363 results about "Model network" patented technology

Disclosed is an industrial question answering model training method based on reinforcement learning and knowledge base matching, comprising the following steps: S1, collecting professional knowledge questions and answers in an industrial field to construct an industrial knowledge base, training a reward model, carrying out, for industrial knowledge questions and answers, matching comparison on outputs of an industrial question answering model and content of the industrial knowledge base, and obtaining reward values on the basis of similarities; S2, sorting the reward values, and using a sorting loss function to train and update parameters of a reward model network; and S3, carrying out industrial question answering model training, incorporating a penalty term for the reward values, and using a reinforcement learning algorithm to train the industrial question answering model multiple times to obtain an optimal strategy. According to the industrial question answering model training method based on reinforcement learning and knowledge base matching of the present invention, the reinforcement learning algorithm is used, and iterative training is carried out multiple times, thereby helping the industrial question answering model to learn and understand industrial professional knowledge and improving the question answering accuracy of the industrial question answering model.

The invention relates to the technical field of image processing, in particular to a dual-branch coding desert segmentation model network structure based on structure state space duality, which adopts multi-dimensional dynamic convolution to replace traditional convolution in the initial stage of an encoder, introduces a mamba2 module based on the structure state space duality into the backbone design of the encoder, and improves the robustness of the encoder. The efficiency and adaptability of the model are remarkably improved, a double-branch parallel design is adopted, one branch uses cavity convolution to extract multi-scale context information, the other branch reinforces feature expression through a mamba2 module, the model is connected in series with a space attention module and a channel attention module between an encoder and a decoder, and the algorithm is more accurate. The method has the advantages that the method is simple and easy to implement, interference of irrelevant information on segmentation results is suppressed, a deformable large kernel attention module is introduced to the tail end of a decoder, and global and local modeling capability of the model in processing desert complex boundary regions is effectively improved by combining flexibility of deformable convolution and global receptive field characteristics of large kernel convolution.

A natural gas pipeline multi-working-condition fault diagnosis method and system based on Bayesian adversarial attack and single-source domain transfer, relating to the technical field of mechanical fault detection and diagnosis. The core of the method is using the transfer learning technology to solve the problem of insufficient generalization ability of existing deep reasoning models when processing pipeline fault diagnosis tasks under different working conditions. The method mainly comprises the following steps: constructing an attack sample generator on the basis of a Bayesian network, wherein the attack sample generator is used for generating, by adding delicately designed tiny disturbance into an input sample, an attack sample that can cause an reasoning model to make an incorrect decision, so as to mine and analyze a defect of the reasoning model; constructing a domain discriminator on the basis of the Bayesian network, wherein the domain discriminator is used for assist in generating a high-concealment attack sample by means of adversarial learning between the domain discriminator and the generator, that is, there is almost no visible difference between the high-concealment attack sample and an original sample; and constructing a classifier on the basis of the Bayesian network, and by expanding the distance between the attack sample and an original decision boundary of the reasoning model, constraining the posterior distribution of network parameters of the reasoning model to be adjusted towards a higher score of the attack sample, thereby enhancing the adaptability and robustness of the model when facing disturbance in different domains. By means of the steps, the present invention effectively solves the problem of missing reporting and false reporting risk improvement caused by poor generalization ability of traditional deep learning models under different working conditions.

The invention provides a geophysical gravity and magnetic anomaly source model construction and rapid forward and reverse modeling method, and relates to the technical field of intelligent three-dimensional geological modeling and simulation. According to geological information of a research area, source body modeling parameters of a gravity and magnetic anomalous field are set for the research area, and a three-dimensional model matrix M capable of describing a plurality of underground anomalous bodies is obtained; constructing a fast forward modeling network, establishing nonlinear mapping from the three-dimensional model matrix to the simulated abnormal data through the fast forward modeling network, and calculating forward modeling response of the three-dimensional model matrix; and constructing an inversion network based on a concurrent module CFTBlock and combining a CNN and a Transform, and establishing nonlinear mapping from the gravity and magnetic abnormal data to a three-dimensional model matrix. The method has the advantages of fast and accurate forward modeling, high-resolution inversion and the like, and is suitable for better interpretation of actually measured gravity and magnetic data.

The invention is suitable for the technical field of short-term power load prediction and intelligent scheduling, and provides a short-term power load prediction method based on improved sparrow search algorithm (ISSA) optimization. The method comprises the following steps: constructing a multi-scene prediction task according to the time resolution and regional seasonal characteristics of a load; local features are extracted in combination with a convolutional neural network (CNN), time sequence dependence is modeled by a long short-term memory (LSTM) network, and an attention mechanism is introduced to strengthen key features; meanwhile, an ISSA is adopted to optimize a model network structure and hyper-parameters, the number of layers, the learning rate and the batch size of the CNN and the LSTM are adjusted in a self-adaptive mode, and the search efficiency and convergence performance of the ISSA are improved through Latin hypercube sampling, cosine annealing, dynamic spiral search and a Levy flight strategy. Simulation results show that the method can maintain high prediction precision under different time resolutions and regional and seasonal conditions, the generalization ability and cross-scene adaptability of the model are enhanced, and a stable and efficient load prediction scheme is provided for power grid dispatching optimization.

The invention relates to the field of computer vision target detection, and provides a chip packaging defect detection method applied to edge equipment based on YOLOv11m, and the method comprises the steps: obtaining a chip packaging defect data set, carrying out the data enhancement of the chip packaging defect data set, and dividing the chip packaging defect data set into a training set, a test set and a verification set; a model network structure is innovated, a backbone network adopts a Starnet network and is combined with C2CGA, SimAM and other modules, feature extraction and key feature capture are enhanced, a neck network introduces a GSConv technology and an improved module based on the GSConv technology, a feature pyramid structure is optimized, a detection head fuses multi-scale features by means of an innovative LWNBDet detection head, and efficient target prediction is achieved. Importing the data set into a detection model for training to obtain an improved model; according to the improved model, the detection speed is remarkably improved while the detection precision is maintained, the size and the calculation amount of the model are reduced, and the feasibility of deployment of edge equipment is improved.

The invention discloses a single-view three-dimensional dressed human body reconstruction method based on pixel and voxel feature fusion. The method comprises the steps of data set preprocessing, data set dividing, three-dimensional dressed human body reconstruction network construction, three-dimensional dressed human body reconstruction network training, three-dimensional dressed human body reconstruction network testing and reconstruction result displaying. In the pixel feature extraction network, the coordinate attention and the stacking hourglass network are combined, so that the pixel feature extraction precision is improved; in the voxel feature extraction network, a three-dimensional channel and space attention are introduced into the voxelization human body parameterization model network, and a multi-scale residual feature extraction module is combined to extract voxel features. And splicing the extracted pixels and voxel feature vectors, inputting the spliced pixels and voxel feature vectors into a multi-layer perceptron, and reconstructing a three-dimensional dressed human body model through Marking Cubes, thereby solving the technical problem of insufficient precision of the existing single-view three-dimensional dressed human body reconstruction method, and being applicable to the technical fields of artificial intelligence and computer vision.

The invention relates to the technical field of geophysical exploration, in particular to a multichannel deep learning magnetotelluric inversion method based on physical information constraint. The method comprises the following steps: generating a synthetic data set containing a geoelectric model and forward modeling response thereof, and adding a noise simulation actual observation condition; constructing a hybrid network architecture combining Transform and U-Net, taking apparent resistivity and impedance phase as dual-channel input, extracting global features by using an encoder, gradually recovering spatial resolution through a decoder, and outputting an underground resistivity model; network training adopts a composite loss function fusing model loss and data loss, and an inversion process is constrained by introducing a magnetotelluric forward modeling physical rule, so that a result is ensured to fit observation data and conform to a physical mechanism; after training is completed, preprocessed actual measurement data are input into the model, and a resistivity image can be directly obtained. The method is used for geological structure identification and reservoir interpretation, and the inversion precision and reliability are effectively improved.

The present invention belongs to the field of indoor and outdoor robot application scene reconstruction, specifically, a deep learning 3D reconstruction method based on FMCW laser radar and visual fusion, comprising the following steps: 1) setting multiple laser radars and cameras to ensure time synchronization; 2) calibrating multiple laser radars and cameras; 3) real-time collection of FMCW laser radar data and camera image data in each scene and angle, corresponding to timestamps; 4) processing the collected data as a training data set; 5) the data are input into the deep learning model network for training to obtain a 3D Gaussian model of dynamic and static objects in the scene; 6) after coordinate conversion and velocity calculation of the collected FMCW laser radar data, combined with the collected camera image data, input into the 3D Gaussian model, and the present invention obtains scene reconstruction information. The present invention uses the native velocity information of the FMCW laser radar, combined with visual information, to better characterize the characteristics of dynamic objects and achieve native dynamic and static distinction.

The invention relates to a face mask detection method based on YOLOv8 shielding and counterfeit attack resistance, and belongs to the field of artificial intelligence security application. According to the method, the precision and generalization of a detection model are improved by constructing a face mask detection picture data set with high sample capacity and high resolution; an original YOLOv8 target detection model is improved by adopting a self-defined convolution module, the convolution efficiency of the detection model is improved, the feature extraction capability of the model is enhanced, and the calculation complexity is reduced; an original YOLOv8 human body posture estimation model is improved by adopting a user-defined module and a loss function, and the feature fusion capability and the target positioning capability of the model are enhanced; by autonomously designing a face mask detection method based on YOLOv8 shielding and counterfeit attack resistance, high-precision, high-robustness and high-real-time face mask detection on a to-be-detected picture is realized. According to the method, real-time face mask detection can be realized, shielding counterfeit attacks in a real detection scene can be defended, and meanwhile, a self-defined data set and a model network structure are designed, so that the method has good detection precision and detection real-time performance.

The invention relates to the technical field of network optimization, and discloses an AI-driven real-time network optimization algorithm, which comprises the following steps: data optimization: preprocessing network state data by using a generative adversarial network, extracting optimization features and generating enhanced data; topology modeling: modeling network topology by adopting a graph neural network, and updating node features; resource optimization: dynamically adjusting calculation, storage and bandwidth resources by using deep reinforcement learning; global scheduling: optimizing resource allocation based on a multi-objective optimization method; secondary optimization: combining a Lagrangian relaxation method and a graph optimization technology to further optimize a scheduling strategy; and adaptive learning: improving the adaptive capacity of the system in a dynamic environment through self-supervised learning and meta-learning. Through a matrix operation technology based on a regular equation and in cooperation with an efficient data preprocessing process, the effect of improving the solving speed of the regression model is achieved, the problem that calculation is slow on a big data set in a traditional method is solved, and rapid and accurate regression analysis is achieved.

The invention discloses a remote sensing image semantic segmentation method based on geometric perception diffusion guidance, and is applied to the technical field of remote sensing image semantic segmentation. Comprising a training stage and a testing stage, in the training stage, original remote sensing images, nDSM corresponding to each original remote sensing image and real semantic segmentation images are selected to form a training sample set, and a segmentation everything model based on geometric perception diffusion guidance is constructed and trained; comprising an enhanced visual converter encoder, a diffusion prompt module, a segmented everything image prompt encoder, a segmented everything image mask decoder and a prompt level supervision strategy. In the test stage, various channel components of a to-be-detected remote sensing image are input into the trained model, and the model network outputs a remote sensing image semantic segmentation prediction map corresponding to an original remote sensing image. According to the method, multi-modal remote sensing data can be effectively fused, a multi-scale space structure is captured, and full-automatic semantic segmentation is realized, so that the segmentation efficiency and accuracy are remarkably improved.

The invention discloses a home scene image generation method based on Stable Diffusion. The method comprises the following steps: firstly collecting a multi-style home scene image-text data set for a home scene generation task; then, on the basis of the data set, a Stable Diffusion model of the text graph is trained by adopting a LoRA training strategy; meanwhile, the content structure of the generated home scene image is controlled in combination with a ControlNet network, so that a Stable Diffusion model which effectively meets a home scene image generation task is obtained; the method comprises the following steps: combining a Stable Diffusion model network with a home scene generation problem, taking a multi-style home scene graph as initial noise, and taking a design constraint as a condition for generating a structural scene graph; according to the invention, a small sample training set is adopted, so that various home scene designs can be generated; meanwhile, the skill requirement for model training of the user is low, and common users can also be well trained and applied.

The invention provides an edge server dynamic activation method and system based on deep reinforcement learning, and the method comprises the following steps: S1, building system models, including building a network model, an energy consumption model, a communication and service delay model and a state switching cost model, the network model including RES and MES; s2, optimizing a network model by calculating the sum of the cost of the energy consumption model, the communication and service delay model and the state switching cost model; s3, constructing a Markov decision process model, and improving RES stability; s4, predicting a traffic load; s5, calculating a baseline value; s6, training the network model through a centralized intelligent scheduling algorithm CDDS to obtain a trained strategy network model; s7, dividing the trained strategy network model through a federated distributed intelligent scheduling algorithm FDDS, and training the model based on federated learning to obtain a DDPG model; and S8, the DDPG model is deployed to each RES.

The invention discloses an OCT (Optical Coherence Tomography) retina image denoising method based on a high-frequency enhanced diffusion model, which is characterized in that a double-branch diffusion model network constructed based on frequency perception Fourier transform attention (FFTA) is used for separating different frequency domains, enhancing specific frequency domain features and fusing the specific frequency domain features into a spatial domain feature map to realize stronger frequency domain perception and processing capability; the invention relates to a time step T coding method for a module of frequency domain attention. The time step T of the diffusion model is used as a parameter to be coded into a frequency domain attention module to participate in self-attention matrix calculation, so that frequency domain feature processing is aligned with the iteration step number of the diffusion model, and different frequency domain information is pointedly processed at different time steps T; the frequency selective hopping mechanism uses pooling operation to obtain a high / low frequency characteristic pattern, so that the network has adaptive frequency domain retention capability for different local parts.

The invention discloses a measurement error prediction method and system for a current transformer, equipment and a storage medium, and the method comprises the steps: firstly collecting the error data of the current transformer under different test conditions to construct a feature data set, then constructing an error prediction model, training the model through the feature data set, and carrying out the prediction of the error of the current transformer. And finally, based on the current current data, the environment data and the trained model, predicting to obtain a measurement error of the current transformer. The model is trained by taking the environmental data as the features, so that the model can learn the influence of the environmental factors on the measurement error, the model prediction error generated by the influence of the environmental factors is reduced, and the prediction accuracy of the measurement error result is improved. And the hyper-parameters of the model are optimized by adopting a chaos guide adaptive algorithm, so that the optimization speed and the optimization precision of the model parameters are improved, the model network can quickly adapt to the influence of environmental factors on the measurement result of the current transformer, and the prediction accuracy is further improved.

The invention relates to a satellite-ground fusion network multi-target space-time switching decision-making method. The method comprises the following steps: S1, establishing system models based on a real environment and a Starlink satellite constellation, wherein the system models comprise a terminal mobile model, a network model and a communication model; s2, defining performance indexes of service quality and experience quality, and constructing a target function; s3, modeling a connection switching decision process: modeling the connection switching decision process as a Markov decision process; s4, proposing a space-time decision method for connection switching based on the double-mask double-depth Q network: obtaining an optimal switching strategy by utilizing continuous interaction between an intelligent agent and an environment; in the exploration stage and the utilization stage, action masks are used respectively to improve the learning efficiency of the intelligent agent in the high-dimensional action space. According to the method provided by the invention, the total switching frequency in the transmission process is reduced, the average connection duration is prolonged, the average throughput is improved, the delay is reduced, and meanwhile, dense switching in the transmission process is avoided.

The invention discloses an echocardiogram video classification method, system and terminal, and the method comprises the steps: constructing a classification model network which comprises a feature extraction module, a feature enhancement module and a feature aggregation module; obtaining an echocardiogram video, obtaining a plurality of standard section views according to the echocardiogram video, performing interpolation processing and feature extraction on the plurality of standard section views through a feature extraction module, and outputting a plurality of video features; inputting the plurality of video features into a feature enhancement module for aggregation enhancement of spatial features and time sequence features, and outputting a plurality of enhanced features; and inputting the plurality of enhanced features into a feature aggregation module for frame-level feature weighted fusion to obtain a plurality of key frame features, selecting related features from the plurality of key frame features, obtaining fusion features according to the related features, and classifying the fusion features to obtain a classification result of the echocardiogram video. According to the method, the classification accuracy of the echocardiogram videos is effectively improved.

The invention relates to the technical field of weather prediction, and discloses a weather prediction method based on an improved quantum long short-term memory network, which comprises the steps of inputting weather data into a CGRU model to perform spatial feature extraction on the weather data, and then inputting an output hidden state sequence into an HAQLSTM model to perform prediction, the HAQLSTM model is an improvement of a quantum long short-term memory network model, and the quantum long short-term memory network model is an improved quantum long short-term memory network model. A parameterized variable component sub-circuit is adopted, an attention mechanism and residual connection are added, a quantum long-short-term memory network serves as a time modeler, the residual connection enhances information transmission, the attention mechanism dynamically balances the importance of different parts of input data, in the processing process, a self-attention mechanism is adopted to calculate the correlation weight of input sequence elements, and the correlation weight of the input sequence elements is calculated. Four parallel parameterized variable component sub-circuits are used for carrying out key calculation so as to improve the model performance; the problems that an existing weather prediction model network is insufficient in expression ability, and the efficiency of capturing the long-term dependency relationship in the sequence is low are solved.

The invention discloses a digital twin three-dimensional city modeling and analysis method based on deep learning, and the method comprises the following steps: S1, collecting multi-view image data, extracting camera parameters, and building a three-dimensional city coordinate system; s2, constructing a volume modeling network based on a NeRF method, and generating a three-dimensional radiation field; s3, extracting semantic features of an intermediate layer, and constructing a spatial feature field; s4, dividing a local area and forming a local feature block; s5, inputting the local feature block into a quaternary capsule coding network, and generating a quaternary capsule unit containing existence, attitude, state and attribute vectors; s6, constructing a dynamic routing mechanism, and generating a structure capsule unit; s7, constructing a structure capsule atlas and binding city operation state data; s8, performing image rendering in combination with the atlas and the radiation field to generate a three-dimensional city model; and S9, performing state recognition, flow clustering and energy consumption classification analysis on the model. According to the invention, an integrated process of city modeling and state analysis is realized.

The invention provides a citrus tree canopy edge segmentation method based on YOLO-BBR. The method comprises the following steps: constructing a citrus tree canopy data set; a YOLO-BBR segmentation model is constructed; a frost ice optimization algorithm RIME is used to optimize hyper-parameters of the YOLO-BBR segmentation model; and the trained YOLO-BBR segmentation model is verified. The BoTNet module, the BiFPN and the frost ice optimization algorithm are fused into the YOLO model to obtain the YOLO-BBR segmentation model, so that the local information extraction capability of the model is improved, parameters are reduced, delay is minimized, features of different scales are fused by the model, image feature expression is improved, and image segmentation by the model is facilitated; through a frost ice optimization algorithm, a model network structure does not need to be changed, rapid convergence is carried out by using a swarm intelligence optimization iteration mechanism, and an optimal hyper-parameter combination is obtained, so that the model performance is improved.

The invention discloses a computer system based on a cross-model Internet of Things equipment access efficiency calculation model, which integrates an equipment performance sub-model, a network state sub-model and a response prediction sub-model. The identification module is used for identifying a device inherent identifier of the Internet of Things device to be accessed so as to obtain a response time statistical characteristic value and a historical communication parameter set; the prediction module is used for acquiring network state parameters in real time, calling the response prediction sub-model to calculate expected response time, and optimizing the historical communication parameter set to generate an initial communication parameter set; the comparison module is used for monitoring the actual response delay and the data packet loss rate of the current access equipment and obtaining a delay deviation value; and the adjusting module is used for dynamically adjusting the bandwidth allocation strategy in the initial communication parameter set when a preset condition is met, and synchronously updating the data analysis priority to generate a target communication parameter set. According to the invention, differentiated access control of different types of Internet of Things equipment can be realized.

The invention discloses an offline-to-online deep reinforcement learning method in a resource scheduling scene, and the method comprises the following steps: S1, obtaining resource scheduling data, and carrying out the data preprocessing; s2, performing offline training of the strategy model in combination with expert imitation learning and a multi-head attention mechanism; s3, using an SAC reinforcement learning algorithm to iteratively optimize the strategy model, refining the strategy model through dynamic interaction with a real-time environment, learning response resource changes, and carrying out online training of the strategy model; s4, a dynamic strategy updating module is used, optimistic and pessimistic strategy updating is combined, strategy model parameters are adjusted according to online feedback, and the trained strategy model network is returned; according to the method, the SAC reinforcement learning algorithm of online learning and the expert imitation learning technology of offline learning are fused, the priority experience playback and multi-head attention mechanism are introduced, and the efficiency and accuracy of resource scheduling can be effectively improved by dynamically updating the proxy strategy model.

A method comprises receiving a request to predict a deployment configuration for at least one application, analyzing code of the at least one application to identify one or more additional applications on which the at least one application will depend, identifying a plurality of network paths between the at least one application and the one or more additional applications, and using one or more machine learning algorithms to predict execution times for the at least one application over the plurality of network paths. The predicted execution times for the at least one application over the plurality of network paths are inputted to a network graph model. The network graph model predicts the deployment configuration for the at least one application based at least in part on the predicted execution times, wherein the deployment configuration comprises a subset of the plurality of network paths.

The invention belongs to the technical field of image processing, and particularly relates to an image tampering detection method fusing noise residual errors and compression artifacts and a program product. According to the scheme, noise residual features of compression artifact features of an original image are extracted through an error level analysis technology and an airspace rich model, and then an image tampering detection model with quality adaptability is constructed by combining the two types of features. The network model firstly extracts tampering features from image compression features and steganography noise dimensions through a convolutional layer in an ELA branch and an SRM branch, and then performs multi-scale feature coding through a lightweight MobileNetV2 network; feature interaction enhancement is realized in combination with an SECA attention mechanism; and finally, generating a pixel-level binary mask representing the tampered area through a feature fusion strategy. According to the scheme, the compression characteristic and the noise characteristic of the image can be fully utilized, and the detection precision of the model on the low-quality image and the robustness in a multi-quality scene are improved.

The invention relates to the field of machine learning, and discloses a semi-supervised multi-view clustering integration method and system based on width learning, and the method comprises the following steps: constructing a width learning-based auto-encoder which can carry out feature processing according to multi-view face image data, and is used for carrying out the feature extraction of an input face image, weights of different node features are calculated based on width learning; constructing a clustering integration model in combination with an auto-encoder based on width learning and bidirectional constraint propagation; and realizing clustering integration of the multi-view face image by using the clustering integration model. According to the method, clustering processing of multi-view data is carried out by utilizing the performance advantages of a width learning system, a model network with both efficiency and performance is obtained, a flow pattern structure and pairwise constraints are introduced, constraint propagation is adopted in the consensus process, sample information is enriched, and the consensus efficiency is improved. The robustness and accuracy of the clustering integrated network model based on width learning are effectively improved, so that the method is more applicable in actual scenes.

The invention belongs to the technical field of power system scheduling, and particularly relates to a photovoltaic energy storage scheduling method and system based on Gaussian kernel adaptive dynamic programming, and the method comprises the steps: constructing a state space containing multiple optimization targets based on a photovoltaic energy storage system; performing similarity measurement on the state space based on a Gaussian kernel function, and calculating a performance index function value of a non-representative point by using the performance index function value of the representative point; generating a control strategy in a state space through an execution network based on the multiple optimization targets; adopting a model network to predict state transition after execution of the control strategy; performing Gaussian kernel weighting by using the evaluation network to update and iterate the control strategy; and scheduling the photovoltaic energy storage system by using the control strategy after iteration convergence. Compared with the prior art, the method has the advantage that the consumption of computing resources is greatly reduced.

The invention discloses a PCB surface defect detection method based on improved YOLOv11. The method comprises the steps that a defect image data set of a PCB is manufactured; performing image processing on the collected data set, enriching the sample capacity, and constructing a complete data set required by model training; using a K-means + + algorithm to improve the generation mode of the anchor frame; constructing an improved YOLOv11 model, and training the improved YOLOv11 model by adopting the complete data set to obtain a trained improved YOLOv11 model; and detecting and identifying the defect image data of the PCB based on the trained improved YOLOv11 model. According to the method, the model network feature extraction capability is improved, redundant calculation is reduced, and the detection performance is improved; the requirement of a training model on hardware is reduced; the defect detection efficiency of the PCB is improved, the defect detection precision is improved, and the production quality of the PCB is improved.

The invention discloses a high-frequency statistical method and application thereof in artificial intelligence large model training or reasoning, and the high-frequency statistical method comprises the following steps: after an ASIC chip and a shared memory are initialized, first statistical data are obtained through self-circulation in an operating system and written into a memory partition corresponding to a first write index value; and starting a timer in the upper-layer application, obtaining the statistical data in the timing period, arranging the statistical data in the timing period, and updating the initial read index value. According to the high-frequency statistical method, accurate millisecond-level even sub-millisecond-level high-frequency statistical data can be provided, the traffic characteristics of a large model network such as the traffic characteristics of each network device in a period of time can be accurately described, accurate data support is provided for optimization of an artificial intelligence large model, and the training efficiency is improved.

The invention discloses an EMSDH-YOLO network-based detectable target detection method for a low-quality image, and relates to the technical field of target detection in industrial automation. According to the method, partial convolution in a fourth C3k2 module of a backbone network of a YOLO11 model and a C3k2 module at the tail end of a Neck network is replaced by efficient multi-scale convolution, and an EMS-ConvBlock module is constructed; adding large-kernel separable convolution into a spatial pyramid structure of a backbone network of the YOLO11 model, fusing a two-dimensional convolution solution idea, and constructing an LSK-SPPF module; the method comprises the following steps: adding the idea of Dynamic Tanh into a C2PSA module of a backbone network of a YOLO11 model, realizing adaptive calibration of attention weight by using hyperbolic tangent gating, and constructing a DyT-C2PSA module; the method comprises the following steps: adding an idea of channel shuffling and space rolling mixing into an up-sampling architecture in a YOLO11 model Neck network, and constructing an EU-SCMixer module; and a loss function of the YOLO11 model is optimized by adopting a Wise-IoU v3 method. According to the method, the detection precision of the mechanical arm on various grabbable objects can be improved when the mechanical arm carries out grabbing operation under the low-quality imaging condition.