2021年智能家电发展解析：现状、趋势与IoT融合

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该文档是GfK发布的关于2021年智能家电发展现状的深度解析和趋势分析报告，由37页组成。报告涵盖了以下几个核心知识点： 1. 新常态下的物联网产业：随着物联网（IoT）与人工智能（AI）的深度融合，它已成为驱动智能硬件和数字经济发展的关键基础设施。物联网被提升为国家发展战略，从"数字产业化"到"产业数字化"转变，成为经济增长的新动能。 2. 全球及中国IoT发展进程：全球物联网市场正处于爆发前期向爆发期的过渡阶段，受到5G网络的快速部署和巨头企业对物联网生态的拓展推动。中国作为全球市场的重要参与者，物联网产业正在实现规模化和部分行业的互联互通。 3. 智能硬件发展：报告详细分析了智能硬件市场的现状，包括市场规模和重点智能场景。智能硬件市场呈现出多元化发展，如智能路由器、可穿戴设备等，以及通用物联网平台和操作系统的崛起。 4. 重点智能场景：智能家电作为其中的关键场景，报告深入探讨了智能家居的发展趋势，强调了物联网技术如何促进家居产品的智能化，提升生活质量，例如智慧家庭、智能驾驶、智慧健康、智慧教育和实时娱乐等领域。 5. 物联网技术融合与场景建设：物联网与5G、AI等技术的融合发展加速了智能硬件、软件服务和数据要素的融合，推动传统产品和服务的数字化转型，从而实现更便捷高效的消费者体验。 6. 市场预期与挑战：虽然物联网市场潜力巨大，但其规模化发展仍需时间来培育和完善，供给端和需求端都面临着技术和市场成熟度的挑战。 总结来说，这份报告为读者提供了全面而深入的视角，分析了当前智能家电和物联网行业的发展动态，以及未来可能的趋势和面临的机遇与挑战。对于关注物联网和智能家电领域的人来说，这是一份重要的参考材料。

将下面这段源码转换为伪代码：def bfgs(fun, grad, x0, iterations, tol): """ Minimization of scalar function of one or more variables using the BFGS algorithm. Parameters ---------- fun : function Objective function. grad : function Gradient function of objective function. x0 : numpy.array, size=9 Initial value of the parameters to be estimated. iterations : int Maximum iterations of optimization algorithms. tol : float Tolerance of optimization algorithms. Returns ------- xk : numpy.array, size=9 Parameters wstimated by optimization algorithms. fval : float Objective function value at xk. grad_val : float Gradient value of objective function at xk. grad_log : numpy.array The record of gradient of objective function of each iteration. """ fval = None grad_val = None x_log = [] y_log = [] grad_log = [] x0 = asarray(x0).flatten() # iterations = len(x0) * 200 old_fval = fun(x0) gfk = grad(x0) k = 0 N = len(x0) I = np.eye(N, dtype=int) Hk = I old_old_fval = old_fval + np.linalg.norm(gfk) / 2 xk = x0 x_log = np.append(x_log, xk.T) y_log = np.append(y_log, fun(xk)) grad_log = np.append(grad_log, np.linalg.norm(xk - x_log[-1:])) gnorm = np.amax(np.abs(gfk)) while (gnorm > tol) and (k < iterations): pk = -np.dot(Hk, gfk) try: alpha, fc, gc, old_fval, old_old_fval, gfkp1 = _line_search_wolfe12(fun, grad, xk, pk, gfk, old_fval, old_old_fval, amin=1e-100, amax=1e100) except _LineSearchError: break x1 = xk + alpha * pk sk = x1 - xk xk = x1 if gfkp1 is None: gfkp1 = grad(x1) yk = gfkp1 - gfk gfk = gfkp1 k += 1 gnorm = np.amax(np.abs(gfk)) grad_log = np.append(grad_log, np.linalg.norm(xk - x_log[-1:])) x_log = np.append(x_log, xk.T) y_log = np.append(y_log, fun(xk)) if (gnorm <= tol): break if not np.isfinite(old_fval): break try: rhok = 1.0 / (np.dot(yk, sk)) except ZeroDivisionError: rhok = 1000.0 if isinf(rhok): rhok = 1000.0 A1 = I - sk[:, np.newaxis] * yk[np.newaxis, :] * rhok A2 = I - yk[:, np.newaxis] * sk[np.newaxis, :] * rhok Hk = np.dot(A1, np.dot(Hk, A2)) + (rhok * sk[:, np.newaxis] * sk[np.newaxis, :]) fval = old_fval grad_val = grad_log[-1] return xk, fval, grad_val, x_log, y_log, grad_log

2023-06-06 上传
alpha, fc, gc, old_fval, old_old_fval, gfkp1 <- _line_search_wolfe12(fun, grad, xk, pk, gfk, old_fval, old_old_fval, amin=1e-100, amax=1e100) 捕获 _LineSearchError： 跳出循环 x1 <- xk + alpha * pk...

将这段代码转换为伪代码：def levenberg_marquardt(fun, grad, jacobian, x0, iterations, tol): """ Minimization of scalar function of one or more variables using the Levenberg-Marquardt algorithm. Parameters ---------- fun : function Objective function. grad : function Gradient function of objective function. jacobian :function function of objective function. x0 : numpy.array, size=9 Initial value of the parameters to be estimated. iterations : int Maximum iterations of optimization algorithms. tol : float Tolerance of optimization algorithms. Returns ------- xk : numpy.array, size=9 Parameters wstimated by optimization algorithms. fval : float Objective function value at xk. grad_val : float Gradient value of objective function at xk. grad_log : numpy.array The record of gradient of objective function of each iteration. """ fval = None # y的最小值 grad_val = None # 梯度的最后一次下降的值 x_log = [] # x的迭代值的数组，n*9，9个参数 y_log = [] # y的迭代值的数组，一维 grad_log = [] # 梯度下降的迭代值的数组 x0 = asarray(x0).flatten() if x0.ndim == 0: x0.shape = (1,) # iterations = len(x0) * 200 k = 1 xk = x0 updateJ = 1 lamda = 0.01 old_fval = fun(x0) gfk = grad(x0) gnorm = np.amax(np.abs(gfk)) J = [None] H = [None] while (gnorm > tol) and (k < iterations): if updateJ == 1: x_log = np.append(x_log, xk.T) yk = fun(xk) y_log = np.append(y_log, yk) J = jacobian(x0) H = np.dot(J.T, J) H_lm = H + (lamda * np.eye(9)) gfk = grad(xk) pk = - np.linalg.inv(H_lm).dot(gfk) pk = pk.A.reshape(1, -1)[0] # 二维变一维 xk1 = xk + pk fval = fun(xk1) if fval < old_fval: lamda = lamda / 10 xk = xk1 old_fval = fval updateJ = 1 else: updateJ = 0 lamda = lamda * 10 gnorm = np.amax(np.abs(gfk)) k = k + 1 grad_log = np.append(grad_log, np.linalg.norm(xk - x_log[-1:])) fval = old_fval grad_val = grad_log[-1] return xk, fval, grad_val, x_log, y_log, grad_log

2023-06-06 上传
gnorm = np.amax(np.abs(gfk)) J = None H = None while (gnorm > tol) and (k ): if updateJ == 1: x_log = np.append(x_log, xk.T) yk = fun(xk) y_log = np.append(y_log, yk) J = jacobian(x0) H = ...

将这段代码转换为伪代码：算法函数3-2：LM 1: def levenberg_marquardt(fun, grad, jacobian, x0, iterations, tol): 2: while (gnorm > tol) and (k < iterations): 3: if updateJ == 1: 4: x_log = np.append(x_log, xk.T) 5: yk = fun(xk) 6: y_log = np.append(y_log, yk) 7: H_lm = H + (lamda * np.eye(9)) 8: gfk = grad(xk) 9: pk = - np.linalg.inv(H_lm).dot(gfk) 10: pk = pk.A.reshape(1, -1)[0] 11: xk = xk + pk 12: fval = fun(xk) 13: if fval < old_fval: 14: lamda = lamda / 10 15: old_fval = fval

2023-06-06 上传
算法函数3-2：LM 输入：目标函数 fun，目标函数的梯度 grad，雅可比矩阵 jacobian，起始点 x0，最大迭代次数 ...18. lamda = lamda * 10 19. end if 20. H = jacobian(xk) 21. k = k + 1 22. end while 23. return xk

ge gfk-2222

2023-07-07 上传
GE GFK-2222是一种工业自动化控制系统中使用的设备。它是由GE（通用电气）公司设计和生产的，具有先进的功能和性能。 GE GFK-2222常被用于工厂和生产线的自动化控制中，能够监控和控制各种工艺和设备。它采用了最新...

pk = - np.linalg.inv(H_lm).dot(gfk)

2023-06-06 上传
这是一个 Python 代码片段，其中使用了 NumPy 库中的 linalg.inv 函数来计算 H_lm 的逆矩阵，并使用 dot 函数计算 H_lm 的逆矩阵与 gfk 的矩阵乘积，最后将结果赋值给变量 pk。这个代码片段的具体实现需要依据上下文...

解释：gnorm = np.amax(np.abs(gfk))

2023-06-06 上传
在这个例子中，给定的数组是 `gfk`，也就是一个一维的 NumPy 数组。在计算 `gnorm` 时，它使用了 `np.abs` 函数，它返回给定数组中每个元素的绝对值，这样可以确保计算的是向量的模长而不是它们的代数和。因此这行...

解释：def steepest_descent(fun, grad, x0, iterations, tol): """ Minimization of scalar function of one or more variables using the steepest descent algorithm. Parameters ---------- fun : function Objective function. grad : function Gradient function of objective function. x0 : numpy.array, size=9 Initial value of the parameters to be estimated. iterations : int Maximum iterations of optimization algorithms. tol : float Tolerance of optimization algorithms. Returns ------- xk : numpy.array, size=9 Parameters wstimated by optimization algorithms. fval : float Objective function value at xk. grad_val : float Gradient value of objective function at xk. grad_log : numpy.array The record of gradient of objective function of each iteration. """ fval = None grad_val = None x_log = [] y_log = [] grad_log = [] x0 = asarray(x0).flatten() # iterations = len(x0) * 200 old_fval = fun(x0) gfk = grad(x0) k = 0 old_old_fval = old_fval + np.linalg.norm(gfk) / 2 xk = x0 x_log = np.append(x_log, xk.T) y_log = np.append(y_log, fun(xk)) grad_log = np.append(grad_log, np.linalg.norm(xk - x_log[-1:])) gnorm = np.amax(np.abs(gfk)) while (gnorm > tol) and (k < iterations): pk = -gfk try: alpha, fc, gc, old_fval, old_old_fval, gfkp1 = _line_search_wolfe12(fun, grad, xk, pk, gfk, old_fval, old_old_fval, amin=1e-100, amax=1e100) except _LineSearchError: break xk = xk + alpha * pk k += 1 grad_log = np.append(grad_log, np.linalg.norm(xk - x_log[-1:])) x_log = np.append(x_log, xk.T) y_log = np.append(y_log, fun(xk)) if (gnorm <= tol): break fval = old_fval grad_val = grad_log[-1] return xk, fval, grad_val, x_log, y_log, grad_log

2023-06-06 上传
这是一个使用最速下降法（steepest descent algorithm）来最小化一个一元或多元标量函数的函数。其中，fun是目标函数，grad是目标函数的梯度函数，x0是参数的初始值，iterations是最大迭代次数，tol是优化算法的收敛...

解释：def conjugate_gradient(fun, grad, x0, iterations, tol): """ Minimization of scalar function of one or more variables using the conjugate gradient algorithm. Parameters ---------- fun : function Objective function. grad : function Gradient function of objective function. x0 : numpy.array, size=9 Initial value of the parameters to be estimated. iterations : int Maximum iterations of optimization algorithms. tol : float Tolerance of optimization algorithms. Returns ------- xk : numpy.array, size=9 Parameters wstimated by optimization algorithms. fval : float Objective function value at xk. grad_val : float Gradient value of objective function at xk. grad_log : numpy.array The record of gradient of objective function of each iteration. """ fval = None grad_val = None x_log = [] y_log = [] grad_log = [] x0 = asarray(x0).flatten() # iterations = len(x0) * 200 old_fval = fun(x0) gfk = grad(x0) k = 0 xk = x0 # Sets the initial step guess to dx ~ 1 old_old_fval = old_fval + np.linalg.norm(gfk) / 2 pk = -gfk x_log = np.append(x_log, xk.T) y_log = np.append(y_log, fun(xk)) grad_log = np.append(grad_log, np.linalg.norm(xk - x_log[-1:])) gnorm = np.amax(np.abs(gfk)) sigma_3 = 0.01 while (gnorm > tol) and (k < iterations): deltak = np.dot(gfk, gfk) cached_step = [None] def polak_ribiere_powell_step(alpha, gfkp1=None): xkp1 = xk + alpha * pk if gfkp1 is None: gfkp1 = grad(xkp1) yk = gfkp1 - gfk beta_k = max(0, np.dot(yk, gfkp1) / deltak) pkp1 = -gfkp1 + beta_k * pk gnorm = np.amax(np.abs(gfkp1)) return (alpha, xkp1, pkp1, gfkp1, gnorm) def descent_condition(alpha, xkp1, fp1, gfkp1): # Polak-Ribiere+ needs an explicit check of a sufficient # descent condition, which is not guaranteed by strong Wolfe. # # See Gilbert & Nocedal, "Global convergence properties of # conjugate gradient methods for optimization", # SIAM J. Optimization 2, 21 (1992). cached_step[:] = polak_ribiere_powell_step(alpha, gfkp1) alpha, xk, pk, gfk, gnorm = cached_step # Accept step if it leads to convergence. if gnorm <= tol: return True # Accept step if sufficient descent condition applies. return np.dot(pk, gfk) <= -sigma_3 * np.dot(gfk, gfk) try: alpha_k, fc, gc, old_fval, old_old_fval, gfkp1 = \ _line_search_wolfe12(fun, grad, xk, pk, gfk, old_fval, old_old_fval, c2=0.4, amin=1e-100, amax=1e100, extra_condition=descent_condition) except _LineSearchError: break # Reuse already computed results if possible if alpha_k == cached_step[0]: alpha_k, xk, pk, gfk, gnorm = cached_step else: alpha_k, xk, pk, gfk, gnorm = polak_ribiere_powell_step(alpha_k, gfkp1) k += 1 grad_log = np.append(grad_log, np.linalg.norm(xk - x_log[-1:])) x_log = np.append(x_log, xk.T) y_log = np.append(y_log, fun(xk)) fval = old_fval grad_val = grad_log[-1] return xk, fval, grad_val, x_log, y_log, grad_log

2023-06-06 上传
这是一个使用共轭梯度算法来最小化一个标量函数的函数。它的输入包括一个目标函数，一个梯度函数，一个初始值，迭代次数和容差。返回值包括参数的估计值，目标函数在该点的值，目标函数在该点的梯度值以及每次迭代中...

解释： while (gnorm > tol) and (k < iterations): if updateJ == 1: x_log = np.append(x_log, xk.T) yk = fun(xk) y_log = np.append(y_log, yk) J = jacobian(x0) H = np.dot(J.T, J) H_lm = H + (lamda * np.eye(9)) gfk = grad(xk) pk = - np.linalg.inv(H_lm).dot(gfk) pk = pk.A.reshape(1, -1)[0] # 二维变一维 xk1 = xk + pk fval = fun(xk1) if fval < old_fval: lamda = lamda / 10 xk = xk1 old_fval = fval updateJ = 1 else: updateJ = 0 lamda = lamda * 10 gnorm = np.amax(np.abs(gfk)) k = k + 1 grad_log = np.append(grad_log, np.linalg.norm(xk - x_log[-1:]))

2023-06-06 上传
这段代码是 Levenberg-Marquardt 算法的主要迭代过程。while 循环条件是当梯度的范数大于指定的容差 tol 并且迭代次数 k 小于指定的最大迭代次数 iterations 时继续迭代。如果 updateJ 的值为 1，则更新 x_log、y_...

解释：算法函数3-2：LM 1: def levenberg_marquardt(fun, grad, jacobian, x0, iterations, tol): 2: while (gnorm > tol) and (k < iterations): 3: if updateJ == 1: 4: x_log = np.append(x_log, xk.T) 5: yk = fun(xk) 6: y_log = np.append(y_log, yk) 7: H_lm = H + (lamda * np.eye(9)) 8: gfk = grad(xk) 9: pk = - np.linalg.inv(H_lm).dot(gfk) 10: pk = pk.A.reshape(1, -1)[0] 11: xk = xk + pk 12: fval = fun(xk) 13: if fval < old_fval: 14: lamda = lamda / 10 15: old_fval = fval

2023-06-06 上传
这是一个实现 Levenberg-Marquardt 算法的函数。Levenberg-Marquardt 算法是一种非线性最小二乘优化算法，用于解决非线性参数估计问题。该算法的基本思想是将高斯-牛顿算法和最小二乘问题的正则化方法相结合，使得...
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