299 results about "Markov chain" patented technology

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.

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 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 relates to the cross technical field of AI multi-modal analysis and dialect processing, in particular to a dialect content crawling and auditing system and method based on AI analysis, a simulation terminal generates a real person behavior entropy interval event stream through a Markov chain and reinforcement learning hybrid model, a dialect exclusive operation library and a differentiated interest strategy are integrated, and a real person behavior entropy interval event stream is generated through a real person behavior entropy interval event stream; anti-crawling is avoided in combination with a proxy IP pool and a Bezier curve trajectory, a search unit realizes directional crawling of dialect keywords through a hot updateable script, a blind patrol mode locates high-risk content depending on a triple knowledge graph and risk prediction, whole-course block chain evidence storage is performed, and a collaborative analysis unit analyzes dialect audio and video features in a layered architecture. The multi-modal large model is combined with the professional small model, the audio spectrum, the text translation and the video picture are fused to realize cross-modal violation detection, the application service unit alarms violation content in real time, a supervision report containing a violation distribution thermodynamic diagram is generated, and the accuracy and traceability of dialect violation content crawling and auditing are improved.

The invention relates to the technical field of pipeline safety monitoring and water seepage prevention early warning, in particular to a water seepage prevention early warning system driven by complex temperature and humidity environment edge calculation, which comprises the following modules: an environment and water immersion data acquisition module used for acquiring temperature, relative humidity, water immersion signals and auxiliary evidence in real time at edge nodes and performing preprocessing; and the temperature and humidity coupling dew point judgment module is used for calculating a dew point at the edge node based on the temperature and humidity data and generating condensation negative evidence. According to the method, temperature and humidity and water immersion data are collected at edge nodes and preprocessed, condensation negative evidence is generated based on the temperature and humidity data and fused with water immersion positive evidence to form a Markov chain, meanwhile, an environment base line is established, and a threshold value is adjusted in a self-adaptive mode, so that continuous monitoring and closed-loop judgment of the water seepage state are achieved; therefore, the problems that most traditional pipeline water seepage monitoring systems depend on a remote server, and judgment lags and misinformation is caused by the complex environment and the static threshold value are solved.

The invention relates to the technical field of market trend prediction, and discloses a market trend prediction system driven by macroeconomic indicators. An index acquisition module of the system dynamically acquires core economic indexes such as GDP growth rate, CPI, PMI and currency supply; the data preprocessing module is used for carrying out layered noise reduction processing on the multi-source heterogeneous data; the feature engineering module constructs a market sensitive feature set through spatio-temporal feature fusion; the prediction model building module is used for building a multi-layer nonlinear prediction model based on a deep belief network; the dynamic adjustment module adopts reinforcement learning to optimize a decision threshold value and combines a Markov chain to carry out state transition planning; and the feedback iteration module analyzes and predicts deviation through Bayesian filtering and realizes strategy updating. According to the method, deep learning and reinforcement learning technologies are creatively fused, the prediction precision is remarkably improved through a dynamic calibration mechanism, and the method can be widely applied to the macroeconomic analysis fields of financial investment, industrial planning and the like.

The invention relates to the technical field of power system inertia evaluation, in particular to a random mode switching driven power distribution network equivalent inertia online evaluation method and system, and the method comprises the steps: carrying out the priori definition of a group of hidden operation modes based on the equivalent inertia of a power distribution network, and constructing an unobservable Markov chain; a double-layer hidden Markov jump system model is constructed, the bottom layer is an unobservable Markov chain, and the upper layer is a continuous dynamic behavior model for describing the power distribution network; on the basis of PMU high-resolution time sequence disturbance data, deducing the probability of a system maximum probability dominant operation mode and the probability of each hidden operation mode; and carrying out multi-mode probability adaptive weighting on each equivalent inertia estimation value based on the probability of the dominant operation mode and each hidden operation mode so as to obtain the equivalent inertia estimation value of the power distribution network on line. Through the method, the problem that the equivalent inertia of the power distribution network is difficult to quickly, accurately, dynamically and adaptively assess online under the conditions of high permeability of new energy and frequent switching of working conditions is effectively solved.

The invention relates to a seismic liquefaction assessment method based on conditional random field simulation, which comprises the following steps: firstly, obtaining a logarithmic normal distribution random field of a target area under a corresponding SPT-N value, then resampling through a Bootstrap method, constructing a weighted prior probability density function of the target area in combination with a likelihood function, and finally calculating the seismic liquefaction of the target area according to a Bayesian theory. A Markov chain Monte Carlo sampling method is combined, through posterior probability density distribution, an optimal horizontal direction correlation distance is determined, a covariance matrix is constructed to generate a conditional random field, and then through multiple times of simulation, the conditional random field is converged; and finally, aiming at the target area, through calculation of a cyclic stress ratio and a cyclic resistance ratio, constructing a liquefaction probability distribution diagram corresponding to the target area. According to the method, a conditional random field simulation method is inferred and improved by combining Bootstrap and Bayesian theories, the precision and reliability of geological parameter simulation are remarkably improved, and reliable data support is provided for seismic liquefaction assessment of deep and uneven site engineering.

The invention relates to the technical field of intelligent tourism management, and discloses a tourist behavior dynamic modeling method based on space-time big data, which comprises the steps of collecting and preprocessing multi-source positioning data, inputting the multi-source positioning data into a multi-source positioning fusion engine, and combining statistical optimization and deep learning model fusion to obtain a high-precision positioning model. Track reconstruction and time sequence aggregation are carried out on continuous position points of tourists based on a high-precision positioning model, a structured activity data graph is generated, activity data of the tourists at all positions are obtained, activity preference characteristics are extracted through an association rule mining and clustering algorithm, and a Markov chain and a spatio-temporal evolution model are combined to predict a tourist flow trend. And forming a flow prediction result, and generating a visual dynamic decision support by using the prediction result. According to the invention, visual and dynamic decision support is provided, the crowd congestion is relieved, and the operation safety of the scenic area is guaranteed.

The invention discloses a method for exploring a heterogeneous catalytic reaction network based on a deep potential energy surface model, and the method comprises the steps: carrying out the accurate prediction of the free energy change of all possible element reactions on a catalyst through the combination of transfer learning and a density functional theory, and constructing a reaction network diagram based on a graph theory method; an improved Dijkstra method is used for searching a reaction network diagram to determine a reaction mechanism, and the selectivity of the catalyst is explored based on a Markov chain method. According to the technical scheme provided by the invention, when the CO2 reduction reaction mechanism on the monatomic alloy is explored, the prediction error MAE of the electron energy of each intermediate is only 0.16 eV, the calculated amount is reduced by 90%, the dominant path of the CO2 reduction reaction is visually displayed, and a new normal form is provided for complex mechanism exploration and catalyst screening.

The embodiment of the invention discloses a pedestrian trajectory prediction method based on an interactive perception diffusion model, and the method comprises the steps: constructing a forward diffusion module, and obtaining a noise-added future trajectory of a target pedestrian; constructing a reverse denoising module, regarding social and scene interaction features as conditions of a parameter-containing reverse Markov chain, and performing denoising operation on a noise-added future trajectory of a target pedestrian; constructing a loss calculation module, and optimizing a pedestrian trajectory prediction model composed of the above modules by using the loss value to obtain an optimal pedestrian trajectory prediction model; and constructing a test denoising module, and denoising test initial random noise based on the optimal pedestrian trajectory prediction model to obtain a pedestrian trajectory prediction result. According to the method, the social encoder and the scene encoder are designed to extract the social interaction features and the scene interaction features respectively, so that the scene space information is reserved, the double guidance decoders are designed to strengthen the cooperative guidance of the two interaction features, and the accuracy and rationality of pedestrian trajectory prediction are further improved.

The invention relates to the technical field of remote sensing and computer vision, and discloses a multi-source image time domain super-division method and system for giant constellations, and the method comprises the steps: obtaining multi-source remote sensing image data which comprises an SAR image, an infrared image and a visible light image; constructing a cross-modal feature space, mapping multi-source remote sensing image data to a unified semantic space, and realizing cross-modal feature alignment; the SAR image or the infrared image is converted into a visible light modal image based on a neural Schrodinger bridge model, and the neural Schrodinger bridge model is decomposed into a plurality of Markov chain sub-problems and solved through adversarial learning and a staged optimization strategy; two-way semantic constraints are introduced into the neural Schrodinger bridge model, and the two-way semantic constraints comprise visual feature matching constraints and text guiding constraints, so that semantic consistency of the generated image in a CLIP feature space is optimized; and the visible light image sequence after time domain super-division is output, and the time resolution is improved.

The invention belongs to the technical field of geographic information science, and particularly relates to a Markov-FLUS model-based feedback land utilization change simulation method and system, and the method comprises the steps: firstly predicting an initial area target vector based on historical data and a Markov chain, and inputting the initial area target vector into a FLUS model to carry out the first space distribution simulation; by calculating the achievement degree of the simulation result and the area target, the space conflict type which cannot be achieved due to the space constraint is identified, the area target vector is dynamically corrected according to the space conflict type, and the corrected target is fed back to the FLUS model for iterative simulation until the result is converged. According to the method, the technical problem of disjunction of area prediction and spatial layout in a traditional coupling model is effectively solved, and the scientificity and accuracy of land utilization change simulation are remarkably improved.

The invention discloses a sewage treatment plant equipment fault diagnosis method based on multi-scale adaptive feature extraction, and belongs to the technical field of equipment intelligent operation and maintenance. According to the method, after adaptive filtering and quality evaluation are carried out on a vibration signal, an adaptive decomposition strategy fusing VMD and improved EMD is adopted, and a modal number and parameters are optimized according to an information entropy criterion; extracting multi-scale features from three levels of micro-scale (instantaneous characteristics), mesoscale (local statistics) and macro-scale (global energy / entropy), and screening key features through mutual information and a two-layer fusion mechanism; a health index model is constructed, and normal, attention, warning and danger four-level dynamic early warning is achieved; and meanwhile, a self-adaptive fault knowledge base capable of being incrementally updated is established in combination with density clustering and a Markov chain. According to the method, the fault identification accuracy and the system self-adaptive capacity are remarkably improved, and the method is suitable for intelligent diagnosis of sewage treatment key equipment such as a centrifugal pump and a Roots blower.

The invention discloses a multi-unmanned aerial vehicle cluster stochastic prediction control method and device based on anti-saturation and packet loss compensation, and the method comprises the steps: building a closed-loop control model of a stochastic system based on a pilot-following multi-unmanned aerial vehicle cluster architecture; through a Markov chain modeling data transmission packet loss process, a forgetting factor is introduced to design a packet loss compensation strategy after random data is randomly lost so as to correct a closed-loop control model of a random system; aiming at the corrected closed-loop control model of the following stochastic system, constructing an anti-actuator saturation control law through convex representation of a saturation function, and further constructing a saturation-limited following stochastic augmentation prediction model; solving a predictive control problem on line based on a random augmentation predictive model; and the control performance index is controlled through rolling optimization, the gain of the feedback controller is updated, and anti-saturation random prediction control following with random following in a packet loss environment is realized. According to the method, the coupling influence of data transmission packet loss and actuator saturation can be effectively solved, and the system performance weakness caused by single problem processing is avoided.

The invention relates to the technical field of environmental protection and monitoring, and discloses an arid region oasis water environment ecological safety early warning method, which comprises the following steps: step 1, constructing a sky-air-land-water three-dimensional monitoring network, the monitoring network comprises a satellite remote sensing monitoring subsystem, an unmanned aerial vehicle remote sensing monitoring subsystem, a ground sensor monitoring subsystem and an underwater monitoring subsystem, and the monitoring network is used for acquiring multi-scale and multi-element water environment ecological data; step 2, establishing an ecological safety evaluation index system of the oasis water environment in the arid region, the evaluation system including four first-level indexes of hydrology and water resource dimension, water environment quality dimension, ecological health dimension and social economy dimension, and setting a plurality of quantifiable second-level indexes under each first-level index. A combined prediction model in which a Markov chain and a long-short-term memory neural network are coupled is adopted, and a system dynamics scene simulation method is combined, so that the perspectiveness and scene adaptability of the early warning method are effectively enhanced.

The invention discloses an image super-resolution reconstruction method and device fusing image restoration and fast diffusion, and the method comprises the steps: constructing a diffusion model, inputting a high-resolution remote sensing image into a forward Markov chain, adding noise, and generating a low-resolution noise graph aligned with a low-resolution remote sensing image; inputting the low-resolution remote sensing image into a reverse Markov chain based on Swindow-UNet for denoising to generate a high-resolution remote sensing image; adopting a fast diffusion mechanism based on high-level feature skipping and multiplexing to accelerate reasoning in the Swinin-UNet; and restoring a new low-resolution remote sensing image by using an image restoration preprocessing module based on a degradation kernel, and inputting the restored low-resolution remote sensing image into the trained reverse Markov chain based on the Swindow-UNet for remote sensing image super-resolution reconstruction. The method can effectively improve the image resolution, gives consideration to the processing efficiency, and can be widely applied to the fields of satellite image analysis, geographic information systems, environment monitoring and the like.

The invention relates to the technical field of digital twinning and simulation, and discloses a full-stack digital twinning simulation method and system for a power grid dispatching master station, and the method comprises the steps: obtaining a voltage power flow result data set and a temperature field distribution data set, building a unified virtual coordinate system, building an initial two-dimensional thermal distribution diagram according to an initial mapping matrix, and carrying out the simulation of the voltage power flow result data set and the temperature field distribution data set; carrying out Delaunay triangulation on the image to generate a mapping error distribution diagram; constructing a Markov chain state sequence, calculating a state transition probability matrix, and predicting and generating a corrected mapping matrix; the edge node loads the correction matrix to generate a lightweight state vector, the lightweight state vector is uploaded to the cloud through multi-level cache to form a set, and the cloud updates the matrix and issues a new version; according to the method, the voltage-temperature rise mapping relation is captured, predicted and corrected in real time along with working condition drifting, hidden coupling deviation caused by inconsistent data calibers is restrained, and it is guaranteed that the high-precision and high-consistency electrical-thermodynamic combined situation is always maintained in the operation process of full-stack digital twin simulation.

The invention discloses an image defogging method and system based on a physical guide diffusion model, and relates to the technical field of image processing. Comprising the following steps: inputting a to-be-processed fog image into a trained physical perception defogging model to obtain a pseudo clear image (namely a preliminarily estimated fog-free image) and a transmissivity image (representing the attenuation degree of a medium to scene light); inputting the to-be-processed fog image into a pre-trained physical guide diffusion model, and gradually adding Gaussian noise to the clear image through a Markov chain to generate a noise image; the pseudo clear image and the transmissivity image serve as input through condition guidance, and feature splicing is adopted to be injected into a diffusion model so as to guide the denoising direction; then capturing the characteristics of noise distribution by using a diffusion model, predicting and removing noise components in the current time step, and obtaining an intermediate image; and taking the intermediate image corresponding to the final time step as a defogged clear image. According to the method, the accuracy of the denoising direction is emphasized, and the definition and quality of the defogged image can be improved.

The invention discloses a GIS latent defect diagnosis method and system based on multi-source signal fusion, and belongs to the field of power equipment fault diagnos.The method comprises the steps that a UWB sensor is adopted to monitor PD signals in a GIS, real-time filtering and spectral analysis are conducted on the signals in combination with an FPGA preprocessing chip, and the sampling sensitivity is dynamically adjusted according to the PD signal strength; gIS internal ultrasonic source three-dimensional imaging is realized by adopting a three-dimensional ultrasonic array; pD and ultrasonic signals are processed based on wavelet transform, PD and ultrasonic data are processed based on CNN, and a signal time sequence is processed by LSTM; constructing a GIS fault knowledge graph, setting expert rules, and calculating a GIS fault occurrence probability by adopting Bayesian reasoning; and predicting a GIS fault evolution path according to the Markov chain model, and performing dynamic adjustment in combination with reinforcement learning. According to the method, the fault state transition model of the GIS equipment is established based on the Markov chain, the evolution probability between different fault states is quantified, long-term steady-state fault risk assessment is provided, and the operation health condition of the GIS equipment can be prejudged.

The invention discloses a land type adjustment and simulation method based on carbon neutralization constraint, and the method comprises the following steps: obtaining land data of a target region, and calculating the carbon flux of each classification space; constructing a carbon profit and loss weight function based on the carbon flux, the ecological benefit and the economic benefit as an overall constraint condition; reversely deducing each land space area corresponding to a preset time node on the basis of a Markov chain model, and taking the land space areas as stage constraint conditions; performing NSGA-III optimization solution based on the constraint condition to obtain a corresponding land use adjustment scheme; an improved PLUS model is adopted to simulate a land use adjustment scheme, the carbon cost of land conversion is incorporated into a land utilization simulation process, and a genetic algorithm is utilized to optimize model parameters to obtain a land space simulation result of a target year. According to the method, a carbon cycle mechanism, a carbon sink optimization target and a land utilization decision full process are coupled, a closed loop of carbon constraint-land simulation-policy assessment is realized, and scientific decision support is provided for regional carbon emission reduction and ecological protection.

A three-dimensional heterogeneous aquifer horizontal equivalent permeability coefficient multi-scale prediction method based on a residual network comprises the steps that a foundation pit dewatering two-dimensional pressure-bearing steady flow model is constructed, the influence of the size of a lenticular body on foundation pit dewatering flow field disturbance is evaluated, and the resolution ratio of a parameter prediction model grid is determined; based on actual drilling data and transition probability / Markov chain T-Progs, according to multiple deposition types formed by a binary deposition structure, a multi-layer aquifer structure, a coarse-grained deposition structure and a fine-grained deposition structure, a three-dimensional stratigraphic random structure model set is constructed, and three-dimensional stratigraphic section random slices are established; obtaining horizontal equivalent permeability coefficients corresponding to stratigraphic sections in various stratigraphic structures based on the underground water flow numerical model FloPy, and establishing a stratigraphic structure-equivalent permeability coefficient training set based on a neural network; a deep learning model is constructed based on the residual network ResNet50; and training, verifying and testing the equivalent permeability coefficient of the stratum section formed by the binary sedimentary structure, the multi-layer aquifer structure, the coarse grain sedimentary structure and the fine grain sedimentary structure.

The invention relates to the technical field of industrial internet communication, and discloses a zero-trust-based large-scale factory 5G private network communication method and system, and the method comprises the steps: constructing a hardware fingerprint; the firmware is subjected to Hash verification by using the national cipher SM4; an equipment activity area is delimited through GPS and UWB fusion positioning, and trust deduction is triggered when equipment crosses a boundary; constructing an equipment behavior baseline based on a Markov chain and historical data, and judging that the equipment behavior is abnormal when the equipment behavior baseline deviates from a threshold value; establishing a dynamic trust evaluation model, and calculating a trust value; the minimum bandwidth of the 5G slice is reduced to 10 Mbps, and the SDN controller completes scheduling within 100 ms; using an LSTM model to predict idle resources; slice isolation is realized through the NFV, the VLAN and the VXLAN; the equipment is accessed by an X.509 certificate and a work order ID, and the unrecorded equipment triggers a stealth strategy; performing session re-authentication and dynamically adjusting the authority; constructing a threat perception engine by using an IDS and a sandbox; and implementing three-level response on the equipment with the trust score less than 40. The network security and the resource utilization rate can be improved.

The invention discloses a wind-solar combined output dynamic coupling modeling method, system and device and a medium, and the method comprises the steps: defining a quantification rule of a plurality of typical weather scenes based on historical weather and output data, and training a model parameter set for each type of scene; respectively constructing time correlation output models of the wind power sequence and the photovoltaic sequence by using a first state algorithm; dimension expansion is carried out, and a two-dimensional Markov chain model reflecting the coupling relation of the wind power and the photovoltaic random process is constructed; modeling a time sequence dependence structure of wind and light output by adopting a first mixed function in a time dimension, and modeling time-varying spatial correlation of the wind and light output by adopting a first dynamic function in a spatial dimension; estimating parameters of the first mixed function by using an expectation maximization algorithm, and estimating parameters of the first dynamic function by using a nonparametric kernel density-based maximum likelihood estimation method to obtain a complete parameter set; and based on the complete parameter set, generating a wind-solar combined output sequence in two stages through a Monte Carlo method.

The invention provides a hidden Markov prediction compensation method and system for gear wear evolution, and belongs to the technical field of gear transmission, and the method comprises the steps: constructing a hidden Markov chain of a gear wear state-compensation parameter, building an encoder coding-decoding frame in a gear compensation system, extracting a hidden Markov chain state sequence by using an encoder, and carrying out the coding-decoding of the hidden Markov chain state sequence. A decoder is used for feeding back a prediction result to the compensation module; the observation sequence of the hidden Markov chain is substituted into the trained hidden Markov model, a corresponding state sequence is obtained, and life end point prediction is carried out; the state sequence is converted into an end-of-life prediction curve, and an optimal end-of-life value with the shortest distance between a prediction value and a target is searched in a pre-drawn compensation optimization solution space to serve as a reference wear state; and the reference wear state is substituted into an encoder in the hidden Markov chain, encoding curve parameters are obtained, parameter optimization is carried out on a compensation curve, and a traditional passive response strategy is converted into an active prevention strategy.

The invention relates to the technical field of power system safety monitoring, in particular to a power transmission line tree flash hidden danger monitoring method based on multi-sensor fusion, which comprises a multi-source data acquisition module, a data fusion and preprocessing module, a risk feature extraction module, a dynamic risk assessment module, an intelligent early warning decision module and a monitoring feedback optimization module. Tree information is collected in real time through multiple sensors such as a laser radar and an infrared thermal imager, data quality is optimized by using improved Kalman filtering and wavelet transformation, risk features are extracted based on a fractional order differential equation, threat indexes are dynamically evaluated in combination with adaptive fuzzy clustering and a Markov chain model, and the risk assessment accuracy is improved. And finally, generating an early warning strategy through mixed integer linear programming. The tree flash hidden danger identification accuracy can be remarkably improved, the potential risk is comprehensively covered, the manual inspection cost is reduced, and intelligent safety guarantee is provided for a power system.

The embodiment of the invention provides an unknown cross-site script security detection method and device generated based on a test script, and intelligent construction of the test script is realized through innovatively constructing a script generation mechanism driven by a Markov chain and through atomic element sequence analysis and a random walk strategy. And designing a DOM tree difference-based filtering analysis mechanism, and establishing a filtering rule identification strategy in combination with multi-dimensional mark extraction and node positioning. And a matrix iterative optimization mechanism is introduced, and self-adaptive optimization of the test script is realized through dynamic updating and convergence calculation of a state transition matrix. According to the method, the defects of the traditional technology in the aspects of script generation, filtering analysis, optimization adjustment and the like are effectively overcome, and the unknown cross-site script detection effect is remarkably improved.

The invention belongs to the technical field of Internet of Vehicles control, and particularly discloses a resource allocation method and system of a cellular Internet of Vehicles based on graph reinforcement learning. The method comprises the following steps: firstly, establishing a two-dimensional Markov chain model, and describing a resource allocation and data packet transmission process in a CAM and DENM coexistence scene; then providing a data packet reception ratio model used for evaluating the successful reception probability of CAM and DENM data packets; meanwhile, a time delay model is provided for describing the time interval between two continuous CAM data packets or DENM data packets successfully received by the same vehicle. On this basis, a multi-agent deep reinforcement learning framework is established to realize intelligent resource selection decision of the vehicle, and a resource allocation method based on graph reinforcement learning is further provided to guide the agents to more effectively extract spatial topological features of vehicle nodes, so that the resource allocation efficiency is improved. According to the invention, based on the extracted features, the optimal resource allocation decision can be learned and acquired for each vehicle, the successful reception rate of data packets of CAM and DENM is improved, and the time delay is reduced.