5results about How to "Quick intervention" patented technology

This application discloses a fall detection method based on MQTT-ROS communication bridging and multidimensional statistical features, comprising: acquiring data packets; decoding the data packets; if a human body is in an active state, sending continuous motion sensing states and active states to two sliding window queues of preset lengths respectively; determining whether the active state meets a first preset condition and / or a second preset condition, and simultaneously determining whether the motion sensing state meets a third preset condition and / or a fourth preset condition; if a fall event is determined, modifying the fall state field in the data packet to a valid state and adding a ROS system timestamp; based on the modified data packet, publishing alarm information to the ROS system through a ROS topic, triggering a single-trigger mechanism to shut down the ROS system node, thereby completing the abnormal event response process. This application effectively avoids false fall detections caused by historical data contamination and significantly improves detection accuracy in scenarios with frequent personnel entry and exit.

The invention discloses a method for realizing water temperature difference adjustment by automatically adjusting water quantity of a crystallizer, and belongs to the technical field of continuous casting of ferrous metallurgy. According to the technical scheme, default cooling water flow of the wide face and the narrow face of the crystallizer is set according to the continuous casting pulling speed (the pulling speed larger than or equal to 1.3 m / min and the pulling speed smaller than 1.3 m / min are set in two gears); the water inlet and outlet temperature difference of the wide surface and the narrow surface is monitored in real time; when the wide surface temperature difference is larger than 9 DEG C, the wide surface water volume is gradually increased step by step, the water volume is 50 L / min every time, the interval is 1 min, and the pulling speed is reduced by 0.1 m / min if the water volume still exceeds the limit after two times of adjustment; when the temperature difference of the narrow surface is greater than 9 DEG C, increasing 30 L / min every time at an interval of 1 min, and reducing the pulling speed if the limit is still exceeded after two times; keeping the water quantity unchanged when the temperature difference is less than or equal to 9 DEG C; and a visual warning signal is sent out when abnormity occurs. According to the method, dynamic partition accurate adjustment of the cooling water amount is achieved, a multi-stage protection mechanism of increasing water first and then decreasing speed is established, surface cracks of the casting blank are remarkably reduced, and the hot delivery rate and production safety of a casting machine are improved.

This invention relates to an artificial intelligence-based data inspection quality control method, system, device, and medium. The method includes: cleaning and determining the distribution of raw inspection data to generate a qualified dataset; adaptively learning key model parameters such as the effective data range and optimal smoothing coefficient through iterative calculation and simulation optimization based on the dataset and its distribution type; training a long short-term memory neural network time-series prediction model using the qualified dataset, and setting quality control limits by combining the learned benchmark dispersion to construct a real-time quality monitoring model; inputting real-time inspection samples into the model, generating alarm signals by calculating an exponentially weighted moving average monitoring statistic and comparing it with the control limits; iteratively optimizing the model and parameters based on historical alarm signals and new data to achieve continuous self-updating of quality control, enabling automatic learning of quality control parameters and intelligent optimization of the model, and improving the real-time monitoring capability of minor system deviations during the inspection process.

This utility model relates to the field of clamp forming technology and discloses a V-shaped clamp stamping forming device, including a worktable. The worktable has a storage component inside, and a mounting block is fixedly connected to one side of the top surface of the worktable. A detection component is fixedly connected to the top surface of the mounting block. This V-shaped clamp stamping forming device, through the storage component, allows for precise placement of clamping strips or similar components onto the storage block during use. The servo motor is then activated, driving the indexing plate to rotate. The rotation of the indexing plate causes the storage block to rotate, accurately placing it under the pressure block. This ensures that each storage block is in the correct position, preventing workpiece displacement, deformation, or damage due to positional deviations. This achieves consistent workpiece processing accuracy in automated production, reduces errors from manual adjustments, improves overall production efficiency and product qualification rate, and lays a precise positional foundation for smooth transitions in subsequent processes.

The application discloses a multi-modal computing power optimization method and system for security monitoring scenes, and relates to the technical field of multi-modal computing power data processing. The multi-modal computing power optimization method for security monitoring scenes comprises the following steps: computing power allocation lag judgment, cooperative scheduling lag monitoring, and scheduling lag level division. The application performs screening processing on security multi-modal data, analyzes the lag degree of computing power allocation after the screening processing is qualified, judges whether to start the computing power allocation lag optimization mechanism, and if not, performs cooperative scheduling lag degree analysis, otherwise, performs cooperative scheduling lag degree analysis after the start is completed. Based on the execution result of the cooperative scheduling lag degree analysis, it is judged whether to start the modal scheduling lag level division mechanism, which improves the real-time and accuracy of the computing power optimization data under security monitoring, and solves the problem of low real-time and accuracy of the computing power optimization data under security monitoring in the prior art.