5. 智能体与业务编排
5.1 Agent技能链
技能定义
智能物流Agent需要具备多种技能,通过技能链组合完成复杂任务:
核心技能
1. 订单解析技能(Order Parsing)
- 功能:解析订单信息,提取关键字段
- 输入:订单数据(JSON、XML、CSV等)
- 输出:解析后的订单信息(地址、重量、体积、时效要求等)
- 实现:基于Python数据处理库(pandas、json等)
- 应用场景:订单接入、订单处理
2. 地址标准化技能(Address Normalization)
- 功能:标准化地址格式,进行地理编码
- 输入:地址文本
- 输出:标准化地址和经纬度
- 实现:基于地图API(高德地图、百度地图等)
- 应用场景:地址处理、地理编码
3. 路径规划技能(Route Planning)
- 功能:规划最优配送路径
- 输入:订单信息、车辆信息、司机信息
- 输出:路径规划方案(路径、车辆分配、时间窗口)
- 实现:基于优化算法(TSP、VRP、遗传算法等)
- 应用场景:路径规划、车辆调度
4. 库存查询技能(Inventory Query)
- 功能:查询库存信息
- 输入:仓库ID、SKU ID
- 输出:库存信息(数量、位置、状态等)
- 实现:基于数据库查询(MySQL、Redis等)
- 应用场景:库存管理、订单处理
5. 拣货优化技能(Picking Optimization)
- 功能:优化拣货路径和顺序
- 输入:订单信息、库存信息、仓储布局
- 输出:拣货方案(拣货顺序、路径、货位)
- 实现:基于优化算法(TSP、贪心算法等)
- 应用场景:拣货管理、仓储优化
6. 风险识别技能(Risk Identification)
- 功能:识别物流过程中的风险
- 输入:物流数据(GPS、订单、天气、交通等)
- 输出:风险识别结果(风险类型、风险等级、影响范围)
- 实现:基于预测模型(LSTM、XGBoost等)
- 应用场景:风险预警、异常检测
7. 延误预测技能(Delay Prediction)
- 功能:预测配送延误情况
- 输入:订单信息、GPS数据、天气数据、交通数据
- 输出:延误预测结果(延误概率、延误时间、原因分析)
- 实现:基于时序预测模型(LSTM、Transformer等)
- 应用场景:延误预警、时效管理
8. 成本计算技能(Cost Calculation)
- 功能:计算配送成本
- 输入:路径信息、车辆信息、订单信息
- 输出:成本计算结果(距离成本、时间成本、总成本)
- 实现:基于成本模型和规则引擎
- 应用场景:成本分析、报价计算
9. 报告生成技能(Report Generation)
- 功能:生成物流分析报告
- 输入:分析结果、报告模板
- 输出:格式化的报告文档
- 实现:基于模板引擎和大语言模型
- 应用场景:报告生成、数据分析
技能链设计
技能链按照业务流程组织,形成完整的处理流程:
5.2 业务流程编排
路径规划流程
流程设计
n8n工作流配置
workflow:
name: 智能路径规划
triggers:
- type: webhook
path: /api/route-planning
nodes:
- id: receive-orders
type: webhook
parameters:
path: /api/route-planning
method: POST
- id: parse-orders
type: function
parameters:
function: |
function parseOrders(orders) {
return orders.map(order => ({
id: order.id,
address: order.address,
weight: order.weight,
volume: order.volume,
timeWindow: order.timeWindow
}));
}
- id: normalize-addresses
type: http-request
parameters:
method: POST
url: http://map-api/normalize
body: ${$json.orders}
- id: group-orders
type: function
parameters:
function: |
function groupOrders(orders) {
// 按区域、时效要求等分组
return groupByRegionAndTime(orders);
}
- id: match-vehicles
type: http-request
parameters:
method: POST
url: http://vehicle-api/match
body: ${$json.orderGroups}
- id: plan-routes
type: http-request
parameters:
method: POST
url: http://route-api/plan
body: ${$json.matchedVehicles}
- id: calculate-costs
type: function
parameters:
function: |
function calculateCosts(routes) {
return routes.map(route => ({
...route,
cost: calculateRouteCost(route)
}));
}
- id: evaluate-solutions
type: if
conditions:
- value1: ${$json.cost}
operation: lessThan
value2: threshold
- id: confirm-solution
type: function
parameters:
function: |
function confirmSolution(solution) {
return {
status: 'confirmed',
solution: solution
};
}
- id: dispatch-tasks
type: http-request
parameters:
method: POST
url: http://task-api/dispatch
body: ${$json.solution}
- id: return-result
type: respond-to-webhook
parameters:
responseBody: ${$json}
仓储管理流程
流程设计
n8n工作流配置
workflow:
name: 智能仓储管理
triggers:
- type: webhook
path: /api/warehouse-management
nodes:
- id: receive-orders
type: webhook
- id: analyze-orders
type: function
parameters:
function: |
function analyzeOrders(orders) {
return {
skuList: extractSKUs(orders),
totalQuantity: calculateTotalQuantity(orders),
priority: calculatePriority(orders)
};
}
- id: check-inventory
type: http-request
parameters:
method: POST
url: http://inventory-api/check
body: ${$json.skuList}
- id: generate-picking-tasks
type: function
parameters:
function: |
function generatePickingTasks(inventory, orders) {
return orders.map(order => ({
orderId: order.id,
pickingItems: order.items.map(item => ({
skuId: item.skuId,
quantity: item.quantity,
location: findLocation(item.skuId, inventory)
}))
}));
}
- id: optimize-picking
type: http-request
parameters:
method: POST
url: http://picking-api/optimize
body: ${$json.pickingTasks}
- id: execute-picking
type: http-request
parameters:
method: POST
url: http://picking-api/execute
body: ${$json.optimizedTasks}
- id: update-inventory
type: http-request
parameters:
method: POST
url: http://inventory-api/update
body: ${$json.pickingResults}
风险预警 流程
流程设计
n8n工作流配置
workflow:
name: 智能风险预警
triggers:
- type: schedule
cron: "*/5 * * * *" # 每5分钟执行一次
nodes:
- id: collect-data
type: http-request
parameters:
method: GET
url: http://data-api/collect
qs:
type: gps,orders,weather,traffic
- id: clean-data
type: function
parameters:
function: |
function cleanData(data) {
return {
gps: cleanGPSData(data.gps),
orders: cleanOrderData(data.orders),
weather: cleanWeatherData(data.weather),
traffic: cleanTrafficData(data.traffic)
};
}
- id: identify-risks
type: http-request
parameters:
method: POST
url: http://risk-api/identify
body: ${$json.cleanedData}
- id: evaluate-risks
type: function
parameters:
function: |
function evaluateRisks(risks) {
return risks.map(risk => ({
...risk,
level: calculateRiskLevel(risk),
impact: calculateImpact(risk)
}));
}
- id: generate-alerts
type: function
parameters:
function: |
function generateAlerts(evaluatedRisks) {
return evaluatedRisks
.filter(risk => risk.level >= 'medium')
.map(risk => ({
alertNo: generateAlertNo(),
type: risk.type,
level: risk.level,
content: generateAlertContent(risk),
recipients: getRecipients(risk)
}));
}
- id: send-notifications
type: http-request
parameters:
method: POST
url: http://notification-api/send
body: ${$json.alerts}
- id: track-risks
type: http-request
parameters:
method: POST
url: http://risk-api/track
body: ${$json.alerts}
5.3 MCP工具集成
地图服务工具
地理编码工具
@mcp_tool
def geocode_address(address: str) -> dict:
"""
地理编码:将地址转换为经纬度
Args:
address: 地址文本
Returns:
{
"lat": 纬度,
"lng": 经度,
"formatted_address": 格式化地址,
"province": 省,
"city": 市,
"district": 区
}
"""
# 调用地图API进行地理编码
result = map_api.geocode(address)
return {
"lat": result["lat"],
"lng": result["lng"],
"formatted_address": result["formatted_address"],
"province": result["province"],
"city": result["city"],
"district": result["district"]
}
路径规划工具
@mcp_tool
def plan_route(origin: dict, destination: dict, waypoints: list = None) -> dict:
"""
路径规划:规划从起点到终点的路径
Args:
origin: 起点 {"lat": 纬度, "lng": 经度}
destination: 终点 {"lat": 纬度, "lng": 经度}
waypoints: 途经点列表(可选)
Returns:
{
"distance": 距离(km),
"duration": 时间(分钟),
"route": 路径坐标列表,
"steps": 路径步骤列表
}
"""
# 调用地图API进行路径规划
result = map_api.route(origin, destination, waypoints)
return {
"distance": result["distance"],
"duration": result["duration"],
"route": result["route"],
"steps": result["steps"]
}
距离计算工具
@mcp_tool
def calculate_distance(point1: dict, point2: dict) -> float:
"""
计算两点之间的距离
Args:
point1: 点1 {"lat": 纬度, "lng": 经度}
point2: 点2 {"lat": 纬度, "lng": 经度}
Returns:
距离(km)
"""
# 使用Haversine公式计算距离
from math import radians, sin, cos, sqrt, atan2
lat1, lng1 = radians(point1["lat"]), radians(point1["lng"])
lat2, lng2 = radians(point2["lat"]), radians(point2["lng"])
dlat = lat2 - lat1
dlng = lng2 - lng1
a = sin(dlat/2)**2 + cos(lat1) * cos(lat2) * sin(dlng/2)**2
c = 2 * atan2(sqrt(a), sqrt(1-a))
distance = 6371 * c # 地球半径6371km
return distance