{"id":476352,"date":"2023-08-09T07:28:31","date_gmt":"2023-08-09T07:28:31","guid":{"rendered":""},"modified":"2023-09-05T11:12:34","modified_gmt":"2023-09-05T11:12:34","slug":"computational-complexity-theory","status":"publish","type":"wiki","link":"https:\/\/oneproxy.pro\/cn\/wiki\/computational-complexity-theory\/","title":{"rendered":"\u8ba1\u7b97\u590d\u6742\u6027\u7406\u8bba"},"content":{"rendered":"

\u8ba1\u7b97\u590d\u6742\u6027\u7406\u8bba\u662f\u8ba1\u7b97\u673a\u79d1\u5b66\u7684\u4e00\u4e2a\u5206\u652f\uff0c\u7814\u7a76\u89e3\u51b3\u8ba1\u7b97\u95ee\u9898\u6240\u9700\u7684\u8d44\u6e90\u3002\u5b83\u63d0\u4f9b\u4e86\u8ba1\u7b97\u673a\u786c\u4ef6\u7684\u6570\u5b66\u62bd\u8c61\u548c\u7b97\u6cd5\u7684\u5206\u6790\uff0c\u4f7f\u5176\u6210\u4e3a\u7406\u89e3\u548c\u8bc4\u4f30\u7b97\u6cd5\u7684\u8ba1\u7b97\u6548\u7387\u4ee5\u53ca\u8ba1\u7b97\u673a\u529f\u80fd\u5c40\u9650\u6027\u7684\u91cd\u8981\u7ec4\u6210\u90e8\u5206\u3002<\/p>\n

\u8ba1\u7b97\u590d\u6742\u6027\u7406\u8bba\u7684\u8d77\u6e90<\/h2>\n

\u8ba1\u7b97\u590d\u6742\u6027\u7406\u8bba\u4f5c\u4e3a\u4e00\u4e2a\u72ec\u7279\u9886\u57df\u7684\u51fa\u73b0\u53ef\u4ee5\u8ffd\u6eaf\u5230 20 \u4e16\u7eaa 50 \u5e74\u4ee3\u548c 60 \u5e74\u4ee3\u3002\u7136\u800c\uff0c\u81ea\u7406\u8bba\u8ba1\u7b97\u673a\u79d1\u5b66\u548c\u7b97\u6cd5\u7406\u8bba\u8bde\u751f\u4ee5\u6765\uff0c\u5176\u57fa\u672c\u539f\u7406\u4e00\u76f4\u5728\u53d1\u5c55\u3002\u6700\u91cd\u8981\u7684\u91cc\u7a0b\u7891\u51fa\u73b0\u5728 1965 \u5e74\uff0c\u5f53\u65f6 Juris Hartmanis \u548c Richard Stearns \u63d0\u51fa\u4e86\u65f6\u95f4\u590d\u6742\u5ea6\u7c7b P\uff08\u591a\u9879\u5f0f\u65f6\u95f4\uff09\u548c EXP\uff08\u6307\u6570\u65f6\u95f4\uff09\uff0c\u5f00\u542f\u4e86\u8ba1\u7b97\u590d\u6742\u6027\u7684\u6b63\u5f0f\u7814\u7a76\u3002\u4ed6\u4eec\u7684\u5de5\u4f5c\u4e3a\u4ed6\u4eec\u8d62\u5f97\u4e86 1993 \u5e74\u7684\u56fe\u7075\u5956\u3002<\/p>\n

P \u4e0e NP \u95ee\u9898\u662f\u8ba1\u7b97\u673a\u79d1\u5b66\u4e2d\u6700\u8457\u540d\u7684\u672a\u89e3\u95ee\u9898\u4e4b\u4e00\uff0c\u6700\u65e9\u7531\u7ea6\u7ff0\u00b7\u7eb3\u4ec0\u4e8e 1955 \u5e74\u63d0\u51fa\uff0c\u968f\u540e\u7531\u53f2\u8482\u82ac\u00b7\u5e93\u514b\u548c\u5217\u6602\u5c3c\u5fb7\u00b7\u83b1\u6587\u4e8e 1971 \u5e74\u72ec\u7acb\u5f62\u5f0f\u5316\u3002\u8be5\u95ee\u9898\u672c\u8d28\u4e0a\u662f\u5173\u4e8e\u53ef\u5feb\u901f\u89e3\u51b3\u7684\u95ee\u9898\u4e0e\u53ef\u5feb\u901f\u68c0\u67e5\u89e3\u51b3\u65b9\u6848\u7684\u95ee\u9898\u4e4b\u95f4\u7684\u5173\u7cfb\uff0c\u5b83\u63a8\u52a8\u4e86\u8ba1\u7b97\u590d\u6742\u6027\u7406\u8bba\u7684\u5927\u90e8\u5206\u7814\u7a76\u3002<\/p>\n

\u6df1\u5165\u7814\u7a76\u8ba1\u7b97\u590d\u6742\u6027\u7406\u8bba<\/h2>\n

\u8ba1\u7b97\u590d\u6742\u6027\u7406\u8bba\u7528\u4e8e\u8861\u91cf\u89e3\u51b3\u95ee\u9898\u6240\u9700\u7684\u8ba1\u7b97\u8d44\u6e90\uff08\u5982\u65f6\u95f4\u3001\u5185\u5b58\u548c\u901a\u4fe1\uff09\u7684\u6570\u91cf\u3002\u95ee\u9898\u7684\u590d\u6742\u6027\u662f\u6839\u636e\u89e3\u51b3\u95ee\u9898\u7684\u6700\u4f73\u7b97\u6cd5\u6240\u9700\u7684\u8d44\u6e90\u6765\u5b9a\u4e49\u7684\u3002<\/p>\n

\u4e3a\u4e86\u8861\u91cf\u7b97\u6cd5\u7684\u590d\u6742\u6027\uff0c\u901a\u5e38\u5b9a\u4e49\u8f93\u5165\u5927\u5c0f\uff08\u901a\u5e38\u662f\u8868\u793a\u8f93\u5165\u6240\u9700\u7684\u4f4d\u6570\uff09\uff0c\u5e76\u5c06\u8d44\u6e90\u63cf\u8ff0\u4e3a\u8f93\u5165\u5927\u5c0f\u7684\u51fd\u6570\u3002\u590d\u6742\u6027\u7c7b\u522b\u6839\u636e\u89e3\u51b3\u95ee\u9898\u6240\u9700\u7684\u7279\u5b9a\u8ba1\u7b97\u8d44\u6e90\u91cf\u5bf9\u95ee\u9898\u8fdb\u884c\u5206\u7c7b\u3002\u590d\u6742\u6027\u7c7b\u522b\u7684\u793a\u4f8b\u5305\u62ec P\uff08\u53ef\u4ee5\u5728\u591a\u9879\u5f0f\u65f6\u95f4\u5185\u89e3\u51b3\u7684\u95ee\u9898\uff09\u3001NP\uff08\u53ef\u4ee5\u5728\u591a\u9879\u5f0f\u65f6\u95f4\u5185\u68c0\u67e5\u5176\u89e3\u51b3\u65b9\u6848\u7684\u95ee\u9898\uff09\u548c NP \u5b8c\u5168\uff08\u4efb\u4f55 NP \u95ee\u9898\u90fd\u53ef\u4ee5\u5728\u591a\u9879\u5f0f\u65f6\u95f4\u5185\u5f52\u7ed3\u4e3a\u7684\u95ee\u9898\uff09\u3002<\/p>\n

\u8ba1\u7b97\u590d\u6742\u6027\u7406\u8bba\u7684\u4e3b\u8981\u5173\u6ce8\u70b9\u662f\u786e\u5b9a\u8ba1\u7b97\u95ee\u9898\u7684\u56fa\u6709\u96be\u5ea6\uff0c\u8fd9\u901a\u5e38\uff08\u4f46\u5e76\u975e\u603b\u662f\uff09\u4ee5\u65f6\u95f4\u590d\u6742\u6027\u6765\u8868\u793a\u3002\u5982\u679c\u968f\u7740\u8f93\u5165\u89c4\u6a21\u7684\u589e\u52a0\uff0c\u89e3\u51b3\u95ee\u9898\u6240\u9700\u7684\u65f6\u95f4\u8fc5\u901f\u589e\u52a0\uff0c\u5219\u8ba4\u4e3a\u8be5\u95ee\u9898\u201c\u56f0\u96be\u201d\u3002<\/p>\n

\u8ba1\u7b97\u590d\u6742\u6027\u7406\u8bba\u7684\u673a\u5236<\/h2>\n

\u95ee\u9898\u7684\u590d\u6742\u6027\u662f\u901a\u8fc7\u6784\u5efa\u8ba1\u7b97\u6570\u5b66\u6a21\u578b\u5e76\u5206\u6790\u8fd9\u4e9b\u6a21\u578b\u6765\u786e\u5b9a\u7684\u3002\u6700\u5e38\u89c1\u7684\u6a21\u578b\u662f\u56fe\u7075\u673a\uff0c\u8fd9\u662f\u4e00\u79cd\u62bd\u8c61\u673a\u5668\uff0c\u5b83\u6839\u636e\u4e00\u7ec4\u6709\u9650\u7684\u89c4\u5219\u64cd\u7eb5\u78c1\u5e26\u4e0a\u7684\u7b26\u53f7\u3002<\/p>\n

\u8ba1\u7b97\u590d\u6742\u6027\u7684\u4e00\u4e2a\u57fa\u672c\u65b9\u9762\u662f\u95ee\u9898\u201c\u7c7b\u522b\u201d\u7684\u6982\u5ff5\uff0c\u5373\u4e00\u7ec4\u5177\u6709\u76f8\u5173\u8d44\u6e90\u590d\u6742\u6027\u7684\u95ee\u9898\u3002\u5982\u524d\u6240\u8ff0\uff0cP\u3001NP \u548c NP-complete \u662f\u95ee\u9898\u7c7b\u522b\u7684\u4f8b\u5b50\u3002\u4ee5\u8fd9\u79cd\u65b9\u5f0f\u5bf9\u95ee\u9898\u8fdb\u884c\u5206\u7c7b\u6709\u52a9\u4e8e\u63cf\u7ed8\u51fa\u54ea\u4e9b\u95ee\u9898\u5728\u8ba1\u7b97\u4e0a\u53ef\u884c\uff0c\u54ea\u4e9b\u95ee\u9898\u4e0d\u53ef\u884c\u3002<\/p>\n

\u8ba1\u7b97\u590d\u6742\u6027\u7406\u8bba\u7684\u4e3b\u8981\u7279\u5f81<\/h2>\n
    \n
  1. \n

    \u95ee\u9898\u5206\u7c7b<\/strong>\uff1a\u8ba1\u7b97\u590d\u6742\u6027\u7406\u8bba\u6839\u636e\u95ee\u9898\u7684\u590d\u6742\u6027\u5c06\u95ee\u9898\u5206\u4e3a\u4e0d\u540c\u7684\u7c7b\u522b\u3002<\/p>\n<\/li>\n

  2. \n

    \u8d44\u6e90\u4f7f\u7528\u60c5\u51b5\u6d4b\u91cf<\/strong>\uff1a\u5b83\u63d0\u4f9b\u4e86\u4e00\u79cd\u6570\u5b66\u65b9\u6cd5\u6765\u6d4b\u91cf\u7b97\u6cd5\u6240\u9700\u7684\u8d44\u6e90\u3002<\/p>\n<\/li>\n

  3. \n

    \u56fa\u6709\u95ee\u9898\u96be\u5ea6<\/strong>\uff1a\u5b83\u7814\u7a76\u8ba1\u7b97\u95ee\u9898\u7684\u56fa\u6709\u96be\u5ea6\uff0c\u800c\u4e0d\u7ba1\u4f7f\u7528\u4f55\u79cd\u7b97\u6cd5\u6765\u89e3\u51b3\u8fd9\u4e9b\u95ee\u9898\u3002<\/p>\n<\/li>\n

  4. \n

    \u8ba1\u7b97\u7684\u5c40\u9650\u6027<\/strong>\uff1a\u5b83\u8bd5\u56fe\u786e\u5b9a\u8ba1\u7b97\u4e0a\u53ef\u80fd\u548c\u4e0d\u53ef\u80fd\u7684\u754c\u9650\u3002<\/p>\n<\/li>\n

  5. \n

    \u8ba1\u7b97\u7b49\u4ef7\u6027<\/strong>\uff1a\u5b83\u901a\u8fc7\u5c55\u793a\u5404\u79cd\u95ee\u9898\u5982\u4f55\u76f8\u4e92\u8f6c\u5316\u6216\u7b80\u5316\u6765\u63ed\u793a\u8ba1\u7b97\u7b49\u4ef7\u6027\u3002<\/p>\n<\/li>\n<\/ol>\n

    \u4e0d\u540c\u7c7b\u578b\u7684\u590d\u6742\u6027\u5ea6\u91cf<\/h2>\n

    \u8861\u91cf\u95ee\u9898\u590d\u6742\u6027\u7684\u65b9\u6cd5\u6709\u5f88\u591a\u79cd\uff0c\u6bcf\u79cd\u8861\u91cf\u65b9\u6cd5\u53ef\u80fd\u5bf9\u5e94\u4e0d\u540c\u7684\u590d\u6742\u6027\u7c7b\u522b\u3002<\/p>\n\n\n\n\n\n\n\n\n\n
    \u7c7b\u578b<\/th>\n\u63cf\u8ff0<\/th>\n<\/tr>\n<\/thead>\n
    \u65f6\u95f4\u590d\u6742\u5ea6<\/td>\n\u6d4b\u91cf\u7b97\u6cd5\u6240\u9700\u7684\u8ba1\u7b97\u65f6\u95f4\u3002<\/td>\n<\/tr>\n
    \u7a7a\u95f4\u590d\u6742\u5ea6<\/td>\n\u6d4b\u91cf\u7b97\u6cd5\u4f7f\u7528\u7684\u5185\u5b58\u91cf\u3002<\/td>\n<\/tr>\n
    \u6c9f\u901a\u590d\u6742\u6027<\/td>\n\u6d4b\u91cf\u5206\u5e03\u5f0f\u8ba1\u7b97\u6240\u9700\u7684\u901a\u4fe1\u91cf\u3002<\/td>\n<\/tr>\n
    \u7535\u8def\u590d\u6742\u6027<\/td>\n\u6d4b\u91cf\u89e3\u51b3\u95ee\u9898\u7684\u5e03\u5c14\u7535\u8def\u7684\u5927\u5c0f\u3002<\/td>\n<\/tr>\n
    \u51b3\u7b56\u6811\u590d\u6742\u6027<\/td>\n\u8861\u91cf\u8ba1\u7b97\u673a\u53ea\u80fd\u505a\u51fa\u7b80\u5355\u4e8c\u5143\u51b3\u7b56\u7684\u6a21\u578b\u4e2d\u95ee\u9898\u7684\u590d\u6742\u6027\u3002<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

    \u8ba1\u7b97\u590d\u6742\u6027\u7406\u8bba\u7684\u5e94\u7528\u3001\u6311\u6218\u548c\u89e3\u51b3\u65b9\u6848<\/h2>\n

    \u8be5\u7406\u8bba\u5728\u7b97\u6cd5\u8bbe\u8ba1\u3001\u5bc6\u7801\u5b66\u3001\u6570\u636e\u7ed3\u6784\u7b49\u9886\u57df\u6709\u7740\u5e7f\u6cdb\u7684\u5e94\u7528\u3002\u5b83\u901a\u8fc7\u63d0\u4f9b\u6240\u9700\u8ba1\u7b97\u8d44\u6e90\u7684\u4e0a\u9650\u6765\u5e2e\u52a9\u8bbe\u8ba1\u9ad8\u6548\u7684\u7b97\u6cd5\u3002<\/p>\n

    \u8be5\u9886\u57df\u9762\u4e34\u7684\u4e00\u4e2a\u4e3b\u8981\u6311\u6218\u662f\u7f3a\u4e4f\u4e00\u4e9b\u6700\u5173\u952e\u95ee\u9898\uff08\u5982 P vs NP \u95ee\u9898\uff09\u7684\u6b63\u5f0f\u8bc1\u660e\u3002\u5c3d\u7ba1\u5b58\u5728\u8fd9\u4e9b\u6311\u6218\uff0c\u4f46\u8bc1\u660e\u6280\u672f\u3001\u8ba1\u7b97\u6a21\u578b\u548c\u590d\u6742\u6027\u7c7b\u522b\u7684\u4e0d\u65ad\u53d1\u5c55\u548c\u5b8c\u5584\u6b63\u5728\u7a33\u6b65\u6269\u5c55\u6211\u4eec\u5bf9\u8ba1\u7b97\u6781\u9650\u7684\u7406\u89e3\u3002<\/p>\n

    \u6bd4\u8f83\u548c\u4e3b\u8981\u7279\u5f81<\/h2>\n

    \u4e0d\u540c\u590d\u6742\u6027\u7c7b\u522b\u4e4b\u95f4\u7684\u6bd4\u8f83\u6784\u6210\u4e86\u8ba1\u7b97\u590d\u6742\u6027\u7406\u8bba\u7684\u5173\u952e\u3002<\/p>\n\n\n\n\n\n\n\n\n
    \u73ed\u7ea7<\/th>\n\u63cf\u8ff0<\/th>\n<\/tr>\n<\/thead>\n
    \u78f7<\/td>\n\u53ef\u4ee5\u5feb\u901f\u89e3\u51b3\u7684\u95ee\u9898\uff08\u591a\u9879\u5f0f\u65f6\u95f4\u5185\uff09<\/td>\n<\/tr>\n
    \u540d\u8bcd\u89e3\u91ca<\/td>\n\u4e00\u65e6\u7ed9\u51fa\u89e3\u51b3\u65b9\u6848\uff0c\u53ef\u4ee5\u5feb\u901f\u68c0\u67e5\u7684\u95ee\u9898<\/td>\n<\/tr>\n
    NP\u5b8c\u5168<\/td>\nNP \u4e2d\u6700\u96be\u7684\u95ee\u9898\uff1b\u4e00\u4e2a\u95ee\u9898\u7684\u89e3\u51b3\u65b9\u6848\u53ef\u7528\u4e8e\u89e3\u51b3 NP \u4e2d\u7684\u6240\u6709\u5176\u4ed6\u95ee\u9898<\/td>\n<\/tr>\n
    \u7ecf\u9a8c\u503c<\/td>\n\u53ef\u4ee5\u5728\u6307\u6570\u65f6\u95f4\u5185\u89e3\u51b3\u7684\u95ee\u9898<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

    \u672a\u6765\u524d\u666f\u548c\u6280\u672f\u8fdb\u6b65<\/h2>\n

    \u91cf\u5b50\u8ba1\u7b97\u548c\u673a\u5668\u5b66\u4e60\u6b63\u5728\u5851\u9020\u8ba1\u7b97\u590d\u6742\u6027\u7406\u8bba\u7684\u672a\u6765\u3002\u91cf\u5b50\u8ba1\u7b97\u5177\u6709\u6bd4\u4f20\u7edf\u8ba1\u7b97\u673a\u66f4\u5feb\u5730\u89e3\u51b3\u67d0\u4e9b\u95ee\u9898\u7684\u6f5c\u529b\uff0c\u8fd9\u4fc3\u4f7f\u4eba\u4eec\u91cd\u65b0\u8bc4\u4f30\u65e2\u5b9a\u7684\u590d\u6742\u6027\u7c7b\u522b\u3002\u53e6\u4e00\u65b9\u9762\uff0c\u673a\u5668\u5b66\u4e60\u63d0\u51fa\u4e86\u65b0\u7c7b\u578b\u7684\u8d44\u6e90\u76f8\u5173\u95ee\u9898\uff0c\u4ece\u800c\u5bfc\u81f4\u5f00\u53d1\u65b0\u7684\u590d\u6742\u6027\u5ea6\u91cf\u548c\u7c7b\u522b\u3002<\/p>\n

    \u4ee3\u7406\u548c\u8ba1\u7b97\u590d\u6742\u6027\u7406\u8bba<\/h2>\n

    \u5728\u4ee3\u7406\u670d\u52a1\u5668\u4e2d\uff0c\u8ba1\u7b97\u590d\u6742\u6027\u7406\u8bba\u53ef\u4ee5\u5e2e\u52a9\u4f18\u5316\u8bf7\u6c42\u5904\u7406\u3002\u4e86\u89e3\u8def\u7531\u7b97\u6cd5\u7684\u8ba1\u7b97\u590d\u6742\u6027\u53ef\u4ee5\u5b9e\u73b0\u66f4\u9ad8\u6548\u7684\u8bbe\u8ba1\u548c\u66f4\u597d\u7684\u8d1f\u8f7d\u5e73\u8861\u3002\u6b64\u5916\uff0c\u590d\u6742\u6027\u7406\u8bba\u53ef\u4ee5\u5e2e\u52a9\u5b9e\u73b0\u4ee3\u7406\u7684\u7a33\u5065\u5b89\u5168\u8bbe\u8ba1\uff0c\u800c\u52a0\u5bc6\u534f\u8bae\u5728\u5176\u4e2d\u53d1\u6325\u7740\u81f3\u5173\u91cd\u8981\u7684\u4f5c\u7528\u3002<\/p>\n

    \u76f8\u5173\u94fe\u63a5<\/h2>\n
      \n
    1. \u65af\u5766\u798f\u54f2\u5b66\u767e\u79d1\u5168\u4e66\uff1a\u8ba1\u7b97\u590d\u6742\u6027\u7406\u8bba<\/a><\/li>\n
    2. \u8ba1\u7b97\u590d\u6742\u6027\uff1a\u4e00\u79cd\u73b0\u4ee3\u65b9\u6cd5\uff08\u4f5c\u8005\uff1aSanjeev Arora \u548c Boaz Barak\uff09<\/a><\/li>\n
    3. P vs NP \u9875\u9762<\/a><\/li>\n<\/ol>","protected":false},"featured_media":467942,"menu_order":0,"template":"","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"class_list":["post-476352","wiki","type-wiki","status-publish","has-post-thumbnail","hentry"],"acf":{"faq_title":"Frequently Asked Questions about Computational Complexity Theory: Unfolding the Intricacies of Computational Power and Efficiency<\/mark>","faq_items":[{"question":"What is Computational Complexity Theory?","answer":"

      Computational Complexity Theory is a branch of computer science that deals with the resources required to solve computational problems. It helps understand and assess the computational efficiency of algorithms and the limitations of computing.<\/p>"},{"question":"When and how did Computational Complexity Theory originate?","answer":"

      Computational Complexity Theory originated as a distinct field in the 1950s and 1960s, but its principles were being developed from the start of theoretical computer science. The significant milestone was in 1965 when Juris Hartmanis and Richard Stearns proposed the time complexity classes P and EXP.<\/p>"},{"question":"What are the key features of Computational Complexity Theory?","answer":"

      The key features of Computational Complexity Theory include problem classification, measurement of resource usage, determination of inherent problem difficulty, identification of computational limits, and discovery of computational equivalences.<\/p>"},{"question":"What types of complexity measures exist in Computational Complexity Theory?","answer":"

      Several complexity measures exist, such as Time Complexity (computational time taken), Space Complexity (memory usage), Communication Complexity (required communication for distributed computation), Circuit Complexity (size of a boolean circuit that solves the problem), and Decision Tree Complexity (complexity of a problem in a binary decision-making model).<\/p>"},{"question":"How is Computational Complexity Theory used, and what are some related challenges?","answer":"

      Computational Complexity Theory finds applications in algorithm design, cryptography, data structures, and more. The major challenge in the field is the lack of formal proofs for crucial questions like the P vs NP problem. Continuous development of proof techniques, computational models, and complexity classes help address these challenges.<\/p>"},{"question":"How does Computational Complexity Theory relate to future technologies like Quantum Computing and Machine Learning?","answer":"

      Quantum computing, capable of solving certain problems faster than classical computers, prompts reevaluation of established complexity classes. Machine learning presents new types of resource-related questions, leading to the development of new complexity measures and classes.<\/p>"},{"question":"What is the relevance of Computational Complexity Theory to Proxy Servers?","answer":"

      Understanding the computational complexity of routing algorithms can lead to more efficient design and better load balancing in proxy servers. Complexity theory can also assist in robust security design for proxies where cryptographic protocols play a vital role.<\/p>"}]},"_links":{"self":[{"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/wiki\/476352","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\/476352\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/media\/467942"}],"wp:attachment":[{"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/media?parent=476352"}],"curies":[{"name":"\u53ef\u6e7f\u6027\u7c89\u5242","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}