实时定位系统（RTLS）在嵌入式导航与物流中的代码应用实战

import wifi_module

def get_location(mac_address):
    # 使用Wi-Fi模块扫描附近的Wi-Fi信号
    wifi_signals = wifi_module.scan_signals()

    # 根据扫描到的信号强度和位置信息进行定位
    location = wifi_module.calculate_location(wifi_signals)

    return location

# 调用函数获取移动目标的实时位置
target_mac_address = "00:11:22:33:44:55"
current_location = get_location(target_mac_address)
print("Current location:", current_location)

import rfid_module

def track_inventory(rfid_tag):
    # 读取RFID标签信息
    tag_info = rfid_module.read_tag(rfid_tag)

    # 获取货物位置和状态信息
    location = tag_info['location']
    status = tag_info['status']

    return location, status

# 调用函数跟踪特定RFID标签的货物
rfid_tag_to_track = "RFID123456"
current_location, current_status = track_inventory(rfid_tag_to_track)
print("Current location:", current_location)
print("Current status:", current_status)

import gps_module

def monitor_vehicle(vehicle_id):
    # 获取车辆的GPS位置信息
    gps_location = gps_module.get_location(vehicle_id)

    # 进行位置信息处理和可视化
    processed_location = process_location_data(gps_location)
    visualize_location(processed_location)

# 调用函数监控特定车辆的实时位置
vehicle_to_monitor = "Vehicle123"
monitor_vehicle(vehicle_to_monitor)

import rtls_module

def visualize_supply_chain():
    # 获取实时供应链数据
    supply_chain_data = rtls_module.get_supply_chain_data()

    # 进行数据分析和可视化
    analyze_and_visualize_data(supply_chain_data)

# 调用函数实现供应链可视化
visualize_supply_chain()

import random
import time

class BLEBeacon:
    def __init__(self, beacon_id):
        self.beacon_id = beacon_id
        self.x = random.uniform(0, 10)  # 模拟x坐标
        self.y = random.uniform(0, 10)  # 模拟y坐标
        self.signal_strength = random.randint(-60, -30)  # 模拟信号强度

def simulate_ble_scanning(target_id, beacons):
    # 模拟BLE扫描
    for beacon in beacons:
        if beacon.beacon_id == target_id:
            # 模拟在实际应用中通过BLE信号强度计算距离
            distance = calculate_distance_from_signal_strength(beacon.signal_strength)

            print(f"目标ID: {target_id}，当前位置：({beacon.x:.2f}, {beacon.y:.2f})，距离：{distance:.2f} 米")

def calculate_distance_from_signal_strength(signal_strength):
    # 在实际应用中，根据信号强度计算距离的算法可能更为复杂
    # 这里简化为线性关系
    reference_distance = 1.0  # 参考距离（1米）
    path_loss_exponent = 2.0  # 路径损耗指数
    distance = reference_distance * (10**((27.55 - (20 * (signal_strength + 100 * path_loss_exponent) / 20)) / path_loss_exponent))

    return distance

# 模拟三个BLE信标
beacon1 = BLEBeacon("Beacon1")
beacon2 = BLEBeacon("Beacon2")
beacon3 = BLEBeacon("Beacon3")

# 设置目标ID
target_id = "Beacon2"

# 模拟实时定位系统
for i in range(5):
    # 模拟每隔一段时间进行一次扫描
    print(f"\n第 {i+1} 次扫描：")
    simulate_ble_scanning(target_id, [beacon1, beacon2, beacon3])

    # 模拟时间间隔
    time.sleep(2)

import random
import time

class UWBAnchor:
    def __init__(self, anchor_id, x, y):
        self.anchor_id = anchor_id
        self.x = x
        self.y = y

class UWBTAG:
    def __init__(self, tag_id):
        self.tag_id = tag_id
        self.x = random.uniform(0, 10)  # 模拟x坐标
        self.y = random.uniform(0, 10)  # 模拟y坐标

def calculate_distance(anchor, tag):
    # 模拟UWB信号传播模型
    distance = ((anchor.x - tag.x)**2 + (anchor.y - tag.y)**2)**0.5
    return distance

def track_vehicle(tag, anchors):
    # 模拟通过多个UWB锚点进行距离测量
    distances = [calculate_distance(anchor, tag) for anchor in anchors]

    # 模拟通过距离测量计算车辆位置
    average_distance = sum(distances) / len(distances)
    print(f"车辆ID: {tag.tag_id}，当前位置：({tag.x:.2f}, {tag.y:.2f})，平均距离：{average_distance:.2f} 米")

# 模拟四个UWB锚点
anchor1 = UWBAnchor("Anchor1", 0, 0)
anchor2 = UWBAnchor("Anchor2", 10, 0)
anchor3 = UWBAnchor("Anchor3", 0, 10)
anchor4 = UWBAnchor("Anchor4", 10, 10)

# 模拟一个运输车辆的UWB标签
vehicle_tag = UWBTAG("VehicleTag")

# 设置UWB锚点
anchors = [anchor1, anchor2, anchor3, anchor4]

# 模拟实时定位系统
for i in range(5):
    # 模拟每隔一段时间进行一次位置跟踪
    print(f"\n第 {i+1} 次位置跟踪：")
    track_vehicle(vehicle_tag, anchors)

    # 模拟时间间隔
    time.sleep(2)

class WarehouseItem:
    def __init__(self, item_id, location):
        self.item_id = item_id
        self.location = location

def track_warehouse_items(items, item_id):
    for item in items:
        if item.item_id == item_id:
            print(f"货物ID: {item.item_id}，当前位置：({item.location.x:.2f}, {item.location.y:.2f})")

# 模拟仓库中的货物
item1 = WarehouseItem("Item1", UWBAnchor("Item1Anchor", 2, 3))
item2 = WarehouseItem("Item2", UWBAnchor("Item2Anchor", 7, 8))
item3 = WarehouseItem("Item3", UWBAnchor("Item3Anchor", 1, 6))

# 跟踪特定货物的位置
track_item_id = "Item2"
track_warehouse_items([item1, item2, item3], track_item_id)

class SupplyChainNode:
    def __init__(self, node_id, location):
        self.node_id = node_id
        self.location = location

def visualize_supply_chain(nodes):
    for node in nodes:
        print(f"节点ID: {node.node_id}，当前位置：({node.location.x:.2f}, {node.location.y:.2f})")

# 模拟供应链中的节点
node1 = SupplyChainNode("Factory", UWBAnchor("FactoryAnchor", 2, 3))
node2 = SupplyChainNode("Distribution Center", UWBAnchor("DistributionCenterAnchor", 7, 8))
node3 = SupplyChainNode("Retail Store", UWBAnchor("RetailStoreAnchor", 1, 6))

# 可视化整个供应链
supply_chain_nodes = [node1, node2, node3]
visualize_supply_chain(supply_chain_nodes)