【专家系统】专家系统概述，应用场景，项目实践及案例分析，附带代码示例

# Python实现
# 疾病和症状字典
illnesses = {
    "flu": ["fever", "headache"],
    "pneumonia": ["cough", "fever"],
    "appendicitis": ["abdominal_pain", "vomiting"]
}
# 诊断函数
def diagnose(symptoms):
    possible_illnesses = []
    for illness, symptom_list in illnesses.items():
        if all(symptom in symptoms for symptom in symptom_list):
            possible_illnesses.append(illness)
    return possible_illnesses
# 测试诊断函数
patient_symptoms = ["fever", "headache"]
diagnosis = diagnose(patient_symptoms)
print("Possible diagnoses:", diagnosis)

% Prolog实现
% 定义疾病和其相关症状
illness(symptoms(fever, headache), flu).
illness(symptoms(cough, fever), pneumonia).
illness(symptoms(abdominal_pain, vomiting), appendicitis).
% 判断病人可能患有的疾病
diagnose(Symptoms, Illness) :-
    illness(Symptoms, Illness).
% 查询示例
?- diagnose(symptoms(fever, headache), Illness).

# Python实现
# 使用NetworkX和PuLP进行物流路径规划
import networkx as nx
from pulp import *
# 创建图
G = nx.DiGraph()
# 添加节点和边
G.add_node("A")
G.add_node("B")
G.add_node("C")
G.add_node("D")
G.add_edge("A", "B", weight=10)
G.add_edge("A", "C", weight=20)
G.add_edge("B", "D", weight=15)
G.add_edge("C", "D", weight=5)
# 计算最短路径
shortest_path = nx.shortest_path(G, source="A", target="D", weight="weight")
print("Shortest Path:", shortest_path)
# 使用PuLP进行优化
# 定义问题
prob = LpProblem("Logistics Optimization", LpMinimize)
# 定义变量
x1 = LpVariable("x1", 0, None, LpInteger)
x2 = LpVariable("x2", 0, None, LpInteger)
x3 = LpVariable("x3", 0, None, LpInteger)
# 定义目标函数
prob += 10*x1 + 20*x2 + 15*x3, "Total Cost"
# 定义约束条件
prob += x1 + x2 == 1, "Constraint1"
prob += x2 + x3 == 1, "Constraint2"
# 解决问题
prob.solve()
# 输出结果
print("Status:", LpStatus[prob.status])
for v in prob.variables():
    print(v.name, "=", v.varValue)
print("Total Cost of Ingredients per can = ", value(prob.objective))

# Python实现
# 使用PyDatalog进行规则推理
from pyDatalog import pyDatalog
# 定义规则
pyDatalog.create_terms('legal_advice, hasIssue, isTypeOfIssue')
+hasIssue('divorce', 'family_law')
+hasIssue('property_dispute', 'real_estate_law')
+hasIssue('breach_of_contract', 'contract_law')
+isTypeOfIssue('family_law', 'civil_law')
+isTypeOfIssue('real_estate_law', 'civil_law')
+isTypeOfIssue('contract_law', 'civil_law')
legal_advice(X) <= (hasIssue(X, Y) & isTypeOfIssue(Y, Z)) 
# 查询示例
print(legal_advice['divorce'])

# Python实现
# 使用Scikit-learn进行回归预测
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
# 加载数据
data = pd.read_csv('chemical_production_data.csv')
# 数据预处理
features = data[['temperature', 'pressure', 'reactant_a_concentration', 'reactant_b_concentration']]
target = data['product_yield']
# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.2, random_state=42)
# 构建线性回归模型
model = LinearRegression()
model.fit(X_train, y_train)
# 预测
y_pred = model.predict(X_test)
# 评估模型
mse = mean_squared_error(y_test, y_pred)
print("Mean Squared Error:", mse)
# 示例预测
new_data = [[100, 5, 0.5, 0.3]]  # 温度、压力、反应物A浓度、反应物B浓度
predicted_yield = model.predict(new_data)
print("Predicted Product Yield:", predicted_yield[0])