{"id":29042,"date":"2025-04-20T05:26:19","date_gmt":"2025-04-19T21:26:19","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/29042.html"},"modified":"2025-04-20T05:26:19","modified_gmt":"2025-04-19T21:26:19","slug":"%e3%80%8cai-%e5%8a%a0%e6%8c%81%e7%9a%84%e9%ab%98%e6%95%88%e6%9e%b6%e6%9e%84%e3%80%8d%e9%ab%98%e5%b9%b6%e5%8f%91%e5%9c%ba%e6%99%af%e4%b8%8b%e7%9a%84%e6%9c%8d%e5%8a%a1%e5%99%a8%e6%88%90%e6%9c%ac","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/29042.html","title":{"rendered":"\u300cAI \u52a0\u6301\u7684\u9ad8\u6548\u67b6\u6784\u300d\u9ad8\u5e76\u53d1\u573a\u666f\u4e0b\u7684\u670d\u52a1\u5668\u6210\u672c\u4f18\u5316"},"content":{"rendered":"

\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\"<\/p>\n

\u7f51\u7f57\u5f00\u53d1<\/b><\/font>
\n （\u5c0f\u7ea2\u4e66\u3001\u5feb\u624b\u3001\u89c6\u9891\u53f7\u540c\u540d）<\/b><\/font> <\/p>\n

\u2003\u2003\u5927\u5bb6\u597d，\u6211\u662f \u5c55\u83f2<\/font>，\u76ee\u524d\u5728\u4e0a\u5e02\u4f01\u4e1a\u4ece\u4e8b\u4eba\u5de5\u667a\u80fd\u9879\u76ee\u7814\u53d1\u7ba1\u7406\u5de5\u4f5c，\u5e73\u65f6\u70ed\u8877\u4e8e\u5206\u4eab\u5404\u79cd\u7f16\u7a0b\u9886\u57df\u7684\u8f6f\u786c\u6280\u80fd\u77e5\u8bc6\u4ee5\u53ca\u524d\u6cbf\u6280\u672f，\u5305\u62eciOS\u3001\u524d\u7aef\u3001Harmony OS\u3001Java\u3001Python<\/font>\u7b49\u65b9\u5411\u3002\u5728\u79fb\u52a8\u7aef\u5f00\u53d1\u3001\u9e3f\u8499\u5f00\u53d1\u3001\u7269\u8054\u7f51\u3001\u5d4c\u5165\u5f0f\u3001\u4e91\u539f\u751f\u3001\u5f00\u6e90<\/font>\u7b49\u9886\u57df\u6709\u6df1\u539a\u9020\u8be3\u3002<\/p>\n

\u56fe\u4e66\u4f5c\u8005：\u300aESP32-C3 \u7269\u8054\u7f51\u5de5\u7a0b\u5f00\u53d1\u5b9e\u6218\u300b \u56fe\u4e66\u4f5c\u8005：\u300aSwiftUI \u5165\u95e8，\u8fdb\u9636\u4e0e\u5b9e\u6218\u300b \u8d85\u7ea7\u4e2a\u4f53：COC\u4e0a\u6d77\u793e\u533a\u4e3b\u7406\u4eba \u7279\u7ea6\u8bb2\u5e08：\u5927\u5b66\u8bb2\u5e08，\u8c37\u6b4c\u4e9a\u9a6c\u900a\u5206\u4eab\u5609\u5bbe \u79d1\u6280\u535a\u4e3b：\u6781\u661f\u4f1a\u9996\u6279\u7b7e\u7ea6\u4f5c\u8005<\/b><\/font><\/b><\/font><\/b><\/font><\/b><\/font><\/b><\/font><\/b><\/font><\/b><\/font><\/b><\/font><\/b><\/font><\/b><\/font><\/p>\n<\/p>\n

\u6587\u7ae0\u76ee\u5f55<\/h4>\n
    \n
  • \n
      \n
    • \u6458\u8981<\/li>\n
    • \u5f15\u8a00<\/li>\n
    • \n
        \n
      • \u9ad8\u5e76\u53d1\u7684\u6311\u6218<\/li>\n
      • \u5b9e\u9645\u573a\u666f\u5206\u6790<\/li>\n
      • \n
          \n
        • \u573a\u666f 1：\u7535\u5546\u79d2\u6740\u6d3b\u52a8<\/li>\n
        • \u573a\u666f 2：\u77ed\u89c6\u9891\/\u76f4\u64ad\u5e73\u53f0<\/li>\n
        • \u573a\u666f 3：AI \u8ba1\u7b97\u670d\u52a1<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/li>\n
        • \u9650\u6d41\u7b56\u7565<\/li>\n
        • \n
            \n
          • \u4ee4\u724c\u6876\u7b97\u6cd5\u9650\u6d41<\/li>\n
          • AI \u667a\u80fd\u9650\u6d41<\/li>\n<\/ul>\n<\/li>\n
          • \u7f13\u5b58\u4f18\u5316<\/li>\n
          • \n
              \n
            • Redis \u7f13\u5b58\u70ed\u70b9\u6570\u636e<\/li>\n
            • CDN \u7f13\u5b58\u9759\u6001\u8d44\u6e90<\/li>\n<\/ul>\n<\/li>\n
            • \u6d88\u606f\u961f\u5217\u4f18\u5316\u8bf7\u6c42\u5904\u7406<\/li>\n
            • \n
                \n
              • RabbitMQ \u4efb\u52a1\u961f\u5217<\/li>\n
              • AI \u8c03\u5ea6\u961f\u5217\u4f18\u5316<\/li>\n<\/ul>\n<\/li>\n
              • QA \u73af\u8282<\/li>\n
              • \n
                  \n
                • Q1: \u4f20\u7edf\u9650\u6d41\u4e0e AI \u9650\u6d41\u7684\u533a\u522b？<\/li>\n
                • Q2: \u6d88\u606f\u961f\u5217\u5982\u4f55\u4f18\u5316\u9ad8\u5e76\u53d1\u8bf7\u6c42？<\/li>\n<\/ul>\n<\/li>\n
                • \u603b\u7ed3<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n

                  \u6458\u8981<\/h3>\n

                  \u5728\u9ad8\u5e76\u53d1\u573a\u666f\u4e0b，\u670d\u52a1\u5668\u8d44\u6e90\u6d88\u8017\u5de8\u5927，\u5bfc\u81f4\u8fd0\u7ef4\u6210\u672c\u98d9\u5347\u3002\u672c\u7bc7\u6587\u7ae0\u5c06\u63a2\u8ba8 \u9650\u6d41\u3001\u7f13\u5b58\u3001\u961f\u5217 \u7b49\u6280\u672f\u624b\u6bb5，\u5e76\u7ed3\u5408 AI \u6280\u672f\u4f18\u5316\u670d\u52a1\u5668\u8d1f\u8f7d，\u964d\u4f4e\u8ba1\u7b97\u6210\u672c\u3002\u901a\u8fc7\u5177\u4f53\u6848\u4f8b\u5206\u6790，\u63d0\u4f9b\u66f4\u7ecf\u6d4e\u9ad8\u6548\u7684\u9ad8\u5e76\u53d1\u67b6\u6784\u89e3\u51b3\u65b9\u6848\u3002<\/p>\n

                  \u5f15\u8a00<\/h3>\n

                  \u9ad8\u5e76\u53d1\u7684\u6311\u6218<\/h4>\n

                  \u968f\u7740\u4e92\u8054\u7f51\u4e1a\u52a1\u589e\u957f，\u7cfb\u7edf\u9700\u8981\u627f\u53d7 \u9ad8\u5e76\u53d1\u8bf7\u6c42，\u9762\u4e34\u4ee5\u4e0b\u6311\u6218：<\/p>\n

                    \n
                  • \u77ac\u65f6\u6d41\u91cf\u6fc0\u589e：\u79d2\u6740\u3001\u62a2\u8d2d\u3001\u70ed\u95e8\u6d3b\u52a8\u5e26\u6765\u7684\u8d85\u9ad8\u6d41\u91cf\u3002<\/li>\n
                  • \u8d44\u6e90\u6d88\u8017\u5de8\u5927：\u6570\u636e\u5e93\u3001\u670d\u52a1\u5668\u3001\u7f51\u7edc\u5e26\u5bbd\u538b\u529b\u5927，\u6210\u672c\u6500\u5347\u3002<\/li>\n
                  • \u54cd\u5e94\u901f\u5ea6\u4e0b\u964d：\u5ef6\u8fdf\u589e\u52a0，\u7528\u6237\u4f53\u9a8c\u53d8\u5dee，\u751a\u81f3\u51fa\u73b0\u7cfb\u7edf\u5d29\u6e83\u3002<\/li>\n
                  • \u67b6\u6784\u590d\u6742\u5ea6\u63d0\u9ad8：\u4f20\u7edf\u6c34\u5e73\u6269\u5c55（\u52a0\u673a\u5668）\u6210\u672c\u9ad8\u6602，\u7ba1\u7406\u590d\u6742\u3002<\/li>\n<\/ul>\n

                    \u5b9e\u9645\u573a\u666f\u5206\u6790<\/h4>\n

                    \u573a\u666f 1：\u7535\u5546\u79d2\u6740\u6d3b\u52a8<\/h5>\n

                    \u7528\u6237\u5728\u540c\u4e00\u65f6\u95f4\u5927\u89c4\u6a21\u8bbf\u95ee\u5546\u54c1\u8be6\u60c5\u9875\u3001\u52a0\u5165\u8d2d\u7269\u8f66\u3001\u4e0b\u5355，\u6570\u636e\u5e93\u538b\u529b\u5de8\u5927，\u5bfc\u81f4\u7cfb\u7edf\u5d29\u6e83\u3002<\/p>\n

                    \u573a\u666f 2：\u77ed\u89c6\u9891\/\u76f4\u64ad\u5e73\u53f0<\/h5>\n

                    \u9ad8\u5e76\u53d1\u8bbf\u95ee\u89c6\u9891\u5185\u5bb9，CDN \u8d1f\u8f7d\u5267\u589e，\u6570\u636e\u5e93\u67e5\u8be2\u538b\u529b\u589e\u5927，\u5f71\u54cd\u89c2\u770b\u4f53\u9a8c\u3002<\/p>\n

                    \u573a\u666f 3：AI \u8ba1\u7b97\u670d\u52a1<\/h5>\n

                    \u5927\u91cf\u7528\u6237\u8bf7\u6c42 AI \u9884\u6d4b\u6216\u63a8\u8350\u670d\u52a1，\u5bfc\u81f4\u63a8\u7406\u670d\u52a1\u5668\u8d85\u8d1f\u8377\u8fd0\u884c，GPU \u8d44\u6e90\u6210\u672c\u98d9\u5347\u3002<\/p>\n

                    \u672c\u6587\u5c06\u4ece \u9650\u6d41\u3001\u7f13\u5b58\u3001\u961f\u5217\u3001AI \u8c03\u5ea6 \u7b49\u65b9\u9762\u63d0\u4f9b\u4f18\u5316\u65b9\u6848，\u964d\u4f4e\u670d\u52a1\u5668\u6210\u672c\u3002<\/p>\n

                    \u9650\u6d41\u7b56\u7565<\/h3>\n

                    \u4ee4\u724c\u6876\u7b97\u6cd5\u9650\u6d41<\/h4>\n

                    \u4f7f\u7528 Redis + Lua \u5b9e\u73b0\u9650\u6d41\u3002<\/p>\n

                    –<\/span>–<\/span> Redis \u4ee4\u724c\u6876\u7b97\u6cd5\u5b9e\u73b0
                    \nlocal key =<\/span> KEYS[<\/span>1<\/span>]<\/span>
                    \nlocal rate =<\/span> tonumber(<\/span>ARGV[<\/span>1<\/span>]<\/span>)<\/span> –<\/span>–<\/span> \u6bcf\u79d2\u751f\u6210\u7684\u4ee4\u724c\u6570
                    \nlocal capacity =<\/span> tonumber(<\/span>ARGV[<\/span>2<\/span>]<\/span>)<\/span> –<\/span>–<\/span> \u6876\u5bb9\u91cf
                    \nlocal now =<\/span> tonumber(<\/span>redis.<\/span>call(<\/span>'TIME'<\/span>)<\/span>[<\/span>1<\/span>]<\/span>)<\/span>
                    \nlocal tokens =<\/span> redis.<\/span>call(<\/span>'GET'<\/span>,<\/span> key)<\/span> or<\/span> capacity
                    \nlocal last_time =<\/span> redis.<\/span>call(<\/span>'GET'<\/span>,<\/span> key .<\/span>.<\/span> ':time'<\/span>)<\/span> or<\/span> now
                    \nlocal elapsed =<\/span> now –<\/span> last_time
                    \nlocal new_tokens =<\/span> math.<\/span>min<\/span>(<\/span>capacity,<\/span> tokens +<\/span> elapsed *<\/span> rate)<\/span>
                    \nif<\/span> new_tokens <<\/span> 1<\/span> then
                    \n return<\/span> 0<\/span> –<\/span>–<\/span> \u8bf7\u6c42\u88ab\u9650\u6d41
                    \nelse<\/span>
                    \n redis.<\/span>call(<\/span>'SET'<\/span>,<\/span> key,<\/span> new_tokens –<\/span> 1<\/span>)<\/span>
                    \n redis.<\/span>call(<\/span>'SET'<\/span>,<\/span> key .<\/span>.<\/span> ':time'<\/span>,<\/span> now)<\/span>
                    \n return<\/span> 1<\/span> –<\/span>–<\/span> \u8bf7\u6c42\u901a\u8fc7
                    \nend<\/p>\n

                    AI \u667a\u80fd\u9650\u6d41<\/h4>\n

                    \u4f7f\u7528 AI \u9884\u6d4b\u6d41\u91cf\u9ad8\u5cf0，\u63d0\u524d\u6269\u5bb9\u6216\u964d\u7ea7\u670d\u52a1\u3002<\/p>\n

                    from<\/span> sklearn.<\/span>ensemble import<\/span> RandomForestRegressor
                    \nimport<\/span> numpy as<\/span> np<\/p>\n

                    # \u6a21\u62df\u8bf7\u6c42\u6d41\u91cf\u6570\u636e<\/span>
                    \ntraffic_data =<\/span> np.<\/span>array(<\/span>[<\/span>[<\/span>10<\/span>,<\/span> 100<\/span>]<\/span>,<\/span> [<\/span>15<\/span>,<\/span> 150<\/span>]<\/span>,<\/span> [<\/span>20<\/span>,<\/span> 200<\/span>]<\/span>]<\/span>)<\/span> # (\u65f6\u95f4, \u8bf7\u6c42\u6570)<\/span>
                    \nlabels =<\/span> np.<\/span>array(<\/span>[<\/span>0<\/span>,<\/span> 1<\/span>,<\/span> 1<\/span>]<\/span>)<\/span> # (0: \u6b63\u5e38, 1: \u9ad8\u5cf0\u671f)<\/span><\/p>\n

                    model =<\/span> RandomForestRegressor(<\/span>)<\/span>
                    \nmodel.<\/span>fit(<\/span>traffic_data,<\/span> labels)<\/span><\/p>\n

                    # \u9884\u6d4b\u672a\u6765\u6d41\u91cf\u662f\u5426\u4e3a\u9ad8\u5cf0\u671f<\/span>
                    \ndef<\/span> predict_traffic<\/span>(<\/span>time,<\/span> requests)<\/span>:<\/span>
                    \n return<\/span> model.<\/span>predict(<\/span>[<\/span>[<\/span>time,<\/span> requests]<\/span>]<\/span>)<\/span><\/p>\n

                    \u7f13\u5b58\u4f18\u5316<\/h3>\n

                    Redis \u7f13\u5b58\u70ed\u70b9\u6570\u636e<\/h4>\n

                    import<\/span> redis
                    \nr =<\/span> redis.<\/span>Redis(<\/span>host=<\/span>'localhost'<\/span>,<\/span> port=<\/span>6379<\/span>,<\/span> db=<\/span>0<\/span>)<\/span><\/p>\n

                    # \u7f13\u5b58\u67e5\u8be2\u7ed3\u679c<\/span>
                    \ndef<\/span> get_product<\/span>(<\/span>product_id)<\/span>:<\/span>
                    \n cache_key =<\/span> f'product:<\/span>{<\/span>product_id}<\/span><\/span>'<\/span><\/span>
                    \n if<\/span> (<\/span>cached :=<\/span> r.<\/span>get(<\/span>cache_key)<\/span>)<\/span>:<\/span>
                    \n return<\/span> cached # \u76f4\u63a5\u8fd4\u56de\u7f13\u5b58\u7ed3\u679c<\/span>
                    \n else<\/span>:<\/span>
                    \n data =<\/span> query_db(<\/span>product_id)<\/span> # \u67e5\u8be2\u6570\u636e\u5e93<\/span>
                    \n r.<\/span>setex(<\/span>cache_key,<\/span> 3600<\/span>,<\/span> data)<\/span> # \u7f13\u5b58 1 \u5c0f\u65f6<\/span>
                    \n return<\/span> data<\/p>\n

                    CDN \u7f13\u5b58\u9759\u6001\u8d44\u6e90<\/h4>\n

                    location \/images\/ {
                    \n root \/var\/www\/html;
                    \n expires 30d;
                    \n add_header Cache-Control "public, max-age=2592000";
                    \n}<\/p>\n

                    \u6d88\u606f\u961f\u5217\u4f18\u5316\u8bf7\u6c42\u5904\u7406<\/h3>\n

                    RabbitMQ \u4efb\u52a1\u961f\u5217<\/h4>\n

                    import<\/span> pika
                    \nconnection =<\/span> pika.<\/span>BlockingConnection(<\/span>pika.<\/span>ConnectionParameters(<\/span>'localhost'<\/span>)<\/span>)<\/span>
                    \nchannel =<\/span> connection.<\/span>channel(<\/span>)<\/span>
                    \nchannel.<\/span>queue_declare(<\/span>queue=<\/span>'task_queue'<\/span>,<\/span> durable=<\/span>True<\/span>)<\/span><\/p>\n

                    # \u751f\u4ea7\u8005<\/span>
                    \nchannel.<\/span>basic_publish(<\/span>exchange=<\/span>''<\/span>,<\/span> routing_key=<\/span>'task_queue'<\/span>,<\/span> body=<\/span>'\u4efb\u52a1\u6570\u636e'<\/span>)<\/span><\/p>\n

                    AI \u8c03\u5ea6\u961f\u5217\u4f18\u5316<\/h4>\n

                    \u5229\u7528 AI \u9884\u6d4b\u8bf7\u6c42\u4f18\u5148\u7ea7，\u667a\u80fd\u8c03\u5ea6\u961f\u5217\u3002<\/p>\n

                    def<\/span> ai_task_priority<\/span>(<\/span>task)<\/span>:<\/span>
                    \n return<\/span> model.<\/span>predict(<\/span>[<\/span>[<\/span>task.<\/span>cpu_usage,<\/span> task.<\/span>response_time]<\/span>]<\/span>)<\/span><\/p>\n

                    QA \u73af\u8282<\/h3>\n

                    Q1: \u4f20\u7edf\u9650\u6d41\u4e0e AI \u9650\u6d41\u7684\u533a\u522b？<\/h4>\n

                    \u4f20\u7edf\u9650\u6d41\u57fa\u4e8e\u56fa\u5b9a\u89c4\u5219，AI \u9650\u6d41\u53ef\u9884\u6d4b\u6d41\u91cf\u53d8\u5316，\u52a8\u6001\u8c03\u6574\u3002<\/p>\n

                    Q2: \u6d88\u606f\u961f\u5217\u5982\u4f55\u4f18\u5316\u9ad8\u5e76\u53d1\u8bf7\u6c42？<\/h4>\n

                    \u5c06\u9ad8\u5e76\u53d1\u8bf7\u6c42\u8f6c\u4e3a\u5f02\u6b65\u4efb\u52a1，\u524a\u5cf0\u586b\u8c37，\u51cf\u5c11\u6570\u636e\u5e93\u538b\u529b\u3002<\/p>\n

                    \u603b\u7ed3<\/h3>\n
                      \n
                    • \u9650\u6d41（Redis + AI）：\u6709\u6548\u63a7\u5236\u6d41\u91cf，\u907f\u514d\u670d\u52a1\u5d29\u6e83\u3002<\/li>\n
                    • \u7f13\u5b58（Redis\u3001CDN）：\u51cf\u5c11\u6570\u636e\u5e93\u67e5\u8be2\u538b\u529b，\u63d0\u5347\u54cd\u5e94\u901f\u5ea6\u3002<\/li>\n
                    • \u6d88\u606f\u961f\u5217（RabbitMQ\u3001Kafka）：\u5f02\u6b65\u5904\u7406，\u4f18\u5316\u8d44\u6e90\u5229\u7528\u3002<\/li>\n
                    • AI \u9884\u6d4b（\u6d41\u91cf\u5206\u6790 + \u8c03\u5ea6）：\u52a8\u6001\u8c03\u6574\u670d\u52a1\u5668\u8d44\u6e90，\u964d\u4f4e\u6210\u672c\u3002<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"

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