15 results about "State transition function" patented technology

The invention discloses a risk perception scheduling method for a multi-energy micro-grid based on distributed Pareto reinforcement learning of a large language model. The method comprises the following steps: 1) simulating and generating a real climate scene of the multi-energy micro-grid by using LLM; 2) constructing a state transition function matrix; 3) designing a multi-energy microgrid risk awareness scheduling framework based on a Markov decision process; 4) obtaining a current real climate scene of the multi-energy microgrid, and determining a state transition function corresponding to the current real climate scene; 5) determining an optimization target in the multi-energy micro-grid risk awareness scheduling framework by using an LLM-DPASC algorithm; and 6) based on the optimization target, determining and executing an action A'corresponding to the new state S ', and realizing risk-aware scheduling of the multi-energy microgrid. According to the method, the modeling capability for risk sensitivity is improved, personalized optimization strategies are provided for different types of agents, and variable environment and task requirements can be flexibly met.

Lane segment-level traversal information is obtained. The lane segment-level traversal information is converted into probabilities for a state transition function. A policy is derived from a decision model using the state transition function. The policy directs vehicle movement of a vehicle between neighboring lane segments based on a cost function integrating a user preference with respect to at least two objectives and a slack time for alternative routes. The slack time indicates an allowable deviation in travel time relative to the user preference. A destination is received. The vehicle is then autonomously controlled on a route to the destination using the policy for lane transitions based on current lane positions.

The invention provides a formal verification method and device for state transition implementation codes and a program product, and the method comprises the steps: compiling a first state transition function implemented by a C language Switch Case statement to obtain a bit code file; obtaining a Cryptol file, wherein the Cryptol file comprises a second state transfer function which is realized by using a Cryptol programming language; acquiring a first input parameter set and a first output value set corresponding to the first state transfer function, and acquiring a second input parameter set and a second output value set corresponding to the second state transfer function; simulating state transition function symbolic execution based on the first output value set, the second output value set, the first input parameter set and the second input parameter set, converting the state transition function symbolic execution into a satisfiability model theory problem, and solving the satisfiability model theory problem through an external solver to obtain a verification result of a state transition implementation code; the problem that whether state transition is implemented correctly or not cannot be guaranteed only through a manual checking mode can be solved, and the state transition code implementation correctness is guaranteed.

The invention provides an agricultural robot-oriented state machine modeling and exception response method, which comprises the following steps of: modularly dividing a main state space, introducing an external driving event set and an exception state set, and constructing a multi-layer state transition function comprising main state transition, exception triggering and exception recovery; and in combination with a multi-sensor observation vector configured by the agricultural robot, a conditional probability distribution model between a state and an observed quantity is established, and parallel reasoning and dynamic switching of a main state and an abnormal state are realized. In addition, an abnormal state evaluation function is designed to carry out quantitative analysis on various abnormities, and a grading response strategy is set. The method is suitable for various agricultural operation scenes, and has strong behavior modeling expression capability and abnormal response capability.

The invention discloses an autonomous mobile robot method and system for an intelligent factory, in the intelligent factory, a Markov decision process model is adopted, a planning range is divided into a plurality of time intervals, a decision is made at the beginning of each interval, and the state of the system is defined by attributes W and O of workers and orders. The individual actions of one worker comprise allocation of a batch of orders, charging of the autonomous mobile robot or idle actions, and an optimal solution is obtained through a state transition function; and on the basis of the obtained optimal solution of the state transfer function, neural approximation dynamic programming is adopted for the state value function after decision making to obtain a high-quality approximate solution, so that a trained and optimized neural network model capable of providing accurate target function coefficient prediction for a new decision making state promotes efficient real-time decision making in uncertainty.

A method for managing order processing includes obtaining a set of time series datasets associated with the order processing for an order processing system, generating a state space for the set of time series datasets using bi-partite graph representations for a mixed integer program (MIP), applying a transition function on the state space to obtain state-to-state transition function, performing a graph embedding on the state-to-state transition function to obtain historical transition data, applying a transformer multi-head attention function on the historical transition data to obtain positional encodings of observation history and feature representation, and perform an agent deployment on an order processing system based on finalized state-to-state transition functions, wherein the finalized state-to-state transition functions are based on the positional encodings.

The invention discloses a block chain unstable factor detection method and related equipment. The method comprises the following steps: constructing a state model; the state model comprises a state set, an event input set, a state transition function and an output function, designing a basic input mode according to the state model, introducing a state transition coverage rate in a block chain operation environment, and constructing a plurality of different transaction sequences based on the basic input mode and a fuzzy test; executing a fuzzy test according to all transaction sequences, and recording actual state transition and actual output results; and comparing the actual state transition with the expected state transition of the state model, comparing the actual output result with the expected output result of the state model, and detecting an abnormal behavior to determine the block chain instability factor. According to the embodiment of the invention, unstable factors caused by competition conditions caused by cross-transaction concurrency can be captured, and the practical applicability of the test is improved. The method can be widely applied to the technical field of software testing.

The application relates to a SQL optimization processing method based on a relational database. The method comprises the following steps: marking whether a state transition function of an aggregation function is a monotone function in a metadata table of a relational database system; pushing a filtering condition of the aggregation function into the state transition function when a query plan is generated; executing the state transition function of the aggregation function on data when the query plan is executed; substituting a result into the filtering condition; and directly processing next grouped data if the filtering condition is not satisfied. Compared with an existing processing mode, the filtering condition of the aggregation function is pushed into the state transition function of the aggregation function, records not satisfying output requirements can be found early, and the SQL execution process is accelerated. The execution time of some SQL of a database can be greatly shortened through the method, and application of the method can help to better exert the performance of the database and improve the working efficiency of the database.

A method for managing order processing includes obtaining a set of time series datasets associated with the order processing for an order processing system, generating a state space for the set of time series datasets using bi-partite graph representations for a mixed integer program (MIP), applying a transition function on the state space to obtain state-to-state transition function, performing a graph embedding on the state-to-state transition function to obtain historical transition data, applying a transformer multi-head attention function on the historical transition data to obtain positional encodings of observation history and feature representation, and perform an agent deployment on an order processing system based on finalized state-to-state transition functions, wherein the finalized state-to-state transition functions are based on the positional encodings.

The present invention discloses a custom feature rule method based on function signatures, which includes statistically analyzing security vulnerabilities of a language to design programming security feature rules; defining corresponding state transition processes according to the programming security feature rules, and recording function signatures that cause state transitions to obtain a set of function signatures; designing multiple finite state automata according to the state transition processes, the set of function signatures, and preset rule meanings; obtaining a set of function signatures for starting each automaton, monitoring the functions called by variables, and starting the automata according to the functions called by variables and the set of function signatures for starting each automaton; after the automata are started, tracking the access and propagation paths of variables, using the signatures of the functions called by variables and their references as input strings for the corresponding automata, and obtaining the converted state in combination with the current state and state transition functions of the automata until the automata end or an error occurs. Without mastering the core technology of static analysis, the feature rule set can be extended.

The present disclosure provides a control method, apparatus, electronic device and storage medium for a state machine, relating to the field of computer technology. The method includes: receiving an event processing instruction, where the event processing instruction includes event information to be processed; parsing the event information to determine an identifier of the event to be processed and context environment information; determining a target state transition function according to the identifier of the event to be processed; obtaining the current state of the state machine associated with the context environment information from a database; and returning an event processing failure message when the current state of the state machine does not match the start state corresponding to the target state transition function. Thus, without adding new state classes, etc., the control of the state machine can be achieved only based on the association relationship between events, thereby reducing the complexity of controlling the state machine and simplifying the process.

The present invention discloses a strategy model for real-time strategy games, describing RTS games as Markov games. The environment size is , and represents the height and width of the grid, respectively. The state space is ; the global observation space is , represents the real number space, and represents the feature channel dimension. The joint action space is , represents the action dimension of the control unit. The state transition function is ; the reward function is ; the strategy is ; and the strategy model is . The present invention solves the problems existing in the prior art. Through innovations in the strategy model architecture and training methods, it can be trained with lower computing resource overhead and is adaptable to various RTS game settings.

A network protocol fuzz testing method and device based on state guidance and seed mutation, the method comprising: 1) manually constructing a high-quality basic seed based on official RFC protocol documents and mapping the seed to the corresponding state; 2) inputting a message sequence into a target program for fuzz testing, interacting with the target program while monitoring the program state and obtaining feedback; 3) selecting a seed with high coverage for information-preserving mutation based on the protocol state code and path coverage feedback, preserving the seed's state transition function to reduce the number of invalid seeds; 4) state-guided message sequence generation: selecting a target test state based on the current state and the returned protocol status code, prioritizing states that have not been reached or have been reached less frequently as targets, and then generating a message sequence that can reach or approach the target state based on the basic seed. The method proposed in the present invention fully utilizes the protocol state information and preserves the seed's state transition capability during the seed mutation process. Through state guidance and targeted message sequence generation, the efficiency and quality of network protocol fuzz testing are improved.

The application provides a double-station multi-target tracking method and device based on WMFS and UKF, comprising: constructing a WMFS model, and reconstructing a state transition function by using a pre-trained WMFS model; combining the optimized WMFS model with a UKF model to obtain a double-station multi-target tracking model; and tracking a target by using the double-station multi-target tracking model. The method fully utilizes the advantages of WMFS in processing system uncertainty and complex modeling, adopts an unscented (UT) framework to solve the nonlinear measurement problem in the WMFS-UKF derivation process, and uses a pre-trained WMFS to reconstruct the state transition function. Through the historical state and expected output of the target, the WM fuzzy reasoning system realizes more accurate state prediction and accurate covariance estimation, improves the performance and stability of target tracking, and verifies the effectiveness and robustness of the algorithm in the target tracking scene.

The application discloses a kind of intelligent power distribution network dynamic state estimation method, system, equipment and medium, it is related to intelligent power distribution network and its automation technical field, method includes: establishing state equation described by state transition function and measurement equation described by measurement function;Based on the noise self-adapting mechanism of extended Kalman filter framework is joined to design adaptive extended Kalman filter;Combined with the sigma point and covariance regularization strategy after adaptive adjustment to design improved unscented Kalman filter;Adaptive extended Kalman filter and improved unscented Kalman filter are weighted fusion;Finally, the root mean square error of state estimation is calculated to carry out state evaluation.The application solves the problem that the estimation accuracy is insufficient and the adaptive ability to time-varying noise and model error is weak in the prior art under strong nonlinear scene.