Merge branch 'cc004:main' into main

autopcr/core/pcrclient.py

@@ -92,7 +92,7 @@ async def travel_receive(self, travel_id: int, ex_auto_recycle_option: Union[Tra
         if ex_auto_recycle_option is None:
             ex_auto_recycle_option = TravelExtraEquipAutoRecycleOptionData(rarity=[], frame=[], category=[])
         req = TravelReceiveRequest()
-        req.travel_quest_id = travel_id
+        req.travel_id = travel_id
         req.ex_auto_recycle_option = ex_auto_recycle_option
         return await self.request(req)
 

autopcr/module/modules/sweep.py

@@ -12,6 +12,7 @@
 import random
 from collections import Counter
 import math
+from typing import Set
 
 
 @description('''
@@ -21,9 +22,10 @@
 代币事件为 30%1000代币70%200代币 或 100%400代币 选项
 赌狗策略为前者，保守策略为后者
 '''.strip())
+@multichoice("travel_speed_up_target", "加速地图", ['1-1', '1-4'], db.travel_quest_candidate)
 @singlechoice('travel_quest_gold_event_strategy', "代币事件策略", '赌狗', ['保守','赌狗','随机'])
 @singlechoice('travel_quest_equip_event_strategy', "装备事件策略", '赌狗', ['保守','赌狗','随机'])
-@inttype('travel_quest_speed_up_paper_hold', "加速券保留", 6, list(range(3001)))
+@inttype('travel_quest_speed_up_paper_hold', "加速券保留", 12, list(range(3001)))
 @name("探险续航")
 @default(True)
 class travel_quest_sweep(Module):
@@ -146,8 +148,15 @@ def get_strategy(event_id: int) -> str:
                 min(top.remain_daily_decrease_count_ticket, client.data.get_inventory(db.travel_speed_up_paper)) 
                 - travel_quest_speed_up_paper_hold, 0)
         if team_count and total_use: # avoid divide by zero
+            travel_speed_up_target = self.get_config("travel_speed_up_target")
             self._log(f"可使用加速券{total_use}张")
-            quest_use = [total_use // team_count + (i < total_use % team_count) for i in range(team_count)]
+            speed_up_quest_id: Set[int] = {db.get_travel_quest_id_from_candidate(i) for i in travel_speed_up_target}
+            speed_up_team_count = sum(1 for quest in new_quest_list if quest.travel_quest_id in speed_up_quest_id)
+            quest_use = [total_use // speed_up_team_count if can_use else 0 for can_use in
+                         [quest.travel_quest_id in speed_up_quest_id for quest in new_quest_list]]
+            if (paper_remain := total_use - sum(quest_use)) and speed_up_team_count:
+                for i in random.choices([i for i, v in enumerate(quest_use) if v], k=paper_remain):
+                    quest_use[i] += 1
             for quest, use in zip(new_quest_list, quest_use):
                 if use:
                     self._log(f"{db.get_quest_name(quest.travel_quest_id)}使用加速券x{use}")
@@ -470,8 +479,8 @@ class starcup1_sweep(starcup_sweep):
     def quest_id(self) -> int:
         return 19001001
 
-@inttype("travel_speed_up_paper_threshold", "加速阈值", 6, list(range(12)))
-@inttype("travel_target_day", "轮转天数", 7, [2, 3, 5, 7, 10])
+@inttype("travel_speed_up_paper_threshold", "加速阈值", 12, list(range(13)))
+@inttype("travel_target_day", "轮转天数", 7, list(range(1, 31)))
 @multichoice("travel_target_quest3", "轮转目标3", ['1-2','1-3','1-5'], db.travel_quest_candidate)
 @multichoice("travel_target_quest2", "轮转目标2", ['1-4'], db.travel_quest_candidate)
 @multichoice("travel_target_quest1", "轮转目标1", ['1-1'], db.travel_quest_candidate)

autopcr/module/modules/tools.py

@@ -55,6 +55,10 @@ async def do_task(self, client: pcrclient):
         teams_go = 3 - len(top.travel_quest_list)
         if not teams_go:
             raise AbortError("已经派遣了3支队伍")
+        if teams_go < len(travel_quest_id):
+            raise AbortError(f"可派队伍数量{teams_go}<需派图数{travel_quest_id}")
+        teams_go = len(travel_quest_id)
+
         memory_unit = 3 * teams_go
 
         forbid_unit = []
@@ -89,9 +93,10 @@ async def do_task(self, client: pcrclient):
         candidate_unit_id = sorted([unit_id for unit_id in unit_power if unit_id not in unit_list and unit_id not in forbid_unit], key=lambda x: unit_power[x], reverse=True)[:teams_go * 7]
         candidate_unit_power = [unit_power[unit] for unit in candidate_unit_id]
 
-        ret, sol = dispatch_solver(start_power, candidate_unit_power, 7)
+        lb = [db.travel_quest_data[quest].need_power for quest in travel_quest_id]
+        ret, sol = dispatch_solver(start_power, candidate_unit_power, lb, 7)
         if not ret:
-            raise AbortError("无解！这不可能！")
+            raise AbortError(f"无法凑出战力满足最低要求的{teams_go}支队伍！")
         for pos, unit in zip(sol, candidate_unit_id):
             teams[pos].append(unit)
 
@@ -104,8 +109,6 @@ async def do_task(self, client: pcrclient):
 
         if travel_team_go:
             self._log('----')
-            if len(travel_quest_id) != teams_go:
-                raise AbortError(f"队伍数量{teams_go}与派遣图数{travel_quest_id}不匹配")
 
             start_travel_quest_list: List[TravelStartInfo] = []
             for id, (team, quest) in enumerate(zip(teams, travel_quest_id), start = 1):

autopcr/util/ilp_solver.py

@@ -29,11 +29,12 @@ def ilp_solver(ub: List[int], target: int, limit: int, effect: List[int]) -> Tup
     print(LpStatus[prob.status])
     return prob.status == LpStatusOptimal, ret
 
-def dispatch_solver(start: List[int], candidate: List[int], choose: int) -> Tuple[bool, List[int]]:
+def dispatch_solver(start: List[int], candidate: List[int], lb: List[int], choose: int) -> Tuple[bool, List[int]]:
     '''
     数字分配，使得不同组的数字和尽可能平均
     :param start: 组的初始数字和
     :param candidate: 候选数字
+    :param lb: 每组数字和的下界
     :param choose: 每组选择的数字个数
     :return: 是否有解，数字分配结果
     '''
@@ -42,6 +43,7 @@ def vname(i, j):
     n = len(start)
     m = len(candidate)
     assert n * choose == m, "候选数需等于安排数"
+    assert len(start) == len(lb), "组数需等于组下界数"
     prob = LpProblem(name='dispatch', sense=LpMinimize)
 
     x = [[LpVariable(vname(j, i), lowBound=0, upBound=1, cat=LpInteger) for i in range(n)] for j in range(m)]
@@ -56,6 +58,7 @@ def vname(i, j):
 
     for i in range(n):
         prob += lpSum([x[j][i] for j in range(m)]) == choose, f"dispatch_{i}"
+        prob += psum[i] >= lb[i], f"dispatch_lb_{i}"
         prob += max >= psum[i], f"max_{i}"
         prob += min <= psum[i], f"min_{i}"