59 results about "State transition probability" patented technology

The invention discloses a mine geological environment characteristic monitoring and recovery treatment method, and relates to the field of geological environment monitoring, and the method comprises the steps: collecting mine geological data; adopting a pre-trained LSTM neural network model to predict membership information of geological disaster evolution in a period of time in the future; adjusting a state transition probability matrix in the Markov chain model by using the membership information; predicting the evolution state of the geological disaster in a future period of time by using the adjusted state transition probability matrix; the method comprises the following steps: mapping mine geological data into nodes of a Bayesian network, and constructing a directed acyclic graph containing environmental factors; based on a Bayesian network inference algorithm, calculating the occurrence probability of geological disasters under different environmental factor combinations; fusing the predicted evolution state of the geological disaster in a future period of time with the geological disaster occurrence probability deduced by the Bayesian network to obtain a geological disaster prediction evaluation result; in view of low mine geological disaster time sequence evaluation precision in the prior art, the evaluation precision is improved.

The invention relates to the technical field of encrypted communication, and discloses an intelligent analysis method and system based on multi-source data, and the method comprises the steps: obtaining multi-dimensional metadata of an encrypted communication flow, and generating a feature vector set; calculating a state transition probability of a communication session by using a Markov chain model, dividing the communication session according to a time window and mapping the communication session to a discrete state, and generating a session state evolution sequence; analyzing a session state evolution sequence based on a hierarchical topic model, mapping the state sequence to a behavior topic through potential Dirichlet allocation, mapping the behavior topic to an intention category through a hierarchical Dirichlet process, and outputting a communication intention category and a confidence score; according to the method, the limitation that static feature analysis cannot reflect the complete life cycle of the session is overcome, the problem of semantic gaps caused by limited metadata information dimensions is solved, and the accuracy and robustness of intention recognition are improved.

The invention discloses a power distribution network power supply recovery method based on safety reinforcement learning, and the method comprises the steps: firstly constructing a Markov decision process MDP according to the characteristics of a power distribution network power supply recovery problem, and the MDP comprises a state, an action, a state transition probability, a reward function and a discount factor; secondly, on the basis of the constructed MDP, a security reinforcement learning model is built to solve the MDP, and the security reinforcement learning model comprises an HSAC algorithm, an invalid action shielding module and a security check layer; and finally, performing offline training on the safety reinforcement learning model, and performing recursive solution on the power supply recovery problem of the power distribution network real-time scene by using the trained safety reinforcement learning model so as to output an optimal power supply recovery decision scheme. According to the method, the invalid action shielding module and the security check layer are introduced, so that the action space is effectively reduced, the decision generated by the model is ensured to meet the key security constraint, and the model learning process is accelerated while the security of power supply recovery is improved.

The invention discloses a statement generation method, which comprises the following steps of: receiving load characteristic information and database mode information, and obtaining characteristic parameters according to the load characteristic information and the database mode information; determining the statement type of the generated statement according to the feature parameters; and calculating a state transition probability according to the load feature information. In the embodiment of the invention, the load type required by the user can be determined according to the database mode information and the load feature information, and the SQL statement of the load of the determined type is generated according to the load feature information. The styles of the SQL statements of the load generated by the method are more, so that the styles of the SQL statements of the load are enriched, and the load generation mobility is improved.

The invention discloses a gateway load balancing method, a gateway load balancing device and electronic equipment. The method comprises the following steps: determining an initial load state of a network element instance in a service communication proxy (SCP) gateway; the state transition probability of the network element instance is determined by adopting a Markov chain model according to the initial load state, the future load state of the network element instance is determined according to the state transition probability, and the state transition probability introduces a time decay factor; the time attenuation factor is used for adjusting the weight of the real-time network element performance data in the preset time window relative to the historical network element performance data; and receiving a network element service request, and executing network element instance allocation according to the importance degree of the network element service request and the future load state of the network element instance. The technical problems that the delay of important services is increased and the system performance is reduced due to the fact that the static load balancing strategy in the related technology cannot effectively predict and respond to the load state change of the network element instance are solved.

The invention belongs to the field of big data, and relates to a content recommendation method and device, equipment and a medium, and the method comprises the steps: obtaining a contact behavior sequence of a target user, the contact behavior sequence comprising an interaction behavior between the target user and a vehicle service platform in a target scene; modeling the contact behavior sequence through a Markov process algorithm, mapping interaction behaviors into state nodes, and generating a state sequence; acquiring a state transition record of a historical user in the target scene; based on the state sequence and the state transition record, a preset deep neural network is adopted for processing, and a state transition probability matrix of the target user is generated; and determining to-be-recommended contents based on the state transition probability matrix, and recommending the contents to the target user. The method can be applied to the business field of finance and the like, and content recommendation accuracy can be improved.

The application discloses a special equipment state recognition method and system based on electrical parameter mode matching, and relates to the technical field of data processing.The method comprises the following steps: verifying the state sensitivity of multiple monitoring indexes of a power distribution cabinet to obtain a dynamic grading sensitive index set; generating an elevator state mode library; calling multiple real-time operation parameters; triggering hierarchical equipment state mapping to output a hierarchical state confidence vector; performing state transition prediction to output a state transition probability matrix; fusing the hierarchical state confidence vector and the state transition probability matrix to output a real-time fault probability distribution; and generating a multi-stage early warning signal according to the real-time fault probability distribution to trigger a step-by-step safety protection response.The technical problem that the state recognition of special equipment in the prior art relies on sensors is solved, the accurate recognition of the running state of an elevator based on electrical parameters is realized, the dependence on external sensors is reduced, and the technical effects of improving the real-time performance and reliability of state monitoring are achieved.

The invention is suitable for the technical field of project management, and provides a project state management method, system, device and medium based on a time axis, and the method comprises the steps: carrying out the unified conversion of the demand state definitions of different teams through employing a state standardization algorithm, and obtaining a demand state data set; based on the demand state data set, constructing a multi-dimensional demand circulation sequence data table, and obtaining demand circulation state parameters; establishing a state transition probability matrix, and identifying an abnormal stagnation demand based on the multi-dimensional demand circulation sequence data table; constructing a demand state time axis with a hierarchical structure according to the demand priority and the stagnation probability; according to the method, the spatial index structure is established in the demand state time axis, the state change event is screened according to the timestamp through the spatial index structure, and the demand query result set is obtained, so that the project management efficiency is improved, the delay risk is reduced, and data support is provided for decision making.

The invention provides a Tor network data parallel node state prediction method and system based on a Markov chain, and relates to the technical field of Tor network security analysis, and the method comprises the steps: S1, obtaining a historical state data sequence of a Tor network relay node; s2, establishing a Tor network relay node state prediction model based on a Markov chain, and predicting a Tor network relay node state transition probability; s3, performing Markov property test on the Tor network relay node state transition probability matrix; and S4, establishing a parallel Tor network relay node state prediction model by using a distributed computing framework Spark, so as to improve the prediction efficiency. Through the time-sharing state data sampling technology, the Markov property test passing rate of the node state sequence is improved, so that the effectiveness of model prediction is improved; a parallelization model is established to process large-scale Tor network data, the prediction speed is remarkably improved, the target range is narrowed for node routing analysis, and support is provided for real-time data analysis.

The invention relates to the technical field of industrial equipment maintenance data processing, in particular to an equipment health state transition probability interval calculation method based on anti-factual reasoning. Through state vector similarity calculation, the problem of data sparsity in an industrial scene is effectively solved, limited historical data is migrated to an unknown state action pair, an improvement, maintenance and deterioration triple probability interval is output based on anti-fact reasoning, and risk buffer is provided for a maintenance decision; by embedding a good and bad relation judgment rule, it is ensured that a probability interval conforms to engineering common knowledge, and anti-intuitionistic estimation is avoided; the calculation process is light, only vector dot product and scalar multiplication are needed, and the real-time requirement is met. According to the method, data driving and domain knowledge are fused, a safety-oriented probability interval can still be generated under the condition of insufficient data, the defects of blind optimistic and static stiffness of traditional point estimation are overcome, and a reliable quantitative basis is provided for intelligent maintenance of high-cost and high-risk equipment.

The present invention discloses a method, system, computer device and storage medium for scenario behavior deduction and reliability prediction analysis of a multi-source data fusion dynamic system. The method, based on the Markov / CCMT dynamic reliability prediction analysis method, combines multi-source data fusion and assimilation methods, and uses Monte Carlo probability model random sampling to simulate and statistically analyze the complex dynamic behavior characteristics of digital process control with strong interactive coupling, nonlinearity and high uncertainty. Then, through dynamic search and analysis of the system state transition probability matrix model, forward deduction analysis and reliability prediction of the system operation state are achieved. The present invention can realize the adaptive update construction of the state transition probability matrix of large-scale complex digital process control systems and the dynamic deduction and analysis of scenario behavior, avoid the problem of high-dimensional system state space search explosion, and can accurately simulate and map the system dynamic behavior characteristics to realize system dynamic reliability prediction analysis.

The invention discloses a water-wind-light complementary space-time scene generation method and device, and the method comprises the steps: discretizing a continuous state space of a multi-dimensional water-wind-light resource into a self-adaptive grid, and constructing a state transition probability matrix in an offline manner; in the online stage, a discrete state sequence is rapidly generated through Markov chain migration, and a continuous scene is reconstructed through kernel density estimation. A layered Markov switching model is introduced to capture long-period features, and smooth interpolation transition is adopted during macroscopic state switching. The device comprises a gridding module, a matrix construction module and a scene generation module, and supports a Z-order curve index and a compressed sparse row format to realize microsecond access. Compared with a traditional VAR iteration method, single-time long-period scene generation time is reduced from an hour level to a millisecond level, and calculation efficiency and scene quality are both considered.

The application provides a behavior control method and device; the method comprises the following steps: determining a current behavior state of a virtual object and a state transition matrix of the virtual object in a current time period; determining a first state transition probability distribution corresponding to the current behavior state from the state transition matrix; in response to the first state transition probability distribution, transferring the behavior state of the virtual object from the current behavior state to a first target behavior state in a behavior state set, generating a first behavior event queue based on a Poisson rate corresponding to the first target behavior state; and controlling the virtual object to execute behavior events in the first behavior event queue in sequence. Through the application, accurate control of the behavior of the virtual object can be realized, thereby improving the fidelity of the behavior of the virtual object.

The invention discloses a visual interaction method, device and equipment based on a state probability transition model, and relates to the technical field of man-machine interaction, and the method comprises the steps: collecting multi-source data, extracting a state feature vector, and fusing the multi-source data; constructing a state transition model based on a state transition graph G = (V, E, P), and dynamically calculating a state transition probability matrix; wherein V is a state set, and the nodes represent different states; e is an edge set, and edges represent possible transfer paths between states; p is an edge weight set, represents a transition probability and can be dynamically updated; a state visualization graph is generated based on the state transition graph G, and dynamic visualization display is rendered through a graphic engine; receiving an interaction operation, triggering the state transition model to update parameters in response to the interaction operation, and recalculating; and responding to different target states and / or strategy parameters of the interaction operation, generating and comparing a plurality of strategy paths, and visually displaying a strategy deduction process. According to the invention, the precision, interaction capability and interpretability of the system are improved.

Provided is a management device, a management method, and a control system that appropriately generate information used for control of a control target. A management device (10) includes: a control characteristic estimation unit (14d) that, on the basis of a state transition probability that is the probability of a state transition in a case where a control target is operated at a predetermined operation amount, calculates an evaluation value for operation amounts when reaching a target candidate state which is a candidate for the target state, from a plurality of states that can be taken by the control target; and a control rule determination unit (14e) that, on the basis of the evaluation value, determines, as a control rule, an optimal operation amount in each state of the control target.

A data estimation device includes an acquisition unit, a stratification unit, an estimation unit, and an output unit. The acquisition unit acquires data sets including pieces of data indicating mutually different probability distributions and an attribute used for stratification of the data sets. The stratification unit stratifies the data sets based on the attribute. The estimation unit estimates a state transition probability between the data sets after the stratification based on a difference in distribution between a state transition probability between the data sets before the stratification and a state transition probability between the data sets stratified for each of the attributes. The output unit outputs the state transition probability between the data sets after the stratification. The use of the state transition probability estimated in this manner enables the data estimation device to support decision making based on an estimation result of a transition destination of data.

The invention discloses a service state identification method and device and electronic equipment. Relates to the big data field. The method comprises the steps that a trusteeship fund allocation instruction sequence sent by a user is received, a classification variable sequence of the trusteeship fund allocation instruction sequence is generated, and the trusteeship fund allocation instruction sequence comprises M trusteeship fund allocation instructions; target parameters in the probability prediction model are obtained, and the target parameters comprise an initial probability vector representing an initial state probability, a state transition probability matrix representing a state transition probability and an observation probability matrix representing an observation probability; and calculating the target parameter and the classification variable sequence through a Viterbi algorithm to obtain a service state sequence corresponding to the trusteeship fund allocation instruction sequence. According to the method and the device, the problem of relatively low accuracy and efficiency of manually determining the operation state of the business indicated by the instruction in the related technology is solved.

The invention relates to the technical field of flow intelligent guidance and control, and discloses an engine training guidance and control flow control method and system. The method comprises the steps that a training process is divided into continuous stages, and execution tracks are extracted to construct state snapshots; analyzing the features to generate a flow operation state mapping table; reconstructing a track and establishing a node state transition sequence, and describing a flow rule through a state transition probability matrix; constructing a flow anomaly propagation network to calculate an anomaly diffusion path; establishing a dynamic weight mechanism to determine key nodes and risk paths; generating a health degree index and constructing a multi-dimensional evaluation system; designing a self-adaptive intervention strategy library to match a guide and control instruction; and dynamically adjusting an execution path and resource allocation according to real-time data to realize flow closed-loop control. According to the method, the operation rule of the modeling process can be quantified, the abnormal propagation can be actively predicted, and the intelligent level of process control and the system robustness are improved.

The application provides a kind of considering the termination strategy optimization method of staged task system, it is related to task termination strategy field.First, for each stage of staged task, the degradation state of system is defined to be subject to a Wiener process, and the number of decision points of each stage is obtained;Second, the degradation state of each stage is converted into a discrete state model to solve the state transition probability of the system;Third, the task termination cost and task continuation cost of each decision point under different system states are solved, and the smaller value is selected as the corresponding expected cost;Finally, using the backward dynamic programming algorithm, based on the expected cost of the current decision point and the system state, the task termination or task continuation action is selected to obtain the task termination strategy set.The optimization method proposed in the application fills the gap in related technologies that do not consider the staged task termination strategy, and uses a stochastic dynamic programming framework to express the optimization problem, which effectively reduces the cost compared to the fixed threshold strategy.

The invention provides an industrial equipment preventive maintenance decision-making method based on a discrete time Markov chain, and relates to the technical field of industrial equipment reliability management and maintenance strategy optimization. The method comprises the following steps: based on historical maintenance record statistics, establishing a discrete time Markov chain maintenance effect state transition model and determining a state transition probability matrix thereof, carrying out eigenvalue decomposition on the state transition probability matrix to obtain state probability distribution after n times of maintenance, and determining each state probability; carrying out weighted fusion on the random intensity functions of the three maintenance processes through corresponding state probabilities, and constructing a mixed fault rate function; calculating an expected failure frequency based on the mixed failure rate function; and based on the expected failure times, establishing a long-term cost rate function, and solving to determine an optimal replacement period. According to the method, three basic maintenance effect types are integrated through the dynamic probability, and accurate prediction of the number of failure times of equipment and calculation of the optimal maintenance period are achieved.

This disclosure provides an autonomous decision-making method and apparatus for a nuclear reactor system based on interpretable artificial intelligence. The autonomous decision-making method for the nuclear reactor system includes: acquiring the state space, action space, and failure probability of the nuclear reactor system; inputting the state space, action space, and failure probability into a state transition model, and outputting the state transition probability of the components transitioning to the corresponding second state after taking different target actions in the first state; inputting the benefits and costs of the nuclear reactor system corresponding to the second state into a reward model, and outputting the reward function corresponding to the second state; aiming to maximize the power generation efficiency of the nuclear reactor system, solving for the optimal operating decision of the nuclear reactor system based on the state transition probability and the reward function; thereby tracing the reason for executing each action based on the relationship between the reward function and the state transition probability, so that operators can understand the reason for recommending the optimal operating decision before taking any action.

The invention discloses a turbo fsk receiving and processing method and system based on a vector DSP, and relates to the field of signal processing, and the method comprises the steps: carrying out the parallel calculation of a state transition probability through a vector after the fusion of channel information and external information, and efficiently generating a probability matrix through batch matrix multiplication and addition, data rearrangement and parallel comparison operation; then, forward and backward probabilities are calculated through vector recursion; and finally calculating the logarithmic posterior probability through vector addition and comparison. According to the invention, the architecture of the vector DSP is fully utilized, traditional serial calculation is converted into batch parallel processing, the calculation complexity and time complexity of a decoding algorithm are remarkably reduced, high-throughput and low-power-consumption Turbo-FSK signal decoding is realized, and the method is particularly suitable for communication equipment with strict requirements on power consumption and real-time performance, such as the Internet of Things.

The invention discloses a special equipment state identification method and system based on electrical parameter mode matching, and relates to the technical field of data processing. The method comprises the following steps: performing special equipment state sensitivity verification on multiple monitoring indexes of the power distribution cabinet to obtain a dynamic grading sensitive index set; generating an elevator state mode library; calling multi-stage real-time operation parameters; triggering hierarchical equipment state mapping, and outputting a hierarchical state confidence vector; performing state transition prediction, and outputting a state transition probability matrix; fusing the hierarchical state confidence vector and the state transition probability matrix, and outputting real-time fault probability distribution; and according to the real-time fault probability distribution, generating a multi-stage early warning signal, and triggering a cascade safety protection response. The technical problem that state recognition of special equipment depends on a sensor in the prior art is solved, accurate recognition of the elevator running state based on the electrical parameters is achieved, and therefore the technical effects that dependence on an external sensor is reduced, and the real-time performance and reliability of state monitoring are improved are achieved.

The invention provides a hydraulic cooler dual protection linkage method and system based on oil temperature and oil pressure, and relates to the technical field of cooling control, and the method comprises the steps: building a running state parameter matrix and a system deviation matrix, building a state transition probability matrix and a stress time sequence matrix, calculating a temperature-pressure coupling stress value, and determining output cooling power according to the temperature-pressure coupling stress value. Accurate control of the hydraulic cooler is achieved. The temperature and pressure parameters of the hydraulic system are monitored and controlled cooperatively, the cooling efficiency is improved, the service life of the system is prolonged, and energy consumption is reduced.

The present invention provides a user portrait-based precision push method and system, which relates to the field of data processing technology. The method includes: obtaining category labels for each data content in the data library; obtaining user behavior data on the data content corresponding to each category label; combining the behavior data and a hidden Markov model to generate state transition probabilities and user preference labels; generating a user portrait with a push time period; obtaining the category labels of the data content currently accessed by the user; calculating the similarity values ​​between each category label and the category label of the data content currently accessed by the user; combining the user portrait and the similarity values ​​to generate the push data content at the next moment; recording the length of time the user stays under the push data content; setting a correction factor to correct the state transition probability corresponding to the category label of the data content currently accessed by the user; using the corrected state transition probability as the new state transition probability and repeatedly executing the push. This improves push accuracy and increases user stickiness.

The invention discloses a polymer formula analysis method and device based on a graph Q learning network, and the method comprises the steps: building a Markov decision process of formula generation through defining a state space, an action space, a state transition probability and a reward function; constructing a graph Q learning network model based on the Markov decision process; training the graph Q learning network model through a double-Q learning algorithm and a strategy function to obtain a trained graph Q learning network model; and performing state transition and action selection processing through the trained graph Q learning network model to generate a polymer formula. According to the method, the problems of low learning speed, high computing resource consumption, difficulty in accurately matching multi-target performance and the like in the prior art are solved.

The invention relates to an offshore wind turbine generator dynamic maintenance decision-making method considering state prediction uncertainty, and the method comprises the steps: obtaining the historical state transition prediction data of a plurality of prediction methods, and considering the local density information of a nearest neighbor set, based on a K-nearest neighbor method, basic probability distribution of historical state prediction results under multiple prediction methods of the unit is dynamically updated through dynamic Bayesian, and a unit component state transition probability interval is obtained through a combined reliability model of unit component state prediction based on a prediction result conflict degree; and constructing a stochastic programming model of a maintenance dynamic decision considering the uncertainty of component state prediction, and optimizing and solving to obtain a unit dynamic maintenance strategy by taking the minimum total maintenance cost as a target. Compared with the prior art, the method can reduce the influence of the uncertainty of state prediction on the maintenance decision, reduces the maintenance cost, and improves the maintenance efficiency.

The present application relates to the technical field of intelligent breeding, and discloses a feed dynamic management method, device and equipment for intelligent breeding and a storage medium, which constructs key time sequence data and performs preprocessing, calculates a heat stress strategy switching action index based on the time sequence data, constructs an input-driven three-state switching state space model based on the action feature sequence and an average feeding residual error sequence, determines an output hidden state estimate and a state probability, recursively generates a future feed demand prediction value and a prediction interval, performs online bias updating on a prediction error based on the action index, and outputs a corrected feed demand prediction. Thus, the present application introduces a strategy switching action index, simultaneously injects continuous hidden state evolution and discrete state transition probability calculation, effectively identifies action intensity from a small amount of key data, quickly completes prediction mechanism switching, simultaneously performs rapid correction, and realizes feed dynamic management for intelligent breeding in a special stress scenario.

The application discloses an electrical appliance state derivation method based on electrical change amount state conversion probability matching, combines a method based on a graph model and a method based on deep learning, derives the state change of the electrical appliance by the method based on deep learning, derives the specific state change condition of the electrical appliance by the method based on the graph model, and can better handle the derivation problem of the low-frequency electrical signal electrical appliance. Because the advantages of the two methods are combined, the required data amount is greatly reduced. In addition, the application adopts a low-frequency non-invasive signal processing technology based on 1-minute sampling once, does not need to make hardware transformation on the electric meter, greatly saves the cost, and has wide application and popularization prospects.

The application discloses a user interaction log-oriented internal and external noise filtering method, which firstly carries out screening and processing of an event log, and extracts useful column information; secondly, a general template in the form of a natural language text is automatically generated; then, the general log template and an operation line are combined, a Markov state transition graph of different operation types and target element combinations is constructed, and corresponding state transition probabilities are output; finally, internal and external noises are marked out through transition probabilities between nodes, and an event log is marked in detail by using a mean value and a total standard deviation variance adaptive threshold of a normal distribution, so that effective detection of internal and external noises in robot process automation is realized, and the accuracy of task segmentation, routine segmentation and process model generation is optimized.