561results about How to "Raise attention" patented technology

The invention discloses a PPY-YOLO-based steel surface defect detection method and a PPY-YOLO-based steel surface defect detection system. The PPY-YOLO-based steel surface defect detection method comprises the following steps: step 1, preprocessing a steel surface defect image; 2, a neck structure Faster-Neck, a deep learning module C3K2Faster and a spatial pyramid pooling module SPPFSim are introduced into the YOLOv11 network, and a PPY-YOLO target detection model is obtained; step 3, training a PPY-YOLO target detection model; and 4, acquiring a steel image, and inputting the steel image into the trained PPY-YOLO target detection model to obtain the category and positioning information of the steel surface defects. A new model is constructed, the backbone network and the neck network are improved, the backbone network is formed by connecting the input end of the C2PSA module with the output end of the SPPFSim module, and an up-sampling group and a down-sampling group are added to the neck network, so that the model has a relatively good detection effect.

The invention relates to the technical field of machine learning, in particular to a supervised time sequence water level data generation method and system and a storage medium. The method comprises the following steps: acquiring multi-source hydrological monitoring data; performing abnormal value identification and data preprocessing according to the hydrological monitoring data to generate standardized hydrological basic data; constructing a data generation model of the conditional generative adversarial network based on the standardized hydrological basic data; hydrological variable driven condition feature regulation and control are carried out on the data generation model, parameter optimization of the generation network is carried out, and hydrological adaptability enhanced condition input feature data are generated; and injecting the condition input characteristic data into the data generation model to generate initial water level time sequence data. According to the method, the rare extreme hydrological events are oversampled by using a hierarchical importance sampling and self-service method (Bootstrap), so that the performance of the generative model in rare event prediction is remarkably enhanced.

The invention relates to a tree neighbor context-based big language model RAG optimization method, which comprises the following steps of: reading a directory structure and content of a document, generating a corresponding directory tree, converting the directory tree into a Markdown format containing a title and a level, and automatically extracting according to a document template if no title exists; segmenting the document into knowledge blocks according to the priority; after the knowledge blocks are recalled, the context is expanded by using a tree adjacent context method, and a new knowledge block list is formed; grouping and sorting the expanded knowledge blocks according to document identifiers, splicing reference data based on different fields and confidence coefficients, and generating a character string set from the original document; and splicing prompt words by using the constructed reference data and the new knowledge block list, and sending the spliced prompt words to the large model for processing. According to the scheme, the performance and practicability of the large model in the information retrieval and answer generation process are comprehensively enhanced by optimizing context information, emphasizing key content, adapting to various document formats, improving user experience and the like.

The invention discloses a network threat deduction system and method based on Transform and a graph attention network model. The system mainly comprises four modules including a threat intelligence extraction module, a network threat entity construction module, a network threat deduction module and a deduction result application module. The method comprises the following steps: preprocessing and mining network security threat intelligence data by utilizing a graph attention network (GAT) model, constructing a complete network threat intelligence knowledge graph based on a data association relationship, and determining a vulnerability utilization weight in the graph; modeling is carried out on a reasoned path sequence based on a Transform model, a next reasoning sequence is predicted in a generative mode, and rapid and efficient network threat intelligence reasoning is achieved. According to the method, the complexity of attack deduction calculation is reduced, the deduction efficiency is improved, and security risk investigation and early warning can be carried out for a large-scale network environment.

The invention discloses a metal surface defect detection method. The method comprises the following steps: establishing a metal surface defect image data set; the metal surface defect image is preprocessed, and it is guaranteed that multiple defect types exist in the same image; a YOLOv7 network model is improved; carrying out training evaluation on the metal surface defect data set by using the improved YOLOv7 model, and selecting proper hyper-parameters in the training process; data obtained after model training are analyzed and judged, and the model is adjusted and optimized; and keeping the optimal model weight, and carrying out experimental testing on the trained model. The method comprises the following steps: establishing a multi-type metal surface defect data set; a polarization self-attention mechanism and an SPDConv module are introduced into the neck network, and important information is captured in a self-adaptive mode while image information is reserved; and an NWD loss function is introduced, and the positioning capability of the network on small-size defects is improved. The method can meet the requirements of metal surface defect detection under different working conditions, and has good robustness.

The invention belongs to the technical field of power distribution network fault recovery, and provides a power distribution network fault recovery method and system based on multi-agent reinforcement learning, and the method comprises the steps: taking a recovered load as a target function based on the load priority of a power distribution network, and taking the operation limitation of each node line in the power distribution network as a constraint condition; constructing a power distribution network fault recovery problem model; based on the power distribution network fault recovery problem model, constructing a multi-agent reinforcement learning agent set, and converting the power distribution network fault recovery problem into a Markov decision process; and a multi-agent reinforcement learning framework is constructed based on the agent set of multi-agent reinforcement learning, multi-agent training is carried out, each agent generates a decision according to respective observation information, and a fault recovery strategy is obtained. The objective function and the constraint condition constructed based on the load priority of the power distribution network and the node line operation limitation can ensure the accuracy and the feasibility of the recovery strategy. And the strategy generation time is shortened through the multi-agent parallel decision-making process.

The invention relates to a multi-modal implicit sentiment analysis method based on a multi-granularity attention mechanism, which belongs to the field of artificial intelligence, and comprises the following steps: S1, extracting visual features in image information by using a visual feature extraction strategy of multi-scale attention fusion; s2, generating a text description of the image information by adopting an image translation engine, splicing the text description with an original text, and extracting text features through a hierarchical attention mechanism; s3, carrying out normalization processing; s4, performing multi-modal fine-grained correlation fusion on the visual features and the text features by adopting a bidirectional cross attention mechanism, and extracting fine-grained features in the visual features and the text features through a multi-head attention mechanism and residual connection; and S5, fusing the fine-grained features by using an adaptive multi-modal classification module to complete sentiment classification. The multi-modal sentiment analysis method solves the problems that multi-modal sentiment analysis is insufficient in inter-modal correlation mining, insufficient in fine-grained information capture and large in noise interference.

The invention provides a data security traceability method and system based on artificial intelligence, relates to the field of data security, and is used for solving the problems of relatively poor data security traceability accuracy and response delay in the prior art. The method comprises the following steps: updating a user behavior attention model, and carrying out multi-dimensional aggregation analysis on all data flow paths comprising abnormal nodes and abnormal edges; determining the path with the highest comprehensive score value in all the data flow paths as a target abnormal path; performing data security traceability analysis on the target abnormal path to obtain a traceability analysis result; and determining a chain creation rule, taking each data circulation event on the target abnormal path as a block in combination with the chain creation rule, connecting the data circulation events in series to form a traceability chain, and starting a corresponding safety response strategy according to a traceability analysis result, the safety response strategy being obtained through self-adaptive generation according to the traceability chain. According to the technical scheme provided by the invention, the accuracy of data security traceability is improved.

The invention belongs to the technical field of Internet of Vehicles and communication security, and relates to a vehicle intrusion detection system and method based on a Hybrid EfficentNet model, and the system is obtained by training the Hybrid EfficentNet model. Wherein the Hybrid OfficientNet model comprises an initial convolutional layer, a backbone network, a self-adaptive global pooling layer, a Flaten layer, a discarding layer, a hidden layer and a classification layer; the backbone network is composed of one Fusion MBConv module and 15 MBConv modules, and each MBConv module comprises an expansion layer, a depth separable convolution module, a channel and space attention mechanism and a projection layer; residual connection is used in the MBConv module; the system has excellent capability of detecting abnormal traffic, realizes high accuracy, high-efficiency processing capability and high response speed, meets the requirement of convenient operation in a vehicle environment, and effectively solves the technical problems that an existing intrusion detection model is complex and inconvenient and cannot be directly deployed in a resource-limited vehicle-mounted network environment.

The invention belongs to the technical field of robot path planning, discloses an autonomous robot path planning method based on deep reinforcement learning, and aims at the problem that an agent is difficult to fully sense the environment in a complex dynamic environment, the State space of SAC is expanded and adjusted to enhance comprehensive sensing of an unknown environment and more flexible omnibearing obstacle avoidance. A heuristic reward function is designed, dynamic adjustment of reward values is achieved, and therefore the learning process of the robot is optimized. In order to solve the problems that the calculation complexity of an algorithm is increased and the convergence speed is reduced in a complex environment, the sample utilization rate is optimized in combination with a prior experience playback (PER) technology and a SumTree data structure. The weight of a high-value sample is increased through the PER technology, so that an intelligent agent can learn an effective strategy more quickly in the training process; and a SumTree data structure is introduced, so that the PER efficiency is further improved.

The invention provides a sick juvenile fish identification method based on an improved YOLOv10 model, and the method comprises the steps: collecting a fish image in a water area as a to-be-detected image, and carrying out the preprocessing of the to-be-detected image, and obtaining an image data set; the method comprises the following steps: constructing a YOLOv10-UDFishNet model; the image data set is input into the YOLOv10-UFFishNet model, the YOLOv10-UFFishNet model is trained through the image data set, and the trained YOLOv10-UFFishNet model is obtained; and inputting a fish image in a water area to be detected into the trained YOLOv10-UFFishNet model to obtain a fish detection result and position information of a fish individual target. According to the method, the sick juvenile fish in the turbid underwater environment can be accurately and efficiently identified.

The invention discloses a cross-modal foreground distillation method for multi-view three-dimensional target detection, and belongs to the technical field of automatic driving perception. According to the method, a laser radar or a multi-modal three-dimensional target detection model is used as a teacher model, a multi-view aerial view BEV perception three-dimensional target detection model is used as a student model, and the model performance is improved through multi-scale feature distillation; a space attention mechanism and a foreground mask module are introduced, and the student model can simulate feature responses of the teacher model in different space positions under multiple scales, focus on a foreground target and suppress background noise; the adaptive module ensures the consistency of the multi-scale features in feature dimensions and spatial resolution, and finally, the difference between the features and regression output is measured through loss functions such as mean square error and KL divergence, so that efficient cross-modal and multi-scale foreground feature distillation is realized. Finally, the multi-view three-dimensional target detection precision and efficiency are improved, and meanwhile, the distillation requirements of multi-scale features are fully considered.

The invention belongs to the technical field of seismic signal monitoring, and discloses a micro-seismic signal spectrum analysis system and method based on DVEC, and the system comprises a data collection device, a data preprocessing module, and a depth variation code-classifier. Acquiring a plurality of original vibration signals through a data acquisition device; performing fast Fourier transform (FFT) processing on the plurality of original vibration signals by using a data preprocessing module to obtain corresponding frequency domain sequence signals; splicing the original micro-seismic signals on a plurality of time domains and the corresponding frequency domain sequence signals in a dual-channel mode to form dual-channel micro-seismic signal input data; and inputting the dual-channel micro-seismic signal input data into the depth variational coding-classifier to obtain the classification category of each original micro-seismic signal. According to the method, different types of seismic signal features can be clearly distinguished, unknown types of seismic signals can be identified, and the method is expected to be an important technical means in the field of seismic signal identification.

The invention discloses an asphalt pavement structure performance analysis method and system, relates to the technical field of road engineering, and aims to solve the problem that an asphalt pavement structure performance analysis model is insufficient in self-adaptability, and the asphalt pavement structure performance analysis method comprises the following steps: obtaining test measurement data of a drop weight deflectometer; combining the adjacent structural layers according to the similarity of the structural layer materials; different numerical simulation models are established according to different configuration parameter combinations, and structural performance evaluation indexes are calculated respectively; different samples are established according to different numerical simulation models, weights of different samples are calculated and normalized, and a sample training structure performance index calculation model is established and input; and calculating a structural performance evaluation index of the target road section according to test measurement data of the drop weight deflectometer of the target road section by applying the structural performance index calculation model. According to the asphalt pavement structure performance analysis method and system provided by the invention, the attention of the trained model to a specific sample can be improved, and the adaptive ability and prediction precision of the trained model are improved.

The invention discloses a self-adaptive multi-hop retrieval knowledge graph construction method for large language model questions and answers. The method comprises the following steps: extracting triples of an input question and a corpus; self-adaptive multi-hop retrieval matching is carried out, and a specific triple related to the question in the corpus is retrieved and recognized; constructing a knowledge graph by using the question in the adaptive multi-hop retrieval matching process and a corpus triple; the answer predictor model comprises node embedding, message passing and selective filtering, and the node embedding is used for encoding questions and a knowledge graph; carrying out message transmission based on the coded problem and the knowledge graph based on a graph convolutional network; selective filtering is used for filtering redundant information of the knowledge graph; according to the method, the problem that the large language model generates illusion can be relieved, the complex reasoning process can be understood, and therefore the question answering and logical reasoning performance of the large language model is improved.

The invention discloses a charging pile electric energy consumption prediction method based on improved deep learning. The method comprises the following steps: preprocessing collected charging pile electric energy consumption data; extracting weather data, time features and special event features from the preprocessed data to form a feature data set; constructing a hybrid deep learning model by using a dynamic sparse connection network and a self-supervised learning deep pseudo-label generator, and generating a preliminary prediction result; a variational information bottleneck mechanism is introduced, and optimized feature representation is generated; an improved tensor fusion network is adopted to realize deep fusion and feature extraction of data; training the new feature representation by using a neural evolutionary strategy optimization algorithm to generate an optimized model; verifying the optimized model through a variational Bayesian verification method; and the reliability and the accuracy of the prediction result in real application are optimized by adopting an adversarial evaluation mechanism.

The invention relates to the technical field of artificial intelligence, and discloses a multi-modal model training fine-tuning optimization method based on artificial intelligence, which comprises the following specific steps: preparing and preprocessing data, collecting a large-scale multi-modal data set, constructing an image text data set containing one million high-quality images and text description accurately corresponding to the images, and performing fine-tuning optimization on the image text data set. According to the method, data are subjected to comprehensive and meticulous cleaning operation, noise data and mistakenly labeled data in the data are strictly removed, so that the accuracy and reliability of the data are ensured, and the effective utilization rate of the data can be increased by at least 20% through data cleaning; wherein the image data is screened, blurred, low-resolution and unclear-content images are removed, it is ensured that each image can clearly convey specific visual information, and text description, grammar error checking, spelling error and semantic ambiguity parts are corrected or deleted.

The invention belongs to the technical field of electroencephalogram signal detection, and particularly discloses an epilepsy electroencephalogram signal detection system and method based on a diffusive attention model.The system comprises an acquisition unit, a signal processing unit, a signal processing unit and a signal processing unit, the reconstruction unit is configured to be used for inputting the preprocessed electroencephalogram signals into a distraction attention model for denoising and extracting features related to epileptic seizure at the same time; wherein the distraction attention model reconstructs a signal by using a diffusion model through step-by-step denoising, and introduces an adaptive gradient correction term to iteratively optimize the signal; in the reconstruction process of each stage, the extraction of epileptic seizure related features is enhanced through a self-adaptive attention mechanism; and the detection unit is configured to use a classifier to classify the epileptic seizure related features to obtain an epileptic seizure detection result. According to the method, the feature extraction capability of the high-dimensional noise electroencephalogram signals is improved, and the accuracy of epileptic seizure detection is remarkably improved.

The invention discloses a multi-modal brain tumor image segmentation system based on a sparse attention mechanism, and relates to the technical field of medical image processing, and the system comprises a data obtaining unit which is used for obtaining multi-modal brain tumor data and carrying out the preprocessing of the multi-modal brain tumor data to obtain multi-modal data; the feature extraction unit is used for extracting a first specific feature Fm of each mode in the multi-mode data based on a first encoder; the feature fusion unit is used for obtaining a first feature f1 based on the first specific feature Fm and a first fusion token Ff; based on a modal sparse mask fusion Transform model, interacting the first feature f1 to obtain a second feature Z, based on the second feature Z, extracting a weight IMm of each modal, based on the weight IMm, obtaining a third feature # imgabs0 #, based on spatial dimensions, and based on the third feature # imgabs0 #, obtaining a second feature Z; the prediction unit is used for inputting the fourth feature # imgabs3 # into a brain tumor segmentation model to obtain a first loss L1, obtaining a prediction segmentation result based on the first loss L1, and obtaining a prediction segmentation result based on the prediction segmentation result; the problems that an existing segmentation mode based on shared feature representation is insensitive to brain tumor information, information redundancy occurs in brain tumor feature fusion when modals are lost, and important brain tumor information is ignored can be solved.

The invention relates to the technical field of sea surface temperature prediction, and discloses a sea surface temperature prediction method based on a spatio-temporal enhanced ConvGRU, which comprises the following steps: preprocessing a sea surface temperature data set obtained by ERA5 reanalysis data of an European mid-term weather forecast center, and dividing the sea surface temperature data set into a training set, a verification set and a test set; inputting the data of the training set into a deep learning algorithm model of spatio-temporal position attention enhancement for training, using the data of the verification set to evaluate the model precision and adjust parameters, and using the test set to evaluate the generalization ability and prediction precision of the model; the model comprises a multi-scale feature extraction module, a position attention enhancement module and a convolution gating cycle unit module. And preprocessing sea surface temperature data of a sea area to be predicted, and inputting the sea surface temperature data into the qualified model for sea surface temperature prediction. According to the method disclosed by the invention, the processing capability of spatial information can be improved, and the prediction accuracy is improved.

The invention discloses a gas outburst monitoring and early warning method and system based on an improved ConvLSTM model, and relates to the technical field of gas monitoring, and the key points of the technical scheme are that the method comprises the following steps: constructing a mine gas data set; cleaning the gas data set, converting unstructured data into structured data, and performing data cleaning, denoising and standardization on the structured data; performing high-order derived feature extraction on the preprocessed gas data set; an improved ConvLSTM model is constructed; using a historical gas data set to train the improved ConvLSTM model; identifying a potential gas high-risk area; based on an analysis result of the improved ConvLSTM model, identifying a potential gas outburst ignition risk area; and constructing a monitoring platform. By improving the ConvLSTM model, efficient collection and processing of gas data are achieved, monitoring accuracy and real-time performance are improved, and powerful support is provided for mine safety management.

The invention discloses an AI algorithm optimization method and system based on a deep learning technology, and relates to the technical field of deep learning model optimization, and the method comprises the steps: carrying out the preprocessing of a data set, carrying out the normalization and standardization processing, and dividing the data set into a training set, a verification set and a test set; a self-attention mechanism is introduced into a convolutional layer of the ResNet-18 deep learning model, and the weight of each channel is dynamically adjusted by calculating a self-similarity matrix of a feature map; training the model by using the training set data, and dynamically adjusting the model learning rate in the training process; an optimal hyper-parameter combination is automatically searched through a hyper-parameter tuning method; the number of model parameters and the calculation amount are reduced through a static quantification method; exporting and deploying the model, importing the test set, and testing the performance of the model; according to the method, a self-attention mechanism is introduced into the convolutional layer of the ResNet-18 model, the weight of each channel is dynamically adjusted, the attention of the model to important features is enhanced, and the feature extraction precision is improved.

The invention discloses a hair care comb control system and method based on hair quality detection, and relates to the technical field of hair quality detection.A personalized application mode of a hair care comb is started according to user hair quality data and preference information, when the user hair quality data is abnormal, multi-level alarm instructions are sent to the outside, and alarm channels are switched; the hair quality management knowledge graph gives improvement measures according to the hair quality data of the user, the hair-care comb product database recommends corresponding products for the user, and recommendation matching of the products and the improvement measures is dynamically adjusted; the method comprises the following steps that: a hair-care comb optimizes improvement measures and then sends the improvement measures to a user, if the hair quality still has specific abnormity, the hair-care comb pushes content to the user and provides online consultation service, and the pushing frequency of paid consultation service is constrained according to a plurality of latest continuous abnormity degrees. The method prevents the user from neglecting the current hair quality problem, updates and switches the alarm channel, and ensures that the user can give feedback when the user neglects the alarm instruction or does not receive the alarm instruction.

The invention discloses a physically-guided critical region fluid nonlinear wave motion prediction method, system and device and a medium. The method comprises the steps that a critical region fluid computational domain and a nonlinear time-varying equation are established; constructing a sample data set; constructing a physical information neural network model; constructing a loss function including nonlinear time-varying equation residual error loss, initial point error loss and boundary point error loss; training the physical information neural network model to obtain a critical region fluid free water surface displacement wave envelope prediction model; weighting internal matching point samples in each iteration round by adopting a gradient-free nonlinear attention mask method based on residual accumulation in the training process; and inputting the time-space coordinates of the to-be-predicted point of the critical region fluid into the prediction model to obtain the free water surface displacement wave envelope of the to-be-predicted point fluid. According to the method, error accumulation is effectively reduced, the model convergence speed is increased, and the prediction precision of the nonlinear wave motion of the fluid in the critical region is improved.

The invention discloses a motor imagery electroencephalogram decoding method based on a multi-attention bridge structure network, and relates to the field of biological signal processing and motor imagery brain-computer interfaces. The method comprises three modules: 1) a depth separable convolution attention module; 2) bridge structure modules; and 3) a multi-scale parallel space-time convolution module. In order to solve the problems of poor robustness and weak generalization ability in a motor imagery electroencephalogram signal decoding process, a deep separable convolution attention module is utilized to focus important channel space topological characteristics of electroencephalogram signals, and a bridge structure module is utilized to effectively capture local and global time domain characteristics; and multi-dimensional attention on spatial-temporal characteristics of the electroencephalogram signals is realized. And finally, spatial-temporal features of different scales are extracted through a multi-scale parallel spatial-temporal convolution module, so that the decoding recognition rate, robustness and generalization ability of the network are improved, and an effective solution is provided for improving the stability and applicability of a motor imagery brain-computer interface.

The invention relates to the technical field of mars surface rocks, and particularly discloses a mars surface stone extraction method and system based on a lightweight network, and the method comprises the steps: obtaining a mars surface image shot by a mars patroller, and screening a rock image; making labels based on the rock images, performing image processing on the labels and the rock images to expand the number of the images, and dividing the rock images into a training set, a test set and a verification set; dynamically adjusting the sampling shape and size of a convolution kernel of the training set through adaptive kernel convolution so as to extract rock features in the rock image; separating the rock image from the surrounding environment by using an improved lightweight Transform model; the attention of rocks covered by sand and dust is improved through an enhanced multi-dimensional convolution attention mechanism; and evaluating the lightweight Transform model based on the pre-training index, if the evaluation passes, obtaining a final model, and based on the final model, carrying out rock surface extraction on the input Mars rock image.

The invention discloses a medical image denoising model training method and device, and the method comprises the steps: obtaining a noise image and a clean image, and carrying out the preprocessing, and obtaining an input image, a label image, and a mask image; inputting the input image into an encoder for feature extraction to obtain an original feature map; calculating a Hessian matrix and a matrix eigenvalue of the original feature map; calculating a Hessian response according to the matrix eigenvalue, and extracting an edge feature map to obtain a depth Hessian attention feature map; splicing the original feature map and the depth Hessian attention feature map, and inputting the spliced original feature map and the spliced depth Hessian attention feature map into a decoder for feature fusion to obtain an output image; and calculating the loss between the output image and the label image according to the mask image, and carrying out back propagation on the updated weight so as to obtain a trained medical image denoising model. The method is beneficial to tissue layering, improves the quality of the denoised image, and enables the diagnosis result to be more accurate.

The invention provides a wave height prediction method for multi-strategy snow ablation optimization, and belongs to the field of ocean engineering. The wave height prediction method comprises the following five steps: preprocessing sea condition data, performing feature extraction on the sea condition data by using a CNN model, training the model, optimizing parameters and evaluating the model. According to the method, the multi-strategy snow ablation optimization algorithm, the convolutional neural network, the bidirectional gating circulation unit and the multi-head attention mechanism are comprehensively utilized, and efficient processing and accurate prediction of the sea condition data are achieved; the introduction of the multi-strategy snow ablation optimization algorithm increases the diversity of the population, prevents the model from falling into local optimum, and improves the robustness of the algorithm. The CNN and the bidirectional GRU are combined, so that the model has excellent performance in the aspect of processing high-dimensional, nonlinear and time sequence data; the application of the attention mechanism can highlight important information, perform weighting processing on features, and further improve the prediction performance of the model.

The invention provides a power industry knowledge question-answering method and system based on a large language model, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining power industry data; constructing a knowledge graph with node weights; acquiring a training set taking the power industry question as a feature and taking the power industry answer as a label; constructing a generative question and answer model with knowledge graph embedded information; establishing a generation loss function; taking the generation loss function value smaller than a preset generation loss function value as a target, training the generative question-answer model to determine an optimal hyper-parameter of the generative question-answer model; obtaining a to-be-processed power industry problem; and inputting a to-be-processed power industry question into the trained generative question and answer model, and outputting an expected power industry answer corresponding to the to-be-processed power industry question. And in combination with the knowledge graph with the node weight and the generative question and answer model, the interference of training set noise on model output answers is reduced, and the consistency and accuracy of the generated answers are improved.

The invention relates to the technical field of information security, in particular to an information security cloud computing platform based on an intelligent computing center, which comprises a user identity and access control module, a self-adaptive threat detection module, a data protection and backup module, a virtualization and resource management module, an edge computing collaborative security module and a security event management module, the user identity and access control module performs identity verification and management; the self-adaptive threat detection module automatically analyzes and identifies novel security threats; the data protection and backup module encrypts the stored and transmitted data; the virtualization and resource management module manages and monitors the security state of the virtual machine; the edge computing collaborative security module deploys a security agent at an edge node; and the security event management module records security events of the platform. According to the method, the accuracy and real-time performance of threat detection and response are improved, and efficient and safe operation of the platform is ensured.