标准多项式表示的B样条曲线是一种常用的曲线表示方法。下面是一个使用Python实现的示例代码：

import numpy as np

def basis_function(i, k, t, knots):
    if k == 1:
        if knots[i] <= t < knots[i+1]:
            return 1
        else:
            return 0
    else:
        denominator1 = knots[i+k-1] - knots[i]
        if denominator1 != 0:
            term1 = ((t - knots[i]) / denominator1) * basis_function(i, k-1, t, knots)
        else:
            term1 = 0
        
        denominator2 = knots[i+k] - knots[i+1]
        if denominator2 != 0:
            term2 = ((knots[i+k] - t) / denominator2) * basis_function(i+1, k-1, t, knots)
        else:
            term2 = 0
        
        return term1 + term2

def b_spline_curve(control_points, knots, k, t):
    n = len(control_points) - 1
    curve_point = np.zeros_like(control_points[0])
    for i in range(n+1):
        basis = basis_function(i, k, t, knots)
        curve_point += control_points[i] * basis
    return curve_point

# 示例使用
control_points = np.array([[0, 0], [1, 2], [3, 1], [4, 3], [6, 2]])
knots = np.array([0, 0, 0, 1, 2, 3, 4, 4, 4])
k = 3  # B样条曲线的次数

# 计算t=0.5时的曲线点
t = 0.5
curve_point = b_spline_curve(control_points, knots, k, t)
print(f"曲线点：{curve_point}")

以上代码实现了一个计算标准多项式表示的B样条曲线在给定参数t处的曲线点的函数b_spline_curve。basis_function函数用于计算B样条曲线的基函数。示例中的control_points是控制点的坐标，knots是节点向量，k是曲线的次数，t是参数。运行示例代码可以得到参数t=0.5处的曲线点。