工作、学习的收益计算
倍率调整为 1 时，工作、学习的基础数据即为基础值，收益计算方式和倍率调整大于 1 时计算方式并不相同，主要区别在于食物口渴心情的消耗不同。
当倍率调整大于 1 时，工作的数据如下：
倍率调整 \(multi\) ：用户自己设置。
奖励倍率 \(bonus\) ：和倍率调整为 1 时相同。
效率 \(efficiency\) ：是根据当前桌宠状态计算的，状态好时一般可以视为 1.1。
饱腹消耗 \(food\)、口渴消耗 \(drink\)、心情消耗 \(feeling\) ：\(base * (0.5 + 0.4 * multi)\)。
等级限制 \(level\) ：\((levelBase + 10) * multi\)。
花费指标 \(spend\) ：\(3*(\frac{food^{1.5}}{3} + \frac{drink^{1.5}}{4} + \frac{feeling^{1.5}}{4} + \frac{level}{10} + \frac{(food+drink+feeling)^{1.5}}{10})\)。
收益指标 \(moneyBase\) ：\(\frac{2*(1.15*spend^{0.8}-1)}{bonus+1}\) (舍入到十分位，后续忽略这个舍入)。
界面显示收益 \(uiMoney\) ：\((\frac{moneyBase(bonus+1)}{2}+1)^{1.25}\)。
每一跳收益 \(addMoney\) ：\(\frac{(2*efficiency-0.5)moneyBase}{20}\)
大致代码如下：
mma:
food := foodBase*(0.5 + 0.4 * multi)
drink := drinkBase*(0.5 + 0.4 * multi)
feeling := feelingBase*(0.5 + 0.4 * multi)
level := (levelBase + 10) * multi
spend := 3*(food^1.5/3 + drink^1.5/4 + feeling^1.5/4 + level/10 + (drink+feeling+food)^1.5/10)
moneyBase := (2*(1.15*spend^0.8 - 1))/(bonus + 1)
uiMoney := ((bonus + 1)*moneyBase/2 + 1)^1.25
addMoney := 0.05*(2*efficiency - 0.5)*moneyBase
python:
food = foodBase * (0.5 + 0.4 * multi)
drink = drinkBase * (0.5 + 0.4 * multi)
feeling = feelingBase * (0.5 + 0.4 * multi)
level = (levelBase + 10) * multi
spend = 3 * (food**1.5 / 3 + drink**1.5 / 4 + feeling**1.5 / 4 + level / 10 + (drink + feeling + food)**1.5 / 10)
moneyBase = (2 * (1.15 * spend**0.8 - 1)) / (bonus + 1)
uiMoney = ((bonus + 1) * moneyBase / 2 + 1)**1.25
addMoney = 0.05 * (2 * efficiency - 0.5) * moneyBase
其中学习的计算方式与工作的计算方式相同，只是金钱收益改成10倍的经验收益。
因此，以状态良好（效率 1.1）为例，每一跳经验收益:
| 名称 | 完成倍率 | 公收益式（学习收益需要再乘以10） |
|---|---|---|
| 文案 | 1.10 | \(0.224194 (5.27287 (0.4 multi+0.5)^{1.5}+multi)^{0.8}-0.0809524\) |
| 学习 | 1.20 | \(0.214004 (4.80098 (0.4 multi+0.5)^{1.5}+multi)^{0.8}-0.0772727\) |
| 直播 | 1.25 | \(0.209248 (21.9144 (0.4 multi+0.5)^{1.5}+3 multi)^{0.8}-0.0755556\) |
1000级内、状态良好（效率 1.1）时每一跳收益表格如下（学习收益需要再乘以10）：
| 等级 | 文案收益 | 学习收益 | 直播收益 |
|---|---|---|---|
| 20 | 1.31271 | 1.17665 | |
| 30 | 1.84295 | 1.65378 | |
| 40 | 2.38652 | 2.14207 | |
| 50 | 2.94246 | 2.64082 | |
| 60 | 3.50976 | 3.14929 | 3.80477 |
| 70 | 4.08755 | 3.66676 | 3.80477 |
| 80 | 4.67507 | 4.19261 | 3.80477 |
| 90 | 5.27165 | 4.72629 | 5.28046 |
| 100 | 5.87673 | 5.26732 | 5.28046 |
| 110 | 6.4898 | 5.81528 | 5.28046 |
| 120 | 7.11043 | 6.3698 | 6.80138 |
| 130 | 7.73824 | 6.93055 | 6.80138 |
| 140 | 8.37286 | 7.49724 | 6.80138 |
| 150 | 9.01399 | 8.06959 | 8.36282 |
| 160 | 9.66136 | 8.64737 | 8.36282 |
| 170 | 10.3147 | 9.23036 | 8.36282 |
| 180 | 10.9738 | 9.81836 | 9.96081 |
| 190 | 11.6384 | 10.4112 | 9.96081 |
| 200 | 12.3083 | 11.0087 | 9.96081 |
| 210 | 12.9834 | 11.6106 | 11.5921 |
| 220 | 13.6635 | 12.217 | 11.5921 |
| 230 | 14.3484 | 12.8275 | 11.5921 |
| 240 | 15.038 | 13.4422 | 13.254 |
| 250 | 15.7321 | 14.0608 | 13.254 |
| 260 | 16.4306 | 14.6833 | 13.254 |
| 270 | 17.1335 | 15.3095 | 14.9442 |
| 280 | 17.8405 | 15.9395 | 14.9442 |
| 290 | 18.5516 | 16.573 | 14.9442 |
| 300 | 19.2667 | 17.2099 | 16.6608 |
| 310 | 19.9857 | 17.8503 | 16.6608 |
| 320 | 20.7085 | 18.4941 | 16.6608 |
| 330 | 21.435 | 19.141 | 18.4021 |
| 340 | 22.1651 | 19.7912 | 18.4021 |
| 350 | 22.8988 | 20.4445 | 18.4021 |
| 360 | 23.636 | 21.1008 | 20.1668 |
| 370 | 24.3766 | 21.7601 | 20.1668 |
| 380 | 25.1205 | 22.4224 | 20.1668 |
| 390 | 25.8677 | 23.0875 | 21.9535 |
| 400 | 26.6181 | 23.7555 | 21.9535 |
| 410 | 27.3717 | 24.4262 | 21.9535 |
| 420 | 28.1284 | 25.0997 | 23.7611 |
| 430 | 28.8882 | 25.7758 | 23.7611 |
| 440 | 29.6509 | 26.4546 | 23.7611 |
| 450 | 30.4166 | 27.136 | 25.5886 |
| 460 | 31.1852 | 27.8199 | 25.5886 |
| 470 | 31.9567 | 28.5063 | 25.5886 |
| 480 | 32.731 | 29.1952 | 27.4351 |
| 490 | 33.508 | 29.8865 | 27.4351 |
| 500 | 34.2878 | 30.5803 | 27.4351 |
| 510 | 35.0702 | 31.2763 | 29.2998 |
| 520 | 35.8553 | 31.9747 | 29.2998 |
| 530 | 36.643 | 32.6754 | 29.2998 |
| 540 | 37.4333 | 33.3784 | 31.182 |
| 550 | 38.2261 | 34.0836 | 31.182 |
| 560 | 39.0214 | 34.791 | 31.182 |
| 570 | 39.8192 | 35.5005 | 33.081 |
| 580 | 40.6194 | 36.2122 | 33.081 |
| 590 | 41.422 | 36.926 | 33.081 |
| 600 | 42.227 | 37.6419 | 34.9961 |
| 610 | 43.0344 | 38.3599 | 34.9961 |
| 620 | 43.844 | 39.0799 | 34.9961 |
| 630 | 44.656 | 39.8019 | 36.9269 |
| 640 | 45.4702 | 40.5259 | 36.9269 |
| 650 | 46.2866 | 41.2518 | 36.9269 |
| 660 | 47.1053 | 41.9797 | 38.8727 |
| 670 | 47.9261 | 42.7096 | 38.8727 |
| 680 | 48.7491 | 43.4413 | 38.8727 |
| 690 | 49.5743 | 44.1749 | 40.8332 |
| 700 | 50.4015 | 44.9104 | 40.8332 |
| 710 | 51.2309 | 45.6477 | 40.8332 |
| 720 | 52.0623 | 46.3868 | 42.8078 |
| 730 | 52.8957 | 47.1277 | 42.8078 |
| 740 | 53.7312 | 47.8704 | 42.8078 |
| 750 | 54.5687 | 48.6149 | 44.7961 |
| 760 | 55.4082 | 49.3611 | 44.7961 |
| 770 | 56.2496 | 50.1091 | 44.7961 |
| 780 | 57.093 | 50.8587 | 46.7977 |
| 790 | 57.9383 | 51.6101 | 46.7977 |
| 800 | 58.7855 | 52.3631 | 46.7977 |
| 810 | 59.6346 | 53.1178 | 48.8123 |
| 820 | 60.4856 | 53.8742 | 48.8123 |
| 830 | 61.3384 | 54.6321 | 48.8123 |
| 840 | 62.1931 | 55.3917 | 50.8395 |
| 850 | 63.0496 | 56.1529 | 50.8395 |
| 860 | 63.9079 | 56.9157 | 50.8395 |
| 870 | 64.768 | 57.6801 | 52.879 |
| 880 | 65.6298 | 58.446 | 52.879 |
| 890 | 66.4935 | 59.2135 | 52.879 |
| 900 | 67.3588 | 59.9825 | 54.9304 |
| 910 | 68.2259 | 60.753 | 54.9304 |
| 920 | 69.0947 | 61.5251 | 54.9304 |
| 930 | 69.9652 | 62.2986 | 56.9936 |
| 940 | 70.8374 | 63.0736 | 56.9936 |
| 950 | 71.7113 | 63.8501 | 56.9936 |
| 960 | 72.5868 | 64.6281 | 59.0681 |
| 970 | 73.464 | 65.4075 | 59.0681 |
| 980 | 74.3428 | 66.1883 | 59.0681 |
| 990 | 75.2232 | 66.9706 | 61.1539 |
| 1000 | 76.1053 | 67.7543 | 61.1539 |
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