Delete Last Element

本文提供了一种使用Python方法删除列表中特定位置元素的技术,特别是删除最后一个元素及其更多元素的方法,避免了使用clearLists方法可能带来的不便。

yo folks, I have an issue with clearing lists. In the current program which I'm coding, I have a method that clears a certain number of lists. This is rather inconvenient since during one part of the program where this method is used, it would be a lot more helpful if it only deleted the last elementfrom the lists. Is there anyway in which I can set index numbers as parameters to my method to solve this problem? 

The code for the method

def clearLists(self):
    del self.Ans[:]
    del self.masses[:]

Whenever I want to use this method, I merely write self.ClearLists() and it deletes every element in a list.

share improve this question
 
2 
I don't see the relation between the question title and body. –  fortran  Dec 2 '11 at 14:54
 
Sorry about that, that was a leftover from an old, unposted question, now fixxxed. –  user1036197  Dec 2 '11 at 14:59
3 
If you don't want to clear your lists, why would you call a method called clearLists? –  Wooble  Dec 2 '11 at 15:07
1 
Am I the only what that do not understand what are you looking for? –  Tadeck  Dec 2 '11 at 15:52

2 Answers

you can use lst.pop() or del lst[-1]

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that pop() method rocks, thanks –  armani  Nov 18 '14 at 22:49
 
pop() removes and returns the item, in case you don't want have a return use del ;) –  flacle  2 days ago

To delete the last element of the lists, you could use:

def deleteLast(self):
    if len(self.Ans) > 0:
        del self.Ans[-1]
    if len(self.masses) > 0:
        del self.masses[-1]
def select_ND_NX_dw_12s( data_one, ND, ND_index, NX, NX_index, peaks_lst, period_points, peak_need_delete_element, period_need_delete_element, baseline_py_lowest, jishu_num, ): ND_index_remodify_throughx = [] ND_remodify_throughx = [] ND_index_finall = [] ND_finall = [] period_len_rate_accum_compute = [] x_distance_all_accum_compute = [] # 初始判断周期的函数 if len(peaks_lst) <= 1: # 确定峰值索引 if len(ND) > 0: # 这里需要加条件 不能简单的根据大小来选取极值点 if len(ND_index) > 1: # 2023-12-18 peak = max(ND) # 2023_12_25 这里的有瑕疵,第一个点就可能选不对 f = ND_index[ND.index(peak)] # 第一步 通过x距离约束来筛选peak_index, 确保顺序不变 for i_dsts in range(len(ND_index)): if ND_index[i_dsts] == f: ND_index_remodify_throughx.append(ND_index[i_dsts]) ND_remodify_throughx.append(ND[i_dsts]) else: distance_NDx = abs(ND_index[i_dsts] - f) # f 是最大值的索引 # 根据周期的最大最小个数确定 if ( distance_NDx > 208 or distance_NDx < 24 ): # 2024_10_31 这里采集10s,周期数范围为6-26,对应长度为104-24 continue else: ND_index_remodify_throughx.append(ND_index[i_dsts]) ND_remodify_throughx.append(ND[i_dsts]) # 第二步 通过y值的一些约束来筛选真正的peak_index for j_dsts in range(len(ND_index_remodify_throughx)): # 2023-12-29 if len(NX) != 0: peak_rate = abs( (ND_remodify_throughx[j_dsts] - min(NX)) / (peak - min(NX)) ) else: peak_rate = abs(ND_remodify_throughx[j_dsts] / peak) if ( peak_rate > 0.30 ): # 这里阈值设置较低,为了把所有正确的点选进去 2023-12-29 这里不应该用二者的绝对值,而是该用二者相对极小值的值 第一次筛选值,阈值稍微高点 ND_index_finall.append(ND_index_remodify_throughx[j_dsts]) # writingto_txt('peak_with_max_peak_value_rate_record',peak_rate ) ND_finall.append(ND_remodify_throughx[j_dsts]) else: continue ( F, G, baseline_py_lowest, peak_need_delete_element, period_need_delete_element, ) = get_true_peak_and_period_index_12( data_one, ND_index_finall, ND_finall, NX_index, NX, peaks_lst, period_points, ) else: # 2023-12-23 这里没办法了 只能令F = ND_index[0],没有获取到周期长度 F, F_value, x_delta_py = ND_len_zero_select_F_peak_index_dw( data_one, ND, ND_index, peaks_lst ) G, baseline_py_lowest = general_select_NX_index( NX, NX_index, F, F_value, peaks_lst, period_points, peak_need_delete_element, period_need_delete_element, baseline_py_lowest, x_delta_py, ) else: F = "Null" G = "Null" else: # 知道周期的大概长度 period_approximate_len = int( (peaks_lst[-1] - peaks_lst[0]) / (len(peaks_lst) - 1) ) # # 确定峰值索引 if len(ND) > 0: # 这里需要加条件 不能简单的根据大小来选取极值点 if len(ND_index) > 1: # 2023-12-18 peak = max(ND) f = ND_index[ND.index(peak)] # 第一步 通过x距离约束来筛选peak_index, 确保顺序不变 for i_dsts in range(len(ND_index)): if ND_index[i_dsts] == f: distance_with_last_peak_index = abs( ND_index[i_dsts] - peaks_lst[-1] ) if ( distance_with_last_peak_index < 24 or distance_with_last_peak_index > 208 ): # 2024_10_31 这里采集10s,周期数范围为6-26,对应长度为104-24 continue else: ND_index_remodify_throughx.append(ND_index[i_dsts]) ND_remodify_throughx.append(ND[i_dsts]) else: distance_NDx = abs(ND_index[i_dsts] - f) distance_with_last_peak_index = abs( ND_index[i_dsts] - peaks_lst[-1] ) if ( distance_NDx > 208 or distance_NDx < 24 or distance_with_last_peak_index < 24 or distance_with_last_peak_index > 208 ): # 2024_10_31 这里采集10s,周期数范围为6-26,对应长度为104-24 continue else: ND_index_remodify_throughx.append(ND_index[i_dsts]) ND_remodify_throughx.append(ND[i_dsts]) # 第二步 通过y值的一些约束来筛选真正的peak_index if len(ND_index_remodify_throughx) > 1: for j_dsts in range(len(ND_remodify_throughx)): # peak_rate = ND_remodify_throughx[j_dsts] / peak # 2023-12-29 if len(NX) != 0: if (peak - min(NX)) != 0: peak_rate = abs( (ND_remodify_throughx[j_dsts] - min(NX)) / (peak - min(NX)) ) else: peak_rate = abs(ND_remodify_throughx[j_dsts] / peak) else: peak_rate = abs(ND_remodify_throughx[j_dsts] / peak) if peak_rate > 0.4: # 0.6变0.4 2023——12-25 # writingto_txt('peak_with_max_peak_value_rate_record', peak_rate) ND_index_finall.append(ND_index_remodify_throughx[j_dsts]) ND_finall.append(ND_remodify_throughx[j_dsts]) else: continue else: for j_dsts in range(len(ND_index_remodify_throughx)): peak_values_lst_dsts = [] for k_imtq in range(len(peaks_lst)): peak_values_lst_dsts.append(data_one[peaks_lst[k_imtq]]) mean_peak_values = np.mean(peak_values_lst_dsts) if ( len(NX) != 0 and (mean_peak_values - min(NX)) != 0 ): # 2024-6-14 这里要防止=0,但是我没有进一步优化,可能还有其他情况 peak_rate = abs( (ND_remodify_throughx[j_dsts] - min(NX)) / (mean_peak_values - min(NX)) ) else: peak_rate = abs( ND_remodify_throughx[j_dsts] / mean_peak_values ) if ( peak_rate > 0.30 ): # 这里阈值设置较低,为了把所有正确的点选进去 2023-12-29 这里不应该用二者的绝对值,而是该用二者相对极小值的值 第一次筛选值,阈值稍微高点 ND_index_finall.append(ND_index_remodify_throughx[j_dsts]) # writingto_txt('peak_with_max_peak_value_rate_record', peak_rate) ND_finall.append(ND_remodify_throughx[j_dsts]) need_whether_num = 0 if len(ND_index_finall) != 0: for j_dst in range(len(ND_index_finall)): period_len_compute = ND_index_finall[j_dst] - peaks_lst[-1] # if peaks_lst[-1] + period_approximate_len + 65< 625: #2024-1-19 后面看看效果 if ( peaks_lst[-1] + period_approximate_len + 25 < 1250 ): # 2024-1-19 后面看看效果 period_len_rate = abs( 1 - period_len_compute / period_approximate_len ) # 20241107 增加为10s数据后,这里也是一个修改点,因为周期变短了,导致分母变小,变化量占比变大,所以这里要修改变小,周期得分,离周期长度最近的得分最高 else: period_len_rate = 0.05 # if period_len_rate < 0.27: if ( period_len_rate < 0.04 ): # 20241107 增加为10s数据后,这里也是一个修改点,因为周期变短了,导致分母变小,变化量占比变大,所以这里要修改变小 need_whether_num += 1 x_distance_khjq = abs( period_len_compute - period_approximate_len ) # 这里不能仅凭x距离判断出点,还得综合y值来看 # if ND_index_finall[j_dst] < (peaks_lst[-1] + 50):# if ND_index_finall[j_dst] < ( peaks_lst[-1] + 0.8 * period_approximate_len ): # 20241107 增加为10s数据后,这里也是一个修改点,加50显然不正确,这里最好加一个可以变化的量 x_distance_khjq = 999 x_distance_all_accum_compute.append(x_distance_khjq) period_len_rate_accum_compute.append(period_len_rate) # if min(period_len_rate_accum_compute) < 0.27: # 2023-12-29 # 2024-1-3 这里,由于第一个周期点没选对,导致周期长度错误,进一步导致正确周期点计算得分不在02范围之内,无法纠正周期 if ( min(period_len_rate_accum_compute) < 0.04 ): # #20241107 增加为10s数据后,这里也是一个修改点,因为周期变短了,导致分母变小,变化量占比变大,所以这里要修改变小 if need_whether_num > 1: F, F_value, G, x_delta_py, baseline_py_lowest = ( get_correct_peak_index( data_one, ND_index_finall, NX_index, ND_finall, NX, x_distance_all_accum_compute, peaks_lst, period_points, period_approximate_len, ) ) else: # baseline_py_peak =0.5* abs(data_one[peaks_lst[-1]] - data_one[peaks_lst[-2]]) + 0.5*() #这里并不能有效处理基线漂移 index_num_peak = period_len_rate_accum_compute.index( min(period_len_rate_accum_compute) ) x_delta_py = ( 0.8 * x_distance_all_accum_compute[index_num_peak] ) F_first_jgba = ND_index_finall[index_num_peak] F_value_first_jgba = ND_finall[index_num_peak] baseline_py_peak = 0.4 * abs( data_one[peaks_lst[-1]] - data_one[peaks_lst[-2]] ) + 0.6 * abs(F_value_first_jgba - data_one[peaks_lst[-1]]) # writingto_txt('峰值距离波动', period_len_rate_accum_compute[index_num_peak]) difference_period_with_ND = ( F_value_first_jgba - data_one[period_points[-1]] ) peak_value_jgjz = ( data_one[peaks_lst[-1]] - data_one[period_points[-1]] ) if F_value_first_jgba > data_one[peaks_lst[-1]]: peak_value_jgjz += baseline_py_peak else: # if difference_period_with_ND !=0: # chaju_big = (peak_value_jgjz - difference_period_with_ND)/ difference_period_with_ND # if chaju_big >1.2: # difference_period_with_ND += 0 # # else: # difference_period_with_ND += baseline_py_peak # # else: difference_period_with_ND += baseline_py_peak if peak_value_jgjz != 0: minus_period_jgnq = abs( 1 - difference_period_with_ND / peak_value_jgjz ) if minus_period_jgnq > 1: minus_period_jgnq = 0.4 else: minus_period_jgnq = 0.4 if ( minus_period_jgnq > 0.40 ): # 2023-12-27 触发周期保护机制,利用周期长度的稳定性来确保周期是否正确 # writingto_txt('相对峰值占比', minus_period_jgnq) # F, F_value, peak_need_delete_element, period_need_delete_element = pan_duan_zhou_qi_zhengque(data_one, ND_index_finall, ND_finall, peaks_lst, period_points) ( F, G, peak_need_delete_element, period_need_delete_element, jishu_num, ) = jiuzheng_zhouqi_1( data_one, ND_index_finall, ND_finall, NX_index, NX, peaks_lst, period_points, ) else: # F, F_value,_ = ND_len_zero_select_F_peak_index_dw(data_one, ND_finall,ND_index_finall,peaks_lst) # 这里得对ND_index_finall 进行筛选,直接传的不一定合格 F, F_value = F_first_jgba, F_value_first_jgba G, baseline_py_lowest = general_select_NX_index( NX, NX_index, F, F_value, peaks_lst, period_points, peak_need_delete_element, period_need_delete_element, baseline_py_lowest, x_delta_py, ) # writingto_txt('相对峰值占比', minus_period_jgnq) # elif min(x_distance_all_accum_compute) < 37 and peaks_lst[-1] + period_approximate_len + int(0.14*period_approximate_len)< 625: # 2024-1-8 为了提升正确率 允许有40距离的周期突变 #2024-1-13 这里设置过大容易把最后一些不是峰值的极大值概括进去 elif ( min(x_distance_all_accum_compute) < 0.6 * period_approximate_len and peaks_lst[-1] + period_approximate_len + int(0.14 * period_approximate_len) < 1250 ): # #20241107 增加为10s数据后,这里也是一个修改点,这个0.14不知道修不修改 index_num_peak_ohhh = x_distance_all_accum_compute.index( min(x_distance_all_accum_compute) ) F_first_jgba = ND_index_finall[index_num_peak_ohhh] F_value_first_jgba = ND_finall[index_num_peak_ohhh] F, F_value = F_first_jgba, F_value_first_jgba x_delta_py = ( 0.8 * x_distance_all_accum_compute[index_num_peak_ohhh] ) G, baseline_py_lowest = general_select_NX_index( NX, NX_index, F, F_value, peaks_lst, period_points, peak_need_delete_element, period_need_delete_element, baseline_py_lowest, x_delta_py, ) else: if peaks_lst[-1] + period_approximate_len + 25 < 1250: ( F, G, peak_need_delete_element, period_need_delete_element, jishu_num, ) = jiuzheng_zhouqi_1( data_one, ND_index_finall, ND_finall, NX_index, NX, peaks_lst, period_points, ) else: F = "Null" if len(NX_index) != 0: last_period_len_rate_accum_compute = [] period_penult_index = period_points[-1] for m_dsts_last_period in range(len(NX_index)): last_period_length = ( NX_index[m_dsts_last_period] - period_penult_index ) last_period_len_rate = abs( 1 - last_period_length / period_approximate_len ) last_period_len_rate_accum_compute.append( last_period_len_rate ) if min(last_period_len_rate_accum_compute) < 0.2: last_period_index = ( last_period_len_rate_accum_compute.index( min(last_period_len_rate_accum_compute) ) ) G = NX_index[last_period_index] min_num_jhbf = NX.index(min(NX)) G_nnbj = NX_index[min_num_jhbf] if G != G_nnbj: # 2024-1-19 zheli 有问题 if ( peaks_lst[-1] + period_approximate_len + 25 < 1250 ): G = G_nnbj else: distance_period_less_htbq = ( period_approximate_len - ( NX_index[last_period_index] - period_penult_index ) ) if ( NX_index[last_period_index] > 1235 and distance_period_less_htbq > 8 ): G = "Null" else: G = G else: # 2024-1-21 例子4787 # 2024_1_22 例子 3795 penult_period_distance_dnbq = ( period_penult_index - period_points[-2] ) distance_period_less_htbq = ( penult_period_distance_dnbq - ( NX_index[last_period_index] - period_penult_index ) ) if ( NX_index[last_period_index] > 1235 and distance_period_less_htbq > 11 ): G = "Null" else: G = G else: G = "Null" else: G = "Null" else: F = "Null" if len(NX_index) != 0: last_period_len_rate_accum_compute = [] period_penult_index = period_points[-1] for m_dsts_last_period in range(len(NX_index)): last_period_length = ( NX_index[m_dsts_last_period] - period_penult_index ) last_period_len_rate = abs( 1 - last_period_length / period_approximate_len ) last_period_len_rate_accum_compute.append( last_period_len_rate ) if min(last_period_len_rate_accum_compute) < 0.2: last_period_index = ( last_period_len_rate_accum_compute.index( min(last_period_len_rate_accum_compute) ) ) last_distance_period_hjqn = period_approximate_len - ( NX_index[last_period_index] - period_penult_index ) if last_distance_period_hjqn < 8: G = NX_index[last_period_index] else: G = "Null" else: G = 1249 F = 1249 # 等于624的原因是 峰值后的极小值不是周期点,所以赋值624 else: F = 1249 G = 1249 else: # 2023-12-23 这里有问题 仅通过选第一个会导致峰值选错,应该结合前面选的峰值 # 2024_8_18 这个周期正确率要高一些,改代码得多跑数据多看看了,不然不如不改 peak = max(ND) f = ND_index[ND.index(peak)] # 第一步 通过x距离约束来筛选peak_index, 确保顺序不变 for i_dsts in range(len(ND_index)): if ND_index[i_dsts] == f: distance_with_last_peak_index = abs( ND_index[i_dsts] - peaks_lst[-1] ) if ( distance_with_last_peak_index < 24 or distance_with_last_peak_index > 208 ): # 2024_10_31 这里采集10s,周期数范围为6-26,对应长度为104-24 continue else: ND_index_remodify_throughx.append(ND_index[i_dsts]) ND_remodify_throughx.append(ND[i_dsts]) else: distance_NDx = abs(ND_index[i_dsts] - f) distance_with_last_peak_index = abs( ND_index[i_dsts] - peaks_lst[-1] ) if ( distance_NDx > 208 or distance_NDx < 24 or distance_with_last_peak_index < 24 or distance_with_last_peak_index > 208 ): # 2024_10_31 这里采集10s,周期数范围为6-26,对应长度为104-24 continue else: ND_index_remodify_throughx.append(ND_index[i_dsts]) ND_remodify_throughx.append(ND[i_dsts]) # 第二步 通过y值的一些约束来筛选真正的peak_index for j_dsts in range(len(ND_remodify_throughx)): # peak_rate = ND_remodify_throughx[j_dsts] / peak # 2023-12-29 if len(NX) != 0: if (peak - min(NX)) != 0: peak_rate = abs( (ND_remodify_throughx[j_dsts] - min(NX)) / (peak - min(NX)) ) else: peak_rate = abs(ND_remodify_throughx[j_dsts] / peak) else: peak_rate = abs(ND_remodify_throughx[j_dsts] / peak) if peak_rate > 0.4: # 0.6变0.4 2023——12-25 # writingto_txt('peak_with_max_peak_value_rate_record', peak_rate) ND_index_finall.append(ND_index_remodify_throughx[j_dsts]) ND_finall.append(ND_remodify_throughx[j_dsts]) else: continue if len(ND_index_finall) != 0: F, F_value, x_delta_py = ND_len_zero_select_F_peak_index_dw( data_one, ND_finall, ND_index_finall, peaks_lst ) else: F, F_value, x_delta_py = "Null", None, 0 G, baseline_py_lowest = general_select_NX_index( NX, NX_index, F, F_value, peaks_lst, period_points, peak_need_delete_element, period_need_delete_element, baseline_py_lowest, x_delta_py, ) # 2024_7_9 这里对ND进行筛选,之后再进行下一步 # f = peaks_lst[-1] # peak = data_one[f] # # # 第一步 通过x距离约束来筛选peak_index, 确保顺序不变 # for i_dsts in range(len(ND_index)): # distance_with_last_peak_index = abs(ND_index[i_dsts] - peaks_lst[-1]) # min_len = int(0.5 * period_approximate_len) # if distance_with_last_peak_index < min_len or distance_with_last_peak_index > 208: # continue # else: # ND_index_remodify_throughx.append(ND_index[i_dsts]) # ND_remodify_throughx.append(ND[i_dsts]) # # # 第二步 通过y值的一些约束来筛选真正的peak_index # # for j_dsts in range(len(ND_remodify_throughx)): # # peak_rate = ND_remodify_throughx[j_dsts] / peak # # 2023-12-29 # if len(NX) != 0: # # if (peak - min(NX)) != 0: # peak_rate = abs((ND_remodify_throughx[j_dsts] - min(NX)) / (peak - min(NX))) # # else: # peak_rate = abs(ND_remodify_throughx[j_dsts] / peak) # # else: # peak_rate = abs(ND_remodify_throughx[j_dsts] / peak) # if peak_rate > 0.4: # 0.6变0.4 2023——12-25 # # writingto_txt('peak_with_max_peak_value_rate_record', peak_rate) # ND_index_finall.append(ND_index_remodify_throughx[j_dsts]) # ND_finall.append(ND_remodify_throughx[j_dsts]) # else: # continue # # if len(ND_index_finall) != 0: # F, F_value, x_delta_py = ND_len_zero_select_F_peak_index_dw(data_one, ND_finall, ND_index_finall, # peaks_lst) # # else: # F, F_value, x_delta_py = 'Null', None, 0 # # G, baseline_py_lowest = general_select_NX_index(NX, NX_index, F, F_value, peaks_lst, period_points, # peak_need_delete_element, period_need_delete_element, # baseline_py_lowest, x_delta_py) else: F = "Null" if len(NX_index) != 0: last_period_len_rate_accum_compute = [] period_penult_index = period_points[-1] for m_dsts_last_period in range(len(NX_index)): last_period_length = ( NX_index[m_dsts_last_period] - period_penult_index ) last_period_len_rate = abs( 1 - last_period_length / period_approximate_len ) last_period_len_rate_accum_compute.append(last_period_len_rate) if min(last_period_len_rate_accum_compute) < 0.2: last_period_index = last_period_len_rate_accum_compute.index( min(last_period_len_rate_accum_compute) ) G = NX_index[last_period_index] else: G = 1249 F = 1249 # 等于624的原因是 峰值后的极小值不是周期点,所以赋值624 else: F = 1249 G = 1249 return ( F, G, peak_need_delete_element, period_need_delete_element, baseline_py_lowest, jishu_num, ) 将以上代码转为 c#实现,
09-21
这是一个基于AI视觉识别与3D引擎技术打造的沉浸式交互圣诞装置。 简单来说,它是一棵通过网页浏览器运行的数字智慧圣诞树,你可以用真实的肢体动作来操控它的形态,并将自己的回忆照片融入其中。 1. 核心技术组成 这个作品是由三个尖端技术模块组成的: Three.js 3D引擎:负责渲染整棵圣诞树、动态落雪、五彩挂灯和树顶星。它创建了一个具备光影和深度感的虚拟3D空间。 MediaPipe AI手势识别:调用电脑摄像头,实时识别手部的21个关键点。它能读懂你的手势,如握拳、张开或捏合。 GSAP动画系统:负责处理粒子散开与聚合时的平滑过渡,让成百上千个物体在运动时保持顺滑。 2. 它的主要作用与功能 交互式情感表达: 回忆挂载:你可以上传本地照片,这些照片会像装饰品一样挂在树上,或者像星云一样环绕在树周围。 魔法操控:握拳时粒子迅速聚拢,构成一棵挺拔的圣诞树;张开手掌时,树会瞬间炸裂成星光和雪花,照片随之起舞;捏合手指时视线会拉近,让你特写观察某一张选中的照片。 节日氛围装饰: 在白色背景下,这棵树呈现出一种现代艺术感。600片雪花在3D空间里缓缓飘落,提供视觉深度。树上的彩色粒子和白色星灯会周期性地呼吸闪烁,模拟真实灯串的效果。 3. 如何使用 启动:运行代码后,允许浏览器开启摄像头。 装扮:点击上传照片按钮,选择温馨合照。 互动:对着摄像头挥动手掌可以旋转圣诞树;五指张开让照片和树化作满天星辰;攥紧拳头让它们重新变回挺拔的树。 4. 适用场景 个人纪念:作为一个独特的数字相册,在节日陪伴自己。 浪漫惊喜:录制一段操作手势让照片绽放的视频发给朋友。 技术展示:作为WebGL与AI结合的案例,展示前端开发的潜力。
【顶级EI复现】计及连锁故障传播路径的电力系统 N-k 多阶段双层优化及故障场景筛选模型(Matlab代码实现)内容概要:本文提出了一种计及连锁故障传播路径的电力系统N-k多阶段双层优化及故障场景筛选模型,并提供了基于Matlab的代码实现。该模型旨在应对复杂电力系统中可能发生的N-k故障(即多个元件相继失效),通过构建双层优化框架,上层优化系统运行策略,下层模拟故障传播过程,从而实现对关键故障场景的有效识别与筛选。研究结合多阶段动态特性,充分考虑故障的时序演化与连锁反应机制,提升了电力系统安全性评估的准确性与实用性。此外,模型具备良好的通用性与可扩展性,适用于大规模电网的风险评估与预防控制。; 适合人群:电力系统、能源互联网及相关领域的高校研究生、科研人员以及从事电网安全分析、风险评估的工程技术人员。; 使用场景及目标:①用于电力系统连锁故障建模与风险评估;②支撑N-k故障场景的自动化筛选与关键脆弱环节识别;③为电网规划、调度运行及应急预案制定提供理论依据和技术工具;④服务于高水平学术论文复现与科研项目开发。; 阅读建议:建议读者结合Matlab代码深入理解模型构建细节,重点关注双层优化结构的设计逻辑、故障传播路径的建模方法以及场景削减技术的应用,建议在实际电网数据上进行测试与验证,以提升对模型性能与适用边界的认知。
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