4results about How to "Guaranteed Time Synchronization" patented technology

The present application relates to the technical field of crane sling deflection detection. The present application relates to a crane sling deflection detection method based on artificial intelligence. It includes the following steps: S1, build a multi-modal perception array, synchronously collect multi-source perception data, the multi-modal perception array contains an industrial camera, an IMU inertial measurement unit, a cart trolley displacement encoder and a wind speed sensor, which respectively obtain real-time image data of the sling, inertial attitude data of the sling, crane walking motion data and on-site wind field environment data; The present application builds a multi-modal perception array composed of an industrial camera, an IMU inertial measurement unit, a displacement encoder and a wind speed sensor, realizes the synchronous collection of multi-source data of the sling image, inertial attitude, crane motion state and on-site wind field environment, breaks through the limitation of single sensor detection in the prior art, and multiple sensors play their respective roles and complement each other's shortcomings, laying a foundation for subsequent high-precision detection from the data collection level.

The invention discloses an unmanned aerial vehicle flight communication state monitoring method and system based on multiple links, and relates to the field of unmanned aerial vehicle communication, and the method comprises the steps: collecting physical layer parameters of at least two heterogeneous communication links at the same time; for each heterogeneous communication link, mapping physical layer parameters into a virtual vibration displacement sequence; extracting the amplitude of the virtual vibration displacement sequence, and performing frequency domain transformation on the virtual vibration displacement sequence of the at least two heterogeneous communication links to obtain a corresponding frequency spectrum function; calculating the cross-power spectral density between the spectrum functions of different heterogeneous communication links, and determining a coherence function and the maximum coherence value of the coherence function; acquiring flight attitude data of the unmanned aerial vehicle, and dynamically adjusting a preset homologous interference judgment threshold value; and generating a communication state monitoring conclusion based on the maximum coherent value, the adjusted homologous interference judgment threshold and the amplitude, and executing a corresponding link control strategy. The flight safety of the unmanned aerial vehicle can be effectively guaranteed.

The application relates to the technical field of virtual reality interaction, and discloses a multi-modal feedback VR device Bluetooth interaction system, which comprises a virtual reality host device, a Bluetooth communication link, a microcontroller unit and a tactile and olfactory feedback execution module. The microcontroller unit receives virtual physical event data by using a ring buffer which is maintained with a read-write pointer, and through an overlap strategy when a write pointer chases a read pointer and a timeout reset mechanism, the real-time performance and stability of instruction processing are ensured. The microcontroller unit runs a segmented control algorithm, maps a wind speed parameter into a pulse width modulation signal, and eliminates a motor starting dead zone. Meanwhile, a hardware timer is used to load a synchronous delay parameter, and a physical response time difference of tactile and olfactory feedback is accurately compensated. The application solves the problems of communication lag, control nonlinearity and sensory timing asynchronization in multi-modal feedback.

This invention relates to the field of intelligent power judgment technology, and more particularly to an AI-based intelligent energy meter fault diagnosis system. The system includes an operational data acquisition module, a jump correlation judgment module, an operational deviation judgment module, a current waveform diagnosis module, and an anomaly alarm module. This invention synchronously acquires current data and cold storage operational data to determine whether current jumps are related to gating actions. Furthermore, by combining multiple cross-validations of baseline energy consumption and temperature changes, it effectively identifies abnormal refrigeration performance in cold storage and the type of intelligent energy meter fault, distinguishing between compressor malfunctions, insufficient airtightness in the cold storage, or faults in the meter itself. An ideal current waveform is constructed using AI algorithms and compared with the measured waveform to achieve intelligent fault type determination and classification. Simultaneously, the diagnostic results are linked with the operation and maintenance system, improving response efficiency. This system is particularly suitable for scenarios with high requirements for meter stability, such as cold chain warehousing, and has good practical value and promising prospects for widespread application.