628 results about "Markov chain" patented technology

The invention relates to the field of welding process control, and discloses a pipeline all-position automatic TIG (Tungsten Inert Gas) welding method which comprises the following steps: collecting pipeline geometric parameters, welding position angles and material attribute data in real time through multi-source data fusion; constructing a Gaussian process regression dynamic response model, coupling a nonlinear mapping relationship among the process parameters, the molten pool morphology and the corrosion tendency index, and dynamically adjusting the weight of the model based on the welding position angle; a hierarchical strategy of Bayesian optimization and model prediction control is adopted to generate a parameter solution set meeting the fusion depth constraint and the corrosion threshold value, and the molten pool oscillation frequency is used as feedback to correct the current in real time; the heat accumulation evolution trend is predicted through a hidden Markov chain, and parameter closed-loop migration and trajectory compensation are achieved in combination with molten pool flow field coupling correction. The problems of uneven forming quality and corrosion risk caused by space displacement, dissimilar metal interface effect and dynamic disturbance in all-position welding are solved.

The invention discloses an unmanned aerial vehicle flight path optimization system and method based on artificial intelligence and Internet of Things, and relates to the technical field of unmanned aerial vehicle control, and the method comprises the steps: collecting communication delay, environment data and an unmanned aerial vehicle state in real time; analyzing historical communication delay data by using a Markov chain, and predicting a basic delay value in a short time in the future; correcting the basic delay value through a machine learning model; outputting a final delay prediction result; calculating a delay risk index, and judging a delay risk level; based on the delay risk level, when the delay risk level is low risk, sending a control instruction in advance according to the final delay prediction result; and when the delay risk level is medium or high risk, calling a pre-trained reinforcement learning strategy library, and generating a self-adaptive control instruction. According to the method, the multi-dimensional risk index is constructed, multiple strategies are provided to adjust the flight path according to the real-time data and the risk index, and the flight stability of the unmanned aerial vehicle is improved.

The invention relates to the technical field of airport safety protection, and discloses an airport bird strike prevention method and system based on multi-sensor fusion. The method comprises the steps of collecting airport peripheral airspace radar detection, radio detection, laser monitoring, weather and other multi-mode sensor data, and performing time synchronization and space registration to generate a fusion data stream; features are extracted based on a dynamic weight distribution algorithm, target types are identified by using a multi-modal classification model, and birds and unmanned aerial vehicles are distinguished; constructing a dynamic risk assessment model based on a Markov chain to generate an intrusion risk level; and when the risk level exceeds a preset threshold value, triggering a multi-level alarm strategy and linking the expelling equipment to execute a self-adaptive expelling instruction. According to the method and system, through multi-sensor fusion, the monitoring precision is improved, the target is accurately recognized, the risk is evaluated in real time, efficient alarming and expelling are achieved, the airport bird strike prevention capacity is effectively improved, and aviation safety is guaranteed.

The invention relates to a method for obtaining a hint injection attack test case for a large language model, which combines a conditional variation auto-encoder cVAE and a Markov chain, and obtains a large language model test case through data generation and context modeling, gradual exposure of malicious instructions and simulation of multiple rounds of dialogue attacks in reality. Realizing multiple rounds of dialogue attacks on the large language model, and challenging the defense capability of the large language model; the design scheme introduces a concealment technology, role play attack, state transition and other technologies, improves the complexity and concealment of attacks, has the core advantages of automation, higher concealment, wide coverage, batch testing and the like, can evaluate the security defense capability of the large language model more truly and comprehensively, and improves the security defense capability of the large language model. The defects of an existing defense mechanism are found, and research on multi-round prompt injection attacks and improvement of a security defense mechanism are promoted.

The invention discloses a railway catenary reliability evaluation method based on a Markov chain and a dynamic Bayesian network, and the method achieves the systematic monitoring and fault early warning of the operation state of catenary equipment through the construction of a catenary monitoring data collection system, and the utilization of multi-source data integration, multi-node multivariable modeling and hierarchical time scale monitoring. According to the scheme, comprehensive acquisition and analysis of key parameters of a contact network system are ensured by integrating various factors such as meteorology, load and equipment state. Through combined application of the dynamic Bayesian network and the Markov chain, multi-node and multi-variable modeling is carried out, and the dynamic influence of factors such as equipment aging, load fluctuation and external environment change on the system reliability can be accurately captured. In addition, by refining the time scale, performing high-frequency sampling on short-term variables such as weather and load fluctuation, and performing low-frequency monitoring on long-term factors such as equipment aging and corrosion, the state of the contact network is finely evaluated on different time scales.

The invention discloses a water surface target tracking method and system based on multi-physical parameter fusion, and the method comprises the steps: obtaining a physical layer observation parameter of a target through a water surface monitoring radar, and constructing a multi-dimensional nonlinear observation vector; establishing a multi-mode motion model set including constant speed, variable speed and turning, and realizing dynamic switching among motion modes through a Markov chain; adjusting a process noise covariance and an observation noise covariance based on a residual covariance estimation result in the sliding window by adopting an adaptive extended Kalman filtering algorithm; distributing weights according to the measurement variance of each physical parameter, optimizing the Kalman gain through a weighted least square method, and completing the updating and estimation of a target state; dynamic switching of motion modes is achieved through a Markov chain, and when state estimation residual errors of continuous preset times exceed a preset threshold value, model mismatch is judged, and a Markov chain model switching mechanism is triggered; according to the method, the sea condition adaptability, the calculation efficiency and the engineering expandability can be improved.

The invention relates to the technical field of corrosion detection, in particular to a grounding grid corrosion intelligent monitoring system which comprises an electrochemical data capture module, a corrosion behavior analysis module, a corrosion probability evaluation module and a monitoring strategy optimization module. According to the method, data errors and abnormal values are filtered and corrected, the overall quality of data is improved, the accuracy of analysis results is ensured, and in corrosion behavior analysis, the future trend of corrosion is effectively predicted through dynamic change detection of electrochemical properties and calculation of the corrosion rate; the time sequence analysis of the electrochemical impedance data not only enhances the prediction accuracy, but also improves the understanding depth of corrosion behaviors, the corrosion probability evaluation module adds consideration of random changes to the whole prediction model through the application of the Markov chain, so that the model is closer to the uncertainty of the real world, and through the optimization of the processing flow, the prediction accuracy is improved. The real-time performance and the early warning capability of the monitoring system are obviously improved, and the reliability and the operation accuracy of the system are enhanced.

The invention relates to an encrypted traffic detection method based on multi-dimensional feature parallel fusion, and belongs to the technical field of network security. According to the method, when a multi-dimensional feature parallel fusion framework is constructed, the limitation of traditional statistical features on dynamic evolution characterization of encryption behaviors and the dependency of graph neural network topology modeling on computing resources are fully considered; through collaborative optimization of a Markov chain dynamic quantization protocol interaction state transition rule and a lightweight graph attention hierarchical compression mechanism, a detection model gives consideration to deep feature perception capability and efficient reasoning capability at the same time; based on the classification decision realized by the fusion mechanism, the recognition robustness and real-time defense efficiency of the encrypted malicious traffic are remarkably improved, and the active security protection level of the network is effectively enhanced.

The invention discloses a back corona rapping electric dust remover fault early warning method based on all-condition monitoring, and relates to the technical field of fault early warning, and the method comprises the steps: obtaining a rapping signal, and extracting back corona features based on wavelet transform and spectral analysis; constructing a dynamic coupling model of rapping strength-dust layer thickness, and combining back corona characteristics to obtain working condition characteristic vectors; obtaining a working condition data set, and obtaining a health degree scoring sequence under each time window in combination with the working condition feature vectors; decomposing the health degree scoring sequence into a plurality of intrinsic mode components, identifying abnormal fluctuation features based on a Markov chain, and performing risk level judgment; through all-condition monitoring and multi-modal data fusion, a back corona feature extraction, dust layer thickness prediction and health degree scoring model is constructed, dynamic identification and risk early warning of the operation state of the electric dust remover are achieved, and the problems that a traditional method is single in monitoring dimension and lags in response are solved.

A dynamic vision SLAM (Simultaneous Localization and Mapping) method based on an extended Bayesian model belongs to the technical field of autonomous robot navigation and computer vision crossing, and mainly comprises the following steps: performing prior dynamic object recognition on a current frame image by using an improved PWt-YOLO network, and outputting a binary mask and semantic probability distribution; oRB feature extraction is carried out by using a hierarchical quadtree algorithm and combining an adaptive threshold value; establishing an extended Bayesian probability model, fusing semantic prior, optical flow residual and epipolar geometric constraints of two-dimensional Gaussian distribution constructed based on dynamic object boundaries, realizing time sequence transmission of dynamic feature probabilities through a Markov chain, and executing feature point filtering according to joint dynamic probabilities; and robust pose estimation is realized through RANSAC-PnP, a sliding window and the like. According to the method, the problem of SLAM system positioning drift in a dynamic environment can be effectively solved by constructing a multi-modal fusion dynamic feature discrimination system.

The invention relates to the field of hydrogen leakage and combustion detection, and discloses a hydrogen leakage and combustion identification method, which comprises the following steps: S1, collecting multiple types of detection signals from acoustic, electrochemical and infrared thermal image sensors; s2, preprocessing the collected signals to generate a multi-dimensional feature space; s3, constructing a topological structure in the multi-dimensional feature space, extracting topological features, and generating topological descriptors; s4, analyzing the time dynamic change of the topology descriptor based on a Markov chain model; s5, calculating the topological similarity between the real-time signal and the known mode, and carrying out mode matching; and S6, calculating a signal state through a comprehensive decision function, judging whether a hydrogen leakage or combustion event exists, and triggering a corresponding alarm. According to the invention, through multi-sensor cooperative monitoring, signal preprocessing, topological structure analysis and dynamic modeling, high-precision real-time monitoring and reliable alarm of hydrogen leakage and combustion are realized, and the system has the advantages of high sensitivity, low missing report rate and rapid response.

The invention relates to the technical field of computers, and discloses a network threat detection method and device, a storage medium and computer equipment, the method can be applied to a high-risk scene involving a user operation process in insurance services, and the method comprises the following steps: preprocessing user behavior data in network operation data to obtain structured data; key behavior features are extracted based on multiple dimensions, an abnormal state transition path is determined by using a Markov chain algorithm, flow deviation degree features are generated, and a user behavior feature set is constructed through feature fusion; performing threat detection based on a security detection rule or by using a machine learning model to obtain a threat detection result; when threats exist in the user behavior feature set, abnormal user behavior data are determined and responded and handled, and a threat detection and processing report is generated. According to the method, the capability of detecting novel, disguise and internal threats is improved, the response time is shortened, the adaptability is enhanced, and a more efficient and reliable solution is provided for network security protection in the insurance industry.

The invention relates to the field of digital core modeling, in particular to a stomatal basalt three-dimensional digital core reconstruction method, which comprises the following steps: extracting target characteristic parameters from CT (Computed Tomography) scanning gray volume data of a stomatal basalt sample, the target characteristic parameters comprising porosity, cluster number, cluster size statistics, spatial uniformity index and simplified compactness; generating an initial three-dimensional digital core model according to the target characteristic parameters; carrying out iterative optimization on the initial three-dimensional digital core model by utilizing a self-adaptive Markov chain-Monte Carlo algorithm so as to enable cluster features of the optimized three-dimensional digital core model to approach target feature parameters; and performing curvature smoothing post-processing on the optimized three-dimensional digital core model to obtain the pore basalt three-dimensional digital core model. The model not only is highly matched with real pore basalt in the aspect of macroscopic statistical characteristics, but also shows natural and smooth curved surface characteristics in the aspect of microscopic pore boundary morphology, and provides a reliable digital basis for subsequent rock physical property analysis.

The invention discloses a dynamic tracking method for an air pollution source, which relates to the technical field of pollution source tracking and comprises the following steps of: acquiring a pollution concentration raster data set, a UAV sniffing set and a meteorological field data set, reconstructing a wind field and predicting pollution transportation, generating a candidate source hypothesis, matching fingerprints and calculating weights, adaptively scheduling sensors, and performing Bayesian inversion fusion and result output. Unified pollution concentration and meteorological data are generated through multi-source data collection and three-dimensional flow field reconstruction, second-level wind field calculation is achieved through a dimensionality reduction CFD model, candidate source hypotheses are generated in combination with Lagrange backtracking and Euler transport, and a weight matrix is formed through UAV mass spectrum fingerprint bidirectional matching. Secondary cruise of the unmanned aerial vehicle and densification sampling of ground nodes are driven through confidence ranking, and sampling density is adaptively optimized. Bayesian joint likelihood and Markov chain are adopted to fuse multi-source data, dynamic source coordinates and emission rate are output, and a closed-loop iterative air pollution source tracking system is realized.

The invention relates to the technical field of energy storage modules, in particular to a direct-current power supply energy storage system suitable for various household appliances. According to the method, the current change value and the power fluctuation range of the energy storage unit are analyzed, statistics of the power change amplitude and the time interval is combined, the work cycle rule of the energy storage unit is defined, the power difference between the partitions is extracted through the Markov chain model, and real-time balance of the partition power supply state is achieved by gradually optimizing path distribution. And resource waste caused by power fluctuation is effectively reduced. Through gradual correction of a real-time output power value and a target power value, dynamic power matching of an energy storage unit is realized, the flexibility and accuracy of energy distribution are improved, voltage fluctuation trend is monitored and adjusted through a variational mode decomposition technology, voltage stability control of an energy storage module is realized, the reliability of an energy storage system is optimized, and the energy storage efficiency is improved. And the overall stability and the energy utilization efficiency are improved by matching the output power and the frequency adjustment range.

The invention discloses an infrared small target detection method based on a conditional diffusion model. Specifically, the conditional diffusion model comprises a conditional perception coding module, a conditional guidance feature fusion coding module and a feature decoding module. According to the method, multi-scale feature extraction and fusion are carried out on an infrared image through a condition-guided condition perception coding module to generate condition features, feature fusion is carried out on the condition features and a noise mask of a time step t, and a model is guided to directionally optimize a target mask. In the training process of the conditional diffusion model, a mean square error loss function based on a time step t is adopted to supervise the accuracy of mask generation, and model parameters are dynamically adjusted through an Adam optimizer to minimize noise prediction errors. In the target detection stage, denoising is carried out step by step from the initial state of Gaussian noise through Markov chain iteration in the reverse diffusion process, and finally a high-precision target mask is generated.

A predictive maintenance method based on an elevator operation and maintenance time sequence knowledge graph comprises the steps that firstly, the elevator operation and maintenance time sequence knowledge graph is constructed, predictive maintenance of electromechanical equipment parts is achieved based on elevator part maintenance period optimization, a theoretical distribution model based on Weibull distribution is constructed, and parameters of Weibull distribution are fitted through a least square method. Therefore, the model can accurately reflect the failure rule of the elevator parts; calculating an inference result of the elevator operation and maintenance time sequence knowledge graph through a graph convolution model and a Horkes process model, and carrying out recursive relation calculation of component fault rate functions in N maintenance cycles; an elevator component maintenance comprehensive cost simulation model is constructed, and the optimal maintenance cycle of elevator components is calculated through a continuous time Markov chain method; the fault prediction accuracy is improved.

The invention provides a method and system for predicting corrosion evolution of a main cable of a suspension bridge, and the method comprises the steps: dividing the main cable into a plurality of cable segments, collecting the non-real-time physical information of the cable segments, and constructing a cable segment-level data structure model; a multi-dimensional input vector is generated by utilizing the characteristics of structural stress, environmental exposure, bridge age, maintenance records and the like, and corrosion risk scoring is realized in combination with the trained XGBoost model; further constructing a multi-state corrosion evolution model based on a Markov chain, dividing the corrosion state into pitting, expansion, precursor and critical stages, and establishing a time evolution path; risk distribution is visually displayed in a corrosion thermodynamic diagram mode, and residual life prediction and early warning information is output in combination with a threshold strategy; according to the method, the limitation of dependence on a real-time sensor is broken through, active prediction can be completed based on historical and structural parameters under a passive condition, and the corrosion identification accuracy and early warning timeliness are effectively improved.

The invention provides a flexible load-oriented day-ahead intra-day inter-group secondary cooperative regulation and control method, and belongs to the technical field of power flexible load scheduling control. The problem that active participation of user side flexible resources in power grid regulation and control is uncertain is solved; comprising the following steps: S10, obtaining related characteristic parameters and historical electric quantity data of user side flexible resources; s20, establishing a flexible resource evaluation index, dividing flexible resources into different flexible load clusters through an improved K-means clustering method, and forming a layered regulation and control framework of flexible loads after aggregation; s30, in combination with a day-ahead load prediction method, calculating the maximum adjustable capacity of user side flexible resources, and in combination with a parallel Markov chain model, preliminarily making a flexible load day-ahead scheduling plan; s40, tracking the load power change of the user side in the day in real time, and correcting the adjustment capacity in time based on a flood filling algorithm to realize secondary cooperative regulation and control of the flexible load cluster; the method is applied to a power system.

The invention relates to the technical field of virtual display video rendering, in particular to a multi-scene adaptive virtual display video rendering method, which comprises the steps of scene feature extraction and dynamic modeling, scene switching prediction and resource pre-allocation, dynamic rendering parameter optimization, multi-thread task scheduling and load balancing, real-time feedback and adaptive adjustment and the like. According to the method, scene switching can be predicted through the Markov chain model, rendering parameters and resource allocation are optimized, the rendering efficiency and the adaptation capability in a complex scene are improved, dynamic adjustment is achieved based on real-time performance monitoring, system resource consumption is reduced, and the multi-scene high-quality rendering requirement is met.

The invention discloses a Markov chain-based power dispatching boundary data behavior anomaly detection method and system. The method comprises the following steps: monitoring historical data of each information object of network boundary equipment of a power dispatching system in an IEC104 protocol, and extracting each dispatching service according to a time interval of a remote control instruction data packet of the information object; constructing a state transition matrix based on all historical remote control instructions for each information object based on a Markov chain algorithm, wherein the state transition matrix records all possible transitions among states and probability distribution thereof; when a new scheduling service is received, calculating the probability that the scheduling service belongs to a normal behavior by using the state transition matrix, and if the calculated probability is lower than a preset safety threshold, determining that the scheduling service has an abnormal behavior; and updating the corresponding state transition matrix according to the abnormal behavior detection result. According to the scheme, the anomaly detection capability of the power dispatching control system is remarkably improved.

The invention provides a self-adaptive volume-variable intelligent air compressor and a control system thereof, and relates to the technical field of air compressors. The control system comprises a multi-source heterogeneous sensing fusion module, a dynamic manifold optimization control module, a quantum probability fault pre-judgment module, a cross-domain energy cooperative scheduling module, a distributed self-organizing communication module and a self-adaptive meta-evolution module. And a grey Markov chain model and a wavelet decomposition-support vector machine are combined to accurately predict the real-time electricity price and the factory gas consumption load. And according to the predictions, the operation mode of the air compressor is intelligently adjusted through dynamic game equilibrium and a multi-target differential evolution algorithm. In the low ebb period of the electricity price, the operation load is automatically increased by 30%-40%, and meanwhile the gas storage capacity is increased by 25%-35%; and in the peak period of electricity price, stored gas is reasonably released, and the operation load is reduced by 20%-30%. And energy waste caused by unreasonable operation is effectively avoided.

The invention relates to the technical field of complex equipment damage detection, in particular to a damage trend prediction method based on a hierarchical graph structure and Markov residual correction, which is superior to a traditional time sequence regression model in the aspects of prediction precision and generalization ability. According to the method, a damage trend prediction model based on a hierarchical graph structure and Markov residual correction is established, the damage trend prediction model based on the hierarchical graph structure and the Markov residual correction comprises a prediction module and a correction module, and the prediction module converts damage size data obtained through engine borescope inspection into a graph structure; a damage prediction network of a hierarchical graph structure is constructed to process the graph structure, a damage trend prediction problem is converted into prediction of edge weights among nodes in the graph, and internal relations among damage data are revealed; and the correction module designs a residual error correction method based on multiple types of Markov chains so as to construct a Markov chain model to correct a prediction result.

The invention provides an electric vehicle charging load space-time prediction method based on hybrid model driving, and relates to the field of electric vehicle charging, and the method comprises the steps: building a user travel model based on a Markov chain; constructing a multi-angle traffic road network model and a load space-time prediction model based on multi-graph feature fusion; acquiring user historical travel data, electric vehicle charging load historical data, traffic network historical information and external historical data of the to-be-predicted area; generating a user MDP trip chain according to the user historical trip data of the to-be-predicted area through a user trip model; extracting a multi-angle spatial relation graph from the traffic road network historical information of the to-be-predicted area through the multi-angle traffic road network model; the electric vehicle charging load of the to-be-predicted area is predicted through the load space-time prediction model based on multi-graph feature fusion according to the user MDP trip chain, the multi-angle spatial relation graph and the external historical data, and the method has the advantage of improving the accuracy of electric vehicle charging load prediction.

The invention discloses a network security analysis method and system based on privatized generative artificial intelligence, and relates to the technical field of network security, and the method comprises the steps: constructing a heterogeneous neural network detection engine based on an adjusted structured feature library, and fusing time sequence association, semantic features and region boundary information to generate a dynamic risk vector; predicting a future resource access probability of the user by utilizing a transfer learning technology, and generating a dynamic caching strategy in combination with the dynamic risk vector; capturing an attack event in the dynamic cache strategy through a comprehensive detection strategy, triggering an automatic response mechanism based on game theory optimization, and generating a defense measure; on the basis of a Markov chain and double failure detection, a hotspot resource access probability curve is dynamically corrected, and an optimal defense strategy combination is solved in combination with Nash equilibrium of the game theory, so that the cache hit rate is increased, and meanwhile, the misjudgment rate of high-risk attacks is reduced.

The invention provides a hydropower-wind power-photovoltaic complementary system long-term random collaborative scheduling method, which comprises the following steps: acquiring attribute information of cascade power stations in a drainage basin, including a resource variable historical value time sequence of each resource variable; based on a Markov chain and a Copula function, time correlation of each resource variable of the drainage basin cascade power station and correlation among the resource variables are quantified, an initial simulation scene set is generated, and scene reduction is carried out; and constructing and solving a watershed cascade hydropower-wind power-photovoltaic complementary system long-term random collaborative scheduling model, obtaining the ex-warehouse flow of the hydropower station in the scheduling period by time period suitable for all the comprehensive scenes, and obtaining the planned water level process and the planned output process of the hydropower station through the ex-warehouse flow. According to the method, long-term random collaborative scheduling of the hydropower-wind power-photovoltaic complementary system is carried out under the condition of considering the uncertainty wind and light output scene, and a reliable scheduling process can be obtained by comprehensively considering multiple actual operation constraints of the power station.

The invention discloses a drainage basin rainfall runoff prediction method and device fusing a mechanism model and a machine learning model, and a program product. The method comprises the following steps: firstly, constructing a seasonal Markov chain model by taking a seasonal rainfall state transition probability as a conditional transition probability; simulating the rainfall state of each day in a preset simulation period by adopting a seasonal Markov chain model, simulating the rainfall capacity of each day according to the distribution obeyed by the rainfall capacity of each seasonal characteristic time period, and generating a rainfall sequence in the preset simulation period; predicting the runoff yield of each day under a specified rainfall sequence in a preset simulation period by adopting a drainage basin mechanism model, and counting the earlier-stage accumulated rainfall capacity and seasonal characteristics as the input characteristics of runoff yield prediction; and finally, taking the input features of all runoff prediction in a preset simulation period and the corresponding runoff simulation values as a data set, training a machine learning model, and predicting the runoff in the future by adopting the trained machine learning model. The method is high in prediction efficiency and high in prediction precision.

The invention discloses a bad weather degraded image restoration method and device based on dynamic degradation generation, and relates to the field of image processing, and the method comprises the steps: employing an E step of an expectation maximization algorithm to optimize a hidden variable in a training process of a noise predictor and a dynamic degradation generator, and obtaining an optimized hidden variable; in M steps of an expectation maximization algorithm, inputting the optimized hidden variables and state variables into a dynamic degradation generator, generating a degradation layer to perform semi-supervised training on a noise predictor and the dynamic degradation generator, and obtaining a trained noise predictor; in an image restoration stage, an image degraded in severe weather and random Gaussian noise are input into a trained noise predictor, an iterative denoising step obeying a Markov chain is executed, and a clean restored image is obtained. The problems that an existing severe weather image restoration model is limited in generalization ability and unstable in restoration effect are solved.

The invention discloses a blurred image segmentation method based on a mixed diffusion model, and relates to the technical field of artificial intelligence image processing and computer vision. The invention provides a novel fast, efficient and accurate hybrid denoising segmentation model HAD-Net. The HAD-Net is an end-to-end architecture composed of a diffusion model and a segmentation model; wherein underlying distribution of data is learned by using a diffusion model, and noise distribution is fully captured through a Markov chain, so that an image is denoised in a reasoning stage; a clear denoised image is used as prior input to a segmentation model, a feature encoder containing wavelet convolution is designed, and high and low frequency information is fully extracted through wavelet convolution to enable a prior image to realize guide segmentation; reasonable construction of the overall architecture greatly reduces training cost, and reasonable network depth and internal module design solve the problem of low segmentation precision caused by too large difference between morphological distribution and size of segmentation targets.

The invention provides a flight data packet data augmentation method, system and medium of a flight simulator. The flight data packet data augmentation method adopts a flight data packet data augmentation method based on the principle of flight mechanics, combines a Markov chain model and a historical flight data packet, performs data augmentation on the basis of real flight data, determines the state transition probability according to the historical actual flight data, then uses the Markov chain model to simulate the sequence change of the flight state, generates a flight state sequence, and calculates the flight attitude, speed, attitude angle, thrust and other changes in different flight stages in each state in combination with a random process, so as to generate flight data that is more in line with actual physical laws, ensure that the changes in engine parameters match the flight state of an aircraft, and constrain a physical model based on flight dynamics and aircraft parameters, so as to better simulate the dynamic changes and coherence in the flight process, and improve the authenticity and scientificity of the augmented data.