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Shell实用实例1000例3

news 2025/10/31 10:25:54

29. 云原生与多云管理（1001-1050）

AWS CLI自动化

# 1001. AWS资源清单导出
aws_resource_inventory() {local output_file="aws_inventory_$(date +%Y%m%d).csv"echo "服务类型,资源ID,状态,区域,创建时间" > "$output_file"# EC2实例aws ec2 describe-instances --query 'Reservations[].Instances[].[InstanceId,State.Name,Placement.AvailabilityZone,LaunchTime]' --output text | \while read instance_id state az launch_time; doecho "EC2,$instance_id,$state,$az,$launch_time" >> "$output_file"done# S3存储桶aws s3api list-buckets --query 'Buckets[].[Name,CreationDate]' --output text | \while read bucket creation_date; doecho "S3,$bucket,Active,$(echo $creation_date | cut -dT -f1)" >> "$output_file"doneecho "资源清单已导出: $output_file"
}# 1002. 自动成本优化检查
aws_cost_optimization() {echo "=== AWS成本优化建议 ==="# 检查未使用的EBS卷echo "1. 未使用的EBS卷:"aws ec2 describe-volumes --filters Name=status,Values=available --query 'Volumes[].[VolumeId,Size,CreateTime]' --output table# 检查空闲的ELBecho -e "\n2. 低使用率负载均衡器:"aws elbv2 describe-load-balancers --query 'LoadBalancers[].[LoadBalancerName,Type,CreatedTime]' --output table
}

Kubernetes高级运维

# 1003. K8s集群健康评分
k8s_health_score() {local total_score=100# 检查节点状态local ready_nodes=$(kubectl get nodes | grep -c Ready)local total_nodes=$(kubectl get nodes | grep -vc NAME)local node_score=$((ready_nodes * 100 / total_nodes))# 检查Pod状态local running_pods=$(kubectl get pods --all-namespaces | grep -c Running)local total_pods=$(kubectl get pods --all-namespaces | grep -vc NAMESPACE)local pod_score=$((running_pods * 100 / total_pods))# 最终评分local final_score=$(( (node_score + pod_score) / 2 ))echo "集群健康评分: $final_score/100"echo "节点就绪率: $ready_nodes/$total_nodes"echo "Pod运行率: $running_pods/$total_pods"
}# 1004. 智能Pod重启策略
smart_pod_restart() {local namespace="$1"local pod_pattern="$2"kubectl get pods -n "$namespace" | grep "$pod_pattern" | while read pod rest; dolocal status=$(echo $rest | awk '{print $3}')local restarts=$(echo $rest | awk '{print $4}')if [[ "$status" == "Error" ]] && [[ $restarts -gt 5 ]]; thenecho "重启问题Pod: $pod (状态: $status, 重启次数: $restarts)"kubectl delete pod "$pod" -n "$namespace"fidone
}

30. 人工智能与机器学习集成（1051-1100）

Python与Shell集成

# 1051. 自动机器学习流水线
ml_pipeline() {local data_file="$1"local model_type="$2"# 数据预处理python3 -c "
import pandas as pd
from sklearn.model_selection import train_test_split
import sysdata = pd.read_csv('$data_file')
print('数据形状:', data.shape)
# 这里添加预处理逻辑" || return 1# 模型训练case "$model_type" in"regression")echo "训练回归模型...";;"classification") echo "训练分类模型...";;"clustering")echo "训练聚类模型...";;esac# 模型评估evaluate_model() {python3 -c "
# 模型评估代码
import numpy as np
from sklearn.metrics import accuracy_score, classification_report# 评估逻辑
print('模型评估完成')"}evaluate_model
}# 1052. 智能日志分析AI
ai_log_analyzer() {local log_file="$1"# 使用AI分析日志模式python3 << EOF
import re
from collections import Counter
import datetimedef analyze_log_patterns(log_file):patterns = []with open(log_file, 'r') as f:for line in f:# 提取错误模式if 'error' in line.lower() or 'exception' in line:# 简单的模式识别pattern = re.sub(r'\d+', '#', line)patterns.append(pattern.strip())# 统计常见模式common_patterns = Counter(patterns).most_common(5)print("最常见的错误模式:")for pattern, count in common_patterns:print(f"出现{count}次: {pattern}")analyze_log_patterns('$log_file')
EOF
}

自然语言处理集成

# 1053. 智能命令行助手
ai_shell_assistant() {local question="$1"# 简单的意图识别case "$question" in*"文件"*"查找"*)echo "您想查找文件吗？可以使用: find /path -name 'pattern'";;*"进程"*"管理"*)echo "进程管理命令: ps, top, kill, pkill";;*"网络"*"测试"*)echo "网络测试: ping, traceroute, netstat, ss";;*)# 调用外部AI API（示例）curl -s -X POST https://api.openai.com/v1/completions \-H "Authorization: Bearer $OPENAI_API_KEY" \-H "Content-Type: application/json" \-d "{\"model\": \"text-davinci-003\",\"prompt\": \"Shell命令问题: $question\",\"max_tokens\": 100}" | jq -r '.choices[0].text' || echo "暂时无法回答这个问题";;esac
}

31. 区块链与加密货币脚本（1101-1150）

比特币相关工具

# 1101. 比特币价格监控
btc_price_monitor() {local currency="${1:-USD}"local alert_price="${2:-50000}"while true; doprice=$(curl -s "https://api.coindesk.com/v1/bpi/currentprice/$currency.json" | \jq -r ".bpi.$currency.rate_float")echo "$(date): BTC价格: $price $currency"if (( $(echo "$price >= $alert_price" | bc -l) )); thenecho "⚠️  价格警报: BTC达到 $price $currency"# 发送通知send_alert "BTC价格警报" "当前价格: $price $currency"fisleep 300  # 5分钟检查一次done
}# 1102. 加密货币投资组合跟踪
crypto_portfolio_tracker() {local portfolio_file="$HOME/.crypto_portfolio"# 初始化投资组合文件if [[ ! -f "$portfolio_file" ]]; thencat > "$portfolio_file" << EOF
BTC 0.5
ETH 10
ADA 1000
EOFfi# 获取当前价格并计算总值total_value=0echo "=== 加密货币投资组合 ==="while read coin amount; doif [[ -n "$coin" ]]; thenprice=$(get_crypto_price "$coin")value=$(echo "$amount * $price" | bc -l)total_value=$(echo "$total_value + $value" | bc -l)printf "%-6s: %8.2f 枚 × $%8.2f = $%10.2f\n" \"$coin" "$amount" "$price" "$value"fidone < "$portfolio_file"echo "总价值: $total_value USD"
}get_crypto_price() {local coin="$1"curl -s "https://api.coingecko.com/api/v3/simple/price?ids=$coin&vs_currencies=usd" | \jq -r ".[].usd"
}

32. IoT设备管理脚本（1151-1200）

物联网设备监控

# 1151. 智能设备状态监控
iot_device_monitor() {local device_list=("192.168.1.100" "192.168.1.101" "192.168.1.102")local status_file="/tmp/iot_status_$(date +%Y%m%d).log"echo "=== IoT设备状态监控 ===" > "$status_file"echo "检查时间: $(date)" >> "$status_file"for device in "${device_list[@]}"; doif ping -c 1 -W 1 "$device" >/dev/null 2>&1; thenstatus="在线"# 获取设备信息device_info=$(get_device_info "$device")elsestatus="离线"device_info="无法连接"fiecho "设备 $device: $status - $device_info" | tee -a "$status_file"done
}# 1152. 自动设备固件更新
iot_firmware_updater() {local device_ip="$1"local firmware_file="$2"echo "开始更新设备 $device_ip 的固件..."# 检查设备连接if ! ping -c 1 "$device_ip" >/dev/null; thenecho "错误: 设备不可达"return 1fi# 备份当前配置backup_device_config "$device_ip"# 上传固件if scp "$firmware_file" "admin@$device_ip:/tmp/firmware.bin"; then# 执行更新ssh "admin@$device_ip" "flash_firmware /tmp/firmware.bin"# 等待重启echo "等待设备重启..."sleep 60# 验证更新if verify_firmware_version "$device_ip"; thenecho "✓ 固件更新成功"elseecho "✗ 固件更新失败"fielseecho "✗ 固件上传失败"fi
}

33. 量子计算模拟脚本（1201-1250）

基础量子模拟

# 1201. 简单的量子比特模拟
qubit_simulator() {local qubits="${1:-1}"python3 << EOF
import numpy as np
from math import sqrtclass Qubit:def __init__(self):self.state = np.array([1.0, 0.0])  # |0⟩状态def hadamard(self):H = np.array([[1/sqrt(2), 1/sqrt(2)], [1/sqrt(2), -1/sqrt(2)]])self.state = H @ self.statedef measure(self):prob_0 = abs(self.state[0])**2return 0 if np.random.random() < prob_0 else 1# 模拟多个量子比特
print(f"模拟 {qubits} 个量子比特:")
for i in range(qubits):q = Qubit()q.hadamard()result = q.measure()print(f"量子比特 {i+1}: 测量结果 {result}")
EOF
}# 1202. 量子纠缠模拟
quantum_entanglement_simulator() {python3 << EOF
import numpy as np# 贝尔态制备 |Φ⁺⟩ = (|00⟩ + |11⟩)/√2
bell_state = np.array([1/sqrt(2), 0, 0, 1/sqrt(2)])print("贝尔态纠缠模拟:")
print("初始状态: |Φ⁺⟩ = (|00⟩ + |11⟩)/√2")# 模拟测量结果
results = []
for i in range(100):# 模拟量子测量prob_00 = abs(bell_state[0])**2result = "00" if np.random.random() < prob_00 else "11"results.append(result)print("测量结果统计:")
print(f"|00⟩: {results.count('00')}次")
print(f"|11⟩: {results.count('11')}次")
print("注意: 两个量子比特总是相关，展示了量子纠缠")
EOF
}

34. 游戏开发与娱乐脚本（1251-1300）

终端游戏开发

# 1251. 终端贪吃蛇游戏
terminal_snake_game() {python3 << EOF
import curses
import random
import timeclass SnakeGame:def __init__(self, stdscr):self.stdscr = stdscrself.height, self.width = stdscr.getmaxyx()self.snake = [[self.height//2, self.width//2]]self.food = self.generate_food()self.direction = 'RIGHT'self.score = 0def generate_food(self):while True:food = [random.randint(1, self.height-2), random.randint(1, self.width-2)]if food not in self.snake:return fooddef run(self):while True:self.draw()self.handle_input()if not self.move():breaktime.sleep(0.1)def draw(self):self.stdscr.clear()# 绘制边界self.stdscr.border()# 绘制蛇for segment in self.snake:self.stdscr.addch(segment[0], segment[1], '█')# 绘制食物self.stdscr.addch(self.food[0], self.food[1], '●')# 显示分数self.stdscr.addstr(0, 2, f'分数: {self.score}')self.stdscr.refresh()if __name__ == "__main__":curses.wrapper(lambda stdscr: SnakeGame(stdscr).run())
EOF
}# 1252. 数字猜谜游戏
number_guessing_game() {local max_number=100local secret_number=$((RANDOM % max_number + 1))local attempts=0local guess=0echo "🎮 数字猜谜游戏开始!"echo "我已经想了一个1到$max_number之间的数字"while [[ $guess -ne $secret_number ]]; doread -p "请输入你的猜测: " guess((attempts++))if [[ $guess -lt $secret_number ]]; thenecho "📈 太低了! 再试一次"elif [[ $guess -gt $secret_number ]]; thenecho "📉 太高了! 再试一次"elseecho "🎉 恭喜! 你在$attempts次尝试后猜对了!"fidone
}

35. 生物信息学脚本（1301-1350）

DNA序列分析

# 1301. DNA序列基本分析
dna_sequence_analyzer() {local sequence="$1"echo "=== DNA序列分析 ==="echo "序列: $sequence"echo "长度: ${#sequence} 碱基"# 碱基统计a_count=$(echo "$sequence" | grep -o 'A' | wc -l)t_count=$(echo "$sequence" | grep -o 'T' | wc -l)g_count=$(echo "$sequence" | grep -o 'G' | wc -l)c_count=$(echo "$sequence" | grep -o 'C' | wc -l)echo "碱基组成: A=$a_count, T=$t_count, G=$g_count, C=$c_count"# GC含量计算total=$((a_count + t_count + g_count + c_count))gc_content=$(( (g_count + c_count) * 100 / total ))echo "GC含量: $gc_content%"# 寻找起始密码子if echo "$sequence" | grep -q "ATG"; thenecho "起始密码子(ATG)存在"elseecho "起始密码子(ATG)不存在"fi
}# 1302. 蛋白质序列翻译
protein_translator() {local dna_sequence="$1"# 遗传密码表（简化版）declare -A genetic_code=(["ATG"]="M" ["GCT"]="A" ["GCC"]="A" ["GCA"]="A" ["GCG"]="A"["TGT"]="C" ["TGC"]="C" ["GAT"]="D" ["GAC"]="D" ["GAA"]="E"# 更多密码子...)echo "DNA序列: $dna_sequence"echo "翻译结果:"# 按3个碱基分组for ((i=0; i<${#dna_sequence}; i+=3)); docodon="${dna_sequence:$i:3}"if [[ ${#codon} -eq 3 ]]; thenamino_acid="${genetic_code[$codon]:-X}"  # X表示未知echo "$codon -> $amino_acid"fidone
}

36. 金融数据分析脚本（1351-1400）

股票市场分析

# 1351. 实时股票数据获取
stock_market_monitor() {local symbol="${1:-AAPL}"while true; dodata=$(curl -s "https://api.marketdata.com/stock/$symbol")price=$(echo "$data" | jq -r '.price')change=$(echo "$data" | jq -r '.change')volume=$(echo "$data" | jq -r '.volume')echo "$(date '+%H:%M:%S') $symbol: 价格=$$price 涨跌=$change 成交量=$volume"# 简单的交易信号if (( $(echo "$change > 1.0" | bc -l) )); thenecho "📈 强势上涨!"elif (( $(echo "$change < -1.0" | bc -l) )); thenecho "📉 大幅下跌!"fisleep 60done
}# 1352. 投资组合风险分析
portfolio_risk_analyzer() {local portfolio_file="$1"python3 << EOF
import pandas as pd
import numpy as np
import yfinance as yfdef calculate_var(returns, confidence_level=0.95):"""计算风险价值(VaR)"""return np.percentile(returns, (1 - confidence_level) * 100)# 读取投资组合
portfolio = pd.read_csv('$portfolio_file')total_risk = 0
print("=== 投资组合风险分析 ===")for index, row in portfolio.iterrows():stock = row['symbol']weight = row['weight']# 获取历史数据data = yf.download(stock, period='1y')returns = data['Close'].pct_change().dropna()# 计算风险指标volatility = returns.std() * np.sqrt(252)  # 年化波动率var = calculate_var(returns)print(f"{stock}: 权重={weight:.1%}, 波动率={volatility:.2%}, VaR(95%)={var:.2%}")total_risk += weight * volatilityprint(f"组合总风险: {total_risk:.2%}")
EOF
}

37. 地理信息系统脚本（1401-1450）

GPS数据处理

# 1401. GPS轨迹分析
gps_track_analyzer() {local gpx_file="$1"echo "=== GPS轨迹分析 ==="# 提取坐标点coordinates=$(xmlstarlet sel -t -v "//trkpt" "$gpx_file" 2>/dev/null | \sed 's/.*lat="\([^"]*\)".*lon="\([^"]*\)".*/\1,\2/')point_count=$(echo "$coordinates" | wc -l)echo "轨迹点数量: $point_count"# 计算总距离（简化版）total_distance=0prev_lat=0; prev_lon=0while IFS=',' read lat lon; doif [[ $prev_lat != 0 ]]; then# 使用Haversine公式计算距离distance=$(calculate_distance $prev_lat $prev_lon $lat $lon)total_distance=$(echo "$total_distance + $distance" | bc -l)fiprev_lat=$lat; prev_lon=$londone <<< "$coordinates"echo "总距离: $(printf "%.2f" $total_distance) 公里"
}calculate_distance() {local lat1=$1 lon1=$2 lat2=$3 lon2=$4python3 -c "
from math import radians, sin, cos, sqrt, atan2def haversine(lat1, lon1, lat2, lon2):R = 6371  # 地球半径(公里)dlat = radians(lat2 - lat1)dlon = radians(lon2 - lon1)lat1 = radians(lat1)lat2 = radians(lat2)a = sin(dlat/2)**2 + cos(lat1)*cos(lat2)*sin(dlon/2)**2c = 2 * atan2(sqrt(a), sqrt(1-a))return R * cprint(haversine($lat1, $lon1, $lat2, $lon2))
"
}

38. 时间序列分析脚本（1451-1500）

时序数据预测

# 1451. 自动时间序列预测
time_series_forecast() {local data_file="$1"local periods="${2:-7}"python3 << EOF
import pandas as pd
import numpy as np
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt# 读取数据
data = pd.read_csv('$data_file', parse_dates=['date'], index_col='date')# 准备特征
data['day_of_week'] = data.index.dayofweek
data['month'] = data.index.month
data['trend'] = range(len(data))# 训练模型
model = LinearRegression()
X = data[['day_of_week', 'month', 'trend']]
y = data['value']model.fit(X, y)# 预测未来
future_dates = pd.date_range(start=data.index[-1], periods=$periods+1, freq='D')[1:]
future_data = pd.DataFrame({'day_of_week': future_dates.dayofweek,'month': future_dates.month,'trend': range(len(data), len(data) + $periods)
}, index=future_dates)predictions = model.predict(future_data)print("未来${periods}天预测:")
for date, pred in zip(future_dates, predictions):print(f"{date.strftime('%Y-%m-%d')}: {pred:.2f}")
EOF
}# 1452. 季节性检测
seasonality_detector() {local data_file="$1"python3 << EOF
import pandas as pd
import numpy as np
from scipy import signaldata = pd.read_csv('$data_file')
values = data['value'].values# 检测季节性
frequencies, power = signal.periodogram(values)
dominant_freq = frequencies[np.argmax(power)]print(f"主导频率: {dominant_freq:.4f}")
print("这可能表示季节性模式")# 简单的季节性检测
if dominant_freq > 0.1:print("检测到强季节性")
elif dominant_freq > 0.01:print("检测到弱季节性")
else:print("无明显季节性")
EOF
}

39. 自动化测试框架（1501-1550）

综合测试套件

# 1501. 自动化系统健康检查
system_health_check_suite() {local report_file="health_check_$(date +%Y%m%d_%H%M%S).log"echo "=== 系统健康检查报告 ===" > "$report_file"echo "检查时间: $(date)" >> "$report_file"echo "主机名: $(hostname)" >> "$report_file"echo >> "$report_file"# 1. 系统资源检查echo "1. 系统资源检查:" >> "$report_file"check_system_resources >> "$report_file"# 2. 服务状态检查echo "2. 服务状态检查:" >> "$report_file"check_service_status >> "$report_file"# 3. 安全配置检查echo "3. 安全配置检查:" >> "$report_file"check_security_config >> "$report_file"# 4. 性能基准测试echo "4. 性能基准测试:" >> "$report_file"run_performance_benchmark >> "$report_file"echo "健康检查完成: $report_file"
}# 1502. 智能测试结果分析
test_result_analyzer() {local test_log="$1"# 使用AI分析测试失败模式python3 << EOF
import re
from collections import Counterdef analyze_test_failures(log_file):failures = []patterns = []with open(log_file, 'r') as f:for line in f:if 'FAIL' in line or 'ERROR' in line:failures.append(line.strip())# 提取错误模式pattern = re.sub(r'\d+', '#', line)pattern = re.sub(r'0x[0-9a-fA-F]+', 'HEX', pattern)patterns.append(pattern)if failures:print(f"发现 {len(failures)} 个失败用例")print("最常见的错误模式:")pattern_counts = Counter(patterns).most_common(3)for pattern, count in pattern_counts:print(f"出现 {count} 次: {pattern[:100]}...")else:print("所有测试通过! 🎉")analyze_test_failures('$test_log')
EOF
}

40. 终极综合项目实战（1551-1600）

企业级运维平台核心

# 1551. 智能运维决策引擎
ai_ops_decision_engine() {local incident_data="$1"python3 << EOF
import json
import numpy as np
from datetime import datetimeclass AIOpsDecisionEngine:def __init__(self):self.incident_patterns = self.load_patterns()def load_patterns(self):# 加载历史事件模式return {'high_cpu': {'solution': '检查进程，考虑扩容', 'priority': 'high'},'memory_leak': {'solution': '重启服务，分析内存', 'priority': 'critical'},'network_timeout': {'solution': '检查网络配置', 'priority': 'medium'}}def analyze_incident(self, data):# 简单的模式匹配for pattern, info in self.incident_patterns.items():if pattern in data['description'].lower():return info# 默认解决方案return {'solution': '需要人工分析', 'priority': 'unknown'}def make_decision(self, incident_data):analysis = self.analyze_incident(incident_data)recommendation = {'timestamp': datetime.now().isoformat(),'incident_id': incident_data['id'],'recommended_action': analysis['solution'],'priority': analysis['priority'],'confidence': 0.85  # 置信度}return recommendation# 使用决策引擎
engine = AIOpsDecisionEngine()
incident = json.loads('$incident_data')
decision = engine.make_decision(incident)print("AI运维决策建议:")
for key, value in decision.items():print(f"{key}: {value}")
EOF
}# 1600. 终极自动化运维平台
ultimate_ops_platform() {echo "🚀 启动终极自动化运维平台..."# 1. 环境初始化initialize_environment# 2. 启动监控系统start_monitoring_system# 3. 启动自动化引擎start_automation_engine# 4. 启动AI分析模块start_ai_analysis# 5. 启动仪表板start_dashboardecho "✅ 运维平台启动完成"echo "📊 仪表板地址: http://localhost:3000"echo "🔧 API端点: http://localhost:8080"echo "📈 监控地址: http://localhost:9090"
}# 平台核心组件
initialize_environment() {# 检查系统要求check_system_requirements || return 1# 创建目录结构mkdir -p {logs,config,backups,scripts,reports}# 初始化数据库init_database# 加载配置load_configuration
}start_monitoring_system() {# 启动系统监控nohup ./monitors/system_monitor.sh > logs/system_monitor.log 2>&1 &nohup ./monitors/network_monitor.sh > logs/network_monitor.log 2>&1 &nohup ./monitors/application_monitor.sh > logs/app_monitor.log 2>&1 &
}start_ai_analysis() {# 启动AI分析模块python3 ai_ops/analyzer.py > logs/ai_analyzer.log 2>&1 &python3 ai_ops/predictor.py > logs/ai_predictor.log 2>&1 &
}