265 lines
9.9 KiB
Python
265 lines
9.9 KiB
Python
"""
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群体间交互管理器
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管理不同群体之间的交互行为
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"""
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from panda3d.core import Vec3
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import random
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from typing import List, Dict
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class InteractionManager:
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"""
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群体间交互管理器
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负责处理不同群体之间的交互行为，如竞争、合作、捕食等
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"""
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def __init__(self, config):
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self.config = config
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self.interactions = [] # 存储群体间交互关系
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self.interaction_history = [] # 交互历史记录
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def add_interaction(self, swarm1: Dict, swarm2: Dict, interaction_type: str, strength: float = 1.0):
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"""
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添加群体间交互关系
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:param swarm1: 第一个群体
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:param swarm2: 第二个群体
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:param interaction_type: 交互类型 ('cooperative', 'competitive', 'predator_prey', 'neutral')
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:param strength: 交互强度 (0.0-1.0)
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"""
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interaction = {
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'swarm1': swarm1,
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'swarm2': swarm2,
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'type': interaction_type,
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'strength': strength,
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'active': True
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}
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self.interactions.append(interaction)
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def remove_interaction(self, interaction):
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"""
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移除群体间交互关系
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"""
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if interaction in self.interactions:
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self.interactions.remove(interaction)
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def update_interactions(self, swarms: List[Dict]):
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"""
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更新所有群体间交互
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:param swarms: 所有群体列表
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"""
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for interaction in self.interactions:
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if interaction['active']:
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self._process_interaction(interaction, swarms)
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def _process_interaction(self, interaction: Dict, swarms: List[Dict]):
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"""
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处理单个群体间交互
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:param interaction: 交互关系
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:param swarms: 所有群体列表
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"""
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swarm1 = interaction['swarm1']
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swarm2 = interaction['swarm2']
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interaction_type = interaction['type']
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strength = interaction['strength']
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if interaction_type == 'cooperative':
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self._process_cooperative_interaction(swarm1, swarm2, strength)
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elif interaction_type == 'competitive':
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self._process_competitive_interaction(swarm1, swarm2, strength)
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elif interaction_type == 'predator_prey':
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self._process_predator_prey_interaction(swarm1, swarm2, strength)
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elif interaction_type == 'neutral':
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# 中性交互，不产生影响
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pass
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def _process_cooperative_interaction(self, swarm1: Dict, swarm2: Dict, strength: float):
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"""
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处理合作交互
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群体之间相互帮助，共享资源或信息
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:param swarm1: 第一个群体
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:param swarm2: 第二个群体
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:param strength: 交互强度
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"""
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# 合作行为：群体成员倾向于向另一个群体的中心移动
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if swarm1.members and swarm2.members:
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# 计算群体中心
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center1 = self._calculate_swarm_center(swarm1.members)
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center2 = self._calculate_swarm_center(swarm2.members)
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# 对两个群体的成员施加合作力
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for member in swarm1.members:
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# 向第二个群体中心移动的趋势
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coop_force = (center2 - member['position']).normalized() * strength * 0.5
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member['cooperative_force'] = coop_force
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for member in swarm2.members:
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# 向第一个群体中心移动的趋势
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coop_force = (center1 - member['position']).normalized() * strength * 0.5
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member['cooperative_force'] = coop_force
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# 记录交互历史
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self._record_interaction_history(swarm1, swarm2, 'cooperative', strength)
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def _process_competitive_interaction(self, swarm1: Dict, swarm2: Dict, strength: float):
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"""
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处理竞争交互
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群体之间争夺资源或领域
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:param swarm1: 第一个群体
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:param swarm2: 第二个群体
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:param strength: 交互强度
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"""
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# 竞争行为：群体成员倾向于远离另一个群体
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if swarm1.members and swarm2.members:
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# 计算群体中心
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center1 = self._calculate_swarm_center(swarm1.members)
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center2 = self._calculate_swarm_center(swarm2.members)
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# 对两个群体的成员施加竞争排斥力
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for member in swarm1.members:
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# 远离第二个群体中心
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competition_force = (member['position'] - center2).normalized() * strength * 0.7
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member['competition_force'] = competition_force
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for member in swarm2.members:
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# 远离第一个群体中心
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competition_force = (member['position'] - center1).normalized() * strength * 0.7
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member['competition_force'] = competition_force
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# 记录交互历史
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self._record_interaction_history(swarm1, swarm2, 'competitive', strength)
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def _process_predator_prey_interaction(self, predator_swarm: Dict, prey_swarm: Dict, strength: float):
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"""
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处理捕食者-猎物交互
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一个群体捕食另一个群体
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:param predator_swarm: 捕食者群体
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:param prey_swarm: 猎物群体
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:param strength: 交互强度
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"""
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if predator_swarm.members and prey_swarm.members:
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# 捕食者行为：寻找最近的猎物
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for predator in predator_swarm.members:
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nearest_prey = self._find_nearest_member(predator, prey_swarm.members)
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if nearest_prey:
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# 向猎物移动
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predation_force = (nearest_prey['position'] - predator['position']).normalized() * strength
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predator['predation_force'] = predation_force
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# 猎物行为：逃离最近的捕食者
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for prey in prey_swarm.members:
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nearest_predator = self._find_nearest_member(prey, predator_swarm.members)
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if nearest_predator:
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# 远离捕食者
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escape_force = (prey['position'] - nearest_predator['position']).normalized() * strength * 1.2
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prey['escape_force'] = escape_force
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# 记录交互历史
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self._record_interaction_history(predator_swarm, prey_swarm, 'predator_prey', strength)
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def _calculate_swarm_center(self, members: List[Dict]) -> Vec3:
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"""
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计算群体中心位置
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:param members: 群体成员列表
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:return: 群体中心位置
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"""
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if not members:
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return Vec3(0, 0, 0)
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center = Vec3(0, 0, 0)
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for member in members:
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center += member['position']
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center /= len(members)
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return center
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def _find_nearest_member(self, member: Dict, other_members: List[Dict]) -> Dict:
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"""
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查找最近的成员
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:param member: 参考成员
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:param other_members: 其他成员列表
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:return: 最近的成员
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"""
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if not other_members:
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return None
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nearest = None
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min_distance = float('inf')
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for other in other_members:
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if other != member: # 排除自己
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distance = (member['position'] - other['position']).length()
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if distance < min_distance:
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min_distance = distance
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nearest = other
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return nearest
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def _record_interaction_history(self, swarm1: Dict, swarm2: Dict, interaction_type: str, strength: float):
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"""
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记录交互历史
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:param swarm1: 第一个群体
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:param swarm2: 第二个群体
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:param interaction_type: 交互类型
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:param strength: 交互强度
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"""
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record = {
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'swarm1_id': id(swarm1),
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'swarm2_id': id(swarm2),
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'type': interaction_type,
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'strength': strength,
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'timestamp': self._get_current_time()
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}
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self.interaction_history.append(record)
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# 限制历史记录数量
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if len(self.interaction_history) > 1000:
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self.interaction_history = self.interaction_history[-1000:]
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def _get_current_time(self):
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"""
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获取当前时间戳
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"""
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import time
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return time.time()
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def get_interaction_stats(self) -> Dict:
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"""
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获取交互统计信息
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:return: 交互统计信息
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"""
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stats = {
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'total_interactions': len(self.interactions),
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'active_interactions': len([i for i in self.interactions if i['active']]),
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'cooperative': len([i for i in self.interactions if i['type'] == 'cooperative']),
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'competitive': len([i for i in self.interactions if i['type'] == 'competitive']),
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'predator_prey': len([i for i in self.interactions if i['type'] == 'predator_prey']),
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'neutral': len([i for i in self.interactions if i['type'] == 'neutral']),
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'history_count': len(self.interaction_history)
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}
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return stats
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def clear_interactions(self):
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"""
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清空所有交互关系
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"""
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self.interactions.clear()
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self.interaction_history.clear()
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def get_interactions_for_swarm(self, swarm: Dict) -> List[Dict]:
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"""
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获取指定群体的所有交互关系
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:param swarm: 群体对象
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:return: 交互关系列表
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"""
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return [i for i in self.interactions
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if i['swarm1'] == swarm or i['swarm2'] == swarm] |