180ax.exe

本文介绍了一个名为180ax.exe的进程,该进程被注册为TROJ.ISTZONE.H下载器,通常与其它病毒捆绑,用于下载更多恶意软件到用户的计算机上。建议立即删除此进程。
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进程知识库

180ax - 180ax.exe - 进程信息 name="google_ads_frame" marginwidth="0" marginheight="0" src="http://pagead2.googlesyndication.com/pagead/ads?client=ca-pub-5572165936844014&dt=1195292630218&lmt=1195292627&format=336x280_as&output=html&correlator=1195292630218&url=file%3A%2F%2F%2FC%3A%2FDocuments%2520and%2520Settings%2Flhh1%2F%E6%A1%8C%E9%9D%A2%2F000stthk.exe.htm&color_bg=FFFFFF&color_text=000000&color_link=000000&color_url=FFFFFF&color_border=FFFFFF&ad_type=text&ga_vid=1397507768.1195292630&ga_sid=1195292630&ga_hid=1390572954&flash=9&u_h=768&u_w=1024&u_ah=740&u_aw=1024&u_cd=32&u_tz=480&u_java=true" frameborder="0" width="336" scrolling="no" height="280" allowtransparency="allowtransparency">

进程文件:180ax 或者 180ax.exe
进程名称: TROJ.ISTZONE.H
 
描述:
180ax.exe是注册为TROJ.ISTZONE.H的下载器。这个进程通常与其它病毒捆绑,用于下载其它病毒到你的计算机上。这个进程的安全等级是建议立即进行删除。

出品者: 未知N/A
属于: TROJ.ISTZONE.H

系统进程:
后台程序:
使用网络:

硬件相关:
常见错误: 未知N/A

内存使用: 未知N/A

 
安全等级 (0-5): 4

间谍软件:
广告软件:
病毒:
木马:

存在安全风险进程列表

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其它进程分类:
- 系统进程列表
- 应用程序进程列表
- 其它进程列表

 

 

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C:\Users\Administrator\PycharmProjects\pythonProject2\.venv\Scripts\python.exe C:\Users\Administrator\PycharmProjects\pythonProject2\study.py a. 子女身高分布形状分析: 偏度: 0.0580 峰度: -0.7953 Traceback (most recent call last): File "C:\Users\Administrator\PycharmProjects\pythonProject2\study.py", line 54, in <module> plt.savefig('/home/user/vibecoding/workspace/height_analysis/child_height_distribution.png', dpi=300, bbox_inches='tight') File "C:\Users\Administrator\PycharmProjects\pythonProject2\.venv\lib\site-packages\matplotlib\pyplot.py", line 1250, in savefig res = fig.savefig(*args, **kwargs) # type: ignore[func-returns-value] File "C:\Users\Administrator\PycharmProjects\pythonProject2\.venv\lib\site-packages\matplotlib\figure.py", line 3490, in savefig self.canvas.print_figure(fname, **kwargs) File "C:\Users\Administrator\PycharmProjects\pythonProject2\.venv\lib\site-packages\matplotlib\backend_bases.py", line 2186, in print_figure result = print_method( File "C:\Users\Administrator\PycharmProjects\pythonProject2\.venv\lib\site-packages\matplotlib\backend_bases.py", line 2042, in <lambda> print_method = functools.wraps(meth)(lambda *args, **kwargs: meth( File "C:\Users\Administrator\PycharmProjects\pythonProject2\.venv\lib\site-packages\matplotlib\backends\backend_agg.py", line 481, in print_png self._print_pil(filename_or_obj, "png", pil_kwargs, metadata) File "C:\Users\Administrator\PycharmProjects\pythonProject2\.venv\lib\site-packages\matplotlib\backends\backend_agg.py", line 430, in _print_pil mpl.image.imsave( File "C:\Users\Administrator\PycharmProjects\pythonProject2\.venv\lib\site-packages\matplotlib\image.py", line 1657, in imsave image.save(fname, **pil_kwargs) File "C:\Users\Administrator\PycharmProjects\pythonProject2\.venv\lib\site-packages\PIL\Image.py", line 2566, in save fp = builtins.open(filename, "w+b") FileNotFoundError: [Errno 2] No such file or directory: '/home/user/vibecoding/workspace/height_analysis/child_height_distribution.png' Process finished with exit code 1 import numpy as np import matplotlib.pyplot as plt from scipy import stats import os # 设置中文字体 plt.rcParams['font.sans-serif'] = ['Microsoft YaHei'] plt.rcParams['axes.unicode_minus'] = False # 创建数据 child_height = np.array([171, 174, 177, 178, 180, 181, 159, 169, 170, 170, 175, 175, 178, 173, 181, 164, 167, 168, 170, 170, 155, 161, 166, 170, 158, 160, 160, 162, 165, 168, 170, 153, 156, 158, 160, 162, 163, 165, 166, 170]) father_height = np.array([166, 171, 179, 174, 173, 170, 168, 168, 170, 170, 172, 175, 174, 170, 178, 175, 163, 168, 170, 172, 165, 182, 166, 178, 173, 170, 171, 167, 175, 172, 168, 163, 168, 174, 170, 170, 173, 172, 181, 180]) mother_height = np.array([158, 158, 168, 160, 162, 160, 153, 153, 167, 160, 160, 165, 160, 160, 165, 161, 166, 155, 160, 158, 157, 165, 156, 160, 160, 165, 150, 158, 160, 162, 163, 152, 155, 155, 162, 158, 160, 161, 158, 165]) # a. 分析子女身高分布形状(包括偏度和峰度) print("a. 子女身高分布形状分析:") # 计算偏度和峰度 def skewness(data): mean = np.mean(data) std = np.std(data) return np.mean(((data - mean) / std) ** 3) def kurtosis(data): mean = np.mean(data) std = np.std(data) return np.mean(((data - mean) / std) ** 4) - 3 child_skewness = skewness(child_height) child_kurtosis = kurtosis(child_height) print(f"偏度: {child_skewness:.4f}") print(f"峰度: {child_kurtosis:.4f}") # 绘制子女身高直方图和Q-Q图 fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5)) # 直方图 ax1.hist(child_height, bins=10, edgecolor='black', alpha=0.7) ax1.set_title('子女身高分布直方图', fontsize=14) ax1.set_xlabel('身高 (cm)') ax1.set_ylabel('频数') ax1.grid(True, alpha=0.3) # Q-Q图 stats.probplot(child_height, plot=ax2) ax2.set_title('子女身高Q-Q图', fontsize=14) ax2.grid(True, alpha=0.3) plt.tight_layout() plt.savefig('/home/user/vibecoding/workspace/height_analysis/child_height_distribution.png', dpi=300, bbox_inches='tight') plt.close() # b. 绘制父亲身高、母亲身高的箱型图 fig, ax = plt.subplots(figsize=(10, 6)) # 准备数据 box_data = [father_height, mother_height] box_labels = ['父亲身高', '母亲身高'] # 创建箱型图 box_plot = ax.boxplot(box_data, labels=box_labels, patch_artist=True) # 设置箱线颜色 colors = ['darkgray', 'lightgray'] for patch, color in zip(box_plot['boxes'], colors): patch.set_facecolor(color) # 添加数据点 for i, data_series in enumerate(box_data): # 为每个点添加随机水平偏移,避免重叠 x = np.random.normal(i+1, 0.04, size=len(data_series)) ax.scatter(x, data_series, alpha=0.6, s=30, edgecolor='black', linewidth=0.5) ax.set_title('父亲身高与母亲身高箱型图对比', fontsize=14) ax.set_ylabel('身高 (cm)', fontsize=12) ax.set_xlabel('父母类型', fontsize=12) ax.grid(True, alpha=0.3, axis='y') # 添加均值点和标注 for i, data_series in enumerate(box_data): mean_val = np.mean(data_series) ax.scatter(i+1, mean_val, color='red', marker='*', s=200, zorder=5) ax.annotate(f'均值: {mean_val:.1f}', (i+1, mean_val), textcoords="offset points", xytext=(0,10), ha='center') plt.tight_layout() plt.savefig('/home/user/vibecoding/workspace/height_analysis/parent_height_boxplot.png', dpi=300, bbox_inches='tight') plt.close() # c. 绘制子女身高与父亲身高、母亲身高的散点图 fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 6)) # 子女身高与父亲身高散点图 ax1.scatter(child_height, father_height, color='blue', marker='o', alpha=0.7, s=60, edgecolor='black', linewidth=0.5) ax1.set_title('子女身高与父亲身高散点图', fontsize=14) ax1.set_xlabel('子女身高 (cm)', fontsize=12) ax1.set_ylabel('父亲身高 (cm)', fontsize=12) ax1.grid(True, alpha=0.3) # 添加趋势线 z1 = np.polyfit(child_height, father_height, 1) p1 = np.poly1d(z1) ax1.plot(child_height, p1(child_height), "r--", alpha=0.8, linewidth=2) # 子女身高与母亲身高散点图 ax2.scatter(child_height, mother_height, color='red', marker='^', alpha=0.7, s=60, edgecolor='black', linewidth=0.5) ax2.set_title('子女身高与母亲身高散点图', fontsize=14) ax2.set_xlabel('子女身高 (cm)', fontsize=12) ax2.set_ylabel('母亲身高 (cm)', fontsize=12) ax2.grid(True, alpha=0.3) # 添加趋势线 z2 = np.polyfit(child_height, mother_height, 1) p2 = np.poly1d(z2) ax2.plot(child_height, p2(child_height), "b--", alpha=0.8, linewidth=2) plt.tight_layout() plt.savefig('/home/user/vibecoding/workspace/height_analysis/height_scatterplots.png', dpi=300, bbox_inches='tight') plt.close() # d. 计算相关系数 print("\nd. 相关系数分析:") def correlation_coefficient(x, y): n = len(x) mean_x = np.mean(x) mean_y = np.mean(y) numerator = np.sum((x - mean_x) * (y - mean_y)) denominator = np.sqrt(np.sum((x - mean_x)**2) * np.sum((y - mean_y)**2)) return numerator / denominator corr_child_father = correlation_coefficient(child_height, father_height) corr_child_mother = correlation_coefficient(child_height, mother_height) print(f"子女身高与父亲身高相关系数: {corr_child_father:.4f}") print(f"子女身高与母亲身高相关系数: {corr_child_mother:.4f}") # e. 计算子女身高的描述性统计量 print("\ne. 子女身高描述性统计量:") mean_child = np.mean(child_height) median_child = np.median(child_height) std_child = np.std(child_height) min_child = np.min(child_height) max_child = np.max(child_height) q25_child = np.percentile(child_height, 25) q75_child = np.percentile(child_height, 75) print(f"均值: {mean_child:.4f}") print(f"中位数: {median_child:.4f}") print(f"标准差: {std_child:.4f}") print(f"最小值: {min_child:.4f}") print(f"最大值: {max_child:.4f}") print(f"25%分位数: {q25_child:.4f}") print(f"75%分位数: {q75_child:.4f}") print(f"偏度: {child_skewness:.4f}") print(f"峰度: {child_kurtosis:.4f}") # 绘制描述性统计表格图 fig, ax = plt.subplots(figsize=(10, 6)) ax.axis('tight') ax.axis('off') # 准备统计数据 stats_data = [ ['均值', f"{mean_child:.2f}"], ['中位数', f"{median_child:.2f}"], ['标准差', f"{std_child:.2f}"], ['最小值', f"{min_child:.2f}"], ['最大值', f"{max_child:.2f}"], ['25%分位数', f"{q25_child:.2f}"], ['75%分位数', f"{q75_child:.2f}"], ['偏度', f"{child_skewness:.4f}"], ['峰度', f"{child_kurtosis:.4f}"] ] table = ax.table(cellText=stats_data, colLabels=['统计量', '数值'], cellLoc='center', loc='center', bbox=[0, 0, 1, 1]) table.auto_set_font_size(False) table.set_fontsize(12) table.scale(1, 2) # 设置表格样式 for i in range(len(stats_data) + 1): for j in range(2): cell = table[(i, j)] if i == 0: # 表头 cell.set_facecolor('#4CAF50') cell.set_text_props(weight='bold', color='white') else: if i % 2 == 0: cell.set_facecolor('#f0f0f0') else: cell.set_facecolor('white') plt.title('子女身高描述性统计量', fontsize=16, pad=20) plt.savefig('/home/user/vibecoding/workspace/height_analysis/child_height_statistics.png', dpi=300, bbox_inches='tight') plt.close() print("\n所有分析完成,图表已保存至工作目录") 如何解决,解决后的完整代码是什么
最新发布
10-18
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