进行一系列大胆的猜测，特别是关于将目标索引转换为约束索引的猜测，并在Python/PuLP中进行演示（您提到您在其他语言中尝试了这一点，但没有指定任何关于您尝试的内容）-这可以简单到

import numpy as np
import pulp

prob = pulp.LpProblem(name='lingo', sense=pulp.LpMinimize)

x = pulp.LpVariable.matrix(
    name='x', indices=range(1, 161),
    cat=pulp.LpInteger, lowBound=0,
)

prob.objective = pulp.lpSum(x)

for starts in (
    (10,  0, 30,      70, 100, 120, 140),
    (20,  5, 35,      75, 105, 125, 145),
    (30, 10, 40, 50,  80, 110, 130, 150),
    (40, 15, 45, 55,  85, 115, 135, 155),
    (50, 20,     60,  90),
    (60, 25,     65,  95),
):
    slices = (x[start: start+5] for start in starts)
    for addends in zip(*slices):
        prob.addConstraint(pulp.lpSum(addends) <= 7)

prob.addConstraint(pulp.lpSum(x[  0:  20]) >= 11.8134)
prob.addConstraint(pulp.lpSum(x[ 20:  30]) >= 15.5714)
prob.addConstraint(pulp.lpSum(x[ 30:  50]) >= 34.2)
prob.addConstraint(pulp.lpSum(x[ 50:  70]) >= 0)  # redundant
prob.addConstraint(pulp.lpSum(x[ 70: 100]) >= 0)  # redundant
prob.addConstraint(pulp.lpSum(x[100: 120]) >= 0)  # redundant
prob.addConstraint(pulp.lpSum(x[120: 140]) >= 0)  # redundant
prob.addConstraint(pulp.lpSum(x[140: 160]) >= 4.1652)
prob.addConstraint(pulp.lpSum(x) >= 65.7501)

print(prob)
prob.solve()
assert prob.status == pulp.LpStatusOptimal
xa = (
    np.array([xi.value() for xi in x], dtype=int)
    .reshape((-1, 10))
)
print(xa)

lingo:
MINIMIZE
1*x_1 + 1*x_10 + 1*x_100 + 1*x_101 + 1*x_102 + 1*x_103 + 1*x_104 + 1*x_105 + 1*x_106 + 1*x_107 + 1*x_108 + 1*x_109 + 1*x_11 + 1*x_110 + 1*x_111 + 1*x_112 + 1*x_113 + 1*x_114 + 1*x_115 + 1*x_116 + 1*x_117 + 1*x_118 + 1*x_119 + 1*x_12 + 1*x_120 + 1*x_121 + 1*x_122 + 1*x_123 + 1*x_124 + 1*x_125 + 1*x_126 + 1*x_127 + 1*x_128 + 1*x_129 + 1*x_13 + 1*x_130 + 1*x_131 + 1*x_132 + 1*x_133 + 1*x_134 + 1*x_135 + 1*x_136 + 1*x_137 + 1*x_138 + 1*x_139 + 1*x_14 + 1*x_140 + 1*x_141 + 1*x_142 + 1*x_143 + 1*x_144 + 1*x_145 + 1*x_146 + 1*x_147 + 1*x_148 + 1*x_149 + 1*x_15 + 1*x_150 + 1*x_151 + 1*x_152 + 1*x_153 + 1*x_154 + 1*x_155 + 1*x_156 + 1*x_157 + 1*x_158 + 1*x_159 + 1*x_16 + 1*x_160 + 1*x_17 + 1*x_18 + 1*x_19 + 1*x_2 + 1*x_20 + 1*x_21 + 1*x_22 + 1*x_23 + 1*x_24 + 1*x_25 + 1*x_26 + 1*x_27 + 1*x_28 + 1*x_29 + 1*x_3 + 1*x_30 + 1*x_31 + 1*x_32 + 1*x_33 + 1*x_34 + 1*x_35 + 1*x_36 + 1*x_37 + 1*x_38 + 1*x_39 + 1*x_4 + 1*x_40 + 1*x_41 + 1*x_42 + 1*x_43 + 1*x_44 + 1*x_45 + 1*x_46 + 1*x_47 + 1*x_48 + 1*x_49 + 1*x_5 + 1*x_50 + 1*x_51 + 1*x_52 + 1*x_53 + 1*x_54 + 1*x_55 + 1*x_56 + 1*x_57 + 1*x_58 + 1*x_59 + 1*x_6 + 1*x_60 + 1*x_61 + 1*x_62 + 1*x_63 + 1*x_64 + 1*x_65 + 1*x_66 + 1*x_67 + 1*x_68 + 1*x_69 + 1*x_7 + 1*x_70 + 1*x_71 + 1*x_72 + 1*x_73 + 1*x_74 + 1*x_75 + 1*x_76 + 1*x_77 + 1*x_78 + 1*x_79 + 1*x_8 + 1*x_80 + 1*x_81 + 1*x_82 + 1*x_83 + 1*x_84 + 1*x_85 + 1*x_86 + 1*x_87 + 1*x_88 + 1*x_89 + 1*x_9 + 1*x_90 + 1*x_91 + 1*x_92 + 1*x_93 + 1*x_94 + 1*x_95 + 1*x_96 + 1*x_97 + 1*x_98 + 1*x_99 + 0
SUBJECT TO
_C1: x_1 + x_101 + x_11 + x_121 + x_141 + x_31 + x_71 <= 7

_C2: x_102 + x_12 + x_122 + x_142 + x_2 + x_32 + x_72 <= 7

_C3: x_103 + x_123 + x_13 + x_143 + x_3 + x_33 + x_73 <= 7

_C4: x_104 + x_124 + x_14 + x_144 + x_34 + x_4 + x_74 <= 7

_C5: x_105 + x_125 + x_145 + x_15 + x_35 + x_5 + x_75 <= 7

_C6: x_106 + x_126 + x_146 + x_21 + x_36 + x_6 + x_76 <= 7

_C7: x_107 + x_127 + x_147 + x_22 + x_37 + x_7 + x_77 <= 7

_C8: x_108 + x_128 + x_148 + x_23 + x_38 + x_78 + x_8 <= 7

_C9: x_109 + x_129 + x_149 + x_24 + x_39 + x_79 + x_9 <= 7

_C10: x_10 + x_110 + x_130 + x_150 + x_25 + x_40 + x_80 <= 7

_C11: x_11 + x_111 + x_131 + x_151 + x_31 + x_41 + x_51 + x_81 <= 7

_C12: x_112 + x_12 + x_132 + x_152 + x_32 + x_42 + x_52 + x_82 <= 7

_C13: x_113 + x_13 + x_133 + x_153 + x_33 + x_43 + x_53 + x_83 <= 7

_C14: x_114 + x_134 + x_14 + x_154 + x_34 + x_44 + x_54 + x_84 <= 7

_C15: x_115 + x_135 + x_15 + x_155 + x_35 + x_45 + x_55 + x_85 <= 7

_C16: x_116 + x_136 + x_156 + x_16 + x_41 + x_46 + x_56 + x_86 <= 7

_C17: x_117 + x_137 + x_157 + x_17 + x_42 + x_47 + x_57 + x_87 <= 7

_C18: x_118 + x_138 + x_158 + x_18 + x_43 + x_48 + x_58 + x_88 <= 7

_C19: x_119 + x_139 + x_159 + x_19 + x_44 + x_49 + x_59 + x_89 <= 7

_C20: x_120 + x_140 + x_160 + x_20 + x_45 + x_50 + x_60 + x_90 <= 7

_C21: x_21 + x_51 + x_61 + x_91 <= 7

_C22: x_22 + x_52 + x_62 + x_92 <= 7

_C23: x_23 + x_53 + x_63 + x_93 <= 7

_C24: x_24 + x_54 + x_64 + x_94 <= 7

_C25: x_25 + x_55 + x_65 + x_95 <= 7

_C26: x_26 + x_61 + x_66 + x_96 <= 7

_C27: x_27 + x_62 + x_67 + x_97 <= 7

_C28: x_28 + x_63 + x_68 + x_98 <= 7

_C29: x_29 + x_64 + x_69 + x_99 <= 7

_C30: x_100 + x_30 + x_65 + x_70 <= 7

_C31: x_1 + x_10 + x_11 + x_12 + x_13 + x_14 + x_15 + x_16 + x_17 + x_18
 + x_19 + x_2 + x_20 + x_3 + x_4 + x_5 + x_6 + x_7 + x_8 + x_9 >= 11.8134

_C32: x_21 + x_22 + x_23 + x_24 + x_25 + x_26 + x_27 + x_28 + x_29 + x_30
 >= 15.5714

_C33: x_31 + x_32 + x_33 + x_34 + x_35 + x_36 + x_37 + x_38 + x_39 + x_40
 + x_41 + x_42 + x_43 + x_44 + x_45 + x_46 + x_47 + x_48 + x_49 + x_50 >= 34.2

_C34: x_51 + x_52 + x_53 + x_54 + x_55 + x_56 + x_57 + x_58 + x_59 + x_60
 + x_61 + x_62 + x_63 + x_64 + x_65 + x_66 + x_67 + x_68 + x_69 + x_70 >= 0

_C35: x_100 + x_71 + x_72 + x_73 + x_74 + x_75 + x_76 + x_77 + x_78 + x_79
 + x_80 + x_81 + x_82 + x_83 + x_84 + x_85 + x_86 + x_87 + x_88 + x_89 + x_90
 + x_91 + x_92 + x_93 + x_94 + x_95 + x_96 + x_97 + x_98 + x_99 >= 0

_C36: x_101 + x_102 + x_103 + x_104 + x_105 + x_106 + x_107 + x_108 + x_109
 + x_110 + x_111 + x_112 + x_113 + x_114 + x_115 + x_116 + x_117 + x_118
 + x_119 + x_120 >= 0

_C37: x_121 + x_122 + x_123 + x_124 + x_125 + x_126 + x_127 + x_128 + x_129
 + x_130 + x_131 + x_132 + x_133 + x_134 + x_135 + x_136 + x_137 + x_138
 + x_139 + x_140 >= 0

_C38: x_141 + x_142 + x_143 + x_144 + x_145 + x_146 + x_147 + x_148 + x_149
 + x_150 + x_151 + x_152 + x_153 + x_154 + x_155 + x_156 + x_157 + x_158
 + x_159 + x_160 >= 4.1652

_C39: x_1 + x_10 + x_100 + x_101 + x_102 + x_103 + x_104 + x_105 + x_106
 + x_107 + x_108 + x_109 + x_11 + x_110 + x_111 + x_112 + x_113 + x_114
 + x_115 + x_116 + x_117 + x_118 + x_119 + x_12 + x_120 + x_121 + x_122
 + x_123 + x_124 + x_125 + x_126 + x_127 + x_128 + x_129 + x_13 + x_130
 + x_131 + x_132 + x_133 + x_134 + x_135 + x_136 + x_137 + x_138 + x_139
 + x_14 + x_140 + x_141 + x_142 + x_143 + x_144 + x_145 + x_146 + x_147
 + x_148 + x_149 + x_15 + x_150 + x_151 + x_152 + x_153 + x_154 + x_155
 + x_156 + x_157 + x_158 + x_159 + x_16 + x_160 + x_17 + x_18 + x_19 + x_2
 + x_20 + x_21 + x_22 + x_23 + x_24 + x_25 + x_26 + x_27 + x_28 + x_29 + x_3
 + x_30 + x_31 + x_32 + x_33 + x_34 + x_35 + x_36 + x_37 + x_38 + x_39 + x_4
 + x_40 + x_41 + x_42 + x_43 + x_44 + x_45 + x_46 + x_47 + x_48 + x_49 + x_5
 + x_50 + x_51 + x_52 + x_53 + x_54 + x_55 + x_56 + x_57 + x_58 + x_59 + x_6
 + x_60 + x_61 + x_62 + x_63 + x_64 + x_65 + x_66 + x_67 + x_68 + x_69 + x_7
 + x_70 + x_71 + x_72 + x_73 + x_74 + x_75 + x_76 + x_77 + x_78 + x_79 + x_8
 + x_80 + x_81 + x_82 + x_83 + x_84 + x_85 + x_86 + x_87 + x_88 + x_89 + x_9
 + x_90 + x_91 + x_92 + x_93 + x_94 + x_95 + x_96 + x_97 + x_98 + x_99
 >= 65.7501

VARIABLES
0 <= x_1 Integer
0 <= x_10 Integer
0 <= x_100 Integer
0 <= x_101 Integer
0 <= x_102 Integer
0 <= x_103 Integer
0 <= x_104 Integer
0 <= x_105 Integer
0 <= x_106 Integer
0 <= x_107 Integer
0 <= x_108 Integer
0 <= x_109 Integer
0 <= x_11 Integer
0 <= x_110 Integer
0 <= x_111 Integer
0 <= x_112 Integer
0 <= x_113 Integer
0 <= x_114 Integer
0 <= x_115 Integer
0 <= x_116 Integer
0 <= x_117 Integer
0 <= x_118 Integer
0 <= x_119 Integer
0 <= x_12 Integer
0 <= x_120 Integer
0 <= x_121 Integer
0 <= x_122 Integer
0 <= x_123 Integer
0 <= x_124 Integer
0 <= x_125 Integer
0 <= x_126 Integer
0 <= x_127 Integer
0 <= x_128 Integer
0 <= x_129 Integer
0 <= x_13 Integer
0 <= x_130 Integer
0 <= x_131 Integer
0 <= x_132 Integer
0 <= x_133 Integer
0 <= x_134 Integer
0 <= x_135 Integer
0 <= x_136 Integer
0 <= x_137 Integer
0 <= x_138 Integer
0 <= x_139 Integer
0 <= x_14 Integer
0 <= x_140 Integer
0 <= x_141 Integer
0 <= x_142 Integer
0 <= x_143 Integer
0 <= x_144 Integer
0 <= x_145 Integer
0 <= x_146 Integer
0 <= x_147 Integer
0 <= x_148 Integer
0 <= x_149 Integer
0 <= x_15 Integer
0 <= x_150 Integer
0 <= x_151 Integer
0 <= x_152 Integer
0 <= x_153 Integer
0 <= x_154 Integer
0 <= x_155 Integer
0 <= x_156 Integer
0 <= x_157 Integer
0 <= x_158 Integer
0 <= x_159 Integer
0 <= x_16 Integer
0 <= x_160 Integer
0 <= x_17 Integer
0 <= x_18 Integer
0 <= x_19 Integer
0 <= x_2 Integer
0 <= x_20 Integer
0 <= x_21 Integer
0 <= x_22 Integer
0 <= x_23 Integer
0 <= x_24 Integer
0 <= x_25 Integer
0 <= x_26 Integer
0 <= x_27 Integer
0 <= x_28 Integer
0 <= x_29 Integer
0 <= x_3 Integer
0 <= x_30 Integer
0 <= x_31 Integer
0 <= x_32 Integer
0 <= x_33 Integer
0 <= x_34 Integer
0 <= x_35 Integer
0 <= x_36 Integer
0 <= x_37 Integer
0 <= x_38 Integer
0 <= x_39 Integer
0 <= x_4 Integer
0 <= x_40 Integer
0 <= x_41 Integer
0 <= x_42 Integer
0 <= x_43 Integer
0 <= x_44 Integer
0 <= x_45 Integer
0 <= x_46 Integer
0 <= x_47 Integer
0 <= x_48 Integer
0 <= x_49 Integer
0 <= x_5 Integer
0 <= x_50 Integer
0 <= x_51 Integer
0 <= x_52 Integer
0 <= x_53 Integer
0 <= x_54 Integer
0 <= x_55 Integer
0 <= x_56 Integer
0 <= x_57 Integer
0 <= x_58 Integer
0 <= x_59 Integer
0 <= x_6 Integer
0 <= x_60 Integer
0 <= x_61 Integer
0 <= x_62 Integer
0 <= x_63 Integer
0 <= x_64 Integer
0 <= x_65 Integer
0 <= x_66 Integer
0 <= x_67 Integer
0 <= x_68 Integer
0 <= x_69 Integer
0 <= x_7 Integer
0 <= x_70 Integer
0 <= x_71 Integer
0 <= x_72 Integer
0 <= x_73 Integer
0 <= x_74 Integer
0 <= x_75 Integer
0 <= x_76 Integer
0 <= x_77 Integer
0 <= x_78 Integer
0 <= x_79 Integer
0 <= x_8 Integer
0 <= x_80 Integer
0 <= x_81 Integer
0 <= x_82 Integer
0 <= x_83 Integer
0 <= x_84 Integer
0 <= x_85 Integer
0 <= x_86 Integer
0 <= x_87 Integer
0 <= x_88 Integer
0 <= x_89 Integer
0 <= x_9 Integer
0 <= x_90 Integer
0 <= x_91 Integer
0 <= x_92 Integer
0 <= x_93 Integer
0 <= x_94 Integer
0 <= x_95 Integer
0 <= x_96 Integer
0 <= x_97 Integer
0 <= x_98 Integer
0 <= x_99 Integer

Welcome to the CBC MILP Solver 
Version: 2.10.3 
Build Date: Dec 15 2019 

Result - Optimal solution found

Objective value:                68.00000000
Enumerated nodes:               0
Total iterations:               0
Time (CPU seconds):             0.00
Time (Wallclock seconds):       0.00

Option for printingOptions changed from normal to all
Total time (CPU seconds):       0.00   (Wallclock seconds):       0.00

[[0 0 0 0 0 5 0 0 0 7]
 [0 0 0 0 0 0 0 0 0 0]
 [0 0 0 0 0 0 7 7 2 0]
 [0 0 0 0 0 0 7 0 0 0]
 [0 0 0 0 0 7 7 7 0 7]
 [0 0 0 0 0 0 0 0 0 0]
 [0 0 0 0 0 0 0 0 0 0]
 [0 0 0 0 0 0 0 0 0 0]
 [0 0 0 0 0 0 0 0 0 0]
 [0 0 0 0 0 0 0 0 0 0]
 [0 0 0 0 0 0 0 0 0 0]
 [0 0 0 0 0 0 0 0 0 0]
 [0 0 0 0 0 0 0 0 0 0]
 [0 0 0 0 0 0 0 0 0 0]
 [0 0 5 0 0 0 0 0 0 0]
 [0 0 0 0 0 0 0 0 0 0]]