4results about How to "Reduced feasibility" patented technology

This invention discloses a high-strength, high-elongation, low-hardness natural rubber and its preparation method. By weight, the high-strength, high-elongation, low-hardness natural rubber comprises the following components: 100 parts natural rubber, 1.5-3 parts crosslinking agent, 0.8-2 parts accelerator, 10-20 parts reinforcing agent, 5-15 parts softener, 1-3 parts antioxidant, and 3-8 parts activator. The high-strength, high-elongation, low-hardness natural rubber has a tensile strength ≥20 MPa, an elongation ≥700%, and a Shore A hardness of 35-45. Under normal operating conditions of -20℃ to 80℃, the fluctuation range of the tensile strength and elongation is controlled within ±5%, and the Shore A hardness fluctuation is ≤±2. This invention solves the technical problem of achieving a balance between high strength, high elongation, and low hardness in existing natural rubber formulations. The formulation components of this invention are widely available, the preparation process is simple, and the production cost is controllable. It can be widely applied in multiple fields and has extremely high practical value and promising prospects for promotion.

The embodiment of the application provides a lightweight model cloud edge collaborative self-training evolution method for an industrial scene, and belongs to the technical field of intelligent industry. The method forms a lightweight representation of a mutation period in a structured log and a window statistical manner on the edge side, and only returns key samples and key contexts when a trigger condition is met, thereby reducing the return bandwidth and return cost. At the same time, the cloud end cognitive writeback provides weak supervision and enhancement instructions for the mutation samples, so that the lightweight edge model has the feasibility of low-cost self-training. Further, through the hierarchical conflict buffer and dynamic gating mechanism, the noise samples are inhibited from damaging the iteration, and the stability after going online is guaranteed through conflict suppression and stability maintenance constraints. Finally, through the effect association of the new inference log and the backflow package, reusable continuous optimization is formed, so as to realize the reduction of edge inference misjudgment rate and the improvement of inference stability, and the real-time and lightweight deployment constraints are also considered.

This invention belongs to the field of flowmeter technology and discloses a method for optimizing the impeller of a turbine flowmeter. The method includes: S1: collecting data on the physical properties, operating range, and performance indicators of the medium through a system to establish clear design constraint data; S2: integrating streamlined and multi-lead design concepts, and determining the blade structural parameters through mathematical modeling and simulation analysis; S3: ensuring precise matching between the processing process and design objectives through parameter modeling, accuracy threshold setting, process coordination, and data linkage; S4: manufacturing an impeller prototype based on the processing technology, and calibrating the blade size and angle parameters using equipment; S5: verifying linearity and starting flow rate indicators through data acquisition and analysis, and iteratively optimizing design and processing parameters; S6: establishing a large-scale batch production process based on the optimized final parameters. This invention solves the problems of large starting flow rate, poor linearity, and insufficient consistency of metering accuracy in turbine flowmeters under low Reynolds number conditions.