16 results about "Safety policy" patented technology

The invention discloses an off-line constraint reinforcement learning method for double-commenter regularization and dynamic coefficient scheduling based on a decision converter, and relates to the technical field of machine learning and intelligent control. The method is oriented to a safe and sensitive robot control task, and historical offline trajectory data is used as a training sample; on the basis of a backbone network of a constraint decision converter, a reward reviewer and a cost reviewer are introduced, and explicit modeling is carried out on state-action long-term return and accumulated cost; designing a joint loss function including optimal trajectory weighting, action prediction loss, a return consistency regular term and a cost consistency regular term, and dynamically scheduling a regularization coefficient by adopting a two-stage cosine annealing strategy; and by alternately optimizing parameters of the commenter and the decision converter, a security policy model considering high return and low cost is obtained through training. The model receives the current observation state of the robot and outputs a high-return motion control instruction meeting the cost constraint.

Embodiments of the present invention provide computer-implemented methods, computer program products and computer systems. Embodiments of the present invention can identify a plurality of constraints on states of data and actions of data associated with a data model. Embodiments of the present invention can then identify constraints on safety policy parameters associated with a computing device. Embodiments of the present invention can then convert the identified constraints into a uniform domain syntax that considers coupled and decoupled constraints and introduce buffer data within the converted constraints, wherein the buffer data filters outlier constraints within the plurality of constraints. Embodiments of the present invention can then dynamically generate optimal safety policies associated with the computing device based on the remaining constraints.

The invention discloses a research, development and operation method and device for security left shift, and relates to the field of software development processes and security management.The method comprises the steps that a security demand baseline is established in the demand analysis stage, security demands and business demands are fused, and risk protection measures are determined; security architecture design review is carried out in the architecture design stage, and a security team carries out threat modeling on the system architecture and carries out one-ticket negative right; in the code development stage, code specifications, open source component vulnerabilities and logic risks are scanned in real time through a static code analysis tool, and a code security closed-loop management mechanism is established; in the compiling and constructing stage, code review and component list filing are implemented, a security review report is formed by combining a dynamic penetration test and a running flow test, and a security strategy is updated through a continuous monitoring mechanism to deal with new threats. Security practice can be systematically integrated in the early stage of the software development life cycle, and the later vulnerability repair cost is remarkably reduced.

This invention discloses an adaptive control method for bearing grinding processes based on reinforcement learning, comprising: S1, real-time acquisition of multi-source state data; S2, construction of a high-dimensional state vector; S3, construction of a historical grinding dataset; S4, construction of an improved IQL model incorporating an uncertainty perception mechanism, offline training using the historical dataset, outputting the Q-value distribution, and learning a safety policy at the safety policy layer; S5, online deployment of the model, generation of candidate commands, and evaluation of uncertainty; S6, judgment by the safety constraint layer: issuing candidate commands when uncertainty is low, and invoking the safety policy to generate safe commands when uncertainty is high; S7, execution of commands and storage of new data in the dataset, periodically optimizing the model. This invention improves the safety and reliability of online decision-making through uncertainty quantification and a safety policy safety net, achieving adaptive and precise control of complex grinding conditions.

The invention discloses a vehicle multi-risk perception and hierarchical automatic takeover system and method, which can integrate multi-dimensional monitoring of vehicle state, driver state and environment information through a condition identification module, cover multiple dangerous scenes, break through the limitation of single function of the existing system, and effectively meet the safety protection requirement under a composite dangerous situation. Through accurate positioning of a tire burst position and a special dynamic control model, a customized stabilizing strategy is executed according to different tire burst conditions of left front tires, right front tires and the like, the out-of-control probability of the vehicle after tire burst is reduced, secondary accidents are reduced, and the emergency control stability is improved; a user-defined security policy is supported, and the system applicability is improved; the air bag is pre-started before collision to shorten the response time, the safety belt is automatically unlocked after collision, passive protection is converted into active front intervention, the passenger injury degree and the secondary disaster risk are effectively reduced, and passenger injury is reduced.

The application discloses a kind of self-adapting risk protection method and system of smart home equipment control, and relates to the technical field of smart home security.The method comprises the following steps: receiving the device control request of control end and extracting context information;Determine the risk analysis module to be called through decision scheduling module;Each risk analysis module carries out risk scoring;Comprehensive scoring engine carries out weighted aggregation and risk amplification to each risk score, and gives comprehensive risk score;According to the comprehensive risk score, the safety policy is executed on the device control request, including release, secondary authentication or interception operation;Finally, the device control request released by terminal is executed.The application realizes multi-dimensional dynamic risk assessment, and improves the security of smart home equipment control.

A virtual augmentation system includes: one or more head-up displays displaying virtual images over an environment forward of a host vehicle; sensors determining parameters indicative of a state of the host vehicle; a telematics module obtaining roadway information indicative of roadway markings and objects along a predicted path of the host vehicle; and a virtual marker and object modification module, based on the parameters and the roadway information, i) detecting one or more roadway markings and objects, ii) generating modified versions of the one or more detected roadway markings and objects as one or more virtual roadway markings and objects, and iii) via the one or more head-up displays, overlaying the virtual one or more roadway markings and objects over at least a portion of the one or more detected roadway markings and objects in a field of view of an occupant of the host vehicle.

This application relates to the field of vehicle active safety technology, and in particular to a vehicle emergency scenario safety prompt system and method. The system includes: a sensor module for real-time acquisition of current vehicle sensor data; a data processing module for determining the current emergency scenario type based on preprocessed sensor data; a safety policy generation module for calling a target safety policy corresponding to the current emergency scenario type from a pre-set safety policy library; and a prompting module for converting the target safety policy into voice prompts and video demonstrations to guide the user in taking appropriate actions. This solves the problem that existing automotive safety systems cannot provide proactive and immediate operational guidance in emergency scenarios. It achieves automatic identification of emergency scenarios through multi-sensor fusion and simultaneous output of voice and video operation guidance, thereby helping users respond correctly in critical moments, avoid misoperation, and reduce the risk of secondary accidents.

The invention provides a power distribution network reconstruction method and system based on multi-agent safety reinforcement learning. The method comprises the following steps: constructing a Markov decision process in a power distribution network reconstruction demand state; constructing a security constraint condition meeting the power distribution network through a security policy embedding mode; a dynamic graph convolutional network GCN is adopted to encode the state characteristics of the power distribution network; constructing a parameterized strategy function by adopting a deep neural network; constructing a multi-objective value function to evaluate the long-term return of taking the action at under the current state st; and multi-agent cooperative training and optimization are carried out, and after the objective function is converged, a safe power distribution network reconstruction topological structure is output by the agents. Through the method, the stability and efficiency of power distribution network reconstruction are improved.

The application discloses a kind of multimodal driving safety monitoring systems based on hydrogel electrode, it is related to driving safety monitoring technical field.The application includes edge device and cloud, wherein edge device includes synchronous acquisition module, real-time monitoring module, safety policy module and channel detection module;Through high-precision data acquisition and synchronization, ensure that each physiological and environmental signal is accurately aligned;From multiple dimensions, capture the change of driver state, timely warning reduces the risk of accident, realize comprehensive real-time monitoring and multidimensional evaluation;Channel failure detection switches standby channel in time, ensure that signal acquisition is continuous and stable;Data management and incremental learning of cloud constantly improve the adaptability of system to new driving scene, prevent model recession;Realize;Continuous self-adaptation and model iteration optimization.In general, the system can not only monitor and warn driving fatigue state in real time, and continuously improve monitoring accuracy and system stability through the closed-loop feedback of edge and cloud, provide guarantee for driving safety.

The application provides a task execution method for an agent, an agent, a device, a medium and a product, wherein the task execution method for the agent comprises: in response to receiving an evidence task package, generating an original plan based on the evidence task package and a corresponding formal constraint specification, and compiling the original plan into a structured execution plan; performing static verification on the structured execution plan based on the formal constraint specification; after the static verification is passed, scheduling the execution of the structured execution plan; recording evidence information during the execution of the structured execution plan, and outputting a task execution result comprising the evidence information. The above scheme defines a decision boundary through the formal constraint specification, cooperates with the compiled structured execution plan and the pre-execution verification mechanism, converts the agent from an open free reasoning mode to a constrained verifiable execution mode, improves the flexibility of task execution, and ensures that the execution process conforms to the predetermined safety policy.

The invention belongs to the technical field of nuclear power, and particularly relates to an elastic capacity expanding and shrinking system applied to the nuclear industry. According to the accurate elastic response provided by the invention, the system adopts the K-ELI model to fuse the business indexes, the security factors and the dynamic weights, the business sensitivity is high, and the decision is more in line with the actual demand. Accuracy, timeliness and effectiveness of capacity expansion and contraction decision are greatly improved, and nuclear industry load fluctuation is effectively dealt with. The security policy adapter performs real-time verification and constraint before decision making and execution, and security context is accurately injected. The problem of conflict between security policies and capacity expansion and shrinkage is solved, a system full link adapts to various types of CPUs, OS, monitoring interfaces of container engines, APIs and security mechanisms, and optimization is carried out according to platform characteristics. And process group-level elastic expansion and contraction are supported. The refined resource control requirement of a special module in a nuclear industry scene is met, and the resource utilization rate is increased.

The invention discloses a hydrogen energy micro-grid dispatching method based on CVaR constraint safety reinforcement learning, and belongs to the technical field of power system operation and control. The method comprises the following steps: firstly, acquiring heterogeneous multi-microgrid community environment state spatial data; constructing a dynamic equivalent carbon intensity tracking model, and checking node carbon intensity and energy storage dynamic equivalent carbon intensity; then, a multi-agent flexible Actor-Critic distributed security policy network is constructed, the carbon emission long tail risk is captured through a non-cross quantile network, and a CVaR constrained original-dual optimization training model is adopted; and finally, outputting a scheduling instruction, and realizing cooperative scheduling by means of a carbon-energy cooperative time shifting mechanism. The method has the core advantages that the energy storage carbon flow is accurately tracked, the tail risk is explicitly optimized, economic and carbon quota constraints are balanced, the extreme carbon standard exceeding risk can be effectively avoided in an uncertain environment, the violation rate is remarkably reduced, the renewable energy consumption rate is increased, and community low-carbon cooperation and safe operation are achieved.

The application discloses a cold-chain logistics data protection method and system based on data analysis, relates to the technical field of data analysis, and guarantees the integrity and timeliness of cold-chain transportation data through an original parameter set Par; a characteristic deviation set Dev is generated, which can reveal implicit data deviations in the cold-chain transportation process due to network fluctuations or sensor abnormalities; a data credibility score Qdc is further calculated, so that each batch of cold-chain transportation data can obtain a quantitative credibility result; the data credibility score Qdc is compared with a system threshold value, an abnormality identification set Jud is generated, so that high-credibility data and low-credibility data can be quickly distinguished, and abnormal information is prevented from being directly mixed into a business database; finally, the abnormality identification set Jud is matched with a safety policy set of the cold-chain logistics, targeted control instructions are generated, and it is ensured that abnormal data can trigger immediate protection measures when being identified.

System for controlling a motor vehicle (MV) for autonomous driving, wherein the system comprises: a bank of control units (110) comprising a number of vehicle control units (ACU1, ..., ACUn) configured to generate target control signals (NCS) configured to cause the motor vehicle (MV) to move autonomously in accordance with a target path; and a monitoring unit (160) configured to receive sensor signals (SS) from the motor vehicle (MV) describing a current state of the motor vehicle (MV) and to generate commands ({cmd}) based on the control signals (SS) that influence how the bank of control units (110) generates the target control signals (NCS), characterized by the fact that the system further comprises: a data storage (140) comprising a set of boundary conditions ({bc}) that the target path must satisfy to be considered safe; and a first data interface unit (163) configured to provide at least one safety policy (P) describing an associated task-related rule to be followed during the operation of the motor vehicle (MV), wherein the first data interface unit (163) is configured to provide at least one safety policy (P) based on a safety event (SC) that prescribes how the motor vehicle (MV) should be controlled in order to meet a functional safety standard, wherein the monitoring unit (160) is configured to repeatedly generate the commands ({cmd}) to update the boundary conditions ({bc}), aiming to keep the target path within limits specified by the sensor signals (SS) and at least one safety policy (P), and wherein the bank of control units (110) is configured to: to read the set of boundary conditions ({bc}) from the data storage (140), and to control the motor vehicle (MV) to move in such a way that the desired path satisfies the boundary conditions ({bc}).