摘要：【中文】本发明公开了一种具有发电功能的健身鞋，包括鞋底和粘接在鞋底上的鞋身，所述鞋底包括后鞋跟、前鞋掌、中部发电组件和包裹胶层，所述中部发电组件位于后鞋跟和前鞋掌之间，所述鞋身包括鞋帮、鞋头和透气网布，且鞋头的两侧和鞋帮相缝接，所述透气网布位于鞋头的顶部外壁上，且鞋帮和透气网布之间缝接有鞋带帮。本发明设有中部发电组件，通过两侧填充物的种类不同，配合上鞋子在跑动挤压上下铜片接触，和两侧填充物之间形成原电池结构，配合上中部的囊型电池组和中置转换器的作用，将原电池结构的电能和压电陶瓷片之间形成电压差，进而产生电荷的迁移，形成电流，保证微型信号发射器和内部计步元件正常使用。 【EN】The invention discloses a body-building shoe with a power generation function, which comprises a sole and a shoe body adhered to the sole, wherein the sole comprises a rear heel, a front shoe heel, a middle power generation assembly and a wrapping adhesive layer, the middle power generation assembly is positioned between the rear heel and the front shoe heel, the shoe body comprises an upper, a toe cap and breathable mesh cloth, two sides of the toe cap are sewn with the upper, the breathable mesh cloth is positioned on the outer wall of the top of the toe cap, and a shoelace upper is sewn between the upper and the breathable mesh cloth. The invention is provided with a middle power generation assembly, the upper shoe is matched with the upper shoe to press the upper copper sheet and the lower copper sheet to contact with the fillers on the two sides through different types of the fillers on the two sides, a primary battery structure is formed between the fillers on the two sides, and the electric energy of the primary battery structure and the piezoelectric ceramic sheet form a voltage difference under the action of a bag-shaped battery pack on the upper middle part and a central converter, so that the migration of charges is generated, the current is formed, and the normal use of the miniature signal transmitter and the internal step-counting element is.

摘要：【中文】本发明的目的是提出一种基于SBS‑BP神经网络的公路客运量预测方法，利用BP神经网络建立公路客运量数学预测模型，并且利用SBS算法优化特征改进BP算法模型，使得公路客运量预测模型性能更加优越。 【EN】The invention aims to provide a road passenger capacity prediction method based on an SBS-BP neural network, wherein a mathematical prediction model of the road passenger capacity is established by using the BP neural network, and the BP algorithm model is improved by using the optimization characteristics of an SBS algorithm, so that the performance of the prediction model of the road passenger capacity is more excellent.

摘要：【中文】本发明的目的是提出一种基于ReliefF算法的区域物流需求影响因素预测方法，利用ReliefF算法计算分析各个影响区域物流需求的特征值和区域物流需求的相关性大小，从而赋予特征不同的权重，从而得到各个影响区域物流需求的影响因素重要性大小。最后利用BP神经网络来验证所选取重要性强的特征的区域物流需求预测能力。 【EN】The invention aims to provide a method for predicting regional logistics demand influence factors based on a Relieff algorithm. And finally, verifying the regional logistics demand prediction capability of the selected features with strong importance by using the BP neural network.