2026数据科学与大数据技术一套基于Spark的DJI无人机数据分析与可视化4.11-AI7.04(论文+程序代码源码+运行指导txt)定稿.zip

下载压缩包加入资源篮

阅读 0
格式 zip
大小 28.58 MB
共449文件
2026-09-18
评论
收藏
赞(0)
海报
分享
微信扫码分享或点击分享到
QQ空间
QQ好友
新浪微博
复制链接
分享赚佣金
举报

收起资源包目录

bdu26bd211jiaosai
- .git
  - hooks
    - applypatch-msg.sample(478 B)
    - commit-msg.sample(896 B)
    - fsmonitor-watchman.sample(4.62 KB)
    - post-update.sample(189 B)
    - pre-applypatch.sample(424 B)
    - pre-commit.sample(1.61 KB)
    - pre-merge-commit.sample(416 B)
    - pre-push.sample(1.34 KB)
    - pre-rebase.sample(4.78 KB)
    - pre-receive.sample(544 B)
    - prepare-commit-msg.sample(1.46 KB)
    - push-to-checkout.sample(2.72 KB)
    - sendemail-validate.sample(2.25 KB)
    - update.sample(3.56 KB)
  - info
    - exclude(240 B)
  - logs
    - refs
      - heads
        main(208 B)
      - remotes
        jay123666
        HEAD(131 B)
        main(155 B)
    - HEAD(208 B)
  - objects
    - 00
      - a6963e28b3b2e9b97187d5dedcdba4e0528eb5(277 B)
    - 01
      - 9082ebd9aa58907c37b63f6877b2033585208a(722 B)
      - 9922fa2198c8f260ca23c8f7f8dfaa9e551c74(425 B)
    - 03
      - 0d28ef086ce911bf2bae35f6328a3b0159ab6f(609 B)
      - d84a6f3b2d89f0e71371959c5072d6582d097a(1.89 KB)
      - dc1c53a44746cca0d70a61d574ee01ce642041(270 B)
    - 05
      - 426a612ea166c452067e70018967787e5e40fe(95.72 KB)
      - b601add03f21ce6a521045de15338b5dfc225d(880 B)
      - db7f8f41070103fdbd85e844a89dafad455055(1.83 KB)
    - 07
      - cd09d50edf8558295767ace971be925ae4a88a(218 B)
    - 0a
      - d5afc7df8a2b4376a6e1cb4918bb3f77fb7ff9(916 B)
    - 0b
      - 2f3957f673de1898df895cb5239e62fabb73da(1.94 KB)
    - 0c
      - 6b7580e361aae0182a6c6087b3bd0208e94f02(2.04 KB)
    - 0e
      - 0e7d98d7697c6d8b780cd68c23fc0f900af0cf(815 B)
      - 7f2620a9482795a969a5ea0bd5577e417f98d1(128 B)
    - 12
      - 13fcc39c9c26f8cd8dae6c35f19017fadf861a(3.67 KB)
      - 2431423b727c4a606a809b3bb2352d5dd5b505(2.8 KB)
      - 95d7b46a4f269edd2f51711fe6850330332ca0(1.06 KB)
    - 14
      - 9906ebdd36cca02ec60c6d6d125a1b68ef42c8(404 B)
      - ac10bc217c613ffc0326346579a0d9724bf826(1.08 KB)
    - 16
      - c298d85397fbbd2f2efd7a19b418efbf4c2b82(520 B)
    - 19
      - ed6417ee2479e35cc7a678a899f0588c75bec1(101 B)
    - 1a
      - b1fbccb63478d46718f2c37a0fe290780f7e15(1.97 KB)
    - 1b
      - b949b890984e4a05a2ffd21d221c9da0b7c219(211 B)
    - 1c
      - 179d60420daedaf02993a11cc4be362b7b8eaf(4.63 KB)
      - 80ca0d4f83fb70d3f4a1aed4a634a0e49399f5(587 B)
    - 1e
      - 392004adc51e0ce205ac4860f526a3d87772fe(181 B)
      - b5cb06594704eefc272fbd0c1aef14df8cbe33(70.7 KB)
    - 1f
      - 1b1ce8df21400ebd1612bb9ce4e22d93c04765(1.7 MB)
    - 20
      - 532cd576d46f04a36751a8fea145bd1481a896(2 KB)
    - 21
      - 38288986bc29e7cc9883cfe248375ca2eedd48(300.95 KB)
      - 413216939dd51c99b80f1a5ce2f31e35d6bee7(198.15 KB)
    - 22
      - 2ded0c0d31f9d5a3513f1006589355be953a7a(65.16 KB)
    - 23
      - a35a3d786cc54988bc1d3660a6da58930ed506(493 B)
    - 24
      - 91f637f5c3ba1b6cc36b403c47d7fbf2d9fa92(651 B)
    - 25
      - 13047829c660c37ea180254f0b6964841f2720(1.05 KB)
    - 27
      - ed0214a03b2c5aa4ef0bbad1fdc711a391b74d(63.59 KB)
    - 29
      - 06afd61f35fa6d686b124f8407e494d30d0551(856 B)
      - 7103a2e2aa7ab3af803aef3149748f764598e1(175 B)
      - a33dc2e3543b9a2fa398b3c41ea3d58e97c039(2.38 KB)
    - 2a
      - d3da90b7df8d8294558420ec344eb762d4b2e6(360 B)
    - 2b
      - 455567aa146125bfb5c7d9fee68b2521cd79c3(93.67 KB)
      - e27763ba0062cab913ec9ff446a4b226966c0f(164 B)
    - 2c
      - 6272ad753a5c22f9b90c827e77c75cdb76c412(901 B)
    - 2f
      - 3e30e1f1d1c0886041bbc9c1413affcd64f75d(37.41 KB)
      - 3fec75cbf76e764bfdc008c1341484d1918f3c(2.32 KB)
    - 30
      - 28ed8e9035842809e7a1e98f68b13f4ab69e6c(54 B)
      - a7b52b23c00fb4f24c7ff95abb046fc434f1a2(136 B)
    - 31
      - e39ee34850ef5aedcf707b628e6ebff62f4f8e(141 B)
    - 32
      - bf50eedc0ea4b40d90d5753d7959d67b7a892a(747 B)
      - c7840463f02fec550bdc20a5fb9f9e2f92088f(2.86 KB)
      - e5f898dc0954520c3314cd4fe1d8590cddfab3(84 B)
      - f609cc457f61d7521a515dedf9bf8a19a3e802(3.72 KB)
      - fbb30ecef9016df08dabe2da4d3dd9d09f5f48(528 B)
    - 34
      - e4182fe145714482623cd68d1feba762b6f75f(565 B)
    - 35
      - 3d5d9c5422a6ebe1824dacf1eca9859370da7c(5.17 KB)
    - 36
      - 12b6649d54923dfeae6dd1f7bc72dc585798d1(156.82 KB)
      - 911acf2e9065be496112da5daa7579de0de04c(115 B)
    - 39
      - 4f815820e367fb54ecd8ac11aaed6214d48c9a(8.08 KB)
    - 3a
      - 91c92df377dce6619a43f089e6f763d95ac4d8(194 B)
    - 3b
      - 24aac40b7113bd011b8fe070c317f6bcc72bf8(6.27 KB)
    - 3c
      - 513596fe35d70f5eee5f282d81b5bffd62ce55(1.49 KB)
      - d3c68f603a712319b842718ea6bc44a05a2ebe(4.04 KB)
    - 3d
      - 6b6b038bf25d559939702322cf1976da2e2dfa(331 B)
    - 3e
      - cf6e695f722abc652df78bf1384402d675b440(397 B)
    - 3f
      - e4292078d9da5b0bb577dff726091275c00c89(2.37 KB)
    - 41
      - 3e611599b96612ea06eb40d4baa3117a3ba01d(981 B)
    - 42
      - 4ce8d0f11ee5b7236818f5856b6bc72af3e7e7(1.12 KB)
      - cec27e4643684b68514e841e1e057899c7d3fb(151 B)
    - 45
      - bd7e828cdb22abd5bc44c2959ead1d3fdcc3d9(107 B)
      - c2e1131b3bc9af2c7058410d456a1fec92b72f(35.24 KB)
    - 47
      - 445ea58a592738345175c1490e1f846dfe178e(6.65 KB)
    - 48
      - 018dc02bf1495a421c8e3f49fad4e9b7768e15(85 B)
      - 4eaa815d49603be494becd6c86e8fa76ddb2b4(149 B)
    - 4a
      - 65f1a7b938cffe3317732ca2d01d3cac0a50ab(3.75 KB)
    - 4c
      - 30b53b5b0a296ec1b23889ebd33c54625cabef(234.04 KB)
    - 4d
      - 6308374c3ef37daa0be491a16ecfa0f1480fdb(59 B)
    - 4f
      - 1b5d816f83b87f7404b52919c8a09178b7292a(141 B)
      - 655554da17a78388d697a3c28fb27d8ae13918(10.53 KB)
    - 50
      - ab750017a21852b6fc11a0f82e54a4317f4d2b(6.33 KB)
      - f44b0a1cf338a00405aea19a8e54ef2d4212bb(283 B)
    - 53
      - 3990120fd0816091e6475464ab8b308036ff29(1.91 KB)
      - c591dad8db4e5711c0274328332ea2a7a06d29(3.88 KB)
    - 55
      - 5bcd1d243edc4b4031ce05296edfcc41a92d91(139 B)
    - 57
      - 14b0781db371c23a506243a66dbb4d16cd5206(463 B)
      - cd538d152665dadef112aeb7535612c1db3570(2.04 KB)
    - 58
      - 204e6d1b8858ac73ecfae9ef1a04208d07d4d5(106.97 KB)
      - 6e48e875232274b8962482e3ceb8b7587abb43(150 B)
    - 59
      - 5bdfea1bd678369209a73ab89f7e9f31a28722(5.49 KB)
    - 5a
      - 1c2d91228a054faf5a91ac824be7acb525349d(122.47 KB)
      - 46619978e35cb966ca5d407410558f397e27ca(6.53 KB)
      - 9393b0689bd5093ccba20678707fef5d91a06f(294 B)
    - 5b
      - 875efbfb7d59a2d359df6f2310cbe18b429827(119.58 KB)
      - d85afee84670dd29b2139da0012e436327d11a(6.85 KB)
    - 5c
      - 882ef3a289ea799fdae0776c735a9576d23a83(2 KB)
    - 5e
      - 1fdd9faa795d031cbed167b2700de52f930969(490.23 KB)
      - e6090b350fd0bf6b3e62ce2b1f1989320d85dc(173 B)
    - 60
      - 560e8d2d314960fd2970f20d85de03a8656e58(4.76 KB)
    - 62
      - 35a27b4ecfcf7c8a86066052aa5719a57ac4e3(174 B)
      - 612b347cfc83e2340ffd5134e501d3e566647e(519.05 KB)
    - 63
      - 5557564039ebe1aa8342984083c698a50bac84(406 B)
    - 64
      - 73f7e8c3779ab0d5acb857841000f395c80a92(54 B)
    - 65
      - 642f982e7ee18d1c110285679323e2ef6d23d9(9.57 KB)
    - 67
      - 36b248846350c34d548a5a4ce7b905a41b745a(96.03 KB)
      - 6add362a7c19938a7e83c13fad92f11bd4c502(1.9 KB)
    - 6a
      - eb151427055ad32e953a5b46f2d71594855854(427 B)
    - 6b
      - 443c9c210b66d70ebc7d3ec617af6b9420b187(405 B)
    - 6c
      - 0e0eed113dc307f3930da61f7e36e910cdb2b3(1.73 KB)
    - 6e
      - c228a50a3b67923e62ff2d3e1aec06894671b0(987 B)
    - 72
      - ac1ddba02f994829a2b7c4aac2753713374c1e(51.42 KB)
    - 74
      - 658c081e765f509f8f2be25c36eb9ef31f6410(1.46 KB)
    - 75
      - ed389dbc1c41b588f6b363bd042de4e5079e00(2.02 KB)
    - 76
      - 672bb8c973cd9c63cf179097f740a4dc453db4(3.93 KB)
      - e33c435757d198ffd262e901d9df431eeab95d(1.47 KB)
    - 77
      - 8f5f7d5a0ac015ac35076f5d6219da37dde250(10.92 KB)
      - d5578ec2017524091d43f40d15ba4c1ee1afa1(155 B)
    - 78
      - 33d508bd7f01ed8621adc7acecd2b942cb8483(350 B)
      - 67202ad0edf517ef55afe4f628141909d8d798(92.54 KB)
    - 79
      - 73d5011df0c3f54ad06b775264d7340a6f9793(1.16 MB)
    - 7a
      - 0d795955956aa7a89186d5cae95571a5759f05(4.62 KB)
      - 0f7ec943ed4147abcb6414ae1065497800290d(1.95 KB)
    - 7c
      - 3f8315bb1de19fdd2088d43456346c55f00b7e(102 B)
      - 8a5abc518283531f671ac1926a2fa23648d9c9(85 B)
      - a36c191cde49c424ef3c71ce2f7c7b70cdbb9b(2.26 KB)
    - 7d
      - 11e987ecb16444385accdd16c842807248de1f(408 B)
      - 4072fabba526d8af40e9fb8b8dd562ffce7ad7(241 B)
      - 7a7ca1a0475043852534c2318b9478cf3e79c0(519 B)
      - bb980c23bc89edea6d59235e178e5164867d9c(5.83 KB)
    - 7f
      - 634aeb7ab410cd4100ddd09e549d0c80d229cb(432 B)
    - 80
      - 3159b1dffb11455a9bf561a2bb71e3cd783c9b(785 B)
      - 9d97ef2dec7672d86e81abf096708c9495ed27(55 B)
      - b13bb221e22da15d40e1e8192d922d5befd9dc(159.95 KB)
    - 81
      - a7c9c23bed5f9b815992fd351d2e55eeb84de3(73.67 KB)
    - 82
      - e60de249f04ac842f01202bae4760b7af4b2b3(765 B)
    - 83
      - 632e3234612354e494d45938c5da24c359e1e6(251 B)
      - a1afdfb6e9ea06bc9ef090f69a975982b57563(58.04 KB)
    - 85
      - 22de4a776f2dd5953798b956484e17df6fca5e(352 B)
      - 5ee229b727d6d6b024cd08e8d11765460a3589(899 B)
    - 86
      - 523a63cb0530ddb181168de3b204bc3a0ce369(92 B)
    - 88
      - 878f562d28001ebb147da8f19be0dd28cbc5da(211 B)
      - d88dbc0cad00ed0d9127b71d79d40162e312d3(2.59 KB)
      - f6b11af779d753977d1d99e218bfc524703c9e(80.39 KB)
    - 89
      - a26f7f666175cf2ce7e8c211e80ab94783e02a(409.4 KB)
    - 8a
      - 17c1ebd88e26fd6d1ee51d0b67fc81a6df455f(454 B)
    - 8b
      - a1b26337cd4062c1ef85c3084848f948113345(859 B)
    - 8c
      - 05619a0bbb2dfb5b577c387388b074f43c0d60(10.18 KB)
    - 8e
      - 18a083bd675fd88495e63c2edfa6f2ce7c5245(3.71 KB)
    - 8f
      - 4acd154459fbbdcc82081c5699d1a55d6b97a6(245 B)
    - 90
      - 7f7092a9fb887204657874458461410808e91b(220.33 KB)
    - 91
      - 035945a21cf2557f77ee57d136ac843764f10f(2.62 KB)
    - 94
      - e8d26706fc6cb9ed1af45ab4653c44c60d5481(92.59 KB)
    - 96
      - 9232775d46355be8c501d97bb279d3dc0d1321(5.84 KB)
    - 97
      - 2a26ed58d42a21c70ac143d86e0a629f70ec6b(544 B)
      - d534b66e21d0ec9624a4a1f0bfa5b110f16787(384 B)
    - 98
      - 47b05130ac5ef6b1d00194f5417b28967266d0(443 B)
      - 5bc2973ab9e95359cec56ac49abe2f1b44f553(214 B)
      - fdfc4b2425a0f1bf2997b6413b43f80c32ee6d(292 B)
    - 9b
      - f458a5057665a224628d59bf622d023324478c(10.35 KB)
    - 9c
      - 0acb223626d80d985e9ffc11810919a25e24cd(4.07 KB)
    - 9e
      - 26dfeeb6e641a33dae4961196235bdb965b21b(17 B)
      - e10a4b7979facff4471c88ebf6bda3454fa5da(85 B)
    - 9f
      - 4403ee3312d348244fa8d8ae2537b7846b3dec(132 B)
      - 9c0ec5f72e9a9ca43d91806d4de98f60742ddc(7.47 KB)
    - a1
      - 0627e496f2aefefc87b588c019853b8bbbb2e0(200 B)
      - 3a149d6fc257e9b995d283d4926abe2a6cd4e8(4.2 KB)
      - d07c96f87cfe6f84d71442830265529010e638(2.41 KB)
    - a2
      - e45f6911fa3d1616d0b75a122161a3896aa3c4(7.19 KB)
    - a5
      - 50a90bc8e241fd8c466bdac301c9127c0763c5(992 B)
    - a7
      - 9e0221efaff5d4cb99bbcb3d7bc4ea558d5a00(85.79 KB)
    - a9
      - 0ab5acb6dfffd370f39e0df1989405236c66bc(151.79 KB)
    - aa
      - 3eb3ebfad5460cb7e01be3000f49a278dcb2b9(3.82 KB)
    - ab
      - 17bf82746c8889c86e2dc354b29c9e0310c658(1.19 KB)
    - ac
      - c0441e55904e728ed959de602124b0a8dcf70b(321 B)
      - db77816a0d4405456fe84dc4c40e9a06525899(245 B)
    - ad
      - 36a48baaafa8536faf13dd24dfcc3a83381878(1018 B)
      - 3a22d7c9b370a9f6e3f7b97f93854e60d315cc(67 B)
      - b26edff2bfbcb5aee4146087cbb09d2d38b74a(81 B)
    - af
      - fa385d1c12ca87db667f7fb34cf7cb71fc1d61(119.43 KB)
    - b0
      - 5cd54cec728a958d3fbd2cbd3ba39fdf12db32(149 B)
    - b1
      - 444264c498ded21a6b00e064365a679d4617b3(232 B)
    - b2
      - 37a237814b9043fcafa0081e05fac4a19926dd(91.75 KB)
    - b3
      - 07605d895808016b8e2ed20897088c5a7df047(211 B)
      - b04e860995685136965a31ab169f19aaa721a3(326 B)
      - baee76f7094684c3f741fe8a92d5bbe10b1e9a(2.28 KB)
    - b6
      - bf9dda0e7cff1e39bc3fb8dfa5db6a2594a285(3.2 KB)
    - b7
      - 21972a611cd35d945165581f95545d898bbd7a(74.38 KB)
      - 3b64a7eaaa2f34f96d1961cb01df9a7c9ae292(1.58 KB)
      - 52ae37e4162498e14fc8e1a9452e51cc620721(959 B)
    - b9
      - 2170cf490167729a7d2404243a3c1d0672667e(478.44 KB)
      - 6a4ae2c6e2d73b026d02e55f4ed94260955ca5(218 B)
    - bb
      - 11cdf2c0676e58ed0093341cc963aaec05019f(281 B)
    - bc
      - 5a6b72a9f3bb8d1b6edb6979716f658fc89211(8.54 KB)
    - be
      - 461de5e73e26f2e8be387ec217edea5064650e(4.05 KB)
      - f1ae61cfdb6f5d2599098976b2bae9c1774d0e(118 B)
    - bf
      - 1ccb3d556fd9858e50e464b6460b1b30027bab(6.5 KB)
      - bd59a0bdac1e054b961bed6840c52ac77c5f18(1.44 KB)
    - c0
      - 8366c9d53c18edc3f5cc4dbd68a7f476a613aa(57 B)
    - c1
      - 105fd622a68375ac5930fbd38cced774c04ccf(204.91 KB)
    - c2
      - 53b6e9c7d70a0ba88910d85f6af15f7a9de326(229 B)
    - c3
      - f74e98e42cf34db7208a30b4d1491c5aa4dd14(228.13 KB)
    - c4
      - a219c69e3e6bf82fe3ee73a7b69f46ffa649d7(2.12 KB)
      - bcc260a9a6d49d02483a00408f4dac9f8a42fc(3.21 KB)
    - c5
      - 3456a438b095c61023268afb540246e779eaa7(113.47 KB)
    - c6
      - 6d3963ce957e744f4b7d33e03a9391e29cc1b1(10.02 KB)
    - c7
      - b4a2c761df0fc0483491eefc2b652d961a638f(431 B)
    - c8
      - 026e71c485ba5d2a2a0bb85a2c043973cb4ce8(106 B)
      - 5da6b61143e83af5010e37a93b673495679f4b(65.03 KB)
    - c9
      - 463e29b33722813d60a8f2a5779319d04a0244(191 B)
    - ca
      - b010ebe494fcbcdfa1011fda18b08eaebe564c(450 B)
    - cc
      - 2b24e2a6f249a214a4f22abdd30c45868b38d1(148 B)
      - 30073e80049fa4ba68973a1c7109fe7dbe0910(104.61 KB)
    - cd
      - 61e9176b3853296128b4925be73775f82d50ec(225 B)
      - 883d1cd028397394e3d244578e61d740ed95d0(286.56 KB)
      - d5870a4549fc7917ba77058def30d6f3ed0f0a(720 B)
    - ce
      - 5f05140306c0b769d780117484f780ca4c2db5(11.04 KB)
      - fb21555dce25b9fbb27e81adaaedfb69b20e00(79.26 KB)
    - d0
      - 2e3eb5e04211f7a0d60bb341676880faa457ae(113.34 KB)
    - d1
      - 07802815fbde74237b76b27bd279df1e819205(1.23 KB)
    - d2
      - 05926c083e89710f099d4c14f4ae811c8e521e(64.17 KB)
      - df3f8eb5978797bc4ff915e8a48ca37d9a78d1(5 KB)
    - d3
      - 14e5a2745366452cb00b068f1b0874a0d9ba56(199 B)
    - d4
      - 5549e8c09e6d3efc57733a452c53cce38f5d91(447 B)
      - 82be948875ee0668e42fd2de4a405fbd9089da(86.26 KB)
      - fab6c2247d881c86042cd420f58f9ea4d4d21f(2.95 KB)
    - d5
      - 7ada21e2623f64355263f3bcd9b9a158e11430(87.28 KB)
      - ab8e890d44d3bce89f21cdfa0cdb6b5bcd550e(483.91 KB)
    - d7
      - f5b5545dbdf06d96fa1b07c59998ad96607466(84.77 KB)
      - fec7c9d06ed0020b37195d592608681a5d8285(21.73 KB)
    - d8
      - 4870b199ce62fda0673fdf0a1d8fa09df916ce(190.58 KB)
    - da
      - daa1fbcefad63ab6a9db55fbef645b8efe1ffe(489.73 KB)
    - db
      - 3a483e4b74971fbfb1cc0fb6499852cedfe650(371 B)
      - 902a5f077684411b273d028faae06d9bb974eb(2.22 KB)
    - dc
      - 526b8c6f8035b8f1fb1a0e3004f1e77aa64881(46 B)
    - dd
      - ba6873a022f34dd726d06849bbf21f587a90ff(137 B)
      - c7c48450b729964f53ac8f14e624449a719d67(5.51 KB)
    - de
      - 5100141f5c40b626ee2c9b94027d85fa4933b9(104.11 KB)
      - cbe80b090c76440fbbf7bdabf854279515789e(12.03 KB)
    - df
      - 883b75271d701c408c1ededa7e7e346f6a7d15(56 B)
      - e03f29f7456d1dbe821ca40c4baad5495392e7(397 B)
    - e2
      - da4df4623022927c08c60b3778b079666233d2(140 B)
    - e3
      - 1f1e05a94c236b6c56fff0ece9077bc61c192a(160 B)
      - 6c6c491c5d5209d7c56740526662d33eba66a4(69 B)
      - 99a3249fa5e67f58a27d3021f101d2e7f29c5a(705 B)
    - e4
      - 6a40e5399df60e9d09fcb0dc06c69702b88fc4(4.32 KB)
      - 83bdd572e93fa68fb66fac2403f05d1bfd1e42(157 B)
    - e6
      - 7ccec6b02d1a9a34458890186b2b7a16630154(3.54 KB)
    - e7
      - 2e2f4711e9e41e44fba72bdc59fb2edc1c55e8(879 B)
      - 31b48af81259b03faf69fab2d3cf3ee751f63c(54 B)
      - 8ac498a2db2fec3abee7391b1cf7387c34b8ad(279 B)
    - ec
      - 07ee2976b04085b5d4639f60c1c8e73a018bc7(881 B)
      - 8e382b3cc126fc83c3b0c23cb474d843a0d14b(2.72 KB)
    - ed
      - 425d07312c3792c28bc32014be16eef3f47389(1.9 KB)
    - ee
      - 195715f13d76d4b9d01f100d4ed00b4b0b089d(93.98 KB)
      - a52e0509d8fe69651c79a34b5c55025028a753(195 B)
    - ef
      - 0bf848bb21fd58876950efc56776fb05695f30(225 B)
    - f2
      - 19fd898a1fdfe337e968cbe8a2f29784232454(848 B)
      - 283a61210aeb7a9cc256a44505c82af3099fb3(2.17 KB)
      - 3a63486edb85bcc28e8e9c542f4d25c6b17686(141 B)
    - f8
      - 43c6feba43abf5b2c1a4ed3443b169db829ec8(13.83 KB)
    - f9
      - 87e6d369d2da9bd6dbad7c9325a4abf504ace6(4.42 KB)
    - fa
      - d9ab1237c61d675c38599bcaaa063b7de5bb12(33.63 KB)
    - fc
      - dae324c239113533f4759a020326bb0f6cda38(182 B)
    - fe
      - 6e7c036fa13f9aaa5dc81e59fb3f9c4a2a5b3f(172 B)
    - ff
      - 472675a99a90796b20a3e47b7a788b9581a491(17.63 KB)
    - info
    - pack
  - refs
    - heads
      - main(41 B)
    - remotes
      - jay123666
        HEAD(33 B)
        main(41 B)
    - tags
  - COMMIT_EDITMSG(305 B)
  - config(305 B)
  - description(73 B)
  - FETCH_HEAD(115 B)
  - HEAD(21 B)
  - index(18.99 KB)
- .spec-workflow
  - approvals
  - archive
  - specs
  - steering
  - templates
    - design-template.md(2.51 KB)
    - product-template.md(1.49 KB)
    - requirements-template.md(1.27 KB)
    - structure-template.md(4.47 KB)
    - tasks-template.md(13.48 KB)
    - tech-template.md(4.24 KB)
  - user-templates
    - README.md(2.34 KB)
- bdu26bd211jiaosai_clustering_comparison
  - code
    - generate_comparison_results.py(11.74 KB)
    - generate_visualizations.py(13.27 KB)
    - kmeans_vs_gmm_comparison.py(17.67 KB)
    - run_comparison.sh(552 B)
    - simple_comparison.py(8.93 KB)
  - results
    - cluster_distribution.csv(139 B)
    - cluster_statistics.csv(527 B)
    - comparison_report.md(4.38 KB)
    - cross_validation_results.csv(918 B)
    - cross_validation_results.json(1.2 KB)
    - evaluation_metrics.csv(318 B)
    - scatter_plot_data.csv(15.39 KB)
  - visualizations
    - comprehensive_comparison.png(1.28 MB)
    - gmm_detailed_analysis.png(559.67 KB)
    - kmeans_detailed_analysis.png(553.17 KB)
  - README.md(2.49 KB)
  - results_generation.py(17.3 KB)
- bdu26bd211jiaosai_data
  - bdu26bd211jiaosai_out
    - analysis_01_city_sales_heat
      - city_price_band.csv(10.7 KB)
      - city_sales_summary.csv(7.17 KB)
      - city_top10.csv(880 B)
      - price_band_distribution.csv(335 B)
    - analysis_02_feature_impact
      - feature_correlation.csv(289 B)
      - feature_impact_report.csv(926 B)
      - feature_sales_box.csv(93.57 KB)
    - analysis_03_keyword_paycount_effect
      - ml_keyword_to_paycount_effect.csv(169.4 KB)
      - ml_metrics.csv(241 B)
      - ml_paycount_prediction.csv(42.69 KB)
      - top_negative_keywords.csv(6.21 KB)
      - top_positive_keywords.csv(7.68 KB)
    - analysis_03_paycount_prediction_boosted
      - gbt_enhanced_evaluation_summary.csv(177 B)
      - gbt_enhanced_predictions.csv(30.6 KB)
    - analysis_03_sales_level_classification
      - classification_report.csv(424 B)
      - confusion_matrix.csv(62 B)
      - fig7_sales_level_model_comparison.png(129.75 KB)
      - fig8_keyword_effect_sales_level.png(149.36 KB)
      - final_model_selection.csv(231 B)
      - final_predictions.csv(44.48 KB)
      - keyword_effect_for_visualization.csv(902 B)
      - keyword_effect_top.csv(3.5 KB)
      - model_comparison_metrics.csv(373 B)
      - prepared_sales_level_data.csv(765.6 KB)
      - table_sales_level_model_comparison.csv(372 B)
      - table_sales_level_model_comparison_cn.csv(391 B)
      - tuning_cv_results.csv(5.5 KB)
    - analysis_04_keyword_factor_matrix
      - ml_keyword_factor_cluster.csv(213.92 KB)
      - ml_keyword_factor_cluster_profile.csv(470 B)
      - ml_keyword_factor_matrix.csv(244.32 KB)
    - analysis_05_shop_trust_effect
      - province_shop_trust.csv(2.98 KB)
      - shop_trust_effect.csv(730 B)
      - shop_year_vs_sales.csv(562 B)
    - analysis_06_product_gmm
      - cluster_profile.csv(655 B)
      - cluster_top_members.csv(14.9 KB)
      - final_model_selection.csv(225 B)
      - gmm_cluster_for_visualization.csv(2.52 KB)
      - gmm_tuning_results.csv(691 B)
      - kmeans_elbow_results.csv(768 B)
      - ml_product_gmm_clusters.csv(354.72 KB)
      - model_comparison_metrics.csv(985 B)
      - result_analysis_for_screenshot.csv(484 B)
    - analysis_06_product_kmeans
      - cluster_profile.csv(697 B)
      - cluster_top_members.csv(19.18 KB)
      - ml_product_clusters.csv(352.48 KB)
      - model_metrics.csv(70 B)
    - analysis_07_sales_driver_regression
      - sales_driver_regression_coefficients.csv(927 B)
      - sales_driver_regression_metrics.csv(206 B)
      - sales_driver_regression_predictions.csv(68.57 KB)
  - bdu26bd211jiaosai_raw
    - products_211jiaosai.csv(2.02 MB)
- bdu26bd211jiaosai_sh
  - export_all_analysis_211jiaosai.sh(5.48 KB)
  - run_all_analyses_211jiaosai.sh(1.95 KB)
  - run_clean_211jiaosai.sh(1.3 KB)
- bdu26bd211jiaosai_spark_jobs
  - analysis_01_recent_hot_topics_211jiaosai.py(3 KB)
  - analysis_02_publisher_influence_211jiaosai.py(3.49 KB)
  - analysis_03_paycount_gbt_211jiaosai.py(8.26 KB)
  - analysis_03_value_regression_211jiaosai.py(6.13 KB)
  - analysis_04_intro_kmeans_211jiaosai.py(7.08 KB)
  - analysis_05_intro_lda_211jiaosai.py(3.86 KB)
  - analysis_06_product_gmm_211jiaosai.py(13.8 KB)
  - analysis_06_product_kmeans_211jiaosai.py(6.23 KB)
  - analysis_common_211jiaosai.py(2.07 KB)
  - step1_clean_211jiaosai.py(7.24 KB)
- bdu26bd211jiaosai_thesis_visualizations
  - gmm_clustering_figures
    - GMM聚类_图1_KMeans与GMM多指标对比.png(258.75 KB)
    - GMM聚类_图2A_KMeans肘部法.png(106.5 KB)
    - GMM聚类_图2_GMM调优结果.png(128.14 KB)
    - GMM聚类_图3_最终簇画像热力图.png(99.02 KB)
    - GMM聚类_图4_最终市场层次气泡图.png(76.13 KB)
    - GMM聚类_表1_KMeans与GMM模型对比.csv(506 B)
    - GMM聚类_表2_最终模型调优结果.csv(156 B)
  - regression_comparison_results
    - regression_evaluation_summary.csv(323 B)
    - regression_model_selection_summary.csv(322 B)
    - regression_tuning_path.csv(1.13 KB)
  - separated_ml_figures
    - 分类分析_图1_高低销量样本分布.png(47.07 KB)
    - 分类分析_图2_模型评估指标对比.png(72.73 KB)
    - 分类分析_图3_最终模型混淆矩阵.png(74.47 KB)
    - 分类分析_图4_关键词权重解释.png(115.19 KB)
    - 分类分析_图5_参数调优路径.png(90.81 KB)
    - 聚类分析_图1_算法与聚类数对比.png(110.07 KB)
    - 聚类分析_图2_市场层次气泡图.png(83.85 KB)
    - 聚类分析_图3_簇画像热力图.png(105.37 KB)
    - 聚类分析_图4_商品规模与GMV对比.png(106.96 KB)
    - 聚类分析_图5_功能关键词占比对比.png(46.48 KB)
  - data_cleaning_flowchart.svg(6.49 KB)
  - data_collection_flowchart.svg(4.38 KB)
  - generate_gmm_clustering_outputs.py(10.89 KB)
  - generate_ml_charts.py(20.42 KB)
  - generate_sales_driver_regression.py(7.56 KB)
  - generate_separated_ml_figures.py(12.5 KB)
  - local_generate_gmm_results.py(9.07 KB)
  - ml_algorithm_analysis_flowchart.svg(8.38 KB)
  - statistical_analysis_flowchart.svg(7.07 KB)
  - visualization_flowchart.svg(2.62 KB)
  - 图1_聚类算法交叉验证对比.png(121.13 KB)
  - 图2_聚类算法多指标雷达图.png(233.11 KB)
  - 图3_回归模型评估指标对比.png(77.7 KB)
  - 图3_岭回归与Lasso模型选择对比.png(107.48 KB)
  - 图4_岭回归与Lasso训练测试评估对比.png(106.69 KB)
  - 图5_岭回归与Lasso超参数调优对比.png(215.85 KB)
  - 图6_聚类算法最终对比结果.png(180.51 KB)
- bdu26bd211jiaosai_visualizations
  - generate_complete_visualizations.py(14.9 KB)
  - generate_visualizations.py(10.05 KB)
  - verify_visualizations.py(1.73 KB)
- bdu26bd211jiaosai_visualizations_complete
  - city_price_band_analysis.png(597.17 KB)
  - city_sales_heatmap.png(317.92 KB)
  - comprehensive_dashboard.png(816.6 KB)
  - feature_impact_analysis.png(353.26 KB)
  - price_band_distribution.png(212.31 KB)
  - top_cities_chart.png(218.12 KB)
- bdu26bd211jiaosai_web
  - css
    - comon0.css(21.77 KB)
  - font
    - DS-DIGIT.TTF(24.88 KB)
  - images
    - bg.jpg(252.24 KB)
    - head_bg.png(7.7 KB)
    - line(1).png(3.85 KB)
  - js
    - chart_polish.js(24.1 KB)
    - chart_redesign.js(40.01 KB)
    - china.js(117.19 KB)
    - echarts.min.js(727.24 KB)
    - jquery.js(82.41 KB)
    - js.js(10.47 KB)
  - picture
    - jt.png(71.9 KB)
    - lbx.png(81.26 KB)
    - loading.gif(701 B)
    - map.png(302.1 KB)
    - weather.png(2.27 KB)
  - index.html(6.25 KB)
- .mcp.json(189 B)
- algorithm_comparison_211jiaosai.py(5.72 KB)
- fix_municipality_cities_211jiaosai.py(4.64 KB)
- ml_sales_level_classification_211jiaosai.py(10.3 KB)
- readme.txt(38.52 KB)
- visualization_requirements.txt(134 B)
数据科学与大数据技术基于Spark的DJI无人机数据分析与可视化4.11-AI7.04_1.docx(10.9 MB)

CAD预览不完整，只显示大致框架可联系客服看全图和细节

在线预览已结束，请下载后查看完整版，加入VIP会员享文档下载特权

加入VIP 下载压缩包加入资源篮

下载提示常见问题

1、当您付费下载文档后，您只拥有了使用权限，并不意味着购买了版权，文档只能用于自身使用.。
2、本站所有内容均由合作方或网友上传，本站不对文档的完整性、权威性及其观点立场正确性做任何保证或承诺！文档内容仅供研究参考，付费前请自行鉴别。
3、如文档内容存在违规，或者侵犯商业秘密、侵犯著作权等，请点击“违规举报”。

碎片内容

2026数据科学与大数据技术一套基于Spark的DJI无人机数据分析与可视化4.11-AI7.04(论文+程序代码源码+运行指导txt)定稿.zip

下载压缩包关闭预览

您可能关注的文档

发表评论取消回复

26年WH + 关注: 实名认证
内容提供者

该用户很懒，什么也没介绍

收藏店铺进入空间

2026数据科学与大数据技术一套基于Spark的DJI无人机数据分析与可视化4.11-AI7.04(论文+程序代码源码+运行指导txt)定稿.zip

微信扫码分享或点击分享到

2026数据科学与大数据技术一套基于Spark的DJI无人机数据分析与可视化4.11-AI7.04(论文+程序代码源码+运行指导txt)定稿.zip

您可能关注的文档

发表评论取消回复

热门下载

相关标签

2026数据科学与大数据技术一套 基于Spark的DJI无人机数据分析与可视化4.11-AI7.04(论文+程序代码源码+运行指导txt)定稿.zip

微信扫码分享或点击分享到

2026数据科学与大数据技术一套 基于Spark的DJI无人机数据分析与可视化4.11-AI7.04(论文+程序代码源码+运行指导txt)定稿.zip

您可能关注的文档

发表评论取消回复

热门下载

相关标签

2026数据科学与大数据技术一套基于Spark的DJI无人机数据分析与可视化4.11-AI7.04(论文+程序代码源码+运行指导txt)定稿.zip

2026数据科学与大数据技术一套基于Spark的DJI无人机数据分析与可视化4.11-AI7.04(论文+程序代码源码+运行指导txt)定稿.zip