22 results about "Steady state distribution" patented technology

The invention discloses an unmanned aerial vehicle random patrol strategy system evaluation method in a confrontation environment. The method comprises the steps of setting an unmanned aerial vehicle intelligent patrol attack and defense confrontation system and a target steady-state distribution model of the unmanned aerial vehicle intelligent patrol attack and defense confrontation system; determining the average strategy entropy of the patrol person; determining conditional probability differences of the patrol persons; determining a transfer strategy dispersion degree of the patrol person; a patrol strategy comprehensive structure difference index is determined; determining a single-time-point key state average relative error; determining the average relative error of the time average key state; and determining a time-weighted key state average relative error. According to the method, the comprehensive structure difference index of the difference between the single-state transition matrix and different matrixes is quantified, and a balanced framework is provided to evaluate the overall unpredictability of the strategy. A multi-dimensional evaluation system based on key state and time weighting is designed, and time importance is directly fused into error calculation through a key state screening mechanism.

The embodiment of the invention provides an intelligent scheduling method for an electric vehicle charging station, and belongs to the technical field of data processing, and the method specifically comprises the steps: constructing a multi-dimensional time sequence feature set, and generating a training set with a scheduling label; training a deep learning model based on a gating circulation unit by using the training set, and performing rolling prediction on the arrival rate and the service rate of the vehicle by using the trained deep learning model; constructing a multi-state Markov queuing model based on scheduling control input, calculating steady-state distribution and calculating performance indexes according to the steady-state distribution; constructing a charging port scheduling optimization model based on a model prediction control method and the performance indexes, constructing an objective function, and outputting a rolling optimization strategy according to the objective function; and executing a rolling optimization strategy, adjusting the number of the AC / DC charging piles, generating indication information, and feeding back an operation result to the charging port scheduling optimization model to realize closed-loop correction. According to the scheme, the resource utilization rate is improved, the user waiting time is shortened, and the system stability is enhanced.

The application discloses a kind of complex environment under unmanned plane patrol path decision method, it is related to unmanned plane path planning technical field, method includes: with each to be patrolled position as node to construct undirected topological graph;According to the topological constraint of undirected topological graph and node importance generates the steady state distribution of each node patrolled by unmanned plane;Generate multiple candidate state transition matrix with steady state distribution;Dynamic environment evaluation is carried out to the obstacle of each candidate state transition matrix corresponding path, to determine the selectable speed and direction of unmanned plane on selected path;According to the weight of decision network dynamic adjustment according to dynamic environment evaluation result, and then generate execution motion command using the decision network after weight adjustment;According to motion command, unmanned plane is controlled and moves between each to be patrolled position.The application can realize the intelligent decision of unmanned plane under complex environment efficiently by dynamic environment evaluation, planning path, adjusting the weight of decision network, generating motion command.

The present application discloses a method for making patrol path decisions of a swarm of unmanned aerial vehicles (UAVs) under resource constraints, which relates to the technical field of UAV path planning. The method comprises: discretizing the actual physical locations to be patrolled to construct an undirected topological graph; generating a steady-state distribution of each patrol node according to the topological constraints and the importance of the nodes; generating a plurality of transfer matrices with the same steady-state distribution but different transfer characteristics according to a multi-stage entropy-driven random matrix optimization algorithm; initializing the position of a navigator and determining the path selected by it according to the transfer matrix; implementing adaptive active positioning decisions under positioning constraints according to the navigator's reference path to ensure path tracking effects; according to the navigator's path selection and tracking, the followers form a humanoid grouping cluster with the navigator through a reward function; according to the multi-state transfer matrix and the humanoid grouping, automatically switching to the next transfer matrix when a transfer number threshold is reached, thereby realizing intelligent patrol path decisions of the UAV swarm under resource constraints.

The invention provides a multi-user joint steady-state distribution tensor determination method and related equipment, which effectively protect the privacy of data. The method comprises the following steps: constructing a user frequency tensor for each user in a plurality of users, and generating a noise tensor; determining a synthetic tensor with a differential privacy protection characteristic according to the noise tensor and the user frequency tensor; performing normalization processing on the user frequency tensor and the synthetic tensor; constructing a DP-2M Markov model based on the normalized user frequency tensor and the synthesized tensor, and solving the DP-2M Markov model to obtain an SJE of each user and a DP-SJE of each user; determining a space-time distance and a semantic distance between the SJE and the DP-SJE; the DP-SJE of each user is optimized according to the space-time distance and the semantic distance; and sending the synthetic tensor and the optimized DP-SJE to a cloud device, so that the cloud device determines a user influence coefficient according to the synthetic tensor, and determines a multi-user DP-SJE according to the user influence coefficient and the optimized DP-SJE.

The invention provides an AI contract tamper-proofing method based on an OCR + large language model, belongs to the technical field of large language models, and improves image quality by performing multi-scale adaptive enhancement and super-resolution reconstruction on a contract scanning image. A semantic bridging fusion model is adopted to deeply fuse an OCR recognition result and an enhanced image under a sparse coding framework to realize context correction of low-confidence characters, semantic normalization is performed on a corrected text, and a term semantic vector sequence is established; the steady-state distribution field of the semantic concentration is calculated based on the fluid dynamic diffusion equation, the concentration gradient matrix is extracted to serve as the text fingerprint, contract tampering detection with robustness to the OCR recognition error is achieved through fingerprint similarity comparison, and the technical problem that the false alarm rate is high due to the fact that contract tampering detection is sensitive to the OCR recognition error is solved.

This invention discloses a real-time monitoring and optimization system for intelligent factory production processes, belonging to the field of intelligent factory production management. Specifically, it includes: acquiring process parameters and production action sequence data of the pipe fitting forming process using the Industrial Internet of Things (IIoT) and transmitting them to a data processing center; constructing a motion waste analysis model based on Markov chain theory and the motion sequence data; identifying motion waste by calculating state transition probabilities and steady-state distributions and comparing them with standard motion flows; and feeding the results back to the production management system; combining process parameters and motion waste identification results, the production management system uses queuing theory and operations research to construct a dynamic bottleneck station identification model, calculating the probability of each station becoming a bottleneck in real time, and determining the dynamic bottleneck station based on the amount of work-in-process inventory and production efficiency; and adjusting the production cycle based on the dynamic bottleneck station identification results to achieve real-time monitoring and optimization of the production process, thereby improving production efficiency and quality.

The invention discloses an unmanned aerial vehicle communication multi-priority queue modeling and performance analysis method, and the method comprises the steps: building an extensible state model through a WiMarkov chain and a hypercube transfer unit, and achieving the low-delay access of different priorities through a threshold and a window in combination with a weighted COS statistics and fixed backoff mechanism; semi-analytical expressions of the success rate and the time delay are obtained through steady-state distribution and generation function approximation, and parameters are learned through optimization constraints. Simulation shows that the low-load success rate is larger than or equal to 99%, the high-priority delay is still kept at the millisecond magnitude during high load, and the method is suitable for an unmanned aerial vehicle network communication system with the strict real-time requirement.

This invention relates to the field of fire safety inspection technology and discloses a method for generating county-level fire safety evaluation standards based on artificial intelligence. The method includes receiving a raw county-level fire data set containing fire records from multiple dimensions; performing multiple rounds of cross-validation within the set to filter for a stable set of elements; inputting this set into a dynamic semantic network that simulates element dependencies and transmission relationships using nodes and associated edges; injecting real-time county-level environmental state parameters to trigger node state migration; and reconstructing the text-based fire safety evaluation standard clauses according to the final steady-state distribution. By obtaining stable elements through multiple rounds of cross-validation, and generating environmentally adaptable clauses through the dynamic semantic network combined with real-time parameters, the method achieves stable standard framework elements and dynamic response clauses to the environment.

This invention discloses a patrol path decision-making method for unmanned systems in adversarial environments, belonging to the field of autonomous unmanned systems and intelligent decision-making technology. The method includes: acquiring an undirected patrol topology graph of the physical environment to be patrolled; calculating the steady-state distribution of the target based on the value parameters, attack time parameters, and degree of each node; and defining a patrol strategy unpredictability index weighted by intra-strategy entropy and inter-strategy entropy. PSUI To address the uncertainty of strategies in terms of microscopic randomness and macroscopic diversity, a simulated annealing dual-entropy balance optimization algorithm is employed to maximize... PSUI To achieve the goal, a set of state transition matrix strategies is generated under the constraints of topological adjacency, row normalization, and target steady-state distribution. The unmanned system is then controlled to perform spatiotemporally decoupled patrols through random matrix switching and probabilistic transitions within the matrix. This invention unifies patrol strategy evaluation and optimization, significantly improves the unpredictability of patrol paths, and enhances the protection capabilities for critical infrastructure.

The application relates to the technical field of fault analysis, and discloses a random fault injection method and an injection system for a train air brake system, which comprises the following steps: a simulation model of a target air brake system is constructed; all historical fault information is classified to obtain fault degree information and fault type information, and a fault degree state transition diagram and a fault type state transition diagram of the target air brake system are drawn; a fault type Markov chain is constructed according to the fault type state transition diagram, and a fault degree Markov chain is constructed according to the fault degree state transition diagram; a state transition probability matrix is determined, and a steady-state distribution is solved; a probability distribution of all fault information is set according to the steady-state distribution, and all fault information is randomly extracted in layers based on the probability distribution; and the extracted fault information is injected into the simulation model; the application solves the problem that the existing fault injection method cannot conveniently and effectively perform random fault injection on the train air brake system.

The invention relates to the technical field of petroleum development, and discloses a rock core water injection mode determination method, device and equipment and a storage medium, and the method comprises the following steps: respectively carrying out steady-state water injection and unsteady-state water injection on an experimental rock core under preset experimental parameters; respectively performing CT scanning on the steady-state water injection process and the non-steady-state water injection process to obtain a steady-state distribution result of an internal fluid of the experimental rock core in a steady-state water injection mode and a non-steady-state distribution result of the internal fluid in a non-steady-state water injection mode; comparing a steady-state distribution result with an unsteady-state distribution result; determining a water injection mode suitable for the actual core in the mine field according to a comparison result. According to the method, the water injection mode suitable for the actual rock core in the mine field is determined by comparing the steady-state distribution result and the non-steady-state distribution result of the experimental rock core, the application conditions of the actual rock core in the mine field can be determined, the situation that an inapplicable water injection mode is adopted in the mine field is avoided, and the oil recovery rate is effectively increased.

The application discloses a kind of unmanned system patrol path decision-making method under hostile environment, belong to autonomous unmanned system and intelligent decision-making technical field.The method includes: obtaining the undirected patrol topology graph of the physical environment to be patrolled, target steady-state distribution is calculated based on the value parameter of each node, attack time parameter and degree;Define the patrol strategy unpredictability index formed by the weight of strategy internal entropy and strategy entropy PSUI , to quantify the uncertainty of strategy in micro-randomness and macro-diversity;Using simulated annealing double-entropy balance optimization algorithm, to maximize PSUI As target, generate state transition matrix strategy set under the constraints of meeting topology adjacency, line normalization and target steady-state distribution;Control unmanned system to carry out space-time decoupling patrol by random switching matrix and probabilistic transition in matrix.The application realizes the unity of patrol strategy evaluation and optimization, significantly improves the unpredictability of patrol path, and enhances the protection ability to key infrastructure.

The invention relates to a multi-state conversion runoff prediction method based on structural fracture recognition, and is suitable for the technical field of hydrology and water resource prediction. The method comprises the following steps: acquiring a historical runoff sequence and performing stationarity test; the global residual sum of squares is minimized under the piecewise linear regression framework; establishing a mean value model for the stationary sequence, and calculating a one-step prediction residual sequence as a disturbance term estimation value; taking the residual error as input, establishing a first-order Markov state transition structure, and jointly estimating a state transition probability matrix of the state transition structure and fluctuation parameters of each hidden state; setting an initial state probability vector as steady-state distribution or uniform distribution of the state transition probability matrix, and obtaining a smooth state probability at each moment through backward recursion by using full sample information; and generating one-step rolling condition prediction under each state, weighting according to a state prediction probability to obtain a rolling point prediction result of the runoff, executing inverse transformation according to recorded difference and transformation parameters, and recovering to an original physical quantity scale.