关键技术和算法：快速静态定位方法的探索

# 定义位置与信号强度的匹配关系
fingerprint = {
    "位置A": {"Wi-Fi1": -70, "Wi-Fi2": -60},
    "位置B": {"Wi-Fi1": -60, "Wi-Fi2": -80},
    "位置C": {"Wi-Fi1": -80, "Wi-Fi2": -70}
}

# 测量当前信号强度
measure = {"Wi-Fi1": -75, "Wi-Fi2": -65}

# 匹配当前信号强度与位置
def fingerprint_location(fingerprint, measure):
    min_distance = float("inf")
    location = ""
    for fp in fingerprint:
        distance = 0
        for signal in fingerprint[fp]:
            distance += abs(fingerprint[fp][signal] - measure[signal])  # 计算欧氏距离
        if distance < min_distance:
            min_distance = distance
            location = fp
    return location

# 调用指纹定位函数
result = fingerprint_location(fingerprint, measure)
print("当前位置：", result)

# 已知参考点的坐标和信号延迟
anchors = {
    "参考点A": {"x": 0, "y": 0, "delay": 1},
    "参考点B": {"x": 3, "y": 0, "delay": 2},
    "参考点C": {"x": 0, "y": 4, "delay": 3}
}

# 测量到达对象的信号延迟
measure = {"参考点A": 2, "参考点B": 4, "参考点C": 5}

# 计算对象的坐标
def trilateration(anchors, measure):
    A = []
    b = []
    for anchor in anchors:
        x = anchors[anchor]["x"]
        y = anchors[anchor]["y"]
        delay = measure[anchor] * 0.5  # 转换为时间
        A.append([x, y, -delay])
        b.append(x ** 2 + y ** 2 - delay ** 2)
    result = np.linalg.lstsq(A, b, rcond=None)[0]  # 最小二乘法求解
    return result[0], result[1]

# 调用三角定位函数
x, y = trilateration(anchors, measure)
print("对象坐标：({0}, {1})".format(x, y))