1332 results about "Sequence prediction" patented technology

The invention discloses a small unmanned aerial vehicle inspection data management method, relates to the technical field of unmanned aerial vehicle inspection, and is used for solving the problem that unmanned aerial vehicle inspection data management is not clear. According to the invention, through a high-precision positioning technology and timestamp synchronization, multi-modal data collected by an unmanned aerial vehicle is associated with an equipment position, space-time consistency of inspection data is realized, multi-modal feature fusion and dimension reduction processing are carried out, unified feature representation is generated, the precision and efficiency of data analysis are improved, historical data and real-time collected data are combined, and the accuracy and efficiency of data analysis are improved. Equipment states are monitored in real time, fault modes are identified and classified, risks are evaluated, fault development trends are predicted through a time sequence prediction model, maintenance suggestions are generated, the timeliness of fault diagnosis is improved, based on fault detection results, routing inspection task priorities and path planning are dynamically optimized, an optimal routing inspection path is generated, and a flight path is corrected; the intelligence and the precision of the inspection task are improved, and the efficiency and the safety of the unmanned aerial vehicle inspection system are remarkably improved.

The invention discloses a decision-making method and model for off-line reinforcement learning and continuous on-line fine tuning, and the method comprises the steps: obtaining original text-image pair data and historical track data, and obtaining an enhanced state sequence through feature matching verification and standardization processing; performing sequence prediction by using a multi-level attention mechanism, and obtaining pre-training model parameters through knowledge distillation and task adaptability enhancement; on the basis of stability evaluation and plasticity control, online knowledge migration is carried out on the model, and optimized model parameters are obtained by combining experience playback optimization; and finally, performing multi-dimensional performance evaluation and resource optimization. According to the method, through multi-modal feature fusion, dynamic knowledge migration and an adaptive optimization mechanism, the problems of insufficient feature representation, disastrous forgetting, low optimization efficiency and the like are effectively solved, and the learning efficiency and generalization ability of the model are improved.

The invention provides an unmanned intelligent inspection equipment cooperative scheduling method and system in a photovoltaic power generation scene, and relates to the technical field of photovoltaic power station intelligent inspection, and the method comprises the steps: carrying out the task priority classification and time sequence correlation analysis based on a dynamic adaptive neural network and a sequence predictor, constructing a task dependency graph, and carrying out the task priority classification and time sequence correlation analysis; a candidate task allocation scheme is generated based on a deep reinforcement learning framework, the candidate scheme is optimized in combination with a collaborative decision-making system constructed by a multi-target genetic algorithm and a graph attention mechanism by taking minimization of completion time, energy consumption and maximization of coverage rate as targets, and task allocation constraints are updated by using a confidence propagation network; and an abnormal condition is judged through a Bayesian reasoning algorithm, and an optimal scheduling scheme is finally output, so that the inspection efficiency of the photovoltaic power station can be effectively improved, the energy consumption is reduced, and the inspection coverage rate is improved.

The invention provides a marine ranch water quality parameter real-time correction and compensation method and system for a multi-source sensor, and relates to the technical field of multi-source sensors, and the method comprises the steps: constructing a double-layer edge computing network, and connecting a sensor through a micro-service architecture to collect water quality data. And carrying out data preprocessing in combination with wavelet transform. And establishing a sensor digital twinborn model, and calculating the real-time credibility. Establishing a multi-dimensional sensor association network, optimizing a weight coefficient by adopting federal learning, and establishing a self-evolution correction parameter matrix; and fusing the sensor data by using a multi-task deep learning model to generate an initial correction value. And calculating a theoretical reference value through a space-time sequence prediction model. A compensation coefficient is adaptively adjusted by adopting a fuzzy decision tree, hierarchical water quality parameter correction is realized, and a closed-loop self-optimization intelligent correction system is formed through verification of a digital twin model. The accuracy and reliability of marine ranch water quality monitoring data are effectively improved.

The invention discloses a power load prediction method based on a two-channel cross attention network, and belongs to the technical field of time sequence prediction in deep learning. The method comprises the following steps of obtaining a power load data set, performing preprocessing on the power load data set, decomposing power load data into a plurality of local components by applying VMD, iteratively optimizing VMD parameters through an APSO algorithm, and screening a subsequence with the lowest complexity to be combined with original features to serve as model input; and then constructing a dual-channel cross attention network model, processing time sequence data by using a parallel mechanism of a BiLSTM channel and a Transform channel, enhancing the feature expression ability through an improved cross attention layer, and performing power load prediction by using an MLP fused KAN network. According to the method, the time sequence prediction of the power load is carried out by constructing the two-channel cross attention network, the prediction precision and robustness are high, and the accuracy of load time sequence prediction is remarkably improved.

The invention provides a distributed computing resource smart evolution method and system based on digital twinning, and relates to the technical field of resource evolution.The distributed computing resource smart evolution method comprises the steps that a smart sensing probe is deployed at a computing node to collect operation data, a double-layer attention mechanism is used for extracting features, and a digital twinning model is constructed based on a graph neural network; a resource allocation strategy and a scheduling decision are optimized in combination with a hierarchical reinforcement learning framework, a prediction sequence is generated through an attention-enhanced sequence prediction network, and finally an optimal configuration scheme is solved by adopting a particle swarm algorithm. According to the method, the utilization rate of distributed computing resources can be improved, the energy consumption of the system is reduced, and intelligent scheduling and optimal configuration of the computing resources are realized.

The invention discloses a thermal imaging temperature rise trend early warning system based on time-space sequence prediction, and particularly relates to the technical field of thermal imaging data prediction and early warning. The thermal imaging temperature rise trend early warning system comprises an image conversion module, a fluctuation feature extraction module, an edge prediction module, an anomaly characterization module and a prediction decision module; a temperature dynamic change rate and gradient intensity are calculated, edge model prediction is carried out based on a fluctuation index combination, when the fluctuation index combination does not exceed a stable interval, a lightweight deep network model deployed at a thermal imaging acquisition end is called, and when the fluctuation index combination exceeds the stable interval, a prediction decision module determines whether to switch to a high-order multi-modal model; the space-time information extraction capability is improved by constructing the temperature evolution data body, the prediction path is dynamically controlled based on the fluctuation index combination, and the prediction stability and efficiency are improved; and the abnormal activation index and the prediction offset index are combined to realize adaptive switching of model calling, so that the accuracy and adaptability of the early warning system are enhanced.

A method for multivariate time-series long-term forecasting based on multi-scale temporal feature enhancements, includes a time-series forcasting model TFEformer. The model utilizes a multi-branch structure and a patch-series attention mechanism to extract global and local time-series features at multiple temporal scales, and designs an adaptive feature fusion mechanism to achieve adaptive fusion of multi-scale temporal features. It employs an variate-wise attention mechanism and a redesigned gated feedforward network to perform feature fusion among multivariate variables and within the time-series, respectively. The time-series forcasting model TFEformer proposed by the present invention significantly improves the prediction of long-term trends in time-series and enhances the fitting ability for short-term local fluctuations, comprehensively increasing prediction accuracy across different prediction time lengths in multivariate time-series forcasting tasks.

The invention relates to the technical field of data processing, and provides a multi-heat-source networking heat supply optimization operation method and system.The method comprises the steps that environmental parameters, heat source data, market dynamic information, user behavior characteristics and a pipe network topological graph are collected by deploying IoT equipment; acquiring a thermal load time sequence predicted value in a future preset time period; the heat source data and the pipe network topological graph are processed, and a pipe network operation state matrix is obtained; performing feature dimension alignment processing on the pipe network operation state matrix to obtain a pipe network spatial topology feature mapping value; performing weighted fusion on the thermal load time sequence prediction value and the pipe network spatial topological feature mapping value to obtain a final thermal load prediction value; and optimal operation of heat supply is realized according to the final heat load predicted value. According to the method, a real-time response mechanism for environmental parameters, market dynamics and user behavior characteristics can be realized, collaborative optimization of multiple heat sources can be realized, the heat source collaborative efficiency is improved, and energy waste and operation cost are reduced.

The invention discloses a visual language navigation method for cross-modal alignment in a dynamic shielding environment, and the method comprises the steps: collecting multi-modal data through a visual sensor, an inertial measurement unit, a laser radar and the like, and carrying out the preprocessing and time synchronization; sensing the dynamic shielding object through a model composed of a convolutional neural network and a long-short-term memory network, and estimating the future change of the dynamic shielding object in combination with a space-time sequence prediction algorithm; a double-branch convolutional neural network and a Transform based on a dynamic attention mechanism are adopted to respectively extract visual and semantic features and fuse the visual and semantic features; on the basis of occlusion prediction, potential occlusion region features are extracted in advance from a time dimension, an occluded image is repaired by using a generative adversarial network and geometric constraints in a space dimension, and cross-modal feature alignment is optimized through an attention mechanism; planning a path by using a hybrid reinforcement learning algorithm based on a deep Q network-space and a fast exploration random tree, and dynamically adjusting according to real-time shielding; according to the method, the accuracy, adaptability and reliability of visual language navigation in a dynamic shielding environment are improved.

The invention discloses a multivariable time series prediction method based on an NMF multi-scale lightweight space-time convolutional neural network, and belongs to the technical field of machine learning and deep learning application. The method specifically comprises the following steps: (1) collecting and processing multivariable time sequence data, and unifying the scale of the data; (2) decomposing the time sequence data by using NMF, extracting basis matrixes and coefficient matrixes of different scales, and constructing a multi-scale feature pyramid; (3) applying a space-time convolution layer on the basis of the output of the feature pyramid, introducing a Bayesian optimization algorithm, and adopting a feature fusion layer fused with a multi-scale attention mechanism; (4) dividing a training data set and a test data set, and carrying out model training; and (5) predicting future time sequence data by using the trained model and outputting an expected value. According to the method, a more powerful and more efficient multivariable time sequence prediction model is effectively constructed, and the method is suitable for analysis and prediction of multivariable time sequence data.

The invention discloses a roadbed settlement prediction method based on IGWO-BP and ARIMA. The roadbed settlement prediction method comprises the following steps: firstly, obtaining a seasonal permafrost region settlement data composition, wherein the seasonal permafrost region settlement data composition comprises short-term linear characteristics and long-term nonlinear change characteristics; preprocessing the original monitoring data, dividing the data into a training set and a test set, and constructing an IGWO-BP model based on the training set; then, an IGWO-BP model is trained on the training set, a training residual sequence is further obtained, and meanwhile, the trained IGWO-BP model is adopted to predict the test set; constructing a model ARIMA, and predicting a network residual error based on the model; finally, the prediction result of the IGWO-BP model and the residual error sequence prediction result of the ARIMA model are connected in series, error correction of the prediction result is completed, and a final combined prediction result is obtained.By means of the method, the freezing injury occurrence characteristics of the seasonal frozen soil region can be well captured, and the defect of prediction of a single model is overcome.

The embodiment of the invention discloses a bridge crack width monitoring method. The method comprises the following steps: acquiring a continuous video image of a bridge surface; generating a pixel-level segmentation mask image of the crack by using the target detection model; when the target detection model is pre-trained, optical flow features between adjacent frames in the first data set are extracted through an optical flow calculation algorithm, and deep fusion is performed on the optical flow features and the image features; contour pixel points of the crack are extracted based on the crack segmentation mask pattern, and the width change information and the width change rate of the crack are obtained; according to the width change information and the width change rate, performing trend prediction on the crack width change by using a time sequence prediction model to obtain a prediction result of the crack width change; in the training process of the time sequence prediction model, a basic model and a complexity capture module are designed, and the weight of a main model is optimized by using a weighted fusion strategy. According to the invention, efficient and accurate crack width monitoring is realized through crack identification and prediction.

The invention belongs to the technical field of turbulent flow field prediction and deep learning, and discloses an incompressible turbulent flow field prediction method based on a potential diffusion model. The method comprises the following steps: acquiring original turbulence data; processing the turbulence data; constructing a turbulence prediction model; model training; and evaluating the model and the like. The model of the technical scheme of the invention specifically comprises the following steps: designing a multi-scale Fourier auto-encoder for extracting multi-scale space and frequency domain features in a turbulence field and obtaining a global structure and a local scale structure of turbulence; a novel accelerated sampling method is proposed and introduced in the diffusion process, namely a diffusion probability model solver greatly shortens the reasoning time in a potential space and keeps high fidelity in long-time-sequence prediction; a physical constraint loss item based on a partial differential equation is introduced, and a Navier-Stokes equation (N-S) is explicitly introduced into a training process, so that the physical consistency of results is effectively improved, and errors are remarkably reduced.

The invention discloses an electric power system time sequence prediction model training method, system and device based on large language model weight fine tuning and a storage medium. The method comprises the steps of obtaining time sequence data of different fields and performing data cleaning to obtain a data set; performing weight freezing on a feedforward neural network and multi-head attention of the large language model, and performing first-stage training on the large language model by using the data set; reconstructing an input layer of the large language model to enable the reconstructed input layer to generate a corresponding natural language instruction according to the time sequence data; and freezing weight parameters of the large language model, and performing second-stage training on the large language model by using source load data of the power system to adjust parameters of the reconstructed input layer so as to obtain a time sequence prediction pre-training model. According to the method, the prediction effect can be improved while the weight of the large language model backbone network adapts to the specific task of time sequence prediction.

The invention discloses a multivariable time sequence prediction method based on GRU and a computer program product. The method comprises the following steps: firstly, introducing the dynamic characteristics of self-adaptive different time sequences of self-defined time sequence decomposition, and splitting an original sequence into trend, seasonal and residual components so as to reduce the data complexity and improve the interpretability; afterwards, a value embedding module is used for unifying feature representation so as to ensure that the model fully captures the time dependency relationship; in the modeling stage, the model adopts a multi-channel recurrent neural network to independently model the three types of components so as to reduce the interference between modes and improve the learning ability. In the prediction stage, a feature splicing strategy is adopted, information of each component is integrated, and richer time sequence representation is provided. In addition, a segmented prediction strategy is designed for the model, the prediction process is divided into multiple time periods, prediction information is combined, error accumulation is reduced, and the stability and robustness of long-sequence prediction are improved. Experimental results show that the prediction precision can be improved on different data sets.

The invention discloses a water quality time sequence prediction method of an SSA-VMD-LSTM-XGBoost hybrid model, and belongs to the technical field of water quality monitoring and prediction. Comprising the following steps: (1) data preparation and preprocessing; (2) optimizing the water quality time sequence decomposition of the VMD based on SSA: optimizing a penalty factor and a modal number of the VMD by adopting a sparrow search algorithm (SSA), and decomposing the water quality time sequence into a plurality of sub-components with high stability and low complexity by utilizing the optimized VMD; (3) construction and training of an LSTM-XGBoost hybrid prediction model: constructing a hybrid prediction model fusing long-short term memory (LSTM) and extreme gradient boost (XGBoost), inputting a high-frequency component into the LSTM model, inputting a low-frequency component into the XGBoost model, and finally performing superposition and integration on prediction results of the models; and (4) multi-component prediction result integration and performance verification. According to the method, adaptive optimization of VMD parameters is realized through SSA, the feature extraction and time sequence modeling capability is improved by combining the advantages of LSTM and XGBoost, and the prediction precision and stability of the water quality time sequence are effectively improved.

The invention relates to the technical field of geological informatization, in particular to a time-space sequence prediction and risk assessment model for geological disaster monitoring data, which comprises a data acquisition module, an edge processing module, a cloud analysis module, a mobile terminal module and a data processing module, the server is connected with the cloud analysis module through a WebSocket protocol, and is used for receiving the prediction result and the visual data; and displaying the three-dimensional illness state evolution graph on augmented reality (AR) equipment. Through the innovative technical scheme, comprehensive breakthrough is achieved in the aspects of multi-modal data fusion, dynamic feature extraction, accurate prediction, robustness design, visual display and the like. The innovation points cooperate with one another and promote one another, and an efficient, reliable and intelligent MS remote monitoring and prognosis prediction system is jointly constructed.

The invention provides an intention guide pushing method and system based on geographic position information and multimedia interaction. The method comprises the following steps: constructing an interest model based on a geofence technology and a deep neural network; generating a user activity intention prediction result based on the interest model, obtaining a behavioral rhythm analysis result by using a Bayesian network and a time sequence prediction algorithm based on the user activity intention prediction result, and constructing a user and place relation graph by using a graph theory algorithm based on determining the optimal pushing opportunity of the behavioral rhythm analysis result. Generating a time sensitivity optimization scheme; generating customized content based on a time sensitivity optimization scheme, processing the customized content by adopting an emotion analysis method, a collaborative filtering algorithm, an auto-encoder and a multi-head attention mechanism to obtain final content, adjusting the final content through an instant feedback mechanism, and generating a multimedia push packet; according to the invention, the performance and service quality of the intelligent pushing system are improved, and more accurate and personalized recommendation experience is provided for the user.

The invention relates to the technical field of personalized pushing, in particular to a personalized music teaching content pushing system. The system comprises a user ability evaluation module, a teaching content matching module, a learning path planning module, a push strategy optimization module, a behavior analysis and prediction module, a teaching effect feedback module, a dynamic adjustment and optimization module and a personalized recommendation module. According to the invention, a clustering algorithm, a support vector machine, a random forest algorithm, accurate assessment of learner ability, K-means clustering and an analytic hierarchy process are adopted, teaching content and ability matching, a Dijkstra algorithm and dynamic planning are realized, a personalized learning path, a Q learning and strategy gradient method are designed, and a push strategy, a long and short term memory network and a seasonal decomposition time sequence are optimized. Learning requirements are predicted, the project reaction theory, the Markov decision process and the Bayesian network are utilized, feedback adjustment precision is improved, deep Q network and Monte Carlo tree search are carried out, personalized recommendation is optimized, and teaching efficiency and achievements are enhanced.

The invention relates to the technical field of marine organism community monitoring, in particular to a marine organism community monitoring method and system based on a memory mixed prototype and knowledge distillation. The method comprises the following steps: carrying out data preprocessing on obtained multi-source heterogeneous monitoring data; constructing a multi-modal joint feature by using the preprocessed data; performing cross-modal teacher model training by using the multi-modal joint features; training a student model based on the historical marine ecological sequence data; performing enhanced knowledge distillation on the student model by using the cross-modal teacher model; performing marine ecological community time sequence prediction by using the trained student model; and performing anomaly detection based on the historical data and the prediction data. Based on the breakthrough of the bottleneck of the existing marine organism community monitoring technology, the multi-dimensional technical improvement and application value are realized.

The invention discloses an intelligent remote follow-up visit method and system for tumor patients, and relates to the technical field of medical intellectualization, and the method comprises the steps: collecting follow-up visit data through a remote medical terminal, carrying out the processing of the follow-up visit data, obtaining a physical sign index sequence and a symptom feature vector, and generating a dynamic follow-up visit evaluation score through a time sequence prediction model; performing semantic analysis on the electronic medical record data of the patient based on the medical knowledge map, extracting tumor stage classification data, chemoradiotherapy measurement data, weight nutrition indexes and laboratory inspection data, determining a basic risk coefficient, a cumulative dose ratio and a body state score, and generating a baseline follow-up assessment score through a prediction model; and according to the dynamic follow-up evaluation score and the baseline follow-up evaluation score, calculating a comprehensive follow-up grade and generating a next follow-up plan. According to the invention, the accuracy of patient state evaluation is improved, accurate configuration of medical resources is realized, and the quality and efficiency of remote follow-up visit are effectively improved.

The invention relates to the technical field of land resource monitoring, in particular to a land resource dynamic monitoring and early warning method and system based on multi-source remote sensing data fusion, and the method comprises the steps: employing an unmanned plane to periodically collect optical images, SAR echoes and LiDAR point clouds, constructing a ground three-dimensional digital model, and carrying out the land parcel division; performing fusion to form a multi-dimensional feature vector, establishing an LSTM land parcel feature evolution model, and predicting a change rate interval of each feature in a current period based on a historical sequence; constructing a time sequence difference change detection algorithm, calculating a land parcel change rate, and screening potential abnormal land parcels by taking a prediction interval as an anomaly judgment threshold value; a double-branch convolutional neural network is adopted to identify crop states, growth stages and construction violation behaviors, abnormity is judged and determined, and confidence is given; spatial clustering is carried out on determined abnormal land parcels, accurate boundaries are obtained in combination with a three-dimensional model, multi-level early warning information is generated, and the decision-making efficiency and response speed of land resource monitoring are improved.

The invention discloses a multivariable time series prediction method based on Patching and multi-scale feature extraction, and the method comprises the steps: dividing obtained multivariable time series data into different independent channels for a multivariable long-term time series prediction task, sharing the same converter backbone network, but enabling a forward process to be independent; the method comprises the following steps of: firstly, performing a Patching operation on a long sequence from a time domain angle, segmenting a single variable of each channel into a plurality of short fragments, and transmitting the short fragments into a Transform framework; in the aspect of a frequency domain, a multi-scale convolutional network is adopted to capture features of different frequency ranges, and meanwhile, a scale attention mechanism is introduced to adaptively weight and fuse features of different scales; and carrying out feature fusion on the features extracted from the frequency domain and the time domain so as to realize accurate modeling of periodic and long-term modes. According to the method, experimental analysis is carried out on eight disclosed data sets, and the result shows that compared with existing models, the model provided by the invention has higher accuracy on prediction of future values in a selected scene.

The invention provides a credit risk dynamic assessment and early warning method based on multi-dimensional data analysis, and relates to the technical field of financial risk control and big data analysis, and the method comprises the steps: obtaining multi-dimensional data from borrower basic information, financial statements, credit records and an external market environment, carrying out cleaning, standardization processing and storage on the multi-dimensional data; based on the multi-dimensional data, through a credit scoring model, cash flow analysis, liability ratio analysis and market environment risk assessment, generating a financial risk level of the borrower; the financial and market data of the borrowers are updated in real time, the dynamic change of the risk state of the borrowers is monitored in combination with a time sequence prediction model and risk correlation analysis, and a dynamic monitoring result is generated; and according to the risk level and the dynamic monitoring result, triggering a grading early warning mechanism, and generating a corresponding control measure suggestion so as to adjust a credit strategy or adopt a risk slow release means.

The invention relates to the technical field of data processing, and discloses a CNN-BiLSTM-MATT time sequence prediction method, which comprises a plurality of feature value inputs and a feature value output. Carrying out data normalization processing; performing normalization processing on the input data, mapping a normalized data set between [-1, 1], and performing reverse normalization processing on the output data after prediction; the hyper-parameters in CNN-BiLSTM-MATT are optimized, the hyper-parameters in CNN-BiLSTM are initialized according to an IHO algorithm, the hyper-parameters comprise the learning rate, the L2 regularization coefficient and the hidden layer number, and the optimal hyper-parameter value is obtained in the iteration process; performing feature extraction: performing convolutional feature extraction by using a convolutional neural network CNN, and taking obtained data features as input of subsequent BiLSTM; and carrying out time sequence analysis. The method overcomes the defects of a current time sequence prediction method. The method can be widely applied to the parameter optimization problem, the convergence degree is improved, and the optimal solution is efficiently found.

The invention discloses a high-rise building settlement prediction method and system, a storage medium and a program product, and the method comprises the steps: separating a trend term, a periodic term and a random term for multi-source monitoring data, and constructing a nonlinear time sequence prediction model; inputting historical multi-source monitoring data into the nonlinear time sequence prediction model to obtain a preliminary settlement prediction result; a continuous settlement distribution diagram of the high-rise building is generated, and spatial distribution characteristics are obtained; determining a plurality of key influence factors and corresponding influence factor weights based on the multi-source monitoring data; constructing a settlement prediction model; inputting the multi-source monitoring data into the settlement prediction model to obtain a final settlement prediction result; and sending the settlement trend curve graph and the prediction and early warning time point to a management terminal. The early settlement prediction of the high-rise building is realized, the timeliness of detecting the settlement risk of the high-rise building is improved, and the probability of potential safety hazards caused by settlement of the high-rise building is reduced.

The invention provides an intelligent storm surge prediction method and device based on deep learning and a medium, and relates to the field of storm surge disaster risk assessment, and the method comprises the steps: obtaining multi-source heterogeneous data of a typhoon storm surge disaster, and carrying out the preprocessing; the multi-source heterogeneous data comprises meteorological and marine observation data and high-precision geographic information data; constructing a typhoon and storm surge prediction network; the typhoon and storm surge prediction network comprises a multi-scale spatial-temporal feature module, a physical mechanism enhancement module and a spatial-temporal sequence prediction model based on a fusion attention mechanism and a graph convolutional network; training a typhoon and storm surge prediction network through multi-source heterogeneous data; acquiring meteorological and tide level live data; and inputting the meteorological and tide level actual data into the trained typhoon and storm surge prediction network to obtain a typhoon and storm surge state prediction result. A physical mechanism is introduced into the neural network to predict the states of the typhoon and the storm surge, and the prediction precision and efficiency are remarkably improved.

The invention belongs to the technical field of network security, and relates to a network attack behavior chain construction and active defense method and device and computer equipment, and the method comprises the steps: collecting full-flow data and a multi-source log from a network environment, and carrying out the preprocessing of the full-flow data and the multi-source log; storing the preprocessed full-flow data and multi-source logs, and establishing an associated index; through a deep learning algorithm and an unsupervised model, abnormal traffic and attack behaviors are identified from the full-traffic data and the multi-source logs; reconstructing the fragmented attack events into a complete behavior chain through a graph neural network and a visualization mode; based on the AI model, a dynamic defense strategy is generated, and a response action is automatically executed; through time sequence prediction and a deep learning model, a future attack trend is predicted, and active defense is realized. The method improves the unknown attack detection capability, optimizes the traceability efficiency, enhances the defense initiative, guarantees the real-time performance and accuracy of network attack prediction, and has compliance adaptability.

The invention relates to an unmanned aerial vehicle group collaboration and task allocation optimization method and system based on edge computing, in particular to the field of communication, efficient task allocation and threat early warning are achieved through dynamic modeling of a multi-modal sequence prediction model and a heterogeneous relation graph, firstly, real-time environment and historical task data are fused, and the real-time environment and historical task data are fused; generating space threat probability distribution and an environment dynamic coefficient; then, a dynamic adjacency matrix is used for adjusting a subgraph embedding vector, a threat-driven topological structure is reconstructed in real time, a decision-making layer outputs a task instruction and value evaluation based on a hierarchical decision-making network, task acceptance, task abandoning and path selection are intelligently optimized, and task conflicts are solved through a federal consensus mechanism; according to the method, the cooperation efficiency and the task execution accuracy of the unmanned aerial vehicle group in a complex environment are effectively improved, and task allocation and resource use are optimized.