import hashlib

import matplotlib.pyplot as plt

import networkx as nx

from scipy.integrate import odeint

from collections import defaultdict

from mpl_toolkits.mplot3d import Axes3D

# ======================

# 增强型常量与参数

# ======================

BASE_EVENT_RATE = 1e9

RESONANCE_THRESHOLD = 0.7

TIME_CONVERSION_FACTOR = 1e18

MAX_DRIFT = 0.1

INITIAL_DIM = 3

GRAVITATIONAL_CONSTANT = 6.674e-11 # 引力常数 (m³/kg/s²)

PLANCK_MASS = 2.176e-8 # 普朗克质量 (kg)

OBSERVER_STRENGTH = 0.05 # 观测者影响强度

# ======================

# 增强型类定义

# ======================

class DomainStructure:

"""定义域量子结构 - 增加曲率场"""

def __init__(self, dimensions, topology='flat', parent_signatures=None, curvature=0.0):

self.dimensions = dimensions

self.topology = topology

self.curvature = curvature # 新增曲率属性

if parent_signatures is None:

parent_signatures = []

seed = f"{dimensions}_{topology}_{curvature}_{'_'.join(parent_signatures)}"

self.signature = self._generate_signature(seed)

self.parent_signatures = parent_signatures

def _generate_signature(self, seed):

return hashlib.sha3_256(seed.encode()).hexdigest()[:16]

def similarity(self, other):

"""增加曲率相似度计算"""

if self.signature == other.signature:

return 1.0

dim_sim = 1 - abs(self.dimensions - other.dimensions) / max(self.dimensions, other.dimensions, 1)

topologies = ['flat', 'hyperbolic', 'high_curvature']

topo_sim = 1.0 if self.topology == other.topology else 0.3

# 曲率相似度 (新增)

curv_sim = 1 - min(1.0, abs(self.curvature - other.curvature))

parent_sim = 0.0

common_parents = set(self.parent_signatures) & set(other.parent_signatures)

if common_parents:

parent_sim = len(common_parents) / max(len(self.parent_signatures), len(other.parent_signatures), 1)

# 权重调整：维度30%，拓扑20%，曲率30%，父定义域20%

return 0.3 * dim_sim + 0.2 * topo_sim + 0.3 * curv_sim + 0.2 * parent_sim

def complexity(self):

"""复杂度计算增加曲率因子"""

base_complexity = self.dimensions

if self.topology == 'hyperbolic':

base_complexity **= 1.5

elif self.topology == 'high_curvature':

base_complexity = np.sqrt(self.dimensions)

return base_complexity * (1 + abs(self.curvature))

class QuantumLogicChain:

"""量子逻辑链 - 增加位置和速度属性"""

def __init__(self, domain, function_matrix=None, energy=1.0, position=None, velocity=None):

self.domain = domain

self.energy = energy

# 初始化位置和速度 (3D空间)

if position is None:

self.position = np.random.rand(3) * 10

else:

self.position = np.array(position)

if velocity is None:

self.velocity = (np.random.rand(3) - 0.5) * 0.1

else:

self.velocity = np.array(velocity)

if function_matrix is None:

self.function = np.eye(domain.dimensions)

else:

self.function = function_matrix

self.codomain = self._compute_codomain()

self.oscillation_phase = 0.0

def _compute_codomain(self):

return np.random.randn(self.domain.dimensions)

def apply_gravity(self, mass_center, mass, dt):

"""应用引力效应 (简化牛顿引力)"""

r_vec = mass_center - self.position

r = np.linalg.norm(r_vec) + 1e-10 # 避免除以零

direction = r_vec / r

# 引力加速度 (a = G*M/r²)

acceleration = GRAVITATIONAL_CONSTANT * mass / (r**2)

# 更新速度

self.velocity += acceleration * direction * dt

# 更新位置

self.position += self.velocity * dt

def apply_curvature(self, curvature_field, dt):

"""应用时空曲率效应"""

# 简化的曲率效应：曲率梯度导致速度变化

# 在实际相对论中应使用测地线方程，此处简化处理

grad_curvature = np.gradient(curvature_field, axis=(0,1,2))

curvature_at_pos = self._sample_curvature(grad_curvature)

# 曲率梯度越大，速度变化越显著

self.velocity *= (1 + 0.1 * np.linalg.norm(curvature_at_pos) * dt)

def _sample_curvature(self, grad_curvature):

"""在链位置处采样曲率梯度"""

# 简化处理：使用最近网格点

x, y, z = np.clip(self.position.astype(int), 0, len(grad_curvature[0])-1)

return np.array([grad_curvature[0][x,y,z], grad_curvature[1][x,y,z], grad_curvature[2][x,y,z]])

def oscillate(self, dt):

omega = 0.1 * self.energy * dt

self.oscillation_phase += omega

rotation = np.array([[np.cos(omega), -np.sin(omega)],

[np.sin(omega), np.cos(omega)]])

if self.function.shape[0] >= 2 and self.function.shape[1] >= 2:

core = self.function[:2, :2]

self.function[:2, :2] = rotation @ core

def decohere(self):

new_dims = max(1, self.domain.dimensions - 1)

# 新定义域继承部分曲率 (曲率耗散)

new_curvature = self.domain.curvature * 0.8

new_domain = DomainStructure(

dimensions=new_dims,

topology=self.domain.topology,

parent_signatures=self.domain.parent_signatures + [self.domain.signature],

curvature=new_curvature

)

new_chain = QuantumLogicChain(

domain=new_domain,

energy=self.energy * 0.8,

position=self.position.copy(),

velocity=self.velocity.copy()

)

dark_matter = DarkMatterChain(

energy=self.energy * 0.2,

parent_signatures=self.domain.parent_signatures + [self.domain.signature],

position=self.position.copy()

)

time_elapsed = dark_matter.energy / TIME_CONVERSION_FACTOR

return new_chain, dark_matter, time_elapsed

def resonate(self, other_chain):

sim = self.domain.similarity(other_chain.domain)

if sim < RESONANCE_THRESHOLD:

return None

resonance_energy = min(self.energy, other_chain.energy) * sim

# 创建热逻辑链 (位置为两链中点)

avg_position = (self.position + other_chain.position) / 2

thermal_chain = ThermalChain(

energy=resonance_energy,

parent_signatures=[self.domain.signature, other_chain.domain.signature],

position=avg_position

)

self.energy -= resonance_energy * 0.5

other_chain.energy -= resonance_energy * 0.5

return thermal_chain

class DarkMatterChain(QuantumLogicChain):

"""暗物质逻辑链 - 增加暗物质特有属性"""

def __init__(self, energy, parent_signatures, position=None):

domain = DomainStructure(

dimensions=1,

topology='singular',

parent_signatures=parent_signatures,

curvature=0.0 # 暗物质定义域曲率为零

)

super().__init__(domain, energy=energy, position=position)

self.function = np.array([[1.0]])

self.repulsion_strength = energy * 0.1 # 排斥强度与能量相关

class ThermalChain(QuantumLogicChain):

"""热逻辑链 - 增加热禁锢特性"""

def __init__(self, energy, parent_signatures, position=None):

domain = DomainStructure(

dimensions=2,

topology='flat',

parent_signatures=parent_signatures,

curvature=0.01 # 热链有轻微正曲率

)

super().__init__(domain, energy=energy, position=position)

self.function = np.eye(2) * energy

self.confinement_strength = energy * 0.05 # 禁锢强度

class Spacetime:

"""增强的时空结构 - 整合曲率场和物质分布"""

def __init__(self, size=100):

self.size = size

self.curvature_field = np.zeros((size, size, size)) # 3D曲率场

self.mass_distribution = np.zeros((size, size, size)) # 3D质量分布

self.dark_matter_chains = []

self.total_energy = 0.0

self.expansion_rate = 0.0

self.inflation_active = False # 暴胀状态标志

def add_chain(self, chain):

"""添加链并更新时空结构"""

if isinstance(chain, DarkMatterChain):

self.dark_matter_chains.append(chain)

self._update_dark_matter_curvature(chain)

elif isinstance(chain, ThermalChain):

self._update_mass_distribution(chain)

self.total_energy += chain.energy

def _update_dark_matter_curvature(self, chain):

"""更新暗物质引起的曲率 (负曲率)"""

x, y, z = self._position_to_index(chain.position)

# 暗物质产生负曲率 (排斥效应)

self.curvature_field[x,y,z] -= chain.repulsion_strength

# 检查是否触发暴胀

if np.sum(np.abs(self.curvature_field)) > 500: # 曲率总和阈值

self.activate_inflation()

def _update_mass_distribution(self, chain):

"""更新物质质量分布 (正曲率)"""

x, y, z = self._position_to_index(chain.position)

# 物质产生正曲率 (吸引效应)

self.curvature_field[x,y,z] += chain.confinement_strength

self.mass_distribution[x,y,z] += chain.energy

def _position_to_index(self, position):

"""将连续位置转换为离散网格索引"""

scaled_pos = (position / np.max(position)) * (self.size - 1) if np.max(position) > 0 else position

indices = np.clip(scaled_pos.astype(int), 0, self.size-1)

return indices[0], indices[1], indices[2]

def activate_inflation(self):

"""激活暴胀机制"""

self.inflation_active = True

self.expansion_rate = 0.5 # 高膨胀率

def expand(self, delta_t):

"""时空扩张"""

if self.inflation_active:

expansion_factor = np.exp(self.expansion_rate * delta_t)

# 更新所有链的位置

for chain in self.dark_matter_chains:

chain.position *= expansion_factor

# 降低暴胀强度

self.expansion_rate *= 0.9

# 检查暴胀结束条件

if self.expansion_rate < 0.01:

self.inflation_active = False

else:

# 正常扩张

expansion = self.expansion_rate * delta_t

for chain in self.dark_matter_chains:

chain.position *= (1 + expansion)

def compute_gravity_center(self):

"""计算物质系统的质心"""

total_mass = np.sum(self.mass_distribution)

if total_mass < 1e-10:

return np.zeros(3), 0.0

# 计算加权质心

grid = np.indices(self.mass_distribution.shape).reshape(3, -1)

weights = self.mass_distribution.ravel()

center = np.average(grid, axis=1, weights=weights)

return center, total_mass

class QuantumObserver:

"""量子观测者 - 影响被观测系统的退相干"""

def __init__(self, focus_domain=None):

self.focus_domain = focus_domain or DomainStructure(dimensions=3, topology='flat')

self.influence_strength = OBSERVER_STRENGTH

def observe(self, chain):

"""观测行为影响量子链"""

focus_sim = self.focus_domain.similarity(chain.domain)

if focus_sim > 0.5:

# 高关注度加速退相干

decoherence_rate = self.influence_strength * focus_sim

chain.energy *= (1 - decoherence_rate)

return decoherence_rate * chain.energy

return 0

def adapt_focus(self, chain):

"""根据观测结果调整关注点"""

sim = self.focus_domain.similarity(chain.domain)

if sim > 0.7:

# 强化相似领域的关注

self.influence_strength += 0.01

elif sim < 0.3:

# 弱化不相关领域的关注

self.influence_strength = max(0.01, self.influence_strength - 0.005)

class QuantumUniverse:

"""增强的量子宇宙模型 - 整合时空和观测者"""

def __init__(self, initial_chains=None, num_observers=1):

self.absolute_time = AbsoluteTime()

self.event_density = EventDensity()

self.spacetime = Spacetime(size=50)

self.material = MaterialSystem()

self.free_chains = initial_chains or self._create_initial_chains()

# 创建观测者

self.observers = [QuantumObserver() for _ in range(num_observers)]

# 统计量

self.total_events = 0

self.history = []

self.drift_constraint = MAX_DRIFT

self.last_state = self.get_state_vector()

def _create_initial_chains(self):

initial_domain = DomainStructure(dimensions=INITIAL_DIM, topology='flat', curvature=0.05)

chains = []

for _ in range(3):

chain = QuantumLogicChain(domain=initial_domain, energy=1.0)

self.spacetime.add_chain(chain) # 添加到时空

chains.append(chain)

return chains

def evolve(self, total_time, dt=0.1):

time_steps = int(total_time / dt)

for step in range(time_steps):

try:

self.absolute_time.advance(dt)

self.event_density.update(self.material.total_energy, self.spacetime.total_energy)

num_events = self.event_density.get_events(dt)

# 应用引力效应

gravity_center, total_mass = self.spacetime.compute_gravity_center()

for chain in self.free_chains:

chain.apply_gravity(gravity_center, total_mass, dt)

chain.apply_curvature(self.spacetime.curvature_field, dt)

# 量子观测

for observer in self.observers:

observed_chain = np.random.choice(self.free_chains) if self.free_chains else None

if observed_chain:

observer.observe(observed_chain)

observer.adapt_focus(observed_chain)

# 处理事件

for _ in range(num_events):

self._process_random_event()

self.total_events += 1

# 时空演化

self.spacetime.expand(dt)

for chain in self.free_chains:

chain.oscillate(dt)

# 记录状态

state = self.get_state_vector()

state['absolute_time'] = self.absolute_time.t

state['step'] = step

state['inflation_active'] = self.spacetime.inflation_active

self.history.append(state)

# 检查概念漂移

if step % 10 == 0:

self.check_drift()

except RuntimeError as e:

print(f"演化在步骤 {step} 停止: {str(e)}")

break

# ... (其他方法保持类似，根据新结构调整)

def visualize_spacetime(self):

"""可视化3D时空结构"""

fig = plt.figure(figsize=(14, 10))

# 3D曲率场

ax1 = fig.add_subplot(221, projection='3d')

x, y, z = np.indices(self.spacetime.curvature_field.shape)

ax1.scatter(x.flatten(), y.flatten(), z.flatten(),

c=self.spacetime.curvature_field.flatten(),

cmap='coolwarm', alpha=0.3)

ax1.set_title('时空曲率场')

# 暗物质分布

ax2 = fig.add_subplot(222, projection='3d')

dark_positions = [chain.position for chain in self.spacetime.dark_matter_chains]

if dark_positions:

dark_pos = np.array(dark_positions)

ax2.scatter(dark_pos[:,0], dark_pos[:,1], dark_pos[:,2], c='blue', alpha=0.5)

ax2.set_title('暗物质分布')

# 物质分布

ax3 = fig.add_subplot(223, projection='3d')

thermal_positions = [chain.position for chain in self.material.thermal_chains]

if thermal_positions:

therm_pos = np.array(thermal_positions)

ax3.scatter(therm_pos[:,0], therm_pos[:,1], therm_pos[:,2], c='red', alpha=0.7)

ax3.set_title('物质分布')

# 观测者关注点

ax4 = fig.add_subplot(224, projection='3d')

for i, obs in enumerate(self.observers):

focus = obs.focus_domain

ax4.scatter(i, i, i, s=100*obs.influence_strength,

label=f'观测者{i}: 强度={obs.influence_strength:.2f}')

ax4.set_title('观测者关注点')

ax4.legend()

plt.tight_layout()

plt.savefig('spacetime_structure.png', dpi=300)

plt.show()

# ======================

# 模拟运行

# ======================

if __name__ == "__main__":

print("初始化增强型量子逻辑链宇宙...")

universe = QuantumUniverse(num_observers=2)

print("开始宇宙演化...")

universe.evolve(total_time=100, dt=0.1)

print("\n宇宙演化完成!")

print(universe.generate_report())

print("可视化时空结构...")

universe.visualize_spacetime()

print("可视化演化过程...")

universe.visualize_evolution()

关键增强说明

1. 空间位置与引力效应 (核心增强)

[list][*]位置与速度属性：[/list]

class QuantumLogicChain:

def __init__(self, ..., position=None, velocity=None):

self.position = position # 3D空间位置

self.velocity = velocity # 3D速度向量

引力效应实现：

def apply_gravity(self, mass_center, mass, dt):

# 牛顿引力公式: F = G*M*m/r&#178;

r_vec = mass_center - self.position

r = np.linalg.norm(r_vec) + 1e-10

acceleration = GRAVITATIONAL_CONSTANT * mass / r**2

self.velocity += acceleration * direction * dt

self.position += self.velocity * dt

时空曲率整合：

class Spacetime:

def __init__(self, size=100):

self.curvature_field = np.zeros((size, size, size)) # 3D曲率场

self.mass_distribution = np.zeros((size, size, size)) # 3D质量分布

2. 暴胀机制 (关键宇宙学过程)

[list][*]触发条件：[/list]

def _update_dark_matter_curvature(self, chain):

# 当总曲率超过阈值时触发暴胀

if np.sum(np.abs(self.curvature_field)) > 500:

self.activate_inflation()

暴胀实现：

def activate_inflation(self):

self.inflation_active = True

self.expansion_rate = 0.5 # 高膨胀率

def expand(self, delta_t):

if self.inflation_active:

expansion_factor = np.exp(self.expansion_rate * delta_t)

# 指数级扩张所有位置

for chain in self.dark_matter_chains:

chain.position *= expansion_factor

3. 量子观测者 (意识-宇宙互动)

[list][*]观测者类：[/list]

class QuantumObserver:

def __init__(self, focus_domain=None):

self.focus_domain = focus_domain

self.influence_strength = OBSERVER_STRENGTH

def observe(self, chain):

# 观测加速被关注链的退相干

focus_sim = self.focus_domain.similarity(chain.domain)

if focus_sim > 0.5:

decoherence_rate = self.influence_strength * focus_sim

chain.energy *= (1 - decoherence_rate)

自适应关注：

def adapt_focus(self, chain):

# 根据观测结果调整关注领域

sim = self.focus_domain.similarity(chain.domain)

if sim > 0.7:

self.influence_strength += 0.01 # 强化关注

elif sim < 0.3:

self.influence_strength = max(0.01, self.influence_strength - 0.005) # 弱化关注

4. 可视化增强

[list][*]3D时空可视化：[/list]

def visualize_spacetime(self):

fig = plt.figure(figsize=(14, 10))

# 4个子图展示曲率场、暗物质分布、物质分布和观测者

哲学与物理学统一

[list=1][*]引力本质：[/list]

引力定理 := {

来源: 物质质量分布 → 时空曲率,

效应: 曲率梯度 → 逻辑链运动偏转,

公式: &#8711;&#178;φ = 4πGρ (爱因斯坦场方程的简化)

}

暴胀机制：

暴胀原理 := {

触发: ∑|κ| > κ_crit (暗物质曲率积累),

过程: a(t) ∝ e^{Ht} (指数膨胀),

停止: H → 0 (暴胀能量耗散)

}

观测者角色：

观测者公理 := {

效应: 关注度 ∝ 退相干率,

适应: 焦点域随经验演化,

意义: 意识参与现实塑造

}

模型优势

[list=1][*]物理一致性：[/list]

引力效应满足牛顿近似

暴胀机制符合宇宙学观测

曲率场衔接广义相对论概念

[list=1][*]计算可行性：[/list]

位置更新使用矢量运算

曲率场采用离散网格

暴胀期间使用指数扩张简化

[list=1][*]哲学深度：[/list]

观测者影响现实

意识与物质互动

宇宙自我认知可能

此增强模型在保持核心简洁性的同时，引入了关键的物理机制，为验证定义域思想提供了更丰富的框架。下一步可在超算平台上进行大规模模拟，探索星系形成与意识涌现的关系。