56 results about "Markov chain" patented technology

The application belongs to the technical field of electricity, and particularly relates to a large language model driven conditional diffusion new energy output scene generation method. First, a conditional diffusion model suitable for new energy output scene generation of the power grid side is constructed, the conditional probability distribution of the actual output of new energy is learned implicitly by embedding the conditional information, and a scene set is generated based on a Markov chain. Second, a thinking chain optimization framework driven by a large language model is proposed, the semantic reasoning capability of the large language model is used to analyze the training state, and the optimal parameter interval is quickly locked under extremely low computing budget. The example verification result shows that the average Euclidean distance of the generated scene set is improved by more than 30.7% compared with the traditional method; the optimization efficiency and timeliness are greatly improved, under the extremely low computing budget of only allowing 10 iterations, the scene generation accuracy is further improved by about 1% compared with the Bayesian optimization method, and the scene set generation time is reduced by 87% compared with the Copula model; the reliability and timeliness of the dispatching decision are significantly improved.

The application relates to the technical field of electrical equipment evaluation, in particular to a multi-dimensional electrical equipment stability evaluation method and device for new energy power grid connection, which comprises the following steps: based on a multi-dimensional evaluation index system, collecting multi-dimensional index data of a target electrical equipment, performing feature enhancement processing, and generating processed enhanced features; based on a grid connection power fluctuation coefficient and an equipment operation stage coefficient of the target electrical equipment, adjusting the weight proportion of each dimension and corresponding indexes in the multi-dimensional index data to obtain dynamic weights; inputting the processed enhanced features and the dynamic weights into an evaluation model combining fuzzy comprehensive evaluation and Markov chain prediction, performing stability evaluation on the target electrical equipment for new energy power grid connection, and outputting a stability evaluation result of the target electrical equipment. Therefore, the problems that in the related art, prospective decision support cannot be provided for preventive maintenance and grid connection scheduling, and it is difficult to guarantee the safe and stable operation of the power grid under high-proportion new energy access are solved.

The application belongs to the technical field of Internet of Vehicles control, and specifically discloses a resource allocation method and system for cellular Internet of Vehicles based on graph reinforcement learning. First, a two-dimensional Markov chain model is established to describe the resource allocation and data packet transmission process in the coexistence scenario of CAM and DENM. Then, a data packet reception ratio model is proposed to evaluate the successful reception probability of CAM and DENM data packets. Meanwhile, a delay model is proposed to depict the time interval between the successful reception of two consecutive CAM data packets or DENM data packets of the same vehicle. On this basis, a multi-agent deep reinforcement learning framework is established to realize intelligent resource selection decision of vehicles. Furthermore, a resource allocation method based on graph reinforcement learning is proposed to guide the agents to more effectively extract the spatial topological features of vehicle nodes. Based on the extracted features, the application can learn and obtain the optimal resource allocation decision for each vehicle, thereby improving the successful reception rate of CAM and DENM data packets and reducing the delay.

The application discloses a kind of three-dimensional geological modeling dynamic updating methods based on tunnel face information, comprising: S1, obtains borehole data, the borehole data is preprocessed, the data after pre-processing is mapped to three-dimensional space and is visualized and presented, preliminary data analysis is carried out, and initial three-dimensional geological model is generated using improved coupled Markov chain model;S2, real-time acquisition tunnel face image, using improved Swin Transformer model extracts stratum feature, and feature is converted into structured face feature data;S3, fusion borehole data and construction stage's face feature data, and the data after fusion is input initial three-dimensional geological model, dynamic updating model, and finally output optimized model.The application uses the above method, realizes the dynamic optimization of three-dimensional geological model, improves the accuracy of geological prediction in the process of tunnel construction, and provides reliable support for real-time decision-making of tunnel construction.

This invention provides a method for assessing the capacity value of wind farm clusters based on the Johnson distribution system. The method comprises the following steps: Step 1: Establishing a wind farm cluster fluctuation model through power output analysis using a first-order Markov chain algorithm; Step 2: Establishing relevant models for different wind farm clusters through time-series power output analysis using the Johnson distribution system; Step 3: Conducting a reliability assessment of the power system based on the given load level and wind farm capacity, according to the power adequacy index; Step 4: Using a bisection method to adjust the load of the wind farm system to search for the reliable capacity of the wind farm cluster. This invention is designed for large-scale wind turbine integration into the power system and can scientifically and accurately assess its capacity value.

The application discloses a ship lock water delivery intelligent collaborative control method based on a hydraulic simulation and relates to the technical field of ship lock hydrodynamics. The application collects hydraulic data such as speed, acceleration and blockage ratio in the ship water delivery process, constructs discrete hydraulic states and calculates attribution coefficients; in combination with historical transfer frequency and physical conservation law verification, a state transfer matrix reflecting state evolution law is established; the future hydraulic state probability distribution is deduced by using a Markov chain model, and then a mooring force threat coefficient is calculated to quantify the risk. The method dynamically adjusts the valve opening degree based on the prediction result, realizes intelligent collaborative control with prediction ability, changes passive response into active prevention, and improves the operation safety and efficiency of the ship lock.

The invention relates to a power space-time poisoning attack defense method and system based on a regression model. The method comprises the following steps: firstly, acquiring multi-source time sequence data of a power system, performing trend fitting and period analysis on the multi-source time sequence data by utilizing a regression model, and generating short-term confidence of each node in the power system by combining space-time consistency detection; generating long-term confidence of each node by using a Markov chain; fusing the short-term confidence coefficient with the long-term confidence coefficient to obtain a node weight; then correcting the node gradient and the node weight; and performing weighted aggregation on the corrected node gradients by using the corrected node weights, and updating the regression model based on an aggregation result, thereby realizing defense of the power space-time poisoning attack. Compared with the prior art, the method has the advantages that continuous offset type and space-time cooperative type poisoning attacks are prevented, and the defense precision and the defense real-time performance are balanced.

The present disclosure provides an intelligent generation method based on layered recognition of painting intention, which comprises: obtaining a painting description text input by a user, and pre-processing the painting description text to extract layered semantic features. A geometric constraint expression model is constructed according to the layered semantic features, and geometric state parameters are generated through the geometric constraint expression model. The state transition stage is determined according to the geometric state parameters, and a spatial state evolution model of Markov chain is constructed based on the state transition stage. The optimal geometric state is determined through the spatial state evolution model, and a layered generation model driven by spatial state constraint is constructed according to the optimal geometric state, so as to generate an initial target image through the layered generation model. The initial target image is subjected to consistency test and optimization, and the optimized target image is output after the test is passed. The present application improves the consistency of the generated image in visual expression and engineering implementation by introducing geometric constraints into the state transition process.

This application provides a method, system, device, and medium for virtual terminal resource preloading and caching management based on the ISOBUS standard. This application innovatively constructs a multi-level caching architecture and combines a Markov chain-based resource prediction engine with adaptive memory management technology to achieve intelligent preloading and efficient caching of interface resources. Addressing the pain points of existing ISOBUS VT systems, such as high resource loading latency, low cache hit rate, and insufficient memory utilization, this invention provides a targeted technical solution. Experimental data verifies that this technical solution can reduce resource loading time by 50% to 70%, increase cache hit rate to over 92%, improve memory utilization by 40%, and reduce system crash rate by over 80%.

This invention relates to the field of automotive performance testing and autonomous driving virtual simulation technology, specifically a method and system for generating continuous vehicle driving conditions based on GIS road networks and data-driven Markov chains. The method includes four main steps: S1, road topology map construction and OD route planning based on GIS big data; S2, random traffic breakpoint simulation based on a probability distribution model; S3, kinematic envelope construction based on bidirectional physical propagation; and S4, data-driven Markov velocity generation with forward aiming. By constructing a smooth envelope that absolutely conforms to the vehicle's kinematic limits through a unique bidirectional physical propagation algorithm, the abnormal problems of velocity steps and acceleration / deceleration exceeding physical limits in the random generation process of traditional Markov chains are completely solved. The generated driving conditions can be directly used for simulation testing without manual correction.

The application relates to the technical field of digital processors, in particular to a reducer control system based on an opportunity constraint, which comprises a dynamic window module, a temperature cooling module, a lubricating pressure module, a gear meshing module and an optimization control module.In the application, reducer torque and rotating speed data are collected, the difference value of adjacent time points is calculated, the state transition probability is estimated in combination with a Markov chain model, a transition time window and an error range are set, the control parameters are dynamically adjusted, the reducer state change is fed back in real time, the real-time performance and adaptability of the control system are improved, the temperature data interval matching and trend judgment avoid over-temperature failure, the stability is ensured, the lubricating oil pressure and the cooling correction result are combined and compared, the lubrication is ensured to be appropriate, wear and failure are avoided, gear vibration and temperature data are analyzed through a probability density function, the gear meshing state is adjusted, and the system robustness and operation efficiency are improved.

A hardware random number generator (HW-RNG) for drawing samples from a multivariate target distribution through simulation of a Markov chain is disclosed. The HW-RNG (100) has a pipeline architecture operating in cycles. The HW-RNG comprises sets of p-bit devices (112-1; 112-2; 112- N), a programming unit (150) to program the sets of p-bit devices according to a corresponding set of adaptive proposal distributions, selection circuitry (121) to select one of the sets of p-bit devices, a sampling circuit (130) configured to produce a candidate sample from the proposal distribution associated with a selected one of the sets of p-bit devices, and a scheduling unit (140). The scheduling unit (140) is configured to accept the candidate sample with an acceptance probability, and recompute at least one of the proposal distributions that is conditionally dependent on the accepted candidate sample. A programming phase for each set of p-bit devices lasts for x cycles, and there are N > x sets of p-bit devices.

The application provides a hybrid vehicle energy management method and system based on energy flow test and working condition adaptive parameter optimization, and belongs to the technical field of hybrid vehicle energy management. The method comprises the following steps: building a power assembly energy flow test platform, and completing a DOE experiment based on a Sobol sequence; collecting multi-scene data of an actual vehicle, extracting typical working conditions through PCA principal component analysis and K-means clustering, and combining a Markov chain to generate random cyclic working conditions; constructing a multi-domain coupled energy flow simulation model and verifying the model; performing strategy optimization on the typical working conditions based on GWO, and combining an online working condition recognition mechanism to construct an adaptive energy management strategy; the system comprises an energy flow test module, a working condition generation module, a simulation modeling module, a strategy optimization module and a vehicle controller module. The application realizes the collaborative optimization of fuel consumption, electricity consumption and battery life, and significantly improves the energy efficiency and reliability of HEV hybrid vehicles under complex working conditions and environments.

The application discloses a text key point mining method based on abstract semantic representation and a graph attention network. The method combines text semantic information and text structure information, introduces label classification loss and spherical projection of an embedding space while narrowing the distance between the same categories and widening the distance between different categories in the embedding space, thereby effectively gathering the text while avoiding feature collapse. In addition, by introducing a random walk strategy of a Markov chain, the model further enhances the discriminability of the clustering space. The algorithm combines the advantages of a graph neural network and a Markov chain, preserves the combination of text semantic information and text structure information, and realizes accurate extraction of text key points.

The present disclosure provides a hydrological ecological state prediction method, device, equipment and medium, and relates to the technical field of data analysis. The method comprises: obtaining historical hydrological ecological data, and preprocessing the historical hydrological ecological data to obtain hydrological ecological optimization data; determining hydrological feature data based on the hydrological ecological optimization data, and determining the hydrological ecological state corresponding to the hydrological feature data; constructing a dynamic Markov chain model about time sequence based on the hydrological feature data and the hydrological ecological state; and taking the current hydrological ecological data and the current hydrological ecological state as the input of the dynamic Markov chain model to predict the hydrological ecological state at a target time. The technical solution in the present disclosure can improve the efficiency and accuracy of hydrological ecological state prediction.

The application discloses a three-dimensional seismic exploration prediction analysis method and system, and relates to the field of three-dimensional seismic exploration.The method comprises data preparation and preprocessing; construction of interpretation and establishment of a geological framework; well-seismic joint analysis and construction of a prior model; construction of a prior probability three-dimensional geological model through Markov chain simulation of lithofacies, Monte Carlo sampling to give elastic parameters, and generation of an adversarial network to expand pseudo-well samples; multi-source information fusion and probabilistic reservoir prediction; extraction of multi-evidence volumes and determination of weights; multi-target threshold decision and delineation of a favorable zone; high-precision target appearance and well site deployment.The method realizes logical judgment before appearance through prior probability modeling and multi-evidence weighted fusion, outputs a three-dimensional probability volume to quantify uncertainty, objectively delineates a favorable zone through multi-target decision, significantly improves reservoir prediction accuracy and micro-geological body recognition capability, and reduces exploration risk.

This invention discloses a method for predicting the probability of electric vehicle charging and discharging based on an improved Markov chain. Taking charging and discharging piles as the research object, firstly, the states of the Markov chain are abstracted. The current total power consumption of the charging and discharging piles at the beginning of the cycle is obtained, and the state intervals are divided according to the adjustable range of the power consumption of the charging and discharging piles. Secondly, the decision-making behaviors of the charging and discharging piles are divided into four types: charging behavior, fast discharging behavior, slow discharging behavior, and stationary behavior. Then, the TPC method is used to establish a control strategy, determine the number of charging and discharging piles allocated for charging and discharging behaviors at a certain moment, and calculate the transition probability to establish a state transition matrix. This method is beneficial for leveraging the role of charging and discharging piles in the control of electric vehicle charging and discharging under the consideration of randomness, and for providing an intuitive and quantitative analysis of the charging and discharging amount of electric vehicles under control.

This invention discloses a method and system for energy consumption management of new energy vehicles based on a Markov chain model, relating to the field of new energy vehicle energy consumption control technology. The method includes: acquiring user travel plans, environmental data, vehicle status, and historical user preference data before the user enters the vehicle; performing state discretization processing and Markov state identification; querying the initial transition probability matrix of the Markov chain model; determining and executing the optimal pre-adjustment parameters for the air conditioning; acquiring environmental, vehicle status, and user preference data in real time during vehicle operation; updating the transition probability matrix of the Markov chain model based on this data; and performing multi-objective optimization decisions based on the updated transition probability matrix, combined with constraints on comfort, energy consumption, and battery status, to generate and execute the optimal control strategy; archiving the current trip data; and updating historical user preference data and the transition probability matrix of the Markov chain model. This invention can achieve precise control of the energy consumption of onboard electrical appliances throughout the entire lifecycle, balancing comfort and energy consumption.

The application discloses an optimization method of a diffusion model, which firstly randomly samples steps from Markov chain paths of a diffusion process in a mini batch to obtain a step t in a current batch, then randomly adds noise to each pixel point in an image of the step t to obtain a noise image of the step t, and predicts an original image through a model based on the noise image to obtain noise predicted by the model, then calculates mean square loss between the noise predicted by the model and real noise, finally samples an additional random label from a label set following a preset distribution, and re-predicts the noise of the noise image based on the random label, and calculates distribution adjustment loss. The method adjusts conditional transition probability in the sampling process, implicitly forces the generated image to approximate the target prior distribution in each sampling step, and fills the research gap in the direction of training a more robust generation model based on long-tail distribution data.

The present application belongs to the technical field of surrounding rock stability monitoring and evaluation, and particularly relates to a coal mining influenced surrounding rock stability intelligent evaluation method and system, which fuses a mining cumulative effect factor, a ground stress environment factor, a surrounding rock structure integrity factor and a support effective factor through a nonlinear product formula, truly reflects the synergistic hazard law of surrounding rock instability under the coupling action of multiple factors, simultaneously adopts a mixed form combining harmonic mean and coupling enhancement for a current comprehensive risk index, can effectively capture the risk jump phenomenon when deformation anomaly and hazard factors deteriorate at the same time, and adopts a situation prediction model fusing a grey prediction and a Markov chain, corrects the residual error of the grey prediction by the Markov chain, significantly reduces the prediction error of a single model, combines the instability probability calculated by a logistic regression, realizes the quantitative trend prediction of the surrounding rock stability within a certain time window in the future, and greatly improves the advance and accuracy of the early warning.

The application relates to a traffic signal fuzzy control method considering individual prior data, which comprises the following steps: step 1, cleaning and analyzing license plate recognition data; step 2, performing support vector machine driver personalized travel time prediction based on Bayesian optimization; step 3, performing Bayesian driver personalized path prediction based on a Markov chain; step 4, fusing vehicle historical travel information and real-time detection information; and step 5, obtaining phase arrival rate and turning rate through vehicle travel time and path prediction, and constructing a traffic signal fuzzy control method. The traffic flow arrival rate calculation idea provided by the application can effectively estimate the traffic flow arrival rate under four-phase control, the calculation result can be used as a data source for traffic signal control, the average vehicle delay can be significantly reduced, the vehicle travel time can be highly accurately predicted, and the service quality of the intersection can be significantly improved.

The application discloses a method and device for determining a lithologic trap boundary, electronic equipment and a storage medium. The method comprises the following steps: simulating a pseudo-well vertical lithologic combination based on a continuous-time Markov chain model, determining a pseudo-well lithofacies curve of different lithologic combinations according to a simulation result; determining a target elastic parameter through the pseudo-well lithofacies curve of the different lithologic combinations based on elastic parameter Monte Carlo random simulation with lithofacies constraints; determining pseudo-well simulation seismic data of the different lithologic combinations through the target elastic parameter based on a wave equation forward modeling method of a propagation matrix; and determining a lithologic trap boundary according to the pseudo-well simulation seismic data of the different lithologic combinations. The technical scheme of the application avoids the problems of strong subjectivity, large deviation of boundaries delineated by different personnel, and insufficient objective basis in the determination of a traditional lithologic trap boundary, thereby avoiding the situation of deviation of the delineated boundary caused by human factors.

This invention discloses a method for assessing and warning of cascading failure probabilities in integrated energy systems, belonging to the technical field of safe and stable operation of integrated energy systems. Addressing the problems in existing technologies such as fuzzy characterization of cascading failure evolution mechanisms, lack of dynamic transition patterns in operating states, static and fixed failure probability assessments, and the inability to adaptively adjust warning thresholds, this invention uses a weighted clustering algorithm with safety weight correction to divide the discrete operating state space. Based on this state space, a Markov state transition probability matrix is ​​constructed, and dynamic updates are achieved by combining a sliding time window and a forgetting factor. The correlation strength coefficient of multi-energy flow coupled failure propagation paths is quantified and incorporated into the Markov chain iterative solution to the cumulative probability of cascading failures. Finally, a two-dimensional dynamic risk model is constructed, and an adaptive fuzzy PID algorithm is used to achieve real-time correction of warning thresholds and hierarchical warnings. Ultimately, this provides quantitative basis and technical support for the safe operation and cascading prevention of integrated energy systems.

The present application relates to the technical field of load simulation test, in particular to a server reliability test method and system based on load simulation, in the present application, the switching direction of the check section is verified, and the section stay time sequence is written, to provide a deterministic time anchor point for subsequent multiple rounds of reproduction experiments, a Markov chain is introduced based on the load sequence to model the request instance generation process, so that the arrival sequence and state transition probability of concurrent requests during the section duration are consistent, so that the request delivery process presents controlled randomness, based on the error response count, request timeout span and service interruption duration collected in the running set, a Bayesian variable point detection is introduced to analyze the probability distribution change of the data sequence in the same section, through the calculation and sorting of the change amplitude, the abnormal identification is changed from the threshold triggering to the posterior probability change judgment, so that the system state mutation position can be located when the load intensity does not reach the upper limit.

This invention discloses a real-time probabilistic inversion method for TBM (Tunnel Boring Machine) extrusion load based on a surrogate model. First, the extrusion load vector and its prior value range are defined for the TBM shield tunneling environment. A surrogate model is constructed and iteratively updated based on a small number of high-fidelity numerical simulation samples to establish a rapid mapping relationship between the extrusion load and the shield structure response. Then, on-site measured shield monitoring data are acquired, and a likelihood function is constructed by combining sensor observation errors and surrogate model prediction errors. Finally, within a Bayesian probabilistic inversion framework, a Markov chain-Monte Carlo algorithm is used to call the surrogate model for sampling, obtaining the posterior probability density distribution, optimal estimate, and confidence interval of the extrusion load, and calculating the shield structure failure probability accordingly. This invention can meet the real-time requirements of inversion calculations and provide a quantitative assessment of the uncertainty of the inversion results, offering a scientific basis for safety decisions when TBMs traverse strata with large extrusion deformation.

The application discloses a high-speed CT intelligent image reconstruction method for transient detection, which is applied to a distributed multi-source transient CT imaging system, acquires sparse angle projection data of multiple fixed X-ray sources under different projection angles, and constructs a linear imaging system equation; an accelerated random differential equation framework is constructed, multi-scale dynamic modeling is carried out by introducing an integer scale index, and time resolution and noise intensity are decoupled; a quasi-equivalent Markov chain solver is used for image reconstruction sampling, and the sampling process is sequentially divided into a non-Markov bridge stage, a link path stage and a Markov bridge stage; after each sampling step, a data consistency constraint is applied to correct the sampling state, and a reconstructed image is obtained; and the application effectively suppresses stripe artifacts under the condition of extremely sparse viewing angles by means of multi-scale decoupling and a three-stage sampling strategy, and ensures the image reconstruction quality.

A consensus-based decentralized multi-robot approach is presented for reconstructing a discrete distribution of features, modeled as an occupancy grid map, that represent information contained in a bounded planar 2D environment, such as visual cues used for navigation or semantic labels associated with object detection. The robots explore the environment according to a random walk modeled by a discrete-time discrete-state (DTDS) Markov chain and estimate the feature distribution from their own measurements and the estimates communicated by neighboring robots, using a distributed Chernoff fusion protocol. Under this decentralized fusion protocol, each robot's feature distribution converges to the ground truth distribution in an almost sure sense.

This invention relates to the field of geophysical exploration technology and proposes a lithology prediction method based on the constraints of geostatistical feature regularities. This method uses a large-scale exploration model to extract geostatistical feature regularities for lithology prediction, including: extracting seismic data; constructing virtual wells, simulating and generating virtual well data, and generating synthetic seismic records based on the seismic data; extracting geostatistical features to obtain their regularities; and constructing a multi-channel deep neural network model using seismic data and geostatistical features as inputs for lithology prediction. This invention expands the virtual wells using a Markov chain model and a sequential Gaussian simulation algorithm, and optimizes the model using K-Fold cross-validation, improving the generalization ability and stability of the seismic dataset. By combining seismic data with geostatistical features, multi-channel input, and a feature fusion layer, multi-source information is integrated. The accuracy is improved from 85.34% to 95.48%, providing a scientific basis for oil and gas reservoir identification and well location deployment.

This invention discloses a differential privacy task offloading method based on Markov chains in a vehicle-to-everything (V2X) environment, belonging to the fields of V2X and privacy protection. The method includes constructing and initializing the discrete Markov state space of each vehicle; updating the state transition probability matrix based on the current vehicle's speed, the number of surrounding vehicles, and the number of edge servers to obtain the current vehicle's privacy parameters; performing local differential privacy protection on the current vehicle's position and generating a confused position for the current vehicle; calculating the latency caused by task transmission between the current vehicle and the edge servers, and constructing a minimum system task offloading latency objective function; using the whale algorithm to process the system task offloading latency objective function, searching for the optimal offloading scheme, and performing differential privacy task offloading. This invention reduces the risk of data leakage and provides an effective and flexible solution for privacy protection in V2X while ensuring low task offloading latency.

A hardware random number generator (HW-RNG) for drawing samples from a multivariate target distribution through simulation of a Markov chain is disclosed. The HW-RNG (100) has a pipeline architecture operating in cycles. The HW-RNG comprises sets of p-bit devices (112-1; 112-2; 112-N), a programming unit (150) to program the sets of p-bit devices according to a corresponding set of adaptive proposal distributions, selection circuitry (121) to select one of the sets of p-bit devices, a sampling circuit (130) configured to produce a candidate sample from the proposal distribution associated with a selected one of the sets of p-bit devices, and a scheduling unit (140). The scheduling unit (140) is configured to accept the candidate sample with an acceptance probability, and recompute at least one of the proposal distributions that is conditionally dependent on the accepted candidate sample. A programming phase for each set of p-bit devices lasts for x cycles, and there are N > x sets of p-bit devices.