency is improved, fusion deviation is eliminated, the problem of data islands of a traditional system is solved, and a precise evaluation foundation is laid; noise suppression and dynamic correlation modeling are adopted, environmental interference is stripped, a vibration and displacement coupling relation is quantified, limitation of a single parameter is broken through, heterogeneous fault features are captured, and evaluation comprehensiveness and sensitivity are improved; a self-adaptive threshold mechanism is constructed based on environment weight and historical data, the bottleneck of a fixed threshold is broken through, an evaluation standard is corrected along with equipment aging and environment change, misjudgment is avoided, and diagnosis robustness in different scenes is enhanced.

The invention discloses a trajectory optimization method based on a multi-source heterogeneous positioning data fusion algorithm, and relates to the technical field of intelligent navigation and high-precision positioning, multi-modal data are acquired through a multi-modal sensor array, positioning redundancy of scenes such as tunnels and indoor scenes is enhanced, a weight distribution strategy is dynamically adjusted through an Actor-Critic network architecture, and the positioning accuracy is improved. The state space input comprises an environment semantic tag, a historical error sequence and a real-time noise variance, the output action space is continuous weight distribution of each data source, a multi-target reward function optimization strategy is combined, scene adaptability is realized, a local SLAM map, inertial navigation error parameters and a weight distribution strategy are shared in real time based on a V2X protocol, and the real-time performance of the system is improved. According to the method, a single device accumulative error is compensated by using adjacent vehicle data, a terminal locally trains an error compensation model, parameters are uploaded to a cloud end through differential privacy encryption, the cloud end adopts a FedAvg algorithm to aggregate a global model and issue the global model, the error difference between devices is inhibited, and dynamic road network updating and scene differentiation model distribution are supported at the same time.

The invention discloses a power load prediction method and system based on a meteorological prediction model, and relates to the technical field of load prediction, and the method comprises the following steps: obtaining historical power load data and corresponding multi-dimensional meteorological data, constructing a meteorological coupling effect analysis model, calculating a dynamic coupling weight matrix among the meteorological parameters through an attention mechanism network; performing feature fusion on the coupling weight matrix and the multi-dimensional meteorological data through a gating circulation unit to generate a space-time coupling feature vector; establishing a power load prediction model, and extracting a short-term fluctuation feature and a long-term trend feature of the power load from the space-time coupling feature vector based on a deep residual network architecture and a parallel convolution channel; the dynamic coupling weight matrix is updated based on prediction error back propagation, and adaptive adjustment of the meteorological parameter coupling relation is achieved. According to the method, dynamic fusion of the spatial-temporal characteristics is realized through the cross gating unit, so that the model can perform power load prediction by using spatial-temporal information, and the prediction precision is improved.

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

Embodiments described herein relate to systems and methods for automatically generating content for a generative answer interface of a collaboration platform. The system receives a natural language user input identifying corresponding blocks of text or snippets using a content extraction service. A prompt is generated using the blocks of text and is used to obtain a generative response. The generative response and links to corresponding content are displayed in the generative answer interface and can be inserted into content of the collaboration platform. The systems and methods described use a network architecture that includes a prompt generation service and a set of one or more purpose-configured large language model instances (LLMs) and / or other trained classifiers or natural language processors used to provide generative responses for content collaboration platforms.

The invention relates to the technical field of chip design, in particular to a system-on-chip design method and system and a medium. The method comprises the following steps that a chip design target and basic parameters are collected, a constraint condition set is generated through demand constraint analysis, the basic parameters of a system are subjected to parallel recombination, functional prototype data are generated, the functional prototype data are used for chip behavior design, functional unit groups are generated, a network architecture is reconstructed to form a functional module network, and the functional module network is used for chip behavior design. And carrying out clock domain mapping and competition compensation processing based on the network to obtain a function scheduling sequence, carrying out resource matrix remapping on the basic parameters according to the function scheduling sequence, generating hardware abstract data, carrying out chip layout simulation, and finally generating a simulation chip blueprint. According to the system-on-chip design method, the more efficient system-on-chip design method is achieved.

The invention belongs to the technical field of bearing residual service life detection, and discloses a rolling bearing residual service life prediction method and system based on a convolution white box. According to the method, time domain and frequency domain feature extraction, noise reduction processing and health state and degradation state division are carried out on an original vibration signal, and deep neural network training is carried out in combination with a Weibull-MSE loss function, so that prediction of RUL conforms to the actual degradation process of a bearing; and synchronous extraction of long-time dependence information and local degradation characteristics of the bearing signal is completed through a convolution CRATE network architecture fusing an expansion causal convolution attention mechanism DCA and a multi-scale convolution MSC. According to the method, the accuracy of RUL prediction is improved in combination with health state evaluation, the local feature extraction capability is enhanced, the local modeling capability of the model is improved through multi-scale information fusion, and the interpretability and the prediction credibility are improved through a CRATE structure.

The embodiment of the invention provides an end-to-end automatic driving control method and device based on multi-camera fusion, and multi-view target detection and tracking are realized through spatial transformation and coordinate mapping by combining front wide-angle camera information and left and right wide-angle camera information. A multi-view feature fusion network architecture is designed, the multi-view feature fusion network architecture comprises three sub-networks of feature extraction, dynamic weight distribution and feature fusion, and the fusion weight is dynamically adjusted based on image definition, detection confidence and view overlapping degree. A geometric consistency constraint between visual angles and a reconstruction loss function are introduced, a deep neural network model is constructed, abnormal conditions such as camera shielding are effectively handled, and an accurate control instruction is output. According to the method, the defects of the traditional technology in the aspects of multi-view information fusion, shielding processing and the like are overcome, and the sensing ability and the control reliability of the automatic driving system are remarkably improved.

The invention discloses an electric vehicle parking lot fire prevention and control system based on multi-source information fusion. A sensing layer captures the environment state of an electric vehicle parking lot and the operation parameters of a vehicle battery in real time through a multi-mode sensor network; the network layer adopts an intelligent fusion network architecture to realize communication between the sensing layer and the data processing layer; the data processing layer is used for performing fusion analysis on the environment state and the operation parameters, judging a fire risk level and generating a dynamic control instruction; the data processing layer is composed of a multi-dimensional analysis engine, and a hierarchical progressive intelligent analysis architecture is adopted to realize multi-level research and judgment of fire risks; and the application layer receives the dynamic control instruction generated by the data processing layer, and realizes full-process closed-loop management of fire prevention and control through a multi-mode terminal device. The method not only improves the fire prevention and control precision of the electric vehicle parking lot, but also has good interpretability, and can be directly applied to a fire prevention and control system of the electric vehicle parking lot.

The invention discloses a multi-label electrocardiogram classification method based on self-supervised pre-training and multi-modal semantic alignment, which belongs to the technical field of artificial intelligence, and comprises the following steps: realizing self-supervised pre-training of unlabeled data through a single-modal contrast enhancement network, generating global and local contrast views by adopting a multi-scale random cutting strategy, and classifying the global and local contrast views in a multi-scale random cutting mode; in combination with a teacher-student network architecture, the potential invariance features of the ECG signals are learned while negative sample dependence is avoided, the problem of annotation data scarcity is effectively relieved, and the feature robustness is improved. A multi-modal fusion mechanism based on label semantic guidance is provided, a time domain signal and a frequency domain time-frequency graph are mapped to a unified semantic space through fine-grained semantic alignment, local feature enhancement and cross-modal complementary information fusion are realized by using a cross attention mechanism, and the problem of semantic difference caused by modal heterogeneity in a traditional method is overcome. A multi-label comparison loss function based on a disease co-occurrence relation is proposed, a category discrimination boundary is dynamically optimized by modeling a label co-occurrence probability, the feature separability of a tail category is improved while the head category discrimination ability is enhanced, and the problem of sample category imbalance in a multi-label scene is remarkably relieved.

Generally, the present disclosure is directed to efficient neural network architectures, techniques for constructing new efficient neural networks, and approaches to low-latency execution of neural networks. In an example aspect, the present disclosure provides a more powerful configuration of an inverted bottleneck block that maintains an efficient execution profile. An example search system can parameterize a universal inverted bottleneck block with a first parameter that categorically activates a spatial mixing operation in the unexpanded state. In this manner, for instance, a large variety of different network architectures can be explored using a relatively compact search space that admits high levels of parameter sharing. Further, the present disclosure introduces an efficiency-optimized multi-query attention block.

Embodiments described herein relate to systems and methods for automatically generating content, generating API requests and / or request bodies, structuring user-generated content, and / or generating structured content in collaboration platforms, such as documentation systems, issue tracking systems, project management platforms, and other platforms. The systems and methods described use a network architecture that includes a generative interface panel having multiple automated assistant services. Each assistant service may access a prompt generation service and a set of one or more purpose-configured large language model instances (LLMs) and / or other trained classifiers or natural language processors used to provide generative responses for content collaboration platforms.

The invention provides a three-dimensional seismic fault identification method based on double-attention multi-scale fusion U-Net. The method comprises the following specific steps: constructing a U-shaped network architecture comprising an encoder, a decoder and jump connection; a constructed multi-scale feature fusion module is embedded in the first level of the encoder, and the extraction capability of fault features of different scales is enhanced through a multi-branch structure; introducing the constructed hole fusion modules into the second and third levels of the encoder, designing and expanding a receptive field by using multiple expansion rates, and capturing fault structures of different scales; a double-attention parallel mechanism is integrated in jump connection, and the sensitivity of channel attention and space attention to fault features is improved; constructing a combined loss function; and finally, performing three-dimensional seismic data training and reasoning based on the optimized model to realize high-precision fault identification. The method has high generalization and accuracy, and especially has good performance in the aspect of seismic image fault identification containing a large fault scale span.

The invention relates to the technical field of data processing, in particular to a multi-source target trajectory fusion method and system based on deep learning. Comprising the following steps: constructing a space-time alignment training set containing radar echo data, satellite telemetry data and truth value trajectory data; designing a multi-channel deep learning network architecture which comprises a radar echo data feature extraction channel and a satellite telemetry data feature extraction channel, and establishing dual-channel feature association through an attention mechanism; optimizing network parameters through back propagation to obtain a weight prediction model; radar echo data and satellite telemetry data are received in real time, and space-time alignment of sampling data is achieved through a sliding window mechanism and coordinate system conversion; inputting the preprocessed double-source features into the trained weight prediction model, and generating a radar-telemetering fusion weight coefficient; and performing weighting processing and optimization on the double-source trajectory coordinates, and outputting a fused target motion trajectory. The problem that a traditional single-source track measurement system is insufficient in precision in a complex environment is solved.

The invention relates to the technical field of traffic control, in particular to a traffic signal cooperative control method based on multi-agent reinforcement learning, and the method comprises the following steps: modeling each signal lamp intersection in a road network as an agent, and constructing a distributed multi-agent control environment; dividing the whole road network into a plurality of small subnets according to spatial correlation, and sharing and aggregating traffic information in the subnets through a neighborhood information sharing mechanism; utilizing a space-time diagram attention network to extract traffic state characteristics including a space-time dependency relationship in an intersection agent and a neighborhood thereof; and defining a traffic state, a traffic signal control strategy, a reward and punishment function, a network architecture and a target function required by training of the distributed intelligent agent, and carrying out joint training on the distributed intelligent agent until a training target is achieved. According to the invention, the real-time sensing capability of the signal control intelligent agent to the traffic flow dynamic state can be improved, and the collaborative decision-making capability among multiple intelligent agents is enhanced.

The invention discloses a heterogeneous computing multi-target adaptive task scheduling method based on deep reinforcement learning, and the method comprises the following steps: S1, constructing a multi-dimensional dynamic perception model of a heterogeneous computing environment, and collecting and computing node performance indexes, task feature parameters and network states in real time; s2, defining a reward function as a multi-target weighted combination, fusing task completion time, energy consumption, resource utilization rate and cost, and dynamically adjusting the weight by a fuzzy comprehensive evaluation algorithm; s3, establishing a dual-channel deep reinforcement learning network architecture based on an attention mechanism; s4, establishing an adaptive exploration mechanism, combining an epsilon-greedy strategy and entropy regularization, and balancing exploration and utilization; the method has the beneficial effects that dynamic balance of multiple indexes such as task completion time, energy consumption and resource utilization rate is realized through combination of deep reinforcement learning and multi-objective optimization, a dual-channel network and a cross attention mechanism are adopted, and a task time sequence characteristic and a topological dependency relationship are modeled at the same time, so that a scheduling strategy is more accurate.

The present invention relates to the technical field of railway inspection, and relates in particular to a railway foreign-object intrusion detection method and system based on deep learning. The method comprises: S10, acquiring image data to undergo detection; and S20, inputting the image data into a trained attention semantic segmentation network to obtain a foreign-object detection result. The attention semantic segmentation network is obtained by first training a preset attention semantic segmentation network architecture using a preconfigured data set, and then re-training the attention semantic segmentation network on specified image data using an adaptive correction algorithm. A backbone network architecture is obtained by inserting a specified attention mechanism at a specified position within a pre-selected residual neural network, and using three parallel dilated convolutions as an initial convolutional layer in the residual neural network. A dual-branch decoder combines an edge recognition branch and a semantic segmentation branch. The method is applicable to various scenarios, achieves improved detection accuracy and maintains a lightweight design.

The invention discloses a multi-mode perception and reinforcement learning engineering machinery intelligent regulation and control method and system, and belongs to the technical field of intelligent control and industrial automation. According to the system, stratum data within the range of 20-50 meters in front of a shield tunneling machine are collected in real time through multi-mode sensing equipment such as a distributed optical fiber sensor, a cutterhead vibration sensor and an electromagnetic wave radar, and original data are processed by adopting a wavelet-Fourier combined noise reduction algorithm; and inputting the processed multi-modal data into a reinforcement learning intelligent decision-making model based on a CNN-LSTM hybrid network architecture. The model is trained through a dynamic reward function, and weight coefficient combinations can be automatically switched according to different construction scenes. According to the system, parameters such as the rotating speed, the thrust and the grouting amount of a shield cutter head are regulated and controlled in real time through the self-adaptive control module, and the slurry utilization rate is increased through the gradient pulse grouting technology. The precision, efficiency and safety of shield construction are remarkably improved, and meanwhile energy consumption and construction cost are reduced.

The invention discloses an industrial internet of things-oriented equipment digital twin operation and maintenance management system, and relates to the technical field of equipment management. The system comprises a data acquisition and preprocessing module, a digital twin model construction module, a data transmission and storage module, a state monitoring and fault diagnosis module, an operation and maintenance decision and optimization module and a visual interaction module. The data acquisition module adaptively acquires data through a sensor and preprocesses the data; the model construction adopts multi-scale and multi-model fusion; a hybrid network architecture and an encryption technology are used for transmission and storage; performing feature fusion and transfer learning for monitoring diagnosis; reinforcement learning and multi-agent collaboration are used for decision optimization; visual interaction supports VR / AR fusion. According to the invention, multiple modules work cooperatively, accurate acquisition, efficient transmission and storage of data are guaranteed, and accurate fault diagnosis and scientific operation and maintenance decision are realized through an advanced algorithm; the operation experience is improved through visual interaction; the method also has energy consumption optimization and supply chain cooperation capabilities, and can improve the operation and maintenance efficiency of industrial equipment and enterprise benefits.

The invention discloses a generative AI endogenous communication network architecture and a hierarchical collaborative scheduling method, and relates to the technical field of communication networks, the communication network architecture comprises an intention extraction layer, a mapping adaptation layer and a network execution layer, the intention extraction layer processes multi-modal data by using a generative AI model to obtain a semantic demand representation of a user, and the mapping adaptation layer carries out mapping on the semantic demand representation of the user; the mapping adaptation layer maps the semantic demand representation into a service quality index by using a generative AI model, maps a real-time network state of a heterogeneous network in the network execution layer into a network performance index, and generates a transmission scheduling strategy based on the service quality index and the network performance index, and the network execution layer works based on the transmission scheduling strategy, so that the network performance of the heterogeneous network is improved. End-to-end deterministic transmission scheduling of data packets of a service is implemented. According to the invention, efficient deterministic transmission scheduling of intelligent computing services in a 6G network can be realized.

The invention discloses a multi-unmanned aerial vehicle negotiation anti-collision method and system based on 5G unmanned aerial vehicle communication, and belongs to the technical field of multi-unmanned aerial vehicle risk avoiding. The method comprises the following steps: collecting multi-source flight state data; after the multi-source flight state data are fused through Kalman filtering, an airspace situation map is constructed; a 5G network architecture is constructed, and real-time data interaction between the unmanned aerial vehicles is realized; a dynamic negotiation protocol is constructed, and fast exchange of obstacle avoidance proposals when collision risks are triggered by multiple machines is supported; and constructing a distributed collaborative obstacle avoidance algorithm, and generating a smooth obstacle avoidance path in real time. According to the invention, through a dynamic negotiation protocol, multiple machines are supported to rapidly exchange obstacle avoidance proposals when a collision risk is triggered; a global optimal strategy is generated through edge node intelligent arbitration; the priority mechanism gives consideration to task urgency and environment dynamic change, ensures that a high-value task unmanned aerial vehicle passes preferentially, balances fairness through distributed voting, avoids path stiffness caused by decision conflict or resource competition, and realizes efficient and reasonable collaborative decision.

The invention relates to the technical field of intelligent weather forecast, and discloses a WRF wind speed simulation correction method, system and device and a storage medium, and the method comprises the steps: obtaining multi-source basic meteorological data, and carrying out the preprocessing; constructing a fan field feature matrix according to the fan field distribution data; inputting the preprocessed data and the wind field feature matrix into a multi-source space-time convolution fusion module, and extracting and fusing time sequence features and space correlation features; inputting the fused features into a normalized convolutional neural network introducing physical constraints, and extracting spatial-temporal features of the wind speed field; and performing channel splicing on the spatial-temporal characteristics of the wind speed field and to-be-corrected wind speed data, inputting the spliced data into a multi-scale convolutional wind speed correction network, and obtaining a correction result of the wind speed simulation data through encoding and decoding operations. Through a multi-source data dynamic fusion mechanism, a spatial-temporal feature collaborative optimization algorithm and a multi-scale correction network architecture, system errors of traditional numerical mode simulation are remarkably reduced, and the accuracy of wind speed forecasting is greatly improved.

The invention discloses a cross-regional vehicle-mounted network switching method and system based on digital twinning, and a medium. The method comprises the following steps: constructing a cloud side end digital twinning network architecture; acquiring vehicle state data and traffic environment sensing data through the terminal sensing layer; carrying out data aggregation on the vehicle state data and the traffic environment perception data through edge computing nodes to obtain a local digital twinborn model; meteorological data, operator base station data and a local digital twinborn model are integrated through a cloud digital twinborn center to obtain a global digital twinborn model, and the traffic congestion condition and the network congestion condition of each road network region in a future time period are predicted according to the global digital twinborn model. Determining a network switching strategy according to the traffic congestion condition and the network congestion condition; and performing a network switching decision on the target vehicle through the edge computing node. According to the method, the timeliness and accuracy of cross-regional vehicle-mounted network switching are improved, so that the stability and reliability of the vehicle-mounted network are improved, and the method can be applied to the technical field of the Internet of Vehicles.

The invention relates to the technical field of security management, in particular to an AI-based intelligent security remote inspection and control method, which comprises the steps of front-end intelligent equipment deployment, intelligent inspection execution, data processing and intelligent analysis and remote control and linkage response. Compared with the defects of incomplete information and high false alarm rate due to the fact that equipment appearance anomaly detection mainly depends on single-modal data in the prior art, the scheme adopts multi-modal data preprocessing and feature extraction, constructs a cross-modal alignment network architecture, dynamically fuses laser radar, RGB-D images and IMU data by using a self-attention mechanism, and improves the detection accuracy of the equipment appearance anomaly. According to the method, more accurate equipment appearance abnormity identification is realized by combining environment self-adaptive adjustment, and the model is compressed and deployed to an edge end through a knowledge distillation technology, so that real-time reasoning and analysis are realized, the intelligent level of security inspection and the accuracy of abnormity detection are remarkably improved, the false alarm rate is reduced, and the practicability and reliability of the system are enhanced.

The embodiment of the invention provides an automatic driving method and device based on a multi-dimensional reward function, and the method and device achieve the control of a driving motion through the combination of an imitation learning framework and a reinforcement learning framework, collection of environment information through a plurality of cameras, and construction of a strategy generation network and a discriminator network. A multi-dimensional reward function model is designed, behaviors such as red light running, line pressing, lane departure and collision are detected and evaluated in real time, and a driving behavior reward and punishment matrix is constructed. Based on an actor evaluation network architecture, environment information and a navigation instruction are input into an actor network to generate an optimal driving action, and the action value is evaluated through the evaluation network to realize dynamic parameter optimization. According to the method, the defects of the traditional technology in the aspects of driving behavior evaluation, action value judgment and the like are effectively overcome, and the safety and reliability of the automatic driving system are remarkably improved.

An unmanned aerial vehicle (UAV) agent for forming a two-tier hybrid smart agriculture network architecture is provided. The UAV agent is configured to support two communication protocols, a short-range communication protocol and a long-range communication protocol. The agriculture sensor gathers agriculture data such as soil temperature and participates in a first-tier short range wireless communication network to send the collected data to at least one UAV agent. A UAV agent participates in one or more first-tier short-range communication networks to pick up sensor data from agriculture sensors in one or more clusters and also participates in a second-tier long-range communication network to route the collected sensor data to at least one cloud server. The tasks in both tier networks are formulated as optimization problems to achieve optimal data delivery and solved by using multi-agent reinforcement learning (MARL), which is implemented by the invented Focus Coordination Multi-Agent Deep Deterministic Policy Gradient (FC-MADDPG) algorithm.

Systems and methods for prioritizing various security findings to allow a security platform to focus on a proper subset of (e.g., the most important) one or more software application stacks of an enterprise are described. In one embodiment, a method includes generating a profile for an enterprise that indicates one or more software application stacks and a network architecture for the one or more software application stacks, determining one or more vulnerability features of the one or more software application stacks, generating one or more exploitability scores by a first machine learning model based at least in part on the one or more vulnerability features, determining a proper subset of the one or more software application stacks based at least in part on the one or more exploitability scores, determining one or more vulnerabilities of the proper subset of the one or more software application stacks and one or more vulnerabilities of the network architecture, providing the one or more vulnerabilities of the proper subset of the one or more software application stacks, the one or more vulnerabilities of the network architecture, and the profile as input to a second machine learning model, generating an inference by the second machine learning model that indicates one or more attack paths for an attacker in the proper subset of the one or more software application stacks and the network architecture, and transmitting the inference to a storage location or a security software application.

The invention relates to a microphone array sound source positioning method and system based on cross-correlation-beam forming closed-loop optimization, and belongs to the technical field of sound source positioning. The method comprises the following steps: collecting multichannel sound signals through a microphone array and preprocessing the multichannel sound signals to extract time-frequency features and suppress noise interference; time delay information among the microphones is estimated by adopting a generalized cross-correlation phase transformation algorithm, and an optimization strategy is introduced to improve estimation stability and anti-interference performance; enhancing the target sound source signal in combination with a minimum variance undistorted response beam forming algorithm and an adaptive Kalman filtering mechanism; constructing a closed-loop feedback optimization mechanism based on the beam output signal to realize feedback adjustment; and adopting a hybrid network architecture, taking the beam output signal amplitude spectrum as input, and outputting the frequency spectrum or mask of the obtained target sound source signal. The method has the advantages of high calculation efficiency, high positioning precision and strong anti-interference capability, and is suitable for real-time acoustic signal processing in a complex environment.

The invention provides a motor defect identification method fusing time sequence space feature extraction and reinforcement learning. The method comprises the following steps: building a Transform-GAT model architecture T-GAT, and learning time sequence relevance and spatial topological structure features by the T-GAT to form a space-time composite representation vector; designing a dual-network architecture reinforcement learning weighted fusion mechanism of a strategy network and a value network, and after the strategy network and the value network of a parallel structure receive the composite vector, constructing a strategy function to select a defect type with the maximum probability; designing potential energy function quantization parameters, and constructing a reward function to generate a reward in combination with a difference value; taking a reward function as a target, training and optimizing model parameters of the dual-network architecture in an off-line manner, running a real-time decision in an on-line manner, and storing a tetrad to an experience pool to form a'perception-decision-feedback-update 'closed-loop mechanism; according to the method, motor defect identification is realized, downtime is reduced, and motor operation reliability and equipment operation efficiency are improved.

The invention discloses an AI generated face image authenticity discrimination method and device and a storage medium, and belongs to the technical field of computer vision and artificial intelligence security. The invention provides a detection framework based on composite scaling optimization, dynamic enhancement and incremental learning collaboration, depth, width and resolution collaboration expansion of a network architecture is optimized through a composite scaling strategy, and the training intensity is dynamically adjusted in combination with an adaptive data enhancement technology. And an incremental learning mechanism is introduced to realize continuous adaptation and knowledge accumulation of the model to a novel generation technology, so that the detection robustness of a diversified generation technology is remarkably improved while the calculation complexity is reduced. Based on verification of an authoritative test set, the method is superior to an existing method in the aspects of detection precision, generalization ability and continuous adaptability, has a better model efficiency balance characteristic, and provides reliable technical support for digital content security verification.