SD 3.01 物理层简化规范:SD/SDIO/SDHC开发者的权威指南

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"《SD Specifications Part 1:物理层简化规格版本3.01最终版(100518)》是一份由SD Group(包括松下、SanDisk和东芝公司)以及SD卡协会共同编撰的文档,于2010年5月18日发布。这份技术规范主要针对SD 3.0标准,适用于SD/SDIO/SDHC等存储设备的软硬件开发者。它详细规定了SD卡的物理层技术细节,旨在提供一个简化易懂的开发指南,便于工程师进行产品设计和实现。 修订历史表明,该规格经历了多个版本的发展。最初的1.10版在2006年4月发布,随后的2.00版在同年9月推出,直至3.01版在2010年5月进行了更新。这个版本的更新可能包含了对先前版本(如1.0和1.01)的集成,并且强调了如果采用该规格,相关成员必须遵循SD-3C, LLC的知识产权政策,确保对1.0或1.01版本SD规范中涉及的部分获取必要的许可,以便合法使用这些部分的技术。 此规格的重点在于物理层,这是SD卡功能的基础,涉及到信号传输、接口规范、电气特性、数据传输速率等方面。它详细描述了卡片与读卡器之间的通信协议,包括数据传输模式、时序、电源管理以及错误检测和纠正机制。这对于保证设备间的兼容性和互操作性至关重要,是开发SD卡产品时不可或缺的技术参考。 对于SD 3.0的开发者来说,理解并遵循这份简化规格,可以确保他们的产品符合行业标准,提高产品的市场接受度和稳定性。此外,随着技术的不断进步,后续的3.01版可能还涵盖了新的功能特性、性能提升以及对现有问题的改进,因此对于保持与最新技术同步的开发者而言,这份文档是至关重要的学习资料和工作指南。"

优化这段代码:def calTravelCost(route_list,model): timetable_list=[] distance_of_routes=0 time_of_routes=0 obj=0 for route in route_list: timetable=[] vehicle=model.vehicle_dict[route[0]] travel_distance=0 travel_time=0 v_type = route[0] free_speed=vehicle.free_speed fixed_cost=vehicle.fixed_cost variable_cost=vehicle.variable_cost for i in range(len(route)): if i == 0: next_node_id=route[i+1] travel_time_between_nodes=model.distance_matrix[v_type,next_node_id]/free_speed departure=max(0,model.demand_dict[next_node_id].start_time-travel_time_between_nodes) timetable.append((int(departure),int(departure))) elif 1<= i <= len(route)-2: last_node_id=route[i-1] current_node_id=route[i] current_node = model.demand_dict[current_node_id] travel_time_between_nodes=model.distance_matrix[last_node_id,current_node_id]/free_speed arrival=max(timetable[-1][1]+travel_time_between_nodes,current_node.start_time) departure=arrival+current_node.service_time timetable.append((int(arrival),int(departure))) travel_distance += model.distance_matrix[last_node_id, current_node_id] travel_time += model.distance_matrix[last_node_id, current_node_id]/free_speed+\ + max(current_node.start_time - arrival, 0) else: last_node_id = route[i - 1] travel_time_between_nodes = model.distance_matrix[last_node_id,v_type]/free_speed departure = timetable[-1][1]+travel_time_between_nodes timetable.append((int(departure),int(departure))) travel_distance += model.distance_matrix[last_node_id,v_type] travel_time += model.distance_matrix[last_node_id,v_type]/free_speed distance_of_routes+=travel_distance time_of_routes+=travel_time if model.opt_type==0: obj+=fixed_cost+travel_distance*variable_cost else: obj += fixed_cost + travel_time *variable_cost timetable_list.append(timetable) return timetable_list,time_of_routes,distance_of_routes,obj

2023-06-11 上传

降低这段代码的重复率:def calTravelCost(route_list,model): timetable_list=[] distance_of_routes=0 time_of_routes=0 obj=0 for route in route_list: timetable=[] vehicle=model.vehicle_dict[route[0]] travel_distance=0 travel_time=0 v_type = route[0] free_speed=vehicle.free_speed fixed_cost=vehicle.fixed_cost variable_cost=vehicle.variable_cost for i in range(len(route)): if i == 0: next_node_id=route[i+1] travel_time_between_nodes=model.distance_matrix[v_type,next_node_id]/free_speed departure=max(0,model.demand_dict[next_node_id].start_time-travel_time_between_nodes) timetable.append((int(departure),int(departure))) elif 1<= i <= len(route)-2: last_node_id=route[i-1] current_node_id=route[i] current_node = model.demand_dict[current_node_id] travel_time_between_nodes=model.distance_matrix[last_node_id,current_node_id]/free_speed arrival=max(timetable[-1][1]+travel_time_between_nodes,current_node.start_time) departure=arrival+current_node.service_time timetable.append((int(arrival),int(departure))) travel_distance += model.distance_matrix[last_node_id, current_node_id] travel_time += model.distance_matrix[last_node_id, current_node_id]/free_speed+\ + max(current_node.start_time - arrival, 0) else: last_node_id = route[i - 1] travel_time_between_nodes = model.distance_matrix[last_node_id,v_type]/free_speed departure = timetable[-1][1]+travel_time_between_nodes timetable.append((int(departure),int(departure))) travel_distance += model.distance_matrix[last_node_id,v_type] travel_time += model.distance_matrix[last_node_id,v_type]/free_speed distance_of_routes+=travel_distance time_of_routes+=travel_time if model.opt_type==0: obj+=fixed_cost+travel_distance*variable_cost else: obj += fixed_cost + travel_time *variable_cost timetable_list.append(timetable) return timetable_list,time_of_routes,distance_of_routes,obj

2023-06-10 上传

优化代码,加背景图import tkinter as tk import numpy as np def change_label(): button.destroy() label.config(text="请输入您的身高体重以及目标体重:") height_label.place(relx=0.5, rely=0.4, anchor="center") height_entry.place(relx=0.5, rely=0.45, anchor="center") weight_label.place(relx=0.5, rely=0.5, anchor="center") weight_entry.place(relx=0.5, rely=0.55, anchor="center") target_label.place(relx=0.5, rely=0.6, anchor="center") target_entry.place(relx=0.5, rely=0.65, anchor="center") submit_button.place(relx=0.5, rely=0.8, anchor="center") def show_buttons(): calculate_low_carb() calculate_medium_carb() calculate_high_carb() label.config(text="您的营养素分配如下:") label.place(relx=0.5, rely=0.2, anchor="center") height_label.destroy() height_entry.destroy() weight_label.destroy() weight_entry.destroy() target_label.destroy() target_entry.destroy() submit_button.destroy() submit_button_1.place(relx=0.5, rely=0.8, anchor="center") def calculate_low_carb(): global low_protein_intake, low_carb_intake, low_fat_intake height = float(height_entry.get()) weight = float(weight_entry.get()) target_weight = float(target_entry.get()) # 根据BMI计算蛋白质摄入量 bmi = weight / (height / 100)**2 if bmi >= 27: low_protein_intake = weight elif bmi >= 24 and bmi < 27: low_protein_intake = weight * 1.5 else: low_protein_intake = weight * 2 # 计算低碳日的碳水摄入量和脂肪摄入量 low_carb_intake = weight low_fat_intake = weight low_carb_label = tk.Label(root, text = "您低碳日的碳水摄入量为{:.1f}克,蛋白质摄入量为{:.1f}克,脂肪摄入量为{:.1f}克".format(low_carb_intake, low_protein_intake, low_fat_intake), font=("Arial", 18)) low_carb_label.place(relx=0.5, rely=0.4, anchor="center") def calculate_medium_carb(): global medium_protein_intake, medium_carb_intake, medium_fat_intake height = float(height_entry.get()) weight = float(weight_entry.get()) target_weight = float(target_entry.get()) # 根据BMI计算蛋白质摄入量 bmi = weight / (height / 100)**2 if bmi >= 27: medium_protein_intake = weight elif bmi >= 24 and bmi < 27: medium_protein_intake = weight * 1.5 else: medium_protein_intake = weight * 2 # 计算中碳日的碳水摄入量和脂肪摄入量 medium_carb_intake = weight * 2 medium_fat_intake = weight * 0.5 medium_carb_label = tk.Label(root, text = "您中碳日的碳水摄入量为{:.1f}克,蛋白质摄入量为{:.1f}克,脂肪摄入量为{:.1f}克".format(medium_carb_intake, medium_protein_intake, medium_fat_intake), font=("Arial", 18)) medium_carb_label.place(relx=0.5, rely=0.5, anchor="center")

2023-05-31 上传