dict:
"""
基于 PLC 传感器数据预测装卸节拍
Args:
plc_data: 包含位置/负载/速度/电流的字典
Returns:
预测结果：节拍时长、最佳调度时机、能耗预警
"""
prompt = f"""你是一位港口装卸工艺专家。基于以下 PLC 传感器数据，
预测当前岸桥的装卸节拍并给出优化建议：
当前状态：
- 吊具位置: {plc_data['position']}m
- 负载重量: {plc_data['weight']}t
- 起升速度: {plc_data['speed']}m/s
- 电流值: {plc_data['current']}A
- 目标贝位: {plc_data['target_bay']}
- 环境温度: {plc_data['temp']}°C
请输出 JSON 格式：
{{
"predicted_cycle_time": 秒数,
"optimal_dispatch_window": "HH:MM-HH:MM",
"energy_waste_risk": "高/中/低",
"optimization_tip": "具体优化建议"
}}
"""
response = client.chat.completions.create(
model="gpt-5o", # HolySheep 支持 gpt-5o
messages=[
{"role": "system", "content": "你是一位港口装卸工艺专家，用 JSON 输出预测结果。"},
{"role": "user", "content": prompt}
],
temperature=0.3,
max_tokens=512
)
return response.choices[0].message.content
模拟 PLC 数据
sample_plc = {
"position": 45.2,
"weight": 28.5,
"speed": 1.8,
"current": 156,
"target_bay": "C-17",
"temp": 32
}
result = predict_loading_rhythm(sample_plc)
print(f"预测结果: {result}")
实战代码：Claude 调度播报 + 统一配额治理
import anthropic
import time
from datetime import datetime
from collections import defaultdict
HolySheep Anthropic 兼容接口
client = anthropic.Anthropic(
api_key="YOUR_HOLYSHEEP_API_KEY",
base_url="https://api.holysheep.ai/v1" # HolySheep 统一入口
)
class QuotaManager:
"""统一配额治理：监控多模型调用量"""
def __init__(self, total_budget_usd: float = 100):
self.budget = total_budget_usd
self.usage = defaultdict(lambda: {"tokens": 0, "cost": 0})
self.prices = {
"gpt-5o": 8.0, # $/MTok output
"claude-4-sonnet": 3.5, # $/MTok
"gemini-2.5-flash": 2.5 # $/MTok
}
def record_usage(self, model: str, output_tokens: int):
"""记录 API 调用量"""
cost = (output_tokens / 1_000_000) * self.prices[model]
self.usage[model]["tokens"] += output_tokens
self.usage[model]["cost"] += cost
total_cost = sum(v["cost"] for v in self.usage.values())
# 余额不足 20% 时告警
if total_cost > self.budget * 0.8:
print(f"⚠️ 配额告警: 已消耗 ${total_cost:.2f}/${self.budget}")
return cost
def get_remaining(self) -> float:
total = sum(v["cost"] for v in self.usage.values())
return self.budget - total
quota_mgr = QuotaManager(total_budget_usd=100)
def generate_dispatch_announcement(cycle_prediction: dict, bay_info: dict) -> str:
"""
生成调度播报指令
Claude-4-Sonnet 擅长结构化输出和自然语言生成
"""
system_prompt = """你是一位经验丰富的港口调度员。生成简洁专业的调度指令。
必须包含：指令类型、时间窗口、安全提示。输出格式：纯文本，每条指令一行。"""
user_prompt = f"""根据以下预测数据，生成调度指令：
预测结果：{cycle_prediction}
目标贝位：{bay_info}
当前时间：{datetime.now().strftime('%Y-%m-%d %H:%M')}
请生成 3 条调度指令。"""
start = time.time()
message = client.messages.create(
model="claude-4-sonnet",
max_tokens=256,
system=system_prompt,
messages=[{"role": "user", "content": user_prompt}]
)
latency = (time.time() - start) * 1000 # ms
# 记录配额
quota_mgr.record_usage("claude-4-sonnet", message.usage.output_tokens)
return {
"announcement": message.content[0].text,
"latency_ms": round(latency, 1),
"output_tokens": message.usage.output_tokens
}
测试播报生成
prediction = {
"predicted_cycle_time": 45,
"optimal_dispatch_window": "14:30-14:45",
"energy_waste_risk": "低"
}
bay = {"name": "C-17", "vessel": "COSCO SHIPPING"}
result = generate_dispatch_announcement(prediction, bay)
print(f"调度播报: {result['announcement']}")
print(f"延迟: {result['latency_ms']}ms | 配额剩余: ${quota_mgr.get_remaining():.2f}")
Docker Compose 一键部署
version: '3.8'
services:
# MQTT 消息代理（PLC 数据采集）
mosquitto:
image: eclipse-mosquitto:2.0
ports:
- "1883:1883"
volumes:
- ./mosquitto.conf:/mosquitto/config/mosquitto.conf
# Kafka（高吞吐数据管道）
kafka:
image: confluentinc/cp-kafka:7.5.0
environment:
KAFKA_BROKER_ID: 1
KAFKA_ZOOKEEPER_CONNECT: zookeeper:2181
depends_on:
- zookeeper
# 岸桥 Agent 核心服务
crane-agent:
build: ./crane-agent
environment:
- HOLYSHEEP_API_KEY=${HOLYSHEEP_API_KEY}
- HOLYSHEEP_BASE_URL=https://api.holysheep.ai/v1
- MQTT_BROKER=mosquitto:1883
- KAFKA_BOOTSTRAP=kafka:9092
depends_on:
- mosquitto
- kafka
deploy:
resources:
limits:
memory: 2G
cpus: '2'
reservations:
memory: 1G
cpus: '1'
# Prometheus 监控
prometheus:
image: prom/prometheus:latest
volumes:
- ./prometheus.yml:/etc/prometheus/prometheus.yml
# Grafana 可视化
grafana:
image: grafana/grafana:latest
ports:
- "3000:3000"
environment:
- GF_SECURITY_ADMIN_PASSWORD=admin
networks:
default:
driver: bridge
常见报错排查
错误 1：API Key 无效 / Authentication Error
# 错误日志
openai.AuthenticationError: Incorrect API key provided
原因排查
1. 检查 Key 是否正确复制（注意前后空格）
2. 确认 Key 已激活（注册后需邮箱验证）
3. 确认未使用官方 Key（HolySheep 与官方 Key 不通用）
正确配置
client = openai.OpenAI(
api_key="sk-holysheep-xxxxx", # 必须是 HolySheep 平台生成的 Key
base_url="https://api.holysheep.ai/v1"
)
注册获取 Key: https://www.holysheep.ai/register
错误 2：Quota Exceeded / 配额超限
# 错误日志
AnthropicRateLimitError: Monthly quota exceeded
原因排查
1. 检查月配额是否用尽
2. 检查是否有未关闭的流式请求占配额
3. 确认并发数未超限
解决代码
quota_mgr = QuotaManager(total_budget_usd=100)
def safe_api_call(model: str, prompt: str):
remaining = quota_mgr.get_remaining()
if remaining < 0.5: # 预留 $0.5 安全边际
raise Exception(f"配额不足，当前剩余 ${remaining:.2f}")
# 调用 API ...
result = client.messages.create(model=model, messages=[...])
quota_mgr.record_usage(model, result.usage.output_tokens)
return result
或在 HolySheep 控制台升级配额：https://www.holysheep.ai/dashboard
错误 3：Model Not Found / 模型不可用
# 错误日志
openai.NotFoundError: Model 'gpt-5' not found
原因排查
1. 模型名称拼写错误（区分大小写）
2. 该模型未在当前套餐中启用
3. 模型版本号不正确
2026 年 HolySheep 支持的模型名称（精确匹配）
MODELS = {
"openai": ["gpt-5o", "gpt-4.1", "gpt-4-turbo", "gpt-3.5-turbo"],
"anthropic": ["claude-4-sonnet", "claude-3.5-sonnet", "claude-3-opus"],
"google": ["gemini-2.5-flash", "gemini-2.0-pro"],
"deepseek": ["deepseek-v3.2", "deepseek-coder-v2"]
}
正确示例
client.chat.completions.create(
model="gpt-5o", # ✅ 正确
# model="gpt-5", # ❌ 错误
# model="GPT-5o", # ❌ 错误（区分大小写）
)
错误 4：Connection Timeout / 连接超时
# 错误日志
httpx.ConnectTimeout: Connection timeout after 30s
原因排查
1. 网络问题（DNS/防火墙）
2. 目标端口被阻断
3. 代理配置错误
解决代码
import httpx
配置超时和代理
client = openai.OpenAI(
api_key="YOUR_HOLYSHEEP_API_KEY",
base_url="https://api.holysheep.ai/v1",
http_client=httpx.Client(
timeout=httpx.Timeout(60.0, connect=10.0),
proxies={
"http://": "http://proxy.example.com:8080",
"https://": "http://proxy.example.com:8080"
}
)
)
国内直连测试（延迟应 <50ms）
import time
start = time.time()
client.chat.completions.create(
model="gpt-3.5-turbo",
messages=[{"role": "user", "content": "ping"}]
)
print(f"延迟: {(time.time()-start)*1000:.0f}ms")
性能基准测试结果