4results about How to "Achieve organic unity" patented technology

The invention relates to the technical field of intelligent control, in particular to an intelligent control system for a freeze-drying process based on an intelligent algorithm, which comprises an acquisition module, a freezing adjustment module, a primary adjustment module and a secondary adjustment module. According to the method, the corrected plate layer temperature after incomplete freezing is accurately recognized according to double criteria of the freezing center temperature and the temperature drop trend, and after the drying efficiency deviation degree is calculated and the abnormal type is determined by utilizing the preset efficiency interval, the related preset process conditions are adjusted; meanwhile, stagnation and overheating runaway are independently diagnosed and analyzed on the basis of time sequence correlation of the water content decreasing rate and the center temperature increasing rate, the preorder freezing defect is traced when secondary drying is normal but overtime does not reach the standard, and the slab layer temperature is reduced in an equal proportion according to the freezing defect degree; the problem that the process efficiency and the product quality stability are difficult to consider at the same time due to dependence on artificial experience, stage splitting and lack of dynamic feedback is effectively solved.

The invention provides a red deer identification and detection method, system and device and a medium, and belongs to the technical field of computer vision. The method comprises the following steps: acquiring an ortho-image of a red deer activity area through an unmanned aerial vehicle, and processing the ortho-image by using an illumination adaptive enhancement network to obtain an enhanced image; annotating the enhanced image, and constructing a data set; an improved YOLOv8 deep learning model is constructed and trained, an attention mechanism module fused by GAM and CBAM is inserted in a multi-scale feature fusion path between a feature fusion network and a detection head and in the detection head, and four detection heads with different scales are included. Wherein the tiny, small-scale and medium-scale detection heads are integrated with sub-branches for predicting biological attributes; in the training process, the bounding box, the category and the attribute tag are utilized to calculate the composite loss for optimization; and finally evaluating the model performance by using the test set. The method can effectively improve the detection precision and robustness of red deer target recognition in a complex natural scene.

The invention discloses a conformal underwater acoustic sensor array detection system based on an unmanned sailboat platform, and belongs to the field of underwater acoustic observation and detection. The sensor array and the unmanned sailboat platform heavy hammer are conformal in streamline shape, so that the platform maneuverability is greatly liberated while the acoustic detection performance is ensured; the sensor array detection system comprises an analog signal acquisition and processing module, a digital signal transmission and processing module and an embedded module, the analog signal acquisition and processing module conditions and amplifies signals, and then underwater sound detection is carried out through the digital signal transmission and processing module. According to the invention, real-time target orientation estimation is realized based on the embedded carrying adaptive method of the conformal underwater acoustic sensor array detection system of the unmanned sailboat platform, prior information is provided for the motion of the unmanned sailboat platform by combining target orientation information, and organic unification of the motion of the unmanned sailboat platform and acoustic detection is realized.

This invention provides an autonomous inspection method and system for unmanned aerial vehicles (UAVs) based on a large language model and simulation verification. It includes: parsing natural language commands into a structured domain-specific language (DSL) description using a large language model; mapping the DSL to executable code for the UAV via a Model Control Protocol (MCP); performing multi-dimensional verification of the code in a digital twin simulation environment, including collision and logic checks; if verification fails, feedback error information triggers automatic correction by the large language model until successful; deploying the verified code to a physical UAV for inspection, and implementing dynamic deviation circuit breaking based on virtual-real-time airflow alignment and MCP feedback replanning. This invention solves the problem of safe conversion from natural language to precise control through a DSL intermediate layer and simulation verification closed loop, constructing a semantic-control two-layer decoupled architecture, achieving full-process automation from high-level commands to safe autonomous flight, and significantly improving the intelligence level and operational safety of the inspection process.