14 results about "Wireless networked control system" patented technology

A wireless communication network transfers a data message to a User Equipment (UE). A wireless network control system registers the UE, and in response, transfers a UE registration notice for the UE and the wireless network control system to a wireless network database system. The wireless network database system receives the UE registration notice, and in response, transfers the UE registration notice to a message center. The message center receives the UE registration notice. The message center receives the data message for the UE, and in response, transfers the data message to the wireless network control system based on the UE registration notice. The wireless network control system receives the data message, and in response, transfers the data message to the UE.

The invention relates to the technical field of train wireless network communication, in particular to an information security risk assessment method for a train wireless network control system, which comprises the following steps of: establishing an attack tree model according to a risk assessment object, assessing the occurrence possibility of a security event by using a triangular fuzzy number, analyzing attack paths, and calculating the interval probability of each attack path, the method comprises the following steps: obtaining a point probability according to an attack path interval probability, calculating an attack occurrence possibility, evaluating a security event influence by using a fuzzy analytic hierarchy process, calculating a security event risk value by quantifying the occurrence possibility and an influence value of the security event, and determining a risk level and a security level of a system according to a system risk evaluation value. And formulating corresponding protection requirements. According to the method, the information security risk of the train wireless network control system is effectively evaluated and managed, a basis is provided for formulating an effective security protection strategy, and the overall security capability of the train wireless network is improved.

The invention discloses a wireless network control system for joint communication control based on deep reinforcement learning, and relates to the technical field of wireless control. On the basis of an existing wireless network control system, a communication controller comprising an uplink estimator is additionally arranged on a base station, a downlink estimator is additionally arranged on a controlled side so as to improve the quality of uplink and downlink transmission information, and a control input and communication resource allocation scheme is jointly generated based on historical data and uplink transmission information by utilizing an A-C neural network, so that the communication quality is improved. According to the invention, the joint optimization control of input, transmitting power and bandwidth allocation is realized under the constraint of limited communication resources, so that the overall performance of a wireless control system is remarkably improved. According to the invention, the problem of difficulty in joint communication control in the scene of fading channels and unknown controlled system rules of the existing wireless network control system is solved.

The invention provides an on-off-policy deep reinforcement learning algorithm based on an optimal communication resource scheduling strategy. The on-off-policiy deep reinforcement learning algorithm based on the optimal communication resource scheduling strategy comprises the steps of S1, collecting state data at a moment by the algorithm for a wireless networked control system with a sensor and a channel, and predicting and updating an equipment state by using Kalman filtering, and S2, calculating a resource allocation action vector based on the collected state data. According to the on-off-policy deep reinforcement learning algorithm based on the optimal communication resource scheduling strategy, the advantages of on-policy and off-policy deep reinforcement learning are combined, and meanwhile, the monotonous characteristic of a value function and the dynamic priority management mechanism of an experience pool are utilized, so that the rapid convergence and global optimal performance of the strategy are realized; the method shows excellent application value and wide applicability in a dynamic complex environment.

The invention provides a collaborative optimization method and device, electronic equipment, a medium and a product, and relates to the technical field of communication, and the method comprises the steps: constructing a three-node topological structure in a network control system; under the condition that an estimator and a controller are decoupled, based on a three-node topological structure, a target link scheduling strategy is determined by taking minimization of information freshness AoI of a target node as an optimization target; and carrying out collaborative optimization on the network control system based on the target link scheduling strategy. The determined target link scheduling strategy can adapt to the closed-loop control requirement of the wireless network control system, the random packet loss characteristic of each link in the three-node topological structure is considered, the state estimation error of the target node is reduced, the precision of the control instruction generated by the controller is improved, and the user experience is improved. The system-level coordination of communication layer link scheduling and control layer closed-loop control is realized, so that the overall performance of the wireless network control system is improved.

The present application relates to train wireless network communication technical field, specifically to a kind of train wireless network control system information security risk assessment method, including according to the object of risk assessment to establish attack tree model, the possibility of security event is assessed using triangular fuzzy number and occurs, attack path is analyzed, and the interval probability of each attack path is calculated, point probability is obtained according to attack path interval probability, the possibility of attack is calculated, the influence of security event is evaluated using fuzzy analytic hierarchy process, the possibility of security event and impact value are quantified, security event risk value is calculated, the risk level and security level of system are determined according to system risk assessment value, corresponding protection requirements are formulated.The present application effectively evaluates and manages the information security risk of train wireless network control system, provides basis for formulating effective security protection strategy, and improves the overall security capability of train wireless network.

The present application discloses a method and device for distributed communication resource allocation and voltage collaborative regulation, belonging to the technical field of power system operation optimization. The method includes: for a new power system, constructing a wireless network control system based on a 5G local area network; determining a wireless transmission model of the wireless network control system and a dynamic model of voltage control; based on the wireless transmission model and the dynamic model of voltage control, determining a cross-domain dependence coupling relationship between the wireless communication and control system performance of the wireless network control system; based on the cross-domain dependence coupling relationship, determining a cross-domain optimization problem; and using the primal-dual graph neural network algorithm to solve the cross-domain optimization problem to obtain an optimal voltage control and communication power allocation strategy.

The invention discloses a control-oriented large-scale industrial 5G-Advanced network interference resource scheduling method, which comprises the following steps: step 1, system architecture modeling: an industrial wireless network control system adopts a centralized-distributed structure; step 2, modeling a communication system; step 3, modeling a control system; 4, defining a real-time instability probability; 5, obtaining interference information by adopting an uplink interference modeling scheme based on a nonlinear regression algorithm, and constructing an interference signal ratio matrix; 6, greedy search clustering optimization is carried out, and clustering is carried out on the industrial control equipment based on an ISR matrix; step 7, resource allocation based on near-end strategy optimization; step 8, executing resource scheduling; according to the method, accurate ICI information is obtained through the IAGC-DRL algorithm by means of interference sensing, the greedy clustering and PPO algorithms are matched, the transmitting power and the RB are reasonably distributed, and the stability of the IWNCS is remarkably improved; the overall stability of the IWNCS system is optimized by optimizing the real-time instability probability, and a better optimization effect on the stability of the system is achieved.

A wireless communication network transfers a data message to a User Equipment (UE). A wireless network control system registers the UE, and in response, transfers a UE registration notice for the UE and the wireless network control system to a wireless network database system. The wireless network database system receives the UE registration notice, and in response, transfers the UE registration notice to a message center. The message center receives the UE registration notice. The message center receives the data message for the UE, and in response, transfers the data message to the wireless network control system based on the UE registration notice. The wireless network control system receives the data message, and in response, transfers the data message to the UE.

The invention relates to the technical field of wireless network control systems (WNCSs) and multi-agent cooperative control, in particular to an estimation-control-scheduling cooperative design method based on deep reinforcement learning (DRL), and discloses an estimation-control-scheduling cooperative design method based on the deep reinforcement learning (DRL). The method is suitable for real-time control and resource optimization of a multi-agent complete cooperation type task in an industrial environment, and an innovative collaborative design framework is provided for solving the problems that in the prior art, system modeling dependency is high, dynamic environment adaptability is poor, and resource allocation efficiency is low. The sequential dependency relationship between the observed quantity and the state quantity in the WNCSs is learned through the recurrent neural network, and the adaptability of the method in the complex industrial environment is effectively enhanced. According to the method, joint optimization of state estimation, a control strategy and resource scheduling can be realized, and high-robustness control is realized under the condition of no precise system dynamics modeling.

The invention relates to the technical field of wireless communication and control, in particular to a wireless network control system based on MEC and URLLC and a resource allocation method thereof. According to the wireless network control system provided by the invention, on one hand, the MEC technology is adopted to distribute calculation tasks to a plurality of base stations, so that the calculation burden of a central server is reduced, and the calculation efficiency and expandability of the system are improved; and on the other hand, a large-scale multiple-input-multiple-output technology is adopted in the base station, and a time division multiple access protocol is adopted in interaction between the base station and the subsystem, so that the utilization rate of communication resources is effectively improved, interference among users is reduced, and implementation of URLLC is ensured. The method supports cooperative control of a plurality of subsystems, is suitable for a large-scale industrial Internet of Things scene, and has a wide application prospect. According to the invention, the problems of large base station calculation load and poor communication control stability in the existing wireless network control system adopting wireless communication are solved.

The invention discloses a communication resource optimization method for a wireless network control system in a low-altitude Internet of Things, and relates to the technical field of wireless communication resource allocation. The method comprises the following steps: constructing a communication resource coupling model of a wireless network control system in the low-altitude Internet of Things, wherein the communication resource coupling model comprises a communication model, a calculation time model and a unified energy constraint; based on the communication resource coupling model, constructing a resource joint optimization problem with minimization of the LQR control cost as a target by using a monotone mapping relation between the lower bound of the LQR control cost and an effective load; based on a condition that an uplink effective information load is equal to a downlink effective information load, namely, an effective load balance condition, a resource joint optimization problem is converted into a single-target optimization problem taking maximization of the uplink effective information load as a target; on the basis of an alternative optimization algorithm, a single-target optimization problem is decomposed into a time and bandwidth optimization sub-problem and a power allocation sub-problem, and an optimal resource allocation scheme is obtained through iterative solution.

The present invention provides a recursive Bayesian network construction system, construction method, computer-readable recording medium, non-transitory computer program product, and wireless network control system. The system is executed by a processor to: establish an initial group and set the initial group as the current group; generate a network for each combination pattern in the current group and establish a corresponding recursive Bayesian network to obtain a set of recursive Bayesian networks corresponding to the current group; evolve the current group using an evolutionary algorithm and a fitness function to obtain a next group; determine whether a termination condition is satisfied based on the fitness function and the set of recursive Bayesian networks corresponding to the current group; and, if the termination condition is not satisfied, repeat the aforementioned steps. If the termination condition is satisfied, select a solution network in the current group as a task network based on the fitness function.

The present application relates to wireless networked control system technology, in particular to a kind of probability scheduling and resource allocation joint optimization method of wireless networked control system.The present application is applicable to the wireless networked control system formed by multiple discrete linear subsystems and shared 5G network.In particular, the linear quadratic Gaussian (LQG) control cost of wireless networked control system is modeled as the closed-form expression of subsystem activation probability, uplink and downlink transmission reliability;With the goal of minimizing LQG control cost, a joint optimization problem of probability scheduling and resource allocation is established;Alternating optimization is used to solve the problem, and the optimal network parameter setting is obtained.