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\u5230 1\uff09\uff0c\u4ee5\u4fbf\u66f4\u597d\u5730\u8fdb\u884c\u6bd4\u8f83\u3002<\/li>\n<li>\u6807\u51c6\u5316\uff1a\u5c06\u6570\u636e\u8f6c\u6362\u4e3a\u5e73\u5747\u503c\u4e3a 0\u3001\u6807\u51c6\u5dee\u4e3a 1\u3002<\/li>\n<\/ul>\n<\/li>\n<li>\n<p><strong>\u6570\u636e\u7f29\u51cf\u6280\u672f\uff1a<\/strong><\/p>\n<ul>\n<li>\u7279\u5f81\u9009\u62e9\uff1a\u9009\u62e9\u5bf9\u5206\u6790\u6709\u91cd\u5927\u8d21\u732e\u7684\u6700\u76f8\u5173\u7279\u5f81\u3002<\/li>\n<li>\u964d\u7ef4\uff1a\u51cf\u5c11\u7279\u5f81\u6570\u91cf\u540c\u65f6\u4fdd\u7559\u57fa\u672c\u4fe1\u606f\uff08\u4f8b\u5982\uff0c\u4e3b\u6210\u5206\u5206\u6790 - PCA\uff09\u3002<\/li>\n<\/ul>\n<\/li>\n<li>\n<p><strong>\u6570\u636e\u4e30\u5bcc\u6280\u672f\uff1a<\/strong><\/p>\n<ul>\n<li>\u6570\u636e\u96c6\u6210\uff1a\u7ec4\u5408\u6765\u81ea\u591a\u4e2a\u6765\u6e90\u7684\u6570\u636e\u4ee5\u521b\u5efa\u7efc\u5408\u6570\u636e\u96c6\u3002<\/li>\n<li>\u7279\u5f81\u5de5\u7a0b\uff1a\u57fa\u4e8e\u73b0\u6709\u7279\u5f81\u521b\u5efa\u65b0\u7279\u5f81\uff0c\u4ee5\u63d0\u9ad8\u6570\u636e\u8d28\u91cf\u548c\u9884\u6d4b\u80fd\u529b\u3002<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n<h2>\u6570\u636e\u9884\u5904\u7406\u7684\u4f7f\u7528\u65b9\u6cd5\u3001\u4f7f\u7528\u4e2d\u9047\u5230\u7684\u95ee\u9898\u53ca\u89e3\u51b3\u65b9\u6cd5<\/h2>\n<p>\u6570\u636e\u9884\u5904\u7406\u662f\u673a\u5668\u5b66\u4e60\u3001\u6570\u636e\u6316\u6398\u548c\u5546\u4e1a\u5206\u6790\u7b49\u5404\u4e2a\u9886\u57df\u7684\u5173\u952e\u6b65\u9aa4\u3002\u5176\u5e94\u7528\u548c\u6311\u6218\u5305\u62ec\uff1a<\/p>\n<ol>\n<li>\n<p><strong>\u673a\u5668\u5b66\u4e60\uff1a<\/strong> \u5728\u673a\u5668\u5b66\u4e60\u4e2d\uff0c\u6570\u636e\u9884\u5904\u7406\u5bf9\u4e8e\u5728\u8bad\u7ec3\u6a21\u578b\u4e4b\u524d\u51c6\u5907\u6570\u636e\u81f3\u5173\u91cd\u8981\u3002\u673a\u5668\u5b66\u4e60\u4e2d\u4e0e\u6570\u636e\u9884\u5904\u7406\u76f8\u5173\u7684\u95ee\u9898\u5305\u62ec\u5904\u7406\u7f3a\u5931\u503c\u3001\u5904\u7406\u4e0d\u5e73\u8861\u6570\u636e\u96c6\u548c\u9009\u62e9\u9002\u5f53\u7684\u7279\u5f81\u3002\u89e3\u51b3\u65b9\u6848\u5305\u62ec\u4f7f\u7528\u63d2\u8865\u6280\u672f\u3001\u91c7\u7528\u91c7\u6837\u65b9\u6cd5\u6765\u5e73\u8861\u6570\u636e\u4ee5\u53ca\u5e94\u7528\u7279\u5f81\u9009\u62e9\u7b97\u6cd5\uff0c\u4f8b\u5982\u9012\u5f52\u7279\u5f81\u6d88\u9664 (RFE)\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u81ea\u7136\u8bed\u8a00\u5904\u7406\uff08NLP\uff09\uff1a<\/strong> NLP \u4efb\u52a1\u901a\u5e38\u9700\u8981\u5927\u91cf\u7684\u6570\u636e\u9884\u5904\u7406\uff0c\u4f8b\u5982\u6807\u8bb0\u5316\u3001\u8bcd\u5e72\u63d0\u53d6\u548c\u5220\u9664\u505c\u7528\u8bcd\u3002\u5904\u7406\u5608\u6742\u7684\u6587\u672c\u6570\u636e\u548c\u6d88\u9664\u5177\u6709\u591a\u91cd\u542b\u4e49\u7684\u5355\u8bcd\u6b67\u4e49\u53ef\u80fd\u4f1a\u5e26\u6765\u6311\u6218\u3002\u89e3\u51b3\u65b9\u6848\u5305\u62ec\u4f7f\u7528\u9ad8\u7ea7\u6807\u8bb0\u5316\u65b9\u6cd5\u548c\u4f7f\u7528\u8bcd\u5d4c\u5165\u6765\u6355\u83b7\u8bed\u4e49\u5173\u7cfb\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u56fe\u50cf\u5904\u7406\uff1a<\/strong> \u5728\u56fe\u50cf\u5904\u7406\u4e2d\uff0c\u6570\u636e\u9884\u5904\u7406\u5305\u62ec\u8c03\u6574\u5927\u5c0f\u3001\u89c4\u8303\u5316\u548c\u6570\u636e\u589e\u5f3a\u3002\u8be5\u9886\u57df\u7684\u6311\u6218\u5305\u62ec\u5904\u7406\u56fe\u50cf\u53d8\u5316\u548c\u4f2a\u5f71\u3002\u89e3\u51b3\u65b9\u6848\u5305\u62ec\u5e94\u7528\u56fe\u50cf\u589e\u5f3a\u6280\u672f\uff08\u5982\u65cb\u8f6c\u3001\u7ffb\u8f6c\u548c\u6dfb\u52a0\u566a\u58f0\uff09\u6765\u521b\u5efa\u591a\u6837\u5316\u7684\u6570\u636e\u96c6\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u65f6\u95f4\u5e8f\u5217\u5206\u6790\uff1a<\/strong> \u65f6\u95f4\u5e8f\u5217\u6570\u636e\u7684\u6570\u636e\u9884\u5904\u7406\u6d89\u53ca\u5904\u7406\u7f3a\u5931\u6570\u636e\u70b9\u548c\u6d88\u9664\u566a\u58f0\u3002\u63d2\u503c\u548c\u79fb\u52a8\u5e73\u5747\u7b49\u6280\u672f\u53ef\u7528\u4e8e\u89e3\u51b3\u8fd9\u4e9b\u6311\u6218\u3002<\/p>\n<\/li>\n<\/ol>\n<h2>\u4e3b\u8981\u7279\u5f81\u4ee5\u53ca\u4e0e\u7c7b\u4f3c\u672f\u8bed\u7684\u5176\u4ed6\u6bd4\u8f83\u4ee5\u8868\u683c\u548c\u5217\u8868\u7684\u5f62\u5f0f<\/h2>\n<table>\n<thead>\n<tr>\n<th>\u7279\u5f81<\/th>\n<th>\u6570\u636e\u9884\u5904\u7406<\/th>\n<th>\u6570\u636e\u6e05\u7406<\/th>\n<th>\u6570\u636e\u8f6c\u6362<\/th>\n<th>\u6570\u636e\u7f29\u51cf<\/th>\n<th>\u6570\u636e\u4e30\u5bcc<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>\u76ee\u7684<\/td>\n<td>\u51c6\u5907\u5206\u6790\u548c\u5efa\u6a21\u7684\u6570\u636e<\/td>\n<td>\u6d88\u9664\u9519\u8bef\u548c\u4e0d\u4e00\u81f4\u4e4b\u5904<\/td>\n<td>\u89c4\u8303\u5316\u548c\u6807\u51c6\u5316\u6570\u636e<\/td>\n<td>\u9009\u62e9\u76f8\u5173\u529f\u80fd<\/td>\n<td>\u96c6\u6210\u5916\u90e8\u6570\u636e\u5e76\u521b\u5efa\u65b0\u529f\u80fd<\/td>\n<\/tr>\n<tr>\n<td>\u6280\u5de7<\/td>\n<td>\u5f52\u56e0\u3001\u5f02\u5e38\u503c\u68c0\u6d4b\u3001\u91cd\u590d\u6570\u636e\u5220\u9664<\/td>\n<td>\u5904\u7406\u7f3a\u5931\u503c\u3001\u5f02\u5e38\u503c\u68c0\u6d4b<\/td>\n<td>\u89c4\u8303\u5316\u3001\u6807\u51c6\u5316<\/td>\n<td>\u7279\u5f81\u9009\u62e9\u3001\u964d\u7ef4<\/td>\n<td>\u6570\u636e\u96c6\u6210\u3001\u7279\u5f81\u5de5\u7a0b<\/td>\n<\/tr>\n<tr>\n<td>\u4e3b\u8981\u7126\u70b9<\/td>\n<td>\u63d0\u9ad8\u6570\u636e\u8d28\u91cf\u548c\u517c\u5bb9\u6027<\/td>\n<td>\u786e\u4fdd\u6570\u636e\u51c6\u786e\u6027\u548c\u53ef\u9760\u6027<\/td>\n<td>\u7f29\u653e\u6570\u636e\u4ee5\u4f9b\u6bd4\u8f83<\/td>\n<td>\u964d\u4f4e\u6570\u636e\u590d\u6742\u6027<\/td>\n<td>\u589e\u5f3a\u6570\u636e\u5185\u5bb9\u548c\u76f8\u5173\u6027<\/td>\n<\/tr>\n<tr>\n<td>\u5e94\u7528\u9886\u57df<\/td>\n<td>\u673a\u5668\u5b66\u4e60\u3001\u6570\u636e\u6316\u6398\u3001\u5546\u4e1a\u5206\u6790<\/td>\n<td>\u6570\u636e\u5206\u6790\u3001\u7edf\u8ba1<\/td>\n<td>\u673a\u5668\u5b66\u4e60\u3001\u805a\u7c7b<\/td>\n<td>\u7279\u5f81\u5de5\u7a0b\u3001\u964d\u7ef4<\/td>\n<td>\u6570\u636e\u96c6\u6210\u3001\u5546\u4e1a\u667a\u80fd<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>\u4e0e\u6570\u636e\u9884\u5904\u7406\u76f8\u5173\u7684\u672a\u6765\u89c2\u70b9\u548c\u6280\u672f<\/h2>\n<p>\u968f\u7740\u6280\u672f\u7684\u8fdb\u6b65\uff0c\u6570\u636e\u9884\u5904\u7406\u6280\u672f\u5c06\u7ee7\u7eed\u53d1\u5c55\uff0c\u91c7\u7528\u66f4\u590d\u6742\u7684\u65b9\u6cd5\u6765\u5904\u7406\u590d\u6742\u591a\u6837\u7684\u6570\u636e\u96c6\u3002\u4e0e\u6570\u636e\u9884\u5904\u7406\u76f8\u5173\u7684\u4e00\u4e9b\u672a\u6765\u89c2\u70b9\u548c\u6280\u672f\u5305\u62ec\uff1a<\/p>\n<ol>\n<li>\n<p><strong>\u81ea\u52a8\u9884\u5904\u7406\uff1a<\/strong> \u901a\u8fc7\u4eba\u5de5\u667a\u80fd\u548c\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\u5b9e\u73b0\u7684\u81ea\u52a8\u5316\u5c06\u5728\u81ea\u52a8\u5316\u6570\u636e\u9884\u5904\u7406\u6b65\u9aa4\u3001\u51cf\u5c11\u4eba\u5de5\u5de5\u4f5c\u91cf\u548c\u63d0\u9ad8\u6548\u7387\u65b9\u9762\u53d1\u6325\u91cd\u8981\u4f5c\u7528\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u6df1\u5ea6\u5b66\u4e60\u9884\u5904\u7406\uff1a<\/strong> \u81ea\u52a8\u7f16\u7801\u5668\u548c\u751f\u6210\u5bf9\u6297\u7f51\u7edc\uff08GAN\uff09\u7b49\u6df1\u5ea6\u5b66\u4e60\u6280\u672f\u5c06\u7528\u4e8e\u81ea\u52a8\u7279\u5f81\u63d0\u53d6\u548c\u6570\u636e\u8f6c\u6362\uff0c\u5c24\u5176\u662f\u5728\u56fe\u50cf\u548c\u97f3\u9891\u7b49\u590d\u6742\u6570\u636e\u9886\u57df\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u6d41\u6570\u636e\u9884\u5904\u7406\uff1a<\/strong> \u968f\u7740\u5b9e\u65f6\u6570\u636e\u6d41\u8d8a\u6765\u8d8a\u666e\u53ca\uff0c\u9884\u5904\u7406\u6280\u672f\u5c06\u4f1a\u9488\u5bf9\u6570\u636e\u5230\u8fbe\u8fdb\u884c\u5904\u7406\uff0c\u4ece\u800c\u5b9e\u73b0\u66f4\u5feb\u7684\u6d1e\u5bdf\u548c\u51b3\u7b56\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u9690\u79c1\u4fdd\u62a4\u9884\u5904\u7406\uff1a<\/strong> \u5dee\u5f02\u9690\u79c1\u7b49\u6280\u672f\u5c06\u88ab\u96c6\u6210\u5230\u6570\u636e\u9884\u5904\u7406\u6d41\u7a0b\u4e2d\uff0c\u4ee5\u786e\u4fdd\u6570\u636e\u7684\u9690\u79c1\u548c\u5b89\u5168\uff0c\u540c\u65f6\u4ecd\u4fdd\u7559\u6709\u7528\u7684\u4fe1\u606f\u3002<\/p>\n<\/li>\n<\/ol>\n<h2>\u5982\u4f55\u4f7f\u7528\u4ee3\u7406\u670d\u52a1\u5668\u6216\u5c06\u5176\u4e0e\u6570\u636e\u9884\u5904\u7406\u5173\u8054<\/h2>\n<p>\u4ee3\u7406\u670d\u52a1\u5668\u53ef\u4ee5\u901a\u8fc7\u591a\u79cd\u65b9\u5f0f\u4e0e\u6570\u636e\u9884\u5904\u7406\u5bc6\u5207\u76f8\u5173\uff1a<\/p>\n<ol>\n<li>\n<p><strong>\u6570\u636e\u6293\u53d6\uff1a<\/strong> \u4ee3\u7406\u670d\u52a1\u5668\u5728\u6570\u636e\u6293\u53d6\u4e2d\u8d77\u7740\u81f3\u5173\u91cd\u8981\u7684\u4f5c\u7528\uff0c\u5b83\u53ef\u4ee5\u9690\u85cf\u8bf7\u6c42\u8005\u7684\u8eab\u4efd\u548c\u4f4d\u7f6e\u3002\u5b83\u4eec\u53ef\u7528\u4e8e\u4ece\u7f51\u7ad9\u6536\u96c6\u6570\u636e\uff0c\u800c\u4e0d\u5b58\u5728 IP \u5c01\u9501\u6216\u9650\u5236\u7684\u98ce\u9669\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u6570\u636e\u6e05\u7406\uff1a<\/strong> \u4ee3\u7406\u670d\u52a1\u5668\u53ef\u4ee5\u5e2e\u52a9\u5728\u591a\u4e2a IP \u5730\u5740\u4e4b\u95f4\u5206\u914d\u6570\u636e\u6e05\u7406\u4efb\u52a1\uff0c\u9632\u6b62\u670d\u52a1\u5668\u963b\u6b62\u6765\u81ea\u5355\u4e00\u6765\u6e90\u7684\u8fc7\u591a\u8bf7\u6c42\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u8d1f\u8f7d\u5747\u8861\uff1a<\/strong> \u4ee3\u7406\u670d\u52a1\u5668\u53ef\u4ee5\u5e73\u8861\u5230\u4e0d\u540c\u670d\u52a1\u5668\u7684\u4f20\u5165\u8bf7\u6c42\u8d1f\u8f7d\uff0c\u4f18\u5316\u6570\u636e\u9884\u5904\u7406\u4efb\u52a1\u5e76\u786e\u4fdd\u9ad8\u6548\u7684\u6570\u636e\u5904\u7406\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u57fa\u4e8e\u5730\u7406\u4f4d\u7f6e\u7684\u9884\u5904\u7406\uff1a<\/strong> \u5177\u6709\u5730\u7406\u5b9a\u4f4d\u529f\u80fd\u7684\u4ee3\u7406\u670d\u52a1\u5668\u53ef\u4ee5\u5c06\u8bf7\u6c42\u8def\u7531\u5230\u7279\u5b9a\u4f4d\u7f6e\u7684\u670d\u52a1\u5668\uff0c\u4ece\u800c\u5b9e\u73b0\u7279\u5b9a\u533a\u57df\u7684\u9884\u5904\u7406\u4efb\u52a1\u5e76\u4f7f\u7528\u57fa\u4e8e\u4f4d\u7f6e\u7684\u4fe1\u606f\u4e30\u5bcc\u6570\u636e\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u9690\u79c1\u4fdd\u62a4\uff1a<\/strong> 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href=\"https:\/\/towardsdatascience.com\/introduction-to-data-cleaning-in-machine-learning-a-complete-guide-8e6c8cdcd704\" target=\"_new\" rel=\"noopener nofollow\">\u6570\u636e\u6e05\u7406\u7b80\u4ecb<\/a><\/li>\n<li><a href=\"https:\/\/towardsdatascience.com\/feature-engineering-in-machine-learning-336d1336118f\" target=\"_new\" rel=\"noopener nofollow\">\u673a\u5668\u5b66\u4e60\u4e2d\u7684\u7279\u5f81\u5de5\u7a0b<\/a><\/li>\n<li><a href=\"https:\/\/towardsdatascience.com\/data-preprocessing-for-nlp-text-data-cleaning-and-preprocessing-ea3ffe0406c1\" target=\"_new\" rel=\"noopener nofollow\">\u81ea\u7136\u8bed\u8a00\u5904\u7406\u7684\u6570\u636e\u9884\u5904\u7406<\/a><\/li>\n<\/ol>\n<p>\u603b\u4e4b\uff0c\u6570\u636e\u9884\u5904\u7406\u662f\u589e\u5f3a\u4ee3\u7406\u670d\u52a1\u5668\u529f\u80fd\u7684\u5173\u952e\u6b65\u9aa4\uff0c\u4f7f\u5176\u80fd\u591f\u66f4\u6709\u6548\u5730\u5904\u7406\u548c\u4f20\u9012\u6570\u636e\u3002\u901a\u8fc7\u5e94\u7528\u5404\u79cd\u6280\u672f\u6765\u6e05\u7406\u3001\u8f6c\u6362\u548c\u4e30\u5bcc\u6570\u636e\uff0c\u50cf OneProxy 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vital step in data analysis and machine learning, where raw data is transformed and prepared for further analysis. For proxy servers, data preprocessing ensures better data quality, faster processing, and improved user experiences. By cleaning, transforming, and enriching data, proxy servers can deliver more efficient and reliable services to users.<\/p>"},{"question":"How does data preprocessing work?","answer":"<p>Data preprocessing involves a series of steps, including data collection, data cleaning, data transformation, data reduction, data enrichment, data integration, data splitting, and model training. These steps are applied sequentially to convert raw data into a more manageable and informative format, suitable for analysis and modeling.<\/p>"},{"question":"What are the key features of data preprocessing?","answer":"<p>Data preprocessing offers several essential features, including improved data quality, enhanced model performance, faster processing, data compatibility, handling missing data, and incorporating domain knowledge. These features play a crucial role in producing accurate and reliable results in data analysis and machine learning tasks.<\/p>"},{"question":"What are the types of data preprocessing techniques?","answer":"<p>Data preprocessing techniques can be categorized into data cleaning, data transformation, data reduction, and data enrichment. Data cleaning involves handling missing values, outliers, and duplicates. Data transformation includes normalization and standardization. Data reduction focuses on feature selection and dimensionality reduction. Data enrichment involves integrating external data and creating new features.<\/p>"},{"question":"How is data preprocessing used in machine learning and other domains?","answer":"<p>In machine learning, data preprocessing prepares the data for model training, handling challenges like missing values and imbalanced datasets. In natural language processing, it involves tokenization and stemming. Image processing involves resizing and normalization. Time series analysis requires handling missing data and smoothing. Data preprocessing is essential across various domains to ensure accurate and reliable results.<\/p>"},{"question":"How can data preprocessing contribute to the future of technology?","answer":"<p>The future of data preprocessing lies in automated techniques, deep learning, streaming data handling, and privacy-preserving methods. Automation will reduce manual efforts, deep learning will enable automatic feature extraction, streaming data handling will facilitate real-time insights, and privacy-preserving methods will protect sensitive information.<\/p>"},{"question":"How are proxy servers associated with data preprocessing?","answer":"<p>Proxy servers and data preprocessing are closely associated in data scraping, load balancing, geolocation-based preprocessing, and privacy protection. Proxy servers help in collecting data without IP blocks, distributing data cleaning tasks, optimizing data handling, and anonymizing user data for privacy compliance.<\/p>"},{"question":"Where can I find more information about data preprocessing?","answer":"<p>For more information about data preprocessing and its applications, you can explore the following resources:<\/p><ol><li>Data Preprocessing in Machine Learning: <a href=\"https:\/\/www.analyticsvidhya.com\/blog\/2020\/07\/types-of-data-preprocessing-techniques-in-machine-learning\/\" target=\"_new\">Link<\/a><\/li><li>A Comprehensive Guide to Data Preprocessing: <a href=\"https:\/\/www.springboard.com\/library\/data-preprocessing-tutorial\/\" target=\"_new\">Link<\/a><\/li><li>Introduction to Data Cleaning: <a href=\"https:\/\/towardsdatascience.com\/introduction-to-data-cleaning-in-machine-learning-a-complete-guide-8e6c8cdcd704\" target=\"_new\">Link<\/a><\/li><li>Feature Engineering in Machine Learning: <a href=\"https:\/\/towardsdatascience.com\/feature-engineering-in-machine-learning-336d1336118f\" target=\"_new\">Link<\/a><\/li><li>Data Preprocessing for Natural Language Processing: <a href=\"https:\/\/towardsdatascience.com\/data-preprocessing-for-nlp-text-data-cleaning-and-preprocessing-ea3ffe0406c1\" target=\"_new\">Link<\/a><\/li><\/ol><p>Join us at OneProxy to dive deeper into the world of data preprocessing and its applications in improving proxy server services.<\/p>"}]},"_links":{"self":[{"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/wiki\/476686","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/wiki"}],"about":[{"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/types\/wiki"}],"version-history":[{"count":0,"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/wiki\/476686\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/media\/468132"}],"wp:attachment":[{"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/media?parent=476686"}],"curies":[{"name":"\u53ef\u6e7f\u6027\u7c89\u5242","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}