如何让查询表查询运行速度更快。
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otUsjqLYGVIbRnyJnv31ccvRI0PKaxYgn8jyvXhsq2LSfg1EkCgbZDMvPp+K2FLRUycMRrO3a7WFe5ZLDu6IfC4rze3hdMjGvqc5J7VurUmysO7dFQsgN7iyF1Wyv3s+imut3c3NRHH31U03OuuuqqkuP++nt9OnTognKzZ3Rpzx7t0T7tvpDT7JlL2jN0yDiG7tHQIfmDKP3DGhuTEXTYI/dw2u/7Tgc1vl0FfwOiLEldKKxrKDmmg1pWbn5W85JKjl9GmQ8O96m2Yx4AAADaJfbuu++GRgncFSaC03DK3d6JNjY2ZFmWb0rJrl27tLGx4d1mWZYsy9K5c+fKJnnd7jY3N7W6ulrxMYODg22qm86fvx/MWWJOw8HWBHOWmNNwsDUffvhhTVOY3MfWlLujQ21sbOiDDz4ouf3aa6/tgO9t5x/zAAAAekHFYEmlBK/d0FkJOyG+7rrrQjv9boe3/SfK7eV2TM1g0pUrV7Rjx46er5tyyiV47cXAW6OVS/BK3W7dhx9+KEmRgx+1Pr6TuVNugvgNAAAAgKtssGQrSwd3krCOFgD0OjNHRzMeDwAAAHSzssESAAAAAACAXlR+SRMAAAAAAIAeRLAEAAAAAADAQLAEAAAAAADAQLAEAAAAAADAsGN2drbdZQAAAAAAAOgYsdW3LrZsNZylpSV9dv8zrXo7tNHffO+MPvF72XYXA11uaWlJd9xxR7uLATQF7btz8dlURx3Vppfrq5f3fSuot+ioq+qWlpb02fs+0+5idJ0dLX/HWKzlbwkAAAAAABBV64MlVxEsAQAAAAAAnasNI0vIKQsAAAAAADoXI0sAAAAAAAAM5CwBAAAAAAAwMLKkqTb1/BNv6v/Ujfq/H/sNfaLdxUH7vXNKX5l8Vv/duOm+r35bX76zbSUCJEkXvv+o/uD5Fe////yBJ/WNe29qY4nQ8TieNVdI/UqSNfKovv/vPtmWInWskLqybvmSnnn8qPa2rVCdIXhs7/n2884pfWVyTf/bc3+kT1W6DaV++i2lvj4vSbKU1J9SXyEu6ruPf1lPnaOOtgtyljTTz3+lU7pGn1q9rB/+/T/VA7/V7gKhMyT1hHvw/Om3dO/Xv6W7OZiijS58/1H9/vNx/elzX3Pa4at68vEf68K9dDRQjXE8e+eUvjL5BX2FQFsDGfWLKqiroNJju/Tjv/iWfqxPUk+o3Z1/pOzUAIGlim7S5x7/tj7nBODQ/Vofubgq1jPbW0vr0l179cjvfKhTS5ttL09LN0Rz04CG2l0G9LhX9ZfPS388ZZ74fFJf5oosavXxo/rGV5M68/x39eN2lwXoea/qL59f0dGv+ju1n/p3dHIBIKrWB0tisR7ZNvXDV6Sjn7xGd/+v/0x65Zd6q+1lauGGSC785Ec6M3KEExe0zzsFrSqugY+3uyDYFu48oqPKa+2ddhcE6HHvFLSqpO5mWhwA1I2cJc3yzmWdit2gEzfFJPXpaOxt/fDvb9QDdEigeT324Lz3v6Nf7eG5w2i/i2s6c8uA+ttdDmwTe7X/lhWdvyiJ37sG8P9eSOSFKc9fVz2fm8N3bCePgl/p98pSsk1lAdDJyFnSJG/95D39v+c+1Gce/Hvvtn/1kyt6ILWrjaVCZwjO8X9U3536mj5HxwLtcm5NBYlpN2iACzp/7oD2k7KkQcjDER115XPTgIa8Yzt5FPwCbYV6AVAGOUuasm3oh6/E9H/8x3+hN553tv/4cemVdb3V9rK1aEM0H/+URtyrsEA7MG0CjfTT0zrFtC6g/T7er0HN6+WftrsgANC9yFnSjO2ddZ3SHv3OTcZtN12rQyuX9MN3OqB85CzpHO/8WAtchUVbfVL/9gHpm5PfMpJyvqonHz+lC20sFbrQO6f0la/PlySUBNAOn9S/feCATn39WyRcBlqNKc7bBjlLmuCtn1zSj/d/XJ/w7esN+p//9Xmd/MmGHrjvmraVDZ2gOFfW0gH9MVNw0GZ77/2antGj+oMHv+Dddt9Xv820HETgn/tPPo1GC8mt0Ou5OBBZ2LH9nz/wJMFM1Oen31Lq6/bx6D88KHLflHhVTz74NX1P7vn9H3EetQ3EVt+6aLXqzZaWlvTZ/+G/tOrt0EZ/88zf6BO/l213MdDllpaWdMcdd7S7GEBT0L47F59NddRRbXq5vnp537eCeouOuqpuaWlJn73vM+0uRtdhZAkAAAAAAIChDavhECwBAAAAAACdqw0jS3pj6WAAAAAAANCdYt899dcty1kiSf/kv/37Vr4d2uj/+xd/1u4iAAAAAEDPI2dJ7WLWz/6gZcGSv/mrV3Xut9Ktejt0gC996YF2FwFd7O9Wzum3D9zS7mIATUH77lx8NtVRR7Xp5frq5X3fCuotur9bOaff+s0b212Mjvbzf/gF7akOrZ+GIzrQ292zzz7f7iIAAAAA6CHz8/P63c9/vuT2v/rOd9pQmvYI7v9ffec7SiaTbSpN92tLsAQAAAAAgEYJC5RI0vDwcItL0jl+9/Of16V33213MboWwRIAAAAAwLbw5vnz7S5C25j7fvP+/W0syfbA0jQAAAAAAAAGgiUAAAAAAAAGgiUAAAAAAAAGgiUAAAAAAAAGgiUAAAAAgK72V9/5Tk8ndw168/z5nlo2uRkIlgAAAAAAABgIlgAAAAAAABgIlgAAAAAAABgIlgAAAAAAABgIlgAAAAAAUNUremL/fu3f/4ReqfLItRce1P4n/I965Yn92r9/f8nt6EwESwAAAAAAaJS1F/Qfjv9AevqbemHNvfEVzT9t/3XPgYF2lQw12NHuAgAAAAAA0DHWXtCDdx/XD8o+4Gl9cf/TIbffoxMvP6f7B+7Xn574nu4+/gMdf/YV3f/YXVp74Zt62nnMfZ8mWNINCJYAAAAAAOAauF/Pnb+/+P9XntD+L4YFRx7Si+cf011hL/Hp+3SPfqAfPP1NvZDM63vH3dDLD3T87v06HvrGbrBlqzuARqhhGs4v9Sd/+I5ebl5ZAAAAAADoGK88sd8JlDykF8+/qIck4++n9cX9+xWagmTgfv3xQ9JDL/6p9E1nlMpDL+r8+fMhm/u66CSRgiUvP7uiw394Uf+52aUBAAAAAKDN1l54UPv375cdJ3lR50tGkNylx86/rBP3SE9/cb/273/QyE/iPOKx83pMz8odVPJQMmwMiumQ4owq6RiRpuHc/aUDeuNLv9Sf/OHlZpcHAAAAAIC2eeUJJ0jievqL8qcoCctZYk+v+d6JF3Xf977oBUjsQMuX9MKDd+v4F/cr+Kx7Trys5+4XOhCr4QAAAAAA4LjrsfM674wakaSHXgybOuNsLzoTaO45oZfPn9dz99+l+58zbpckDSh+KPBaLzLxptMRLAEAAAAAADCwGg4AAAAAAGU8HTJ9phNeC83FyBIAAAAAAMqINA2n1tdiGk7HI1gCAAAAAEC7rOV1tt1lQIlI03BefnZF4z+y//6//vCy/tUXbtZ//szOZpYLAAAAAIC2YxpOb6ph6eBmFwUAAAAAgE4woPufO68tr+r79Be1/+mH9JAz6+ahF8/rsbsCj1lz/r3ngAa2+n5oGBK8AgAAAADgWHvhQd19/Ad1PfeeEy/rufsHtJYvTqx56MXHlJx/uuyIkrUffU8/kKRDcYIlHYRgCQAAAAAAjoH7n9P5LQ4pWVv5gaSH9OL5x3SXJN11Xucfc+98QQ/efVy+cMw9J/RyyZATtBPBEgAAAAAAGuiux4zgSNDA/Xpuq9EYNB2r4QAAAAAAABgIlgAAAAAAABgIlgAAAAAAABgIlgAAAAAAABgIlgAAAAAAABhYDQcAAAAA0NV+9/OflyS9ef58m0vSGW7ev183S7r07rvtLkrXYmQJAAAAAACAgWAJAAAAAACAgWAJAAAAAACAgZwlaLgvfekB/d3KOf32gVvaXRQAAAAAPeTm/ft9/++lHCbBfcfWECxBQz377PPe3//Pj/5Wkh08AQAAAIBWW1xcbHcRWubmdhdgm2lLsMTsUGP7ICgCAAAAoB3KrfqSbHE52omVbxor9sM/+5dWK9/w3G+lW/l2aKH/6dP/ozeaJHg7AAAAAKA9SJFQux2f+L1s5Ue8c0pP/uRT+vK9N0mSLnz/Uf3+335azzx+VHtrfLOlpSXp739eX0nR8X77wC2hwRK+mKgXuW+wndG+OxefTXXUUW16ub56ed+3gnqLjrqq7u9WzrW7CF2p+jScjx/Vl+8t/nfvv/y0hp5fU0GqOVgiMVUDAAAAAAB0tppzllz4yY905pZPq78ZpUHXc4NhRHgBAAAAAN3qqtoe/qr+8vkVHf03tU/BAQAAAAAA6AY1BEte1ZMPfk2rDzypL9/ZvAIBAAAAAAC0U8RpOHagRF/9tr5BoAQAAAAAAGxjse+e+uuWLh38uaP/SyvfDgAAAACAnmZZLe32bws77rjjjpa92dLSEh9Sj4jFYnzW2DLaEbYz2nfn4rOpjjqqTS/XVy/v+1ZQb9FRV9XFYrF2F6Er1ZjgFQAAAAAAYHsjWAIAAAAAAGAgWAIAAAAAAGAgWAIAAAAAAGAgWAIAAAAAAGAgWAIAAAAAAGAgWAIAAAAAAGAgWAIAAFCzvGZGYxqdyW/5lRYmY4rFYoqNzijSq+VnNBr6eLtMsVhjytVQbpljo6q3aD1RT0F111sb2+c20W37nZ8Ztcsbi2lyoT1l6LY6A6ohWAIAAFCTvGZGBzWRlbITg1vumMQPp+w/si9pvkIPY2EyrPOb18yo25HO642sJKV0LBkPKbbb8Y661R/YaAbqKar2tM+6lK3rSS14QS1//eZnRhVzdsoMENS6VQsk1bXfC5NG+Vsrnjwmp8SaPlXh3UPrfFILVdt99X3qtjoDqiFYAgAAUJO4xudWlXH6BdMnol1FLdexG5zIOo/IamKwTKcuP6MT01J24uFAx/FhTWSzmji5IC2c0rQk6TYdCIkBFKWVsyxZZbec0nXUisft/FS9ulwc4WF26HumnoK2WG9FbWifW5TOFes1V6FS8zOjdnmmE5pckOLjcyGfS3Hflc6V/fzmxuMN3+/8yuv2H6nDqti0tsILLgS2wQm5JdZ0onpQL5XR6mrGC7AoPq65iu19SiPufnZbnQF1IlgCAABQs7jGn3I6GtkJPVxDhzGVWQ3tcJsdxpJOXXxcc7m0lLpNB0peMa3c1Eix06FpJcxOSkmPOnB/yZZwggntRT1tRYvbZwssTDqd8VRGq5alqZEyD8zP6yWnz54+Wu5BperZb2/aSTBYkJ3QYLXRLVueK1MtmBchqJd/ww6wpI8qek0VdV+dAbUhWAIAAFCP+Lieci5hZydO1jeE3BvlkFbFft3CpGKJaSk7rUTCuYKcnVBiIitpWidmFjT/UlbFDpR7dd0OEPi1eMREI1BPtWtl+9wyZ7RMmc7wGydHlZh2Oudz474RCMHOtznCYjpRZ6c74n6PTIWMiKkwmsW3lY321GJBk4ERRm59RBnx4wYOU4cbEPDqmjoDoiNYAgAAUKf4gducv6ZVKU2A37wm3U5bwh2bUG4UgzN0fmTK7iyYcxScK+yWZWkuueJcTX9dK768HGG6ZcQE9bRVLWufTXb4kbmqo1jSOeNzNzrfxZsC7aLEFvbbmf4lpZR5pIUd+nxcj6xm9HoiVldS1ZU3srJz98ib2lVpKw3AdGGdATUgWAIAAFCnhVPF7nLU3BBSUlORr6rOyesf5mc0mjC6586w9ckFaeGkka/AZOYBqJqToMJ718McVu9d7TdzGdhJSHu+noK2XG9FLW2fXa/+/fbaVfq4xvNlcor4ti0kM3UDgtaURuJxxePjmrNWldGEBkNHzIxoKvSzekkvTUtKHVMyHtf4nLF/Xi4T/+iq0mBVl9QZUCeCJQAAAHVZ0ClzaEF2Qicjn80Xn2vnVVhwrtCW6RDkZzQ6OKGs0sqZSRllTzUoxgayemk+L+VX9Lok3XZAcYVMVahlqzVPgNeZC+t8pZRZLe1IlR9dv43rqan1JrW0fX/MbI4AABxnSURBVDbctBJeUCg8WWjF+jYSnCZqGv5Tx357IySc54R9jpal1UyxNaZzU3XlCPEX1Q4w2NXgBDum4nJT8txWOXuxlM1609Sit4vQgnRPnQE1IlgCAABQh/zMCXsaRjrnndRXXLLTVGMuiIWTE8qmczJXpLCnl6wqk0opnU4plU4rJSn7Rl75+ZeUVTHB5ciU0fE2pqUU826YV5DdPB5OB72deQKop7q1sn02Xlo5X/069W+OXKhU3yHTcCKpY7/LjlYyHxM1OW0N8vFHlEsX87LUHKtLZbTqVI476sgOFgamy/iCMiG6qM6AWvVcsMRb6sqZ1+f+f9S/vpxGmzAH036vkChr5KXi0CztbBceYym4Wn/wiku4OeWjTfUwd0nJettquecXl6psxDKR6CL5GY1u8cq5d7V+Wx2TFnTSmQuRPjqi+PhxuzM3fSLSd684PcKd3+/mvwjO97fPG0amnM7hwqQ9NSOV0ercuOKKa3xuTlNTx3X86CM6lrLL8PBLdi6C0LyNvlUnwt43+jSPsHqZrOW3zPvtC78KvX3rqWRPG1pvrW6fzRfX4ZSk6VNNfb+a93thsvrIFe8xaeUCyWm3Ih6P2+09l5aU1uF4XsUcPGXadNDIUbtdZN9Q3hsh4l9Se8UZqvL6SnjD6aY6awVzWeWoP5nBfohxD+debdZbwZKFSWd5qgpfPHf4ZvqYkiVP38pQzLyTfX1aJ4KNPX7YWdrtJc2X/R7kIyVe8rZtdULaZG1tF94baMYdj5jKKEqeK/NgPDiRdZZps+eDetnNjyW76gcGTbJQZS5wlOPFwkmnU5DSsSStqnfkNfOwcQWwzo5K/LAzlLri71x3WZhMeFft7SueI5rKpSVlNfFw+Hcq72QSve1A3LciRPmRC5Y3QsI75ru9DF9HflQz+RGNjMRl5/PMKpuVnQsg7OsaecREe1BPW9fq9rl1die//KoscSWPpVRMzFtFDdNwtrLfC6emlc4Z7WDFCC67W7nEp3Weq5vnf8XXn9bE4KARqIg6dckJQknFKWlm/h5JB5LH7JFYL8175e22OitTkaXv6wV1wi8c5WdGq140iDv1JUUfyeUFM4NTooxzr9tK10JHC/ROsMRI9pXKPBJ6YM/PjCo2+JKOrVqypsYVL3e8rpC8qOxQP2/N97SOj+eLmaNjsTLJu8pH6/1rmge2msYaou3twrEwaVydqrLOvHuMjo/PlczndErsBOak7MRg6GsQne5RwTYa+XixoEnvxCXsBIyka9uVd2xKpZVOSdK0EhVOFEtO4o2Ari38BL7rjkneVc/AKg4jj9gdgOyEHm7wPsXH57zfEu88wJsuckxuDHPkkWKejnR75k+0FfWktrTPujnJdKfidkc9O3FSmgoPwtidUCfXTDX1TsOp0chUYHrIgZDkwMXsp/7gQZ0jJtzzv9LNWMraF+gztgpzWVacKWklF9riSXskVoOC3e2os0rsi43V20p+ZtT+LZtO2Ofi5S5EDZoXGBIhj3EDMAtGf7C4spZvuWvj3Gs6MRh4PlqhR4IlC5ocrDw3LjsxqIf1lJqV1dudm+frkJc7kHXwlYrtpf3tQrJHpiSmVSHYYvz4RZkP6kShw4Jq7o9A1aRf6Ep2RzWYEC96IMMeJRV4/uRC8Qpl4JjlnVQErkJhe/COTUorNzelqTnnWDSdqHplzX/8KR7DzJNSc6u0HGjnKQYP07ngb0Nc40/ZnfDsxMMlU9nsQX9pHV4JCyqVG7ruXA02EiJmJwY1OrPgjfpJHy92IMxcAMHVT7z7Ik8vKT8KoXmop61pU/vcIjd3jDStRLnRA06nPTtxsoEB+vbud+PkNTNqLGXttN2KgejshAZjg5rIppRZfUR6yV1GOHg8Lo7EsgNV26XOoqucPyU4gqbcZp7PS8VVisL7gOV+L7fXClSdL2Kw5FU9+eAXlHK2e//i1eaWqtEWTmnaSG5ls4dXeVe70jn/yVpD81O4cwDTOn7gpC96WI9yowX8EUhU1fZ2EQiUTEmTIa/tdVSVUma12nBXdzpPWsdLjqRuO4w4jxVdx77aFEg4GLw6514JCTlh8SU2dJ9/9FRxCLM5PNTrkKSUeaq75hOjGvtqlxco8drQiKYsI2ASdTh02xNFNtKCJmPB6Q0B8XE9ZV++18SgEax0R5imDisZemW43NB152qwu5ytE6XMTiTsjrpRDu83JZVRJq2SEQSlw+WrvG+TruRWRD1tQRvb5xbLfXIiW0xEmw1ZAjc/o9HReSWPpxU6pb1ebd3vRlnQpBu4cy9qrLpBscHyIxG8CyBzGpc7At6fr8Q1ctT5Pr2R3yZ1ZnKm3JQJ5LxxclSJaeciQNnvuf27ab6EO02/60ZOwm/1rYtW1e2//on1lf/q/v8n1p+P/4H154sRnhfYvnvqr612Ws2kLElWKpOx0pKVzlmWlUtbkiylMtaq+WD39nTOuEn2bVE243nu+yqds/9Op610pNdJWzmrO0lqdxEia3e7SGVWLctatTKp0s/dfO10oDGEv2/KyqzmrExm1V/eVMZaXc1YqbB96mDd1I46h9uWUpbbDHztNuxvs024t5nPtyzLctuP0lbOaK+pTLe0ps7Tie3b+72q+BtkHq/8bSB4PI12bAy0tQ4Q/tnkivsUPCCHPTrtr0f/MT/0Gc7rl//t9x337R+rkHp2n+/eZ9dvTb9VwS3kd6N8+w0rU/Rznu1WT9XrqzH11gnts5zK++4cT7y6M39fzONI6bmRu5t1fWbOkxuz38Uye1Xv/WYWtwgfi0/1NuM/Foe+vlkO9wFh54OrOSudMh5Tcn5g10M6s2rlOrDO6vo9dd4vnXPKks45bck8z6nw++SdL6Wt3OpqyXm22y6L9eQ/1vhfI/pWazty1VVHsKIFS3zbnPWVxJ9YL9X8vE4Klvh6AL4DTfgP0NYED+LpnPNl6aJOa6266QvZrnZRKuc7KUh77aZSRyLkx8b3kmWCPl2im9pR59hKsMRs96XtruSEtN5fbFiW1Xnte0udxDIdD+81u6ythH02te+L8X1yL5ZU/P2o3MGo9v6rmVTpsd49eW9C/Tes/ZodDsvadvXkavj3PVBv7W6flVTa91w65BxlNWOl0jmjM1um7IFOaaRdD1z0auZ+b1X5esuFBpEqMuuy0sWzQOc9rE47sc7aEiwJf9HAa9V2UamZv5mdds7RLWoIlvzE+vPx37EO1hko6YRgSXuZB/aoVw9SVma1Uqc9wtamTjJfyDqVRNWr/NAYjy8eV902k7ZyRmc4U8vJRIegHdWi3LEiZWUyxZNDX2c2GCwJtj9fYyl/3OqmNtVJOrZ9+0YRhd2dCmkf/vvsk8Nim7Ef2r5OR6069rPpINRRbXq5vnp537eCeouuscGS6BcILMvyzqX8P4lVLmaGF6iO50RHe6pPDQleb9LnHv+2vv/cEb384KP67jvRn9kZalx615cUccG/ek0wg3/ZZTmNOYJu0s2SpVzTyjlz+9I5J2GiL49GXONzVTJDV9o6Yg5tJ2tzuzAtTPozaKdzpfkmgmU3lvScTjhzJY25pL6l35y14V6PtOYeuo97rAjmLJnTeMTl5uykhimlnOendaIkYZuXcMxIGT+d6N6kbahf+SU+HdsqVwkAoDeklfOdSzk5WMyFGIzcK/n4I8qli6vYRD4dKuknFFfG9K2IE7JxytU6dayG80ndPbKi8xcbX5hWKZ9d2ExuGMbtfJRZWspLlBTMduwm3XQSLY2e0OuSUqnalrzJ26mnWXmiSVrfLmxuAqiS5Lyhy40ZiaK8tdfd8uekxKgmTzoZ5W874Gsn7rrv2ZfmOyRjP5ojrzfc1RlOlv6a5u07y6+IlD6mY86fh48eVy6TltnGvfODkSnfd2PbLb2JMopLk1dbVWvhlLcQorMiQrkVElh6GgDQqeI6nJI0fSr0tyoej9tJoe0r3joct1cMsk+3Kiyq4J5HWZZxkbx4vlVp68g8udtUpGDJhe9/yxhJ8qpeXjig/Tc1r1CtUxwZ0NQIna9Tm1Lm+LFKjy7L7eT4l7Irs0VdpQAhWtMu3CBJMUZSacRQ6XLSbkekGHQb0ZQ1p6POWJOSzquz5J6yL2mexrF95Vf0uvv3dCLQhlfkxlynEzFNnvI/NX44pfTRpHHDiEbGp5RLu8sQh2yDE8qWW3UB24d3Bay44sIjIZ+5GYwrXVGk3AoJ1Vb5AgCgWezARvnRknElj6UkvS5zcHZ+JrBscmJa0rQmBgeNiwNlzp/8y+YUV+FMHVPJys1oq0jBkr03SU9NuksHf0366tf0uY83u2gt4HUqWjBE2Bu6NadxvaGsUrrtNsm+wmZ/oaYTTsd5OlH80hjCl7Lzb15kMjCqADVoUbsYmcoprbRyFUetVHj+0bSUysgfeysuU11a9rgO3CZJWb1EtGT7yr9hh8vSGWVS0nRiVDNx5+rFI3KWBnQcnfJN14snnwrtALuCo69WgxE8bF/GFbBim1kIXe4cAICO5ywzPhW3z/uzEyelqfAAvj0623/+HA9dNjkwktwbXV5mCeX8jEYT05LSyrjLZle5IM4UnNaKNg3nzj9S9rlve9uX72xyqZqimPvDa5/zznQFTSvRzGHAI1Pel2Jh0o083qbDh6XqOUtq440+QURtbBca0dRWrqjGDytzPJCTxs0RkD4a+rojR+0pFbdFzGGB7uNO10sdTmr8qYzvx93OSRJ8woxG3ZFo8ThBVkSXX9Hrympi0D1O5p2RS2kdXhkNOckrNw2HnDcAgPbwnfeXG5nvjM7OTpys0i/Ia2bU/a2TF/wYDbuqkJ/R6OCEskorZ01p3Bd8cQMupdNyGMnbWnXkLNkmzCFPkryTt8E3dLwJw4LdKRen3Fhj+qiSlZ9Sh7yKaU3o8tSlxe1iS+LjGg8UJh8/qkyqQv6IkSlZ1pymRmgf25ObTyKlY8m4d9Vkbjyu/MyoPXItlVHODMbm31A2O6FBOqtwxQ/oNknBIcdB3gmmG5w1kksnQ6+4lZuG40+W13PMJH9Vp9DaScnLfV29HFjbcSou9eTxhv+XlL+YtD60cyYV6zFYOfkZjXZ7DiF3H+qpl17V83W2oJMT2eKF6rDzofyMRkfnlTyeljStE2XrY0GTxlTVVSOvW3ZisGSBh4WTE8qmMlrtpL4FSvRcsMT7gXGGPJknbHZyTveKV9iPRXHeWTAXp323O3TKiCjK/lE+ddR+j6Pu2gBbnd/h/aiFZFFO5zQ3Tme4Fu1oF6VCrraGZMiuJB4f0fhcSNSZaVm9wc2PlD4u8xCwMBmzg4CpjFYDK2QVR6JUbyHB7Oxh0wWxHYxoKpdW2bnW5uefymjVOeC4wZPSVd9Q3oImQ384yjx60v4tmE702vQn6skUHz9uBx6zE/Ll8fZy5JUfQerlO3v9hP877a3GV3ou0umd5YXJsDLmNTPqfv7FZJvHwhJChJ5TV9q6v11RZ5I3CsT5HYuPP2XnB5xOaHRmvpgsP/GGjs+NKz4ypVzaDnz44yluYMlYNdA913IuWtlBE+c31XnyyJT9OAVzn/j6DOG/w53+ndxOeiJYYibgGZzIKpVZDU0qV8wJklNa00qUfLHrW/VkZKrYeXXzVPhjJSOacoZVjUxZysnJWVIpyY/75as0Dw4VtbtdlKotwWtF7pUjeyiBMpUSUWDbWFh5XanA570waQfxUplVIzeJvTLSdMINeKR1PEKAlZwlPSSYo6TcVrI8fbS2BMm+ChkIolcZ5TUy5f4WZDXxcHeOiqgd9WQrJp83L774gtheQCmr6cRgSCfVzWuW0rGnAqO/vPxppeciHX0BLj+jE9NSduJh37lZfuZhTWSdVeG8ZcxvU+VFvCqdh0U5j+sS1JkkJ6gq8yJS3J6+nM5pLim9Lve8p9gvcI8t0ydmlPe+k4OayBbrIbQbZgZNAsn3y+c+Kb919Hdyu1l966LVqu27p/7aQm+Q1O4iYBugHWE7o313ruZ/NjkrLVmSLCllZVYty8qlnf/LUjpX/qmrGSuVylirYa+adp5f5v5Gak377f56cjWnvlatTMrel0pV4XHrLmy/VzNWSrKktBXlpWrR9LaSS1tKpa1cztmHVMbKZVLevqxmUsU2Y25upXn7HnVz2mKTNbXetlmd8XtaHXVUn54YWQIAANAR8jMaNUYGpDJP2dPmRqaKo7WmE+XzacTHNVcyomcbop4azpuCcyypuJGTouI0nE7PZ7UwaY+oyU4rkXD2ITuhxERWdn6JBSeXl3vl3x11lFauZAhA946SqAl1BkRGsAQAAKAFFibNTqk9Pc4cTh0fnytO53TyXVXqq+YDc92LszACy092WSJT6qkMM8ltIJ9ZMKdUyTKjztSLUqWrbViWJSt0XnEHcqcMmuU1lmudS67Yiae9hNVuLo4wlXLHRck91yWoMyAygiUAAABN5uYPMtkrJMTCO/KO6YR9dd9bwcVINh6c6+71fYyOjxWaW6ZzUU8VBHIJefuRzpW9su8OBCguj7oN5Wc0ajYII4C2cHIifL9Th4ufdaU8gKHbnLo+ZQR1BkRCsAQAAKDJisnC69imRuzne0k4ty/qKaq8nMXMIq2wmC87NKDMqlc1rD7UVvkZjQ5OKKu0coHPfTphBtWyemk+L+VX9LrkrRLoD67VuHX6FKVyqDMgMoIlAAAAQCerYwqOORUnfjilVCoshNTd03AWTk4om875ViyxRwytKpNKKZ1OKZVOKyUp+0beG2HjBph8wTXfSCM314aZk8PN3eHUWZeuPkmdAdHtaHcBAAAAekNeM6PFTm5VKXNZy15CPZUYmZJlTUmyc7DYy76nlFmNOr3huI4dO6FsSZ3aI0smGlvalhmZsmRJxaSlRlsYn5uTtKCFhbhWXp9WdvqEHk7Z9XY4rM6yExqMBWtiWolYl4yyiYg6A6JjZAkAAECLpXMVppNEvKqfnxmtmNh0O6CeAhYmnUCJpNQxJaNGiEZGlAy9o7tHlnjJe925I76kvaOayY9oZCSuA7dJUtYOFqWPhweYIo+S6G7UGRAdwRIAAICuYq9AMThxmyKkq+hh26yevKScaWUyqdLVfCqthlNWd+csMVdGSmVW7Q66N0WkGEwaeaSYmyNKjpftjDoDoiNYAgAA0HXSypk5B1DGNqknMymnNaVx3wo/7hX90lEi1VNEdPfIEnNJ5OzEoEZnFjTzsL2aS/p4cWqWucLL9An/EtHefb7gk7vkrbk0rjs1LKuJhzt8melKqDMgMoIlAAAA3SD/htN52SYBgGbZhvW0cHJC2VRGq9tkfxrGXcLWCe5kJxJ25zyd8wJF3nLUqYwyaUnZCT08U+y2h6/AFDalxNi6OUcOdQZERrAEAACgxSquYFJuCsTII8qkUsqs9k6HmXqyjUzZnU25+SZ8m3tFP3xKzehM8Hq+mUC3yjSc6YRio509ImBh0ihvOmd32qcT3r7Yd6WVmxvX+JTdoc9OPKyZfKVlcMNGSQS2Dq+XSqgzIJrY6lsXrVa92dLSkj5732da9XZoo1gsJstqWdPCNkU7wnZG++5czftsip3UdK7CFImQVSo6TXPb7/apJ1cvf9+bue/eCIh0LnRZ2vzMqAZfOuZvH+6UpjLP6RTNqrftWGe9/P2KijqqD8ESNAVfSDQC7QjbGe27c/HZVEcd1aaX66uX930rqLfoqKvqqKP6MA0HAAAAAADAQLAEAAAAAADAQLAEAAAAAADAsGNpaamlbxiLxVr6fmgfPms0Au0I2xntu3Px2VRHHdWml+url/d9K6i36KgrNMOOO+64o2VvtrS0RGKZHkESITQC7QjbGe27c/HZVEcd1aaX66uX930rqLfoqKvqCCbVZ0fb3jnsA6ORAwAAAABQu2Afm/71lrQnZwmRLQAAAAAAmod+95a0b2SJRKQLAAAAAIBGMPvXBEq2jNVwAAAAAAAADARLAAAAAAAADARLAAAAAAAADARLAAAAAAAADO1J8EpiVwAAAAAAmoM+95bVPLLkx3/xBd37+CldaEZpAAAAAAAA2qymYMmP/+IL+k9KaqhZpQEAAAAAAGizyMGSC99/VP9p35P6xtGBZpYHAAAAAACgrSIFSy58/1H9mf53fePem5pdHgAAAAAAgLaKkOD1ok7/7Yr++7kvK/V88dbff1x65vGj2tu8sgEAAAAAALRchGDJTfrc49/W59z/vnNKX3lK+vcESgAAAAAAwDZU82o4AAAAAAAA2xnBEgAAAAAAAEOEaTgBHz+qbzzehJIAAAAAAAB0AEaWAAAAAAAAGNoTLInF7A0AAAAAADQWfe4tY2QJAAAAAACAgWAJAAAAAACAgWAJAAAAAACAofbVcBopOIfKstpTDgAAAAAAuhk5ShqKkSUAAAAAAACG9owsYQQJAAAAAACNQz+7oXYsLS219A1jDA3qGXzWaATaEbYz2nfn4rOpjjqqTS/XVy/v+1ZQb9FRV2iGmGURfgIAAAAAAHCRswQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMBAsAQAAAAAAMDw/wMsbc6WIXyaGQAAAABJRU5ErkJggg==有如上的查询表查询货品资料各个仓库的入库合计（初始库存+入库库存合计）出库合计，实际库存，账面库存的一个查询表，如何让运行查询的速度更快呢。有两三万调明细。现在是先导入货品资料明细实际库存和账面库存，然后再匹配入库合计和出库合计数，在计数差值，但是运行很慢，有时还会卡死。请问有什么更好的方法吗？如果要做中间表单是要怎么做
？
先导入货品资料明细是怎么样的一个操作？
复制货品基本资料就很慢，然后在匹配各个仓库的出入库数量就更久。
用填表公式这么写肯定很慢，每一行要执行一遍公式。可以尝试用普通模板，勾选只有一份表单，然后用业务公式去查询试试。 想要加快查询，那就减少查询。出库量、入库量、出入库差值可以作为货品资料的常驻数据，仅被出入库影响值，这样可以大幅减少每次盘点的计算量。 先插入货物明细创建一个数据项来触发后续的计算结果 并行执行能大量提高计算速度
李根 发表于 2023-10-16 09:35
用填表公式这么写肯定很慢，每一行要执行一遍公式。可以尝试用普通模板，勾选只有一份表单，然后用业务公式 ...
目前截图的是用【新建报表】 做的，【新建报表】是不储存数据的，不能执行业务公式，换成用【新建模板】来设计这个功能
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