{"id":476984,"date":"2023-08-09T09:06:01","date_gmt":"2023-08-09T09:06:01","guid":{"rendered":""},"modified":"2023-09-05T11:13:47","modified_gmt":"2023-09-05T11:13:47","slug":"double-precision-floating-point-format","status":"publish","type":"wiki","link":"https:\/\/oneproxy.pro\/cn\/wiki\/double-precision-floating-point-format\/","title":{"rendered":"\u53cc\u7cbe\u5ea6\u6d6e\u70b9\u683c\u5f0f"},"content":{"rendered":"<p>\u53cc\u7cbe\u5ea6\u6d6e\u70b9\u683c\u5f0f\uff0c\u901a\u5e38\u79f0\u4e3a\u201cdouble\u201d\uff0c\u662f\u4e00\u79cd\u7528\u4e8e\u8ba1\u7b97\u7684\u6570\u503c\u8868\u793a\u65b9\u6cd5\uff0c\u7528\u4e8e\u5b58\u50a8\u548c\u5904\u7406\u5b9e\u6570\uff0c\u4e0e\u5355\u7cbe\u5ea6\u683c\u5f0f\u76f8\u6bd4\uff0c\u7cbe\u5ea6\u66f4\u9ad8\u3002\u5b83\u5e7f\u6cdb\u5e94\u7528\u4e8e\u79d1\u5b66\u8ba1\u7b97\u3001\u5de5\u7a0b\u3001\u56fe\u5f62\u548c\u91d1\u878d\u5e94\u7528\u7b49\u5bf9\u7cbe\u5ea6\u548c\u8303\u56f4\u8981\u6c42\u4e25\u683c\u7684\u9886\u57df\u3002<\/p>\n<h2>\u53cc\u7cbe\u5ea6\u6d6e\u70b9\u683c\u5f0f\u7684\u8d77\u6e90\u5386\u53f2\u4ee5\u53ca\u9996\u6b21\u63d0\u53ca\u3002<\/h2>\n<p>\u6d6e\u70b9\u6570\u7684\u6982\u5ff5\u53ef\u4ee5\u8ffd\u6eaf\u5230\u8ba1\u7b97\u673a\u53d1\u5c55\u7684\u65e9\u671f\u3002\u968f\u7740 20 \u4e16\u7eaa 40 \u5e74\u4ee3\u6570\u5b57\u8ba1\u7b97\u673a\u7684\u53d1\u5c55\uff0c\u5bf9\u5b9e\u6570\u7684\u6807\u51c6\u8868\u793a\u7684\u9700\u6c42\u4e5f\u968f\u4e4b\u4ea7\u751f\u30021957 \u5e74\uff0cIBM 704 \u5927\u578b\u8ba1\u7b97\u673a\u5f15\u5165\u4e86\u7b2c\u4e00\u79cd\u53cc\u7cbe\u5ea6\u683c\u5f0f\uff0c\u8be5\u683c\u5f0f\u4f7f\u7528 36 \u4f4d\u6765\u8868\u793a\u5b9e\u6570\uff0c\u5176\u4e2d\u6709 1 \u4e2a\u7b26\u53f7\u4f4d\u30018 \u4f4d\u6307\u6570\u548c 27 \u4f4d\u5c0f\u6570\u3002\u7136\u800c\uff0c\u8fd9\u79cd\u683c\u5f0f\u5e76\u672a\u5f97\u5230\u5e7f\u6cdb\u91c7\u7528\u3002<\/p>\n<p>\u73b0\u4ee3\u53cc\u7cbe\u5ea6\u6d6e\u70b9\u683c\u5f0f\u7531 IEEE 754 \u6807\u51c6\u5b9a\u4e49\uff0c\u4e8e 1985 \u5e74\u9996\u6b21\u53d1\u5e03\u3002\u8be5\u6807\u51c6\u6307\u5b9a\u4e86\u53cc\u7cbe\u5ea6\u6570\u7684\u4e8c\u8fdb\u5236\u8868\u793a\u548c\u7b97\u672f\u8fd0\u7b97\u89c4\u5219\uff0c\u786e\u4fdd\u4e86\u4e0d\u540c\u8ba1\u7b97\u673a\u67b6\u6784\u4e4b\u95f4\u7684\u4e00\u81f4\u6027\u3002<\/p>\n<h2>\u5173\u4e8e\u53cc\u7cbe\u5ea6\u6d6e\u70b9\u683c\u5f0f\u7684\u8be6\u7ec6\u4fe1\u606f\u3002\u6269\u5c55\u4e3b\u9898\u53cc\u7cbe\u5ea6\u6d6e\u70b9\u683c\u5f0f\u3002<\/h2>\n<h3>IEEE 754 \u6807\u51c6<\/h3>\n<p>IEEE 754 \u6807\u51c6\u5c06\u53cc\u7cbe\u5ea6\u6d6e\u70b9\u683c\u5f0f\u5b9a\u4e49\u4e3a 64 \u4f4d\u4e8c\u8fdb\u5236\u8868\u793a\u3002\u5b83\u4f7f\u7528\u4e00\u4e2a\u7b26\u53f7\u4f4d\u6765\u8868\u793a\u6570\u5b57\u7684\u7b26\u53f7\uff0c\u4f7f\u7528 11 \u4f4d\u6307\u6570\u6765\u8868\u793a\u6570\u5b57\u7684\u5927\u5c0f\uff0c\u4f7f\u7528 52 \u4f4d\u5c0f\u6570\uff08\u4e5f\u79f0\u4e3a\u6709\u6548\u6570\u5b57\u6216\u5c3e\u6570\uff09\u6765\u5b58\u50a8\u6570\u5b57\u7684\u5c0f\u6570\u90e8\u5206\u3002\u4e0e\u5355\u7cbe\u5ea6\u683c\u5f0f\u76f8\u6bd4\uff0c\u8be5\u683c\u5f0f\u5141\u8bb8\u66f4\u5e7f\u6cdb\u7684\u503c\u8303\u56f4\u548c\u66f4\u9ad8\u7684\u7cbe\u5ea6\u3002<\/p>\n<h3>\u8868\u73b0\u548c\u7cbe\u5ea6<\/h3>\n<p>\u5728\u53cc\u7cbe\u5ea6\u683c\u5f0f\u4e2d\uff0c\u6570\u5b57\u8868\u793a\u4e3a \u00b1 m \u00d7 2^e\uff0c\u5176\u4e2d m \u662f\u5206\u6570\uff0ce \u662f\u6307\u6570\u3002\u7b26\u53f7\u4f4d\u51b3\u5b9a\u6570\u5b57\u7684\u7b26\u53f7\uff0c\u800c\u6307\u6570\u5b57\u6bb5\u63d0\u4f9b\u7f29\u653e\u56e0\u5b50\u3002\u5206\u6570\u5305\u542b\u6570\u5b57\u7684\u6709\u6548\u6570\u5b57\u300252 \u4f4d\u5206\u6570\u5141\u8bb8\u5927\u7ea6 15 \u5230 17 \u4f4d\u5c0f\u6570\u7684\u7cbe\u5ea6\uff0c\u4f7f\u5176\u9002\u5408\u51c6\u786e\u8868\u793a\u5404\u79cd\u5b9e\u6570\u3002<\/p>\n<h3>\u503c\u7684\u8303\u56f4<\/h3>\n<p>\u4e0e\u5355\u7cbe\u5ea6\u683c\u5f0f\u76f8\u6bd4\uff0c\u53cc\u7cbe\u5ea6\u683c\u5f0f\u63d0\u4f9b\u4e86\u66f4\u5927\u8303\u56f4\u7684\u53ef\u8868\u793a\u503c\u3002\u6307\u6570\u7684 11 \u4f4d\u5141\u8bb8\u503c\u7684\u8303\u56f4\u4ece\u5927\u7ea6 10^-308 \u5230 10^308\uff0c\u6db5\u76d6\u4e86\u4ece\u6781\u5c0f\u5230\u6781\u5927\u7684\u5927\u91cf\u5b9e\u6570\u3002<\/p>\n<h3>\u7b97\u672f\u8fd0\u7b97<\/h3>\n<p>\u53cc\u7cbe\u5ea6\u6570\u7684\u7b97\u672f\u8fd0\u7b97\u9075\u5faa IEEE 754 \u6807\u51c6\u4e2d\u6307\u5b9a\u7684\u89c4\u5219\u3002\u8fd9\u4e9b\u8fd0\u7b97\u5305\u62ec\u52a0\u6cd5\u3001\u51cf\u6cd5\u3001\u4e58\u6cd5\u548c\u9664\u6cd5\u3002\u867d\u7136\u53cc\u7cbe\u5ea6\u7b97\u672f\u6bd4\u5355\u7cbe\u5ea6\u7b97\u672f\u63d0\u4f9b\u66f4\u9ad8\u7684\u7cbe\u5ea6\uff0c\u4f46\u5b83\u4e0d\u80fd\u907f\u514d\u820d\u5165\u8bef\u5dee\uff0c\u5728\u5173\u952e\u5e94\u7528\u4e2d\u5e94\u8c28\u614e\u4f7f\u7528\u3002<\/p>\n<h2>\u53cc\u7cbe\u5ea6\u6d6e\u70b9\u683c\u5f0f\u7684\u5185\u90e8\u7ed3\u6784\u3002\u53cc\u7cbe\u5ea6\u6d6e\u70b9\u683c\u5f0f\u7684\u5de5\u4f5c\u539f\u7406\u3002<\/h2>\n<p>\u53cc\u7cbe\u5ea6\u6d6e\u70b9\u683c\u5f0f\u4ee5\u4e8c\u8fdb\u5236\u683c\u5f0f\u5b58\u50a8\u6570\u5b57\uff0c\u8fd9\u5141\u8bb8\u5728\u73b0\u4ee3\u8ba1\u7b97\u673a\u67b6\u6784\u4e0a\u8fdb\u884c\u9ad8\u6548\u8ba1\u7b97\u3002\u5185\u90e8\u7ed3\u6784\u7531\u4e09\u4e2a\u4e3b\u8981\u90e8\u5206\u7ec4\u6210\uff1a\u7b26\u53f7\u4f4d\u3001\u6307\u6570\u5b57\u6bb5\u548c\u5c0f\u6570\uff08\u6216\u6709\u6548\u6570\u5b57\uff09\u3002<\/p>\n<h3>\u7b26\u53f7\u4f4d<\/h3>\n<p>\u7b26\u53f7\u4f4d\u662f 64 \u4f4d\u8868\u793a\u6cd5\u4e2d\u6700\u5de6\u8fb9\u7684\u4f4d\u3002\u6b63\u6570\u8bbe\u7f6e\u4e3a 0\uff0c\u8d1f\u6570\u8bbe\u7f6e\u4e3a 1\u3002\u8fd9\u79cd\u7b80\u5355\u7684\u8868\u793a\u6cd5\u5141\u8bb8\u5728\u7b97\u672f\u8fd0\u7b97\u671f\u95f4\u5feb\u901f\u786e\u5b9a\u6570\u5b57\u7684\u7b26\u53f7\u3002<\/p>\n<h3>\u6307\u6570\u5b57\u6bb5<\/h3>\n<p>11 \u4f4d\u6307\u6570\u5b57\u6bb5\u4f4d\u4e8e\u7b26\u53f7\u4f4d\u4e4b\u540e\u3002\u5b83\u8868\u793a\u6570\u5b57\u7684\u5927\u5c0f\u5e76\u63d0\u4f9b\u5206\u6570\u7684\u7f29\u653e\u56e0\u5b50\u3002\u4e3a\u4e86\u89e3\u91ca\u6307\u6570\u503c\uff0c\u5c06 1023 \u7684\u504f\u5dee\u6dfb\u52a0\u5230\u5b58\u50a8\u7684\u503c\u4e2d\u3002\u6b64\u504f\u5dee\u5141\u8bb8\u8868\u793a\u6b63\u6307\u6570\u548c\u8d1f\u6307\u6570\u3002<\/p>\n<h3>\u5206\u6570\uff08\u6709\u6548\u6570\u5b57\uff09<\/h3>\n<p>\u5c0f\u6570\u5b57\u6bb5\u662f 64 \u4f4d\u8868\u793a\u6cd5\u7684\u5269\u4f59 52 \u4f4d\u3002\u5b83\u4ee5\u4e8c\u8fdb\u5236\u5f62\u5f0f\u5b58\u50a8\u6570\u5b57\u7684\u6709\u6548\u6570\u5b57\u3002\u7531\u4e8e\u5c0f\u6570\u7684\u5bbd\u5ea6\u56fa\u5b9a\u4e3a 52 \u4f4d\uff0c\u56e0\u6b64\u5728\u67d0\u4e9b\u7b97\u672f\u8fd0\u7b97\u8fc7\u7a0b\u4e2d\uff0c\u524d\u5bfc\u96f6\u6216\u4e00\u53ef\u80fd\u4f1a\u88ab\u622a\u65ad\u6216\u820d\u5165\uff0c\u4ece\u800c\u53ef\u80fd\u5bfc\u81f4\u8f7b\u5fae\u7684\u4e0d\u51c6\u786e\u6027\u3002<\/p>\n<p>\u53cc\u7cbe\u5ea6\u683c\u5f0f\u4f7f\u7528\u89c4\u8303\u5316\u6765\u786e\u4fdd\u5c0f\u6570\u7684\u6700\u9ad8\u6709\u6548\u4f4d\u59cb\u7ec8\u4e3a 1\uff08\u96f6\u503c\u9664\u5916\uff09\u3002\u6b64\u6280\u672f\u4f18\u5316\u4e86\u53ef\u8868\u793a\u6570\u5b57\u7684\u7cbe\u5ea6\u548c\u8303\u56f4\u3002<\/p>\n<h2>\u53cc\u7cbe\u5ea6\u6d6e\u70b9\u683c\u5f0f\u7684\u5173\u952e\u7279\u6027\u5206\u6790\u3002<\/h2>\n<p>\u53cc\u7cbe\u5ea6\u6d6e\u70b9\u683c\u5f0f\u7684\u4e3b\u8981\u7279\u70b9\u5305\u62ec\uff1a<\/p>\n<ol>\n<li>\n<p><strong>\u7cbe\u786e<\/strong>\uff1a\u53cc\u7cbe\u5ea6\u683c\u5f0f\u4f7f\u7528 52 \u4f4d\u4e13\u95e8\u8868\u793a\u5c0f\u6570\uff0c\u53ef\u4ee5\u9ad8\u7cbe\u5ea6\u5730\u8868\u793a\u5b9e\u6570\uff0c\u9002\u5408\u9700\u8981\u7cbe\u786e\u8ba1\u7b97\u7684\u79d1\u5b66\u548c\u5de5\u7a0b\u5e94\u7528\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u8303\u56f4<\/strong>\uff1a11 \u4f4d\u6307\u6570\u63d0\u4f9b\u4e86\u53ef\u8868\u793a\u7684\u5e7f\u6cdb\u503c\uff0c\u4ece\u6781\u5c0f\u7684\u6570\u5b57\u5230\u6781\u5927\u7684\u6570\u5b57\uff0c\u4f7f\u5f97\u53cc\u7cbe\u5ea6\u683c\u5f0f\u9002\u7528\u4e8e\u5404\u79cd\u5e94\u7528\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u517c\u5bb9\u6027<\/strong>\uff1aIEEE 754 \u6807\u51c6\u786e\u4fdd\u4e0d\u540c\u8ba1\u7b97\u673a\u67b6\u6784\u4e4b\u95f4\u7684\u4e00\u81f4\u6027\uff0c\u5141\u8bb8\u4e0d\u540c\u7cfb\u7edf\u4e4b\u95f4\u65e0\u7f1d\u4ea4\u6362\u53cc\u7cbe\u5ea6\u6570\u5b57\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u6548\u7387<\/strong>\uff1a\u5c3d\u7ba1\u4e0e\u5355\u7cbe\u5ea6\u76f8\u6bd4\u53cc\u7cbe\u5ea6\u7b97\u6cd5\u66f4\u5927\uff0c\u4f46\u73b0\u4ee3\u5904\u7406\u5668\u53ef\u4ee5\u9ad8\u6548\u5904\u7406\u53cc\u7cbe\u5ea6\u7b97\u6cd5\uff0c\u8fd9\u4f7f\u5176\u6210\u4e3a\u6027\u80fd\u5173\u952e\u578b\u5e94\u7528\u7a0b\u5e8f\u7684\u5b9e\u7528\u9009\u62e9\u3002<\/p>\n<\/li>\n<\/ol>\n<h2>\u5199\u51fa\u53cc\u7cbe\u5ea6\u6d6e\u70b9\u683c\u5f0f\u6709\u54ea\u4e9b\u7c7b\u578b\u3002\u4f7f\u7528\u8868\u683c\u548c\u5217\u8868\u6765\u5199\u3002<\/h2>\n<p>\u5728\u8ba1\u7b97\u9886\u57df\uff0c\u6700\u5e38\u89c1\u7684\u53cc\u7cbe\u5ea6\u6d6e\u70b9\u683c\u5f0f\u662f IEEE 754 \u6807\u51c6\uff0c\u5b83\u4f7f\u7528 64 \u4f4d\u4e8c\u8fdb\u5236\u8868\u793a\u3002\u4e0d\u8fc7\uff0c\u5728\u4e13\u95e8\u7684\u5e94\u7528\u7a0b\u5e8f\u4e2d\uff0c\u7279\u522b\u662f\u5728\u786c\u4ef6\u548c\u5d4c\u5165\u5f0f\u7cfb\u7edf\u4e2d\uff0c\u8fd8\u4f7f\u7528\u4e86\u5176\u4ed6\u8868\u793a\u5f62\u5f0f\u3002\u5176\u4e2d\u4e00\u4e9b\u66ff\u4ee3\u683c\u5f0f\u5305\u62ec\uff1a<\/p>\n<ol>\n<li>\n<p><strong>\u6269\u5c55\u7cbe\u5ea6<\/strong>\uff1a\u67d0\u4e9b\u5904\u7406\u5668\u548c\u6570\u5b66\u5e93\u5b9e\u73b0\u4e86\u6269\u5c55\u7cbe\u5ea6\u683c\u5f0f\uff0c\u5206\u6570\u4f4d\u6570\u66f4\u591a\uff08\u4f8b\u5982 80 \u4f4d\uff09\u3002\u8fd9\u4e9b\u683c\u5f0f\u4e3a\u67d0\u4e9b\u8ba1\u7b97\u63d0\u4f9b\u4e86\u66f4\u9ad8\u7684\u7cbe\u5ea6\uff0c\u4f46\u4e0d\u540c\u7cfb\u7edf\u4e4b\u95f4\u5e76\u672a\u6807\u51c6\u5316\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u81ea\u5b9a\u4e49\u786c\u4ef6\u683c\u5f0f<\/strong>\uff1a\u67d0\u4e9b\u4e13\u7528\u786c\u4ef6\u53ef\u80fd\u4f1a\u4f7f\u7528\u9488\u5bf9\u7279\u5b9a\u5e94\u7528\u7a0b\u5e8f\u5b9a\u5236\u7684\u975e\u6807\u51c6\u683c\u5f0f\u3002\u8fd9\u4e9b\u683c\u5f0f\u53ef\u4ee5\u4f18\u5316\u7279\u5b9a\u4efb\u52a1\u7684\u6027\u80fd\u548c\u5185\u5b58\u4f7f\u7528\u7387\u3002<\/p>\n<\/li>\n<\/ol>\n<h2>\u53cc\u7cbe\u5ea6\u6d6e\u70b9\u683c\u5f0f\u7684\u4f7f\u7528\u65b9\u6cd5\uff0c\u4f7f\u7528\u4e2d\u9047\u5230\u7684\u95ee\u9898\u53ca\u89e3\u51b3\u65b9\u6cd5\u3002<\/h2>\n<h3>\u4f7f\u7528\u53cc\u7cbe\u5ea6\u6d6e\u70b9\u683c\u5f0f\u7684\u65b9\u6cd5<\/h3>\n<ol>\n<li>\n<p><strong>\u79d1\u5b66\u8ba1\u7b97<\/strong>\uff1a\u53cc\u7cbe\u5ea6\u683c\u5f0f\u901a\u5e38\u7528\u4e8e\u79d1\u5b66\u6a21\u62df\u3001\u6570\u503c\u5206\u6790\u548c\u6570\u5b66\u5efa\u6a21\uff0c\u8fd9\u4e9b\u9886\u57df\u5bf9\u9ad8\u7cbe\u5ea6\u548c\u51c6\u786e\u5ea6\u6709\u5f88\u9ad8\u7684\u8981\u6c42\u3002<\/p>\n<\/li>\n<li>\n<p><strong>\u56fe\u5f62\u548c\u6e32\u67d3<\/strong>\uff1a3D 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OneProxy 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href=\"https:\/\/ieeexplore.ieee.org\/abstract\/720193\" target=\"_new\" rel=\"noopener nofollow\">IEEE 754 \u6807\u51c6<\/a><\/li>\n<li><a href=\"https:\/\/www.mathworks.com\/help\/matlab\/matlab_prog\/floating-point-numbers-with-double-precision.html\" target=\"_new\" rel=\"noopener nofollow\">\u53cc\u7cbe\u5ea6\u6570\u503c\u8ba1\u7b97<\/a><\/li>\n<li><a href=\"https:\/\/docs.oracle.com\/cd\/E19957-01\/806-3568\/ncg_goldberg.html\" target=\"_new\" rel=\"noopener nofollow\">\u6d6e\u70b9\u8fd0\u7b97\u7b80\u4ecb<\/a><\/li>\n<\/ul>","protected":false},"featured_media":468266,"menu_order":0,"template":"","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"class_list":["post-476984","wiki","type-wiki","status-publish","has-post-thumbnail","hentry"],"acf":{"faq_title":"Frequently Asked Questions about <mark>Double-precision floating-point format<\/mark>","faq_items":[{"question":"What is Double-precision floating-point format?","answer":"<p>Double-precision floating-point format, also known as \"double,\" is a numerical representation method used in computing to store and manipulate real numbers with increased precision compared to single-precision formats. It uses 64 bits to represent a number, allowing for approximately 15 to 17 decimal digits of precision.<\/p>"},{"question":"How does Double-precision floating-point format work?","answer":"<p>The format uses a sign bit to indicate the sign of the number, an 11-bit exponent to represent the magnitude of the number, and a 52-bit fraction to store the fractional part. The numbers are represented as \u00b1 m \u00d7 2^e, where m is the fraction and e is the exponent. This allows for a wide range of values, from about 10^-308 to 10^308.<\/p>"},{"question":"Where is Double-precision floating-point format used?","answer":"<p>Double-precision format finds applications in scientific computing, engineering, graphics, financial analysis, and more. Any field that requires high precision and a broad range of representable values can benefit from double-precision format.<\/p>"},{"question":"What is the difference between Double-precision and Single-precision formats?","answer":"<p>The main difference is in the number of bits used for representation. Double-precision uses 64 bits, while single-precision uses 32 bits. As a result, double-precision provides higher precision and a larger range of representable values.<\/p>"},{"question":"Are there alternative formats to Double-precision?","answer":"<p>Yes, there are alternative formats, such as extended precision formats with more than 64 bits for the fraction. However, these formats are not standardized and may vary across different systems and applications.<\/p>"},{"question":"How is Double-precision used in graphics and rendering?","answer":"<p>In 3D graphics rendering and image processing applications, double-precision format is used to avoid artifacts and maintain visual fidelity, especially in complex and high-precision calculations.<\/p>"},{"question":"Can Double-precision format suffer from rounding errors?","answer":"<p>Yes, like any floating-point format, double-precision arithmetic can suffer from rounding errors, particularly in iterative calculations. Careful consideration of numerical methods can help mitigate these errors.<\/p>"},{"question":"How does the future of computing impact Double-precision format?","answer":"<p>Advancements in hardware and computing technologies may lead to improved precision and performance. Quantum computing, mixed-precision algorithms, and improved standards are some of the potential future developments.<\/p>"},{"question":"How are proxy servers associated with Double-precision floating-point format?","answer":"<p>While proxy servers themselves are not directly related to double-precision format, they can indirectly benefit applications that rely on double-precision computations. Proxy servers can enhance secure data transmission, accelerate communication, and optimize content delivery for such applications.<\/p>"}]},"_links":{"self":[{"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/wiki\/476984","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\/476984\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/media\/468266"}],"wp:attachment":[{"href":"https:\/\/oneproxy.pro\/cn\/wp-json\/wp\/v2\/media?parent=476984"}],"curies":[{"name":"\u53ef\u6e7f\u6027\u7c89\u5242","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}