8 results about "Safety policy" patented technology

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 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 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 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.