A big data genetic algorithm data modeling processing device

Abstract

The invention discloses a data modeling processing structure of a big data genetic algorithm, which comprises an information encoder, a data modeling processor and an information compressor; an abutting data pin is arranged on the information encoder, the information encoder is connected with a temporary storage stacker; the inner part of the temporary storage stacker is provided with a managementport, and a storage grain is arranged on the temporary storage stacker; a storage plug slot is further arranged on the temporary storage stacker, the data modeling processor is arranged beneath the temporary storage stacker. A radiating fan is arranged on the data modeling processor, the side of the radiating fan is provided with an assembling groove. The data modeling processing structure has the beneficial effects that the data modeling processing structure has active radiating system and can guarantee good performance in working process; meanwhile, the data modeling processing structure has data stacking and temporary storage functions, and can store files under the emergency power failure; moreover, the data modeling processing structure has the functions of modeling, feedback and re-modeling, thus the modeling quality is improved.

Description

technical field [0001] The invention relates to the field of 3D printing equipment, and the invention relates to a large data genetic algorithm data modeling processing device. Background technique [0002] Genetic Algorithm (Genetic Algorithm) is a kind of randomized search method evolved from the evolution law of the biological world (survival of the fittest, survival of the fittest genetic mechanism). It was first proposed by Professor J.Holland in the United States in 1975. Its main feature is to directly operate on structural objects, without restrictions on derivation and function continuity; it has inherent implicit parallelism and better global optimization capabilities. ; Using a probabilistic optimization method, it can automatically obtain and guide the optimized search space, and adjust the search direction adaptively, without definite rules. These properties of genetic algorithms have been widely used in fields such as combinatorial optimization, machine learni...

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Problems solved by technology

[0003] At present, there are still many deficiencies in the big data genetic algorithm data modeling and processing device. For example, the data does not have a temporary storage stack function, which is prone to data loss and collapse. At the same time, it does not have a feedback function and cannot perform multiple modeling based on the data. A more suitable mathematical model

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