4results about How to "Efficient start" patented technology

The invention discloses an air-cooled fuel cell system and a low-temperature start control method, and belongs to the technical field of fuel cells, and the air-cooled fuel cell system comprises a fuel cell stack, a bidirectional cooling fan, a mainboard, a first housing, a second housing, a first temperature sensor and a second temperature sensor. In cooperation with cycle control of low-efficiency reaction and normal reaction, the problem of thermal runaway of the fuel cell stack caused by long-term continuous heating in the same area due to the fact that low-temperature starting temperature regulation cannot be performed in the prior art is avoided, and efficient, safe and quick starting of the air-cooled fuel cell system in a low-temperature environment is realized; and the air-cooled fuel cell system is particularly suitable for wide application in cold regions.

The invention relates to a multi-unmanned aerial vehicle water lifesaving material cooperative delivery path planning method and system. The method comprises the following steps: acquiring lifesaving target data of a lifesaving task; acquiring lifesaving unmanned aerial vehicle state data of each lifesaving unmanned aerial vehicle, wherein the lifesaving unmanned aerial vehicle state data comprises unmanned aerial vehicle flight state data, unmanned aerial vehicle position state data and lifesaving material strength data; generating first unmanned aerial vehicle lifesaving task allocation information according to the lifesaving target data and the lifesaving unmanned aerial vehicle state data, wherein the first unmanned aerial vehicle lifesaving task allocation information is used for representing the lifesaving unmanned aerial vehicle state data of the lifesaving unmanned aerial vehicle participating in the lifesaving task; based on the first unmanned aerial vehicle lifesaving task allocation information and the lifesaving target data, path planning is carried out on the lifesaving unmanned aerial vehicle participating in the lifesaving task, and first path planning result information is generated. By adopting the method, lifesaving scene information can be comprehensively mastered, lifesaving unmanned aerial vehicle resources are reasonably allocated, and unmanned aerial vehicle path planning information is accurately and efficiently generated.

The present application relates to fuel cell temperature control technical field, especially in kind, a kind of unmanned aerial vehicle fuel cell optimal temperature curve tracking control method, device, equipment and computer storage medium are pointed out.The present application constructs the optimal temperature curve model that changes dynamically with current and environmental temperature and humidity, determines the optimal temperature point corresponding to the power peak of different working conditions of electric pile;Real-time monitoring of electric pile temperature, compared with the optimal temperature point determined by model generates deviation signal;Based on the deviation signal continuous closed loop adjustment fan speed, so that the electric pile temperature tracks the optimal temperature curve;When extremely cold start, electrochemical self-heating is used to warm up, and after reaching the safe temperature, the fan is controlled to intervene according to the preset curve smoothly.The present application can track the optimal temperature curve according to load and environment, improve power generation efficiency, reduce hydrogen consumption, and inhibit temperature overshoot and chattering in extremely cold environment, reduce thermal stress damage, and ensure the wide temperature range and efficient and stable operation of unmanned aerial vehicle.

The application discloses a kind of geometric defect identification and processing method based on AI big model, it is related to aerospace engineering field.The method aims at solving the problem of low efficiency and poor accuracy of aircraft CAD model geometric defect artificial repair.The steps include: first, CAD assembly body model is intelligently segmented into multiple sub-components;Second, the part geometry is converted into a structured feature representation, and a multi-task hierarchical graph neural network model incorporating domain knowledge is trained using labeled data;Then, the model is used to identify defects in sub-components, outputting defect type and spatial location information;Finally, according to the identification results, the repair topology is connected first, and the geometric anomaly is processed in sequence, and a watertight surface mesh is generated.The application realizes the whole process automation of geometric defect identification and repair, greatly improves the pre-processing efficiency and quality, and reduces the dependence on expert experience.