update

2025-03-11 17:21:49 +08:00
parent 0445128fd0
commit c482bbaeb4
9 changed files with 226 additions and 10 deletions
@@ -114,7 +114,31 @@ if __name__ == '__main__':
    # x = cal_dv(338.2, 55000, 70000)
    # xx = cal_fuel_burned(21000, 400, 1)

-    print(cal_dv(298.7, 8.5, 1.5))
+    # payload_mass = 30
+    # structural_mass = 8
+    # return_fuel_mass = 8
+    # outbound_fuel_mass = 47
+    # return_dv = cal_dv(475, structural_mass, return_fuel_mass)
+    # out_bound_dv = cal_dv(475, payload_mass+structural_mass+return_fuel_mass, outbound_fuel_mass)
+    # total_mass = payload_mass + structural_mass + return_fuel_mass + outbound_fuel_mass
+
+    payload_mass = 45
+    structural_mass = 10
+    return_fuel_mass = 10
+    outbound_fuel_mass = 65
+    return_dv = cal_dv(475, structural_mass, return_fuel_mass)
+    out_bound_dv = cal_dv(475, payload_mass + structural_mass + return_fuel_mass, outbound_fuel_mass)
+    total_mass = payload_mass + structural_mass + return_fuel_mass + outbound_fuel_mass
+
+    # payload_mass = 30
+    # structural_mass = 10
+    # return_fuel_mass = 14
+    # outbound_fuel_mass = 71
+    # return_dv = cal_dv(382, structural_mass, return_fuel_mass)
+    # out_bound_dv = cal_dv(382, payload_mass + structural_mass + return_fuel_mass, outbound_fuel_mass)
+    # total_mass = payload_mass + structural_mass + return_fuel_mass + outbound_fuel_mass
+
+    tank_mass = 0.000001 * 179380.1868 + 0.00003 * 41395.42773

    x = cal_kinetic_energy(350, 2450, 1, 1)
    # x = cal_kinetic_energy(314.3, 931, 1, 1)
@@ -10,11 +10,11 @@ def plot_stage_performance(isp, gross_mass, dry_mass, payload, stage_name):

 payload_list = [i for i in range(0, 15200, 200)]
 # plot_stage_performance(450.5, 20830 + 2247, 2247, payload_list, 'Centaur III')
-# plot_stage_performance(438, 21000, 2800, payload_list, 'Long March 3m')
-# plot_stage_performance(450, 23500 + 2900, 2900, payload_list, 'Long March 3.35m')
-plot_stage_performance(453.8, 54000+4500, 5000, payload_list, 'Centaur V')
-plot_stage_performance(348, 112000, 4000, payload_list, 'Falcon 9')
-plot_stage_performance(367, 82000, 2000, payload_list, 'Neutron S2')
+plot_stage_performance(438, 21000, 2800, payload_list, 'Long March 3m')
+plot_stage_performance(450, 26500 + 3100, 3100, payload_list, 'Long March 3.35m')
+# plot_stage_performance(453.8, 54000+4500, 5000, payload_list, 'Centaur V')
+# plot_stage_performance(348, 112000, 4000, payload_list, 'Falcon 9')
+# plot_stage_performance(367, 82000, 2000, payload_list, 'Neutron S2')
 # plot_stage_performance(451, 68000, 8000, payload_list, 'Long March 10 S3')
 # plot_stage_performance(442.6, 36000, 5100, payload_list, 'Long March 5 S2')
 # plot_stage_performance(462, 30710, 3490, payload_list, 'DCSS 5m')
@@ -295,8 +295,14 @@ if __name__ == '__main__':
    # Neutron.run_sim_new()
    # Neutron.plot_sim(start_payload=13000, step=100)

-    S1 = Stage(12000, 2500, 80, 335, 'kerolox', 'Service Module')
-    S2 = Stage(7000, 3800, 50, 330, 'kerolox', 'Ascent Stage')
-    Lanyue = Vessel([[S1], [S2]], name='Lanyue', payload=200, duration=0.05)
-    Lanyue.run_sim_new()
+    # S1 = Stage(12000, 2500, 80, 335, 'kerolox', 'Service Module')
+    # S2 = Stage(7000, 3800, 50, 330, 'kerolox', 'Ascent Stage')
+    # Lanyue = Vessel([[S1], [S2]], name='Lanyue', payload=200, duration=0.05)
+    # Lanyue.run_sim_new()
+
+    booster = Stage(163560, 16000, 4740, 285, 'solid', 'SRB')
+    S1 = Stage(350000, 25000, 3500, 456.5, 'hydrolox', 'Hydrolox Core Stage')
+    S2 = Stage(54000, 5000, 392, 450, 'hydrolox', 'Upper Stage')
+    SMART_LV = Vessel([[booster, S1], [S2]], name='SMART_LV', payload=20000, duration=0.05)
+    SMART_LV.run_sim_new()
    print('done')
@@ -0,0 +1,11 @@
+import os
+import time
+import operator
+
+from typing import TypedDict, List, Annotated
+from langchain_openai import ChatOpenAI
+from langgraph.graph import END, StateGraph
+from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
+from langchain_core.messages import HumanMessage
+
+llm = ChatOpenAI(model="gpt-4o", temperature=0.1)
@@ -0,0 +1,148 @@
+import os
+import time
+import operator
+import difflib
+
+from typing import TypedDict, List, Annotated
+from langchain_openai import ChatOpenAI
+from langgraph.graph import END, StateGraph
+from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
+from langchain_core.messages import HumanMessage
+
+from pydantic import BaseModel, Field
+
+llm = ChatOpenAI(model="gpt-4o", temperature=0.1, openai_api_key='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')
+
+MEMBERS = {'天气查询模块': '输入一个城市和日期，查询当日的天气状况',
+           '网络查询模块': '在网上搜索指定的内容',
+           '诗歌创作模块': '根据给定的要求，进行诗歌创作',
+           '结束节点': '完成所有任务或者任务执行失败的情况下进入'}
+
+
+class AgentState(TypedDict):
+    user_query: str
+    messages: Annotated[List[str], operator.add]
+    plan: str
+    members: dict
+    next_agent: str
+    next_plan: str
+
+
+def planner(state: AgentState):
+    user_query = state.get('user_query', '')
+    planner_prompt = ChatPromptTemplate.from_messages([
+        ('system', '''你是一个团队的管理者，你负责根据用户输入的要求，制定一个计划来完成。你的团队成员和能力如下：
+1. 天气查询模块：输入一个城市和日期，查询当日的天气状况。
+2. 网络查询模块：输入一个需要查询的内容，在网上搜索指定的内容。
+3. 诗歌创作模块：根据给定的要求，进行诗歌创作
+请你根据用户的需求，制定一个处理计划'''),
+        ('user', f'当前的用户需求是:'),
+        MessagesPlaceholder(variable_name="query"),
+    ])
+    planner_chain = (planner_prompt | llm)
+
+    result_dict = {'plan': '为了完成这个任务，我们需要按照以下步骤进行：\n\n1. **网络查询模块**：\n   - 任务：查询2024年中国GDP最高的城市。\n   - 行动：在网上搜索2024年中国GDP最高的城市的信息。\n\n2. **天气查询模块**：\n   - 任务：查询该城市今天的天气状况。\n   - 行动：使用天气查询模块，输入城市名称和当前日期，获取该城市的天气信息。\n\n3. **诗歌创作模块**：\n   - 任务：根据查询到的城市名称和天气状况，创作一首诗。\n   - 行动：利用诗歌创作模块，结合城市的特点和天气状况，创作一首诗。\n\n请各模块按照上述计划执行任务，并在完成后汇总信息。'}
+
+    return result_dict
+    # llm_resp = planner_chain.invoke({'query': [('user', user_query)]})
+    # return {'plan': llm_resp.content}
+
+
+def decision_node(state: AgentState):
+    class NextStep(BaseModel):
+        next_agent: str = Field(description='下一步应该交给哪个模块来完成')
+        action: str = Field(description='下一步应该执行的行动计划是什么')
+
+    members = state.get('members', {})
+    member_keys = list(members.keys())
+    decision_prompt = ChatPromptTemplate.from_messages([
+        ('system', f'你负责针对一个给定的任务计划，以及当前的任务执行状态，制定下一步应该由谁来执行什么内容。可以在这些模块中进行选择：{", ".join(member_keys)}。'),
+        ('user', f'当前的计划是:'),
+        MessagesPlaceholder(variable_name="plan"),
+        ('user', '以下是各个模块之间的消息记录'),
+        MessagesPlaceholder(variable_name="messages")
+    ])
+    decision_chain = (decision_prompt | llm.with_structured_output(NextStep))
+    llm_resp = decision_chain.invoke({'plan': [('user', state.get('plan', ''))], 'messages': state.get('messages', [])})
+    next_agent = llm_resp.next_agent
+    action = llm_resp.action
+    return {'messages': [HumanMessage(content=f'接下来由{next_agent}来执行{action}')], 'next_agent': next_agent, 'next_plan': action}
+
+
+def router_node(state: AgentState):
+    members = state.get('members', {})
+    member_keys = list(members.keys())
+    best_match = difflib.get_close_matches(state.get('next_agent', ''), member_keys, n=1, cutoff=0)
+    if not best_match:
+        raise ValueError(f'Select a wrong agent: {state.get("next_agent")}')
+    return {'next': best_match}
+
+
+def weather_node(state: AgentState):
+    result = '上海的天气是25℃，下雨'
+    return {'messages': [HumanMessage(content=result)]}
+
+
+def websearch_node(state: AgentState):
+    result = '2024年中国GDP最高的城市是上海'
+    return {'messages': [HumanMessage(content=result)]}
+
+
+def composer_node(state: AgentState):
+    composer_prompt = ChatPromptTemplate.from_messages([
+        ('system', f'你负责根据用户的要求以诗歌的形式进行创作'),
+        ('user', f'当前用户的要求是:'),
+        MessagesPlaceholder(variable_name="request"),
+    ])
+    composer_chain = composer_prompt | llm
+    llm_resp = composer_chain.invoke({'request': [('user', state.get('next_plan', ''))]})
+    return {'messages': [HumanMessage(content=llm_resp.content)]}
+
+
+def end_node(state: AgentState):
+    print('placeholder')
+
+
+workflow = StateGraph(AgentState)
+
+workflow.add_node('planner', planner)
+workflow.add_node('决策节点', decision_node)
+workflow.add_node('router', router_node)
+workflow.add_node('天气查询模块', weather_node)
+workflow.add_node('网络查询模块', websearch_node)
+workflow.add_node('诗歌创作模块', composer_node)
+workflow.add_node('结束节点', end_node)
+
+workflow.add_edge('planner', '决策节点')
+workflow.add_edge('决策节点', 'router')
+workflow.add_conditional_edges(
+    'router',
+    lambda x: x['next'],
+    {
+        '天气查询模块': '天气查询模块',
+        '网络查询模块': '网络查询模块',
+        '诗歌创作模块': '诗歌创作模块',
+        '结束节点': '结束节点'
+    }
+)
+
+workflow.add_edge('天气查询模块', '决策节点')
+workflow.add_edge('网络查询模块', '决策节点')
+workflow.add_edge('诗歌创作模块', '决策节点')
+workflow.add_edge('结束节点', END)
+
+workflow.set_entry_point('planner')
+
+graph = workflow.compile()
+print(graph.get_graph().draw_ascii())
+
+enter = {'user_query': "查询2024年中国GDP最高的城市，以这个城市今天的天气，创作一首诗",
+         "members": MEMBERS}
+for s in graph.stream(enter):
+    if "__end__" not in s:
+        print(s)
+        print('-'*80)
+
+
+
+
@@ -0,0 +1,27 @@
+from tavily import TavilyClient
+import requests
+
+
+query = '乔布斯是谁'
+
+base_url = 'https://api.tavily.com/search'
+json = {
+    'api_key': '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',
+    'query': query,
+    'search_depth': 'basic',
+    'include_answer': False,
+    'include_images': False,
+    'include_raw_content': False,
+    'max_results': 5,
+    'include_domains': [],
+    'exclude_domains': []
+}
+
+response = requests.post(base_url, json=json, verify=False)
+
+if response.status_code == 200:
+    search_result = response.json()
+else:
+    raise Exception(f'Error: {response.status_code}: {response.reason}')
+
+print('done')