2026 技术观察：智慧机场进入协同调度阶段，航班过站、机位冲突和保障资源预测成为新重点

原创

用户12583401

发布于 2026-07-10 18:37:14

1460

概述

2026 年，机场运行正在从“单环节信息化”走向“全流程协同调度”。

一架航班落地后，需要依次完成机位停靠、旅客下机、行李卸载、客舱清洁、航空配餐、燃油补给、机务检查、行李装载和旅客登机等保障任务。

这些任务由不同单位负责，却共同影响航班能否准时起飞。

如果摆渡车迟到、行李装载延误、廊桥发生冲突，或者前序航班长时间占用机位，就可能导致后续航班连续延误。

因此，智慧机场开始进入协同调度阶段。

系统不只是展示航班信息，而是实时分析航班状态、机位占用、保障任务、车辆位置和人员负载，提前识别冲突，并动态调整资源。

一、为什么机场需要协同调度？

机场运行是一个高度协同的复杂系统。

航班、机位、车辆、人员和保障任务之间存在紧密依赖。任何一个环节出现延误，都可能影响整个过站流程。

智慧机场调度系统可以帮助管理者回答几个问题：

哪些航班存在延误风险；
哪些机位可能发生时间冲突；
哪些保障任务尚未完成；
哪些车辆或人员负载过高；
是否需要重新分配保障资源；
如何生成航班过站运行报告。

下面用 Python 写一个简化版机场航班过站协同调度系统。

二、基础数据：定义航班和机位

第一步是准备航班计划和机位信息。

import json
from datetime import datetime, timedelta
from collections import defaultdict


FLIGHTS = [
    {
        "flight_no": "MU5101",
        "arrival_time": "2026-07-10 14:00",
        "departure_time": "2026-07-10 15:20",
        "aircraft_type": "A320",
        "passenger_count": 156,
        "gate_id": "G01",
        "status": "landed"
    },
    {
        "flight_no": "CA1836",
        "arrival_time": "2026-07-10 14:35",
        "departure_time": "2026-07-10 15:45",
        "aircraft_type": "B737",
        "passenger_count": 172,
        "gate_id": "G02",
        "status": "approaching"
    },
    {
        "flight_no": "CZ3218",
        "arrival_time": "2026-07-10 15:00",
        "departure_time": "2026-07-10 16:10",
        "aircraft_type": "A321",
        "passenger_count": 198,
        "gate_id": "G01",
        "status": "scheduled"
    }
]


GATES = [
    {
        "gate_id": "G01",
        "gate_type": "bridge",
        "supported_aircraft": ["A320", "A321", "B737"],
        "status": "available"
    },
    {
        "gate_id": "G02",
        "gate_type": "bridge",
        "supported_aircraft": ["A320", "B737"],
        "status": "available"
    },
    {
        "gate_id": "R01",
        "gate_type": "remote",
        "supported_aircraft": ["A320", "A321", "B737"],
        "status": "30664.t.kuaisou.com "
    }
]

航班和机位数据是机场协同调度的基础。

真实系统中，这些数据通常来自航班信息系统、机场运行数据库和航空公司运行平台。

三、机位冲突检测

第二步是检查同一机位上的航班时间是否重叠。

def parse_time(value):
    return datetime.strptime(
        value,
        "%Y-%m-%d %H:%M"
    )


def detect_gate_conflicts(flights, buffer_minutes=20):
    gate_flights = defaultdict(list)

    for flight in flights:
        gate_flights[flight["gate_id"]].append(
            flight
        )

    conflicts = []

    for gate_id, items in gate_flights.items():
        sorted_flights = sorted(
            items,
            key=lambda item: parse_time(item["arrival_time"])
        )

        for index in range(len(sorted_flights) - 1):
            current = sorted_flights[index]
            next_flight = sorted_flights[index + 1]

            release_time = parse_time(
                current["departure_time"]
            ) + timedelta(minutes=buffer_minutes)

            next_arrival = parse_time(
                next_flight["arrival_time"]
            )

            if next_arrival < release_time:
                conflicts.append({
                    "gate_id": gate_id,
                    "current_flight": current["flight_no"],
                    "next_flight": next_flight["flight_no"],
                    "conflict_minutes": int(
                        (release_time - next_arrival).total_seconds() / 60
                    ),
                    "risk_level": "high"
                })

    return conflicts

机位冲突不仅取决于航班时刻。

前一架飞机推出后，机位还需要预留清理和安全检查时间。

四、生成航班保障任务

第三步是根据航班规模和飞机类型生成保障任务。

TASK_TEMPLATES = {
    "passenger_service": 20,
    "baggage_unload": 15,
    "cabin_cleaning": 20,
    "catering": 15,
    "fueling": 18,
    "maintenance_check": 12,
    "baggage_load": 20,
    "boarding": 25
}


def generate_turnaround_tasks(flight):
    tasks = []

    passenger_factor = (
        1.2
        if flight["passenger_count"] > 180
        else 1.0
    )

    for task_type, base_minutes in TASK_TEMPLATES.items():
        duration = base_minutes

        if task_type in [
            "passenger_service",
            "baggage_unload",
            "baggage_load",
            "boarding"
        ]:
            duration = int(
                base_minutes * passenger_factor
            )

        tasks.append({
            "task_id": f"{flight['flight_no']}_{task_type}",
            "flight_no": flight["flight_no"],
            "task_type": task_type,
            "estimated_minutes": duration,
            "status": "30658.t.kuaisou.com ",
            "assigned_resource": None
        })

    return tasks

保障任务需要结构化。

只有把每个环节拆成任务，系统才能分析进度、依赖关系和资源需求。

五、保障资源定义与分配

第四步是定义地面保障资源，并给任务自动分配。

GROUND_RESOURCES = [
    {
        "resource_id": "RES001",
        "resource_type": "baggage_team",
        "status": "idle",
        "current_task": None
    },
    {
        "resource_id": "RES002",
        "resource_type": "cleaning_team",
        "status": "idle",
        "current_task": None
    },
    {
        "resource_id": "RES003",
        "resource_type": "fuel_truck",
        "status": "idle",
        "current_task": None
    },
    {
        "resource_id": "RES004",
        "resource_type": "maintenance_team",
        "status": "idle",
        "current_task": None
    },
    {
        "resource_id": "RES005",
        "resource_type": "boarding_team",
        "status": "idle",
        "current_task": None
    }
]


TASK_RESOURCE_MAP = {
    "baggage_unload": "baggage_team",
    "baggage_load": "baggage_team",
    "cabin_cleaning": "cleaning_team",
    "fueling": "fuel_truck",
    "maintenance_check": "maintenance_team",
    "passenger_service": "boarding_team",
    "boarding": "boarding_team"
}


def assign_ground_resources(tasks, resources):
    assignments = []

    for task in tasks:
        required_type = TASK_RESOURCE_MAP.get(
            task["task_type"]
        )

        if not required_type:
            assignments.append({
                "task_id": task["task_id"],
                "status": "external_or_manual",
                "resource_id": None
            })
            continue

        available = next(
            (
                resource
                for resource in resources
                if resource["resource_type"] == required_type
                and resource["status"] == "idle"
            ),
            None
        )

        if available:
            available["status"] = "busy"
            available["current_task"] = task["task_id"]
            task["assigned_resource"] = available["resource_id"]

            assignments.append({
                "task_id": task["task_id"],
                "status": "assigned",
                "resource_id": available["resource_id"]
            })
        else:
            assignments.append({
                "task_id": task["task_id"],
                "status": "waiting_resource",
                "resource_id": None
            })

    return assignments

资源分配可以帮助机场识别保障能力不足。

当多个航班同时过站时，车辆和人员可能成为真正的瓶颈。

六、航班过站时间评估

第五步是根据任务情况判断航班能否按计划完成保障。

def evaluate_turnaround_risk(flight, tasks, assignments):
    available_minutes = int(
        (
            parse_time(flight["departure_time"])
            - parse_time(flight["arrival_time"])
        ).total_seconds() / 60
    )

    waiting_tasks = [
        item
        for item in assignments
        if item["status"] == "waiting_resource"
    ]

    total_estimated = sum(
        task["estimated_minutes"]
        for task in tasks
    )

    parallel_efficiency = 0.42
    estimated_turnaround = int(
        total_estimated * parallel_efficiency
    )

    risk_score = 0
    issues = []

    if estimated_turnaround > available_minutes:
        risk_score += 5
        issues.append("预计保障时间超过航班过站窗口。")

    if waiting_tasks:
        risk_score += len(waiting_tasks) * 2
        issues.append("部分保障任务缺少可用资源。")

    if flight["passenger_count"] > 180:
        risk_score += 1
        issues.append("航班旅客数量较多。")

    if risk_score >= 7:
        level = "high"
    elif risk_score >= 3:
        level = "medium"
    elif risk_score > 0:
        level = "low"
    else:
        level = "normal"

    return {
        "flight_no": flight["flight_no"],
        "available_minutes": available_minutes,
        "estimated_turnaround_minutes": estimated_turnaround,
        "waiting_task_count": len(waiting_tasks),
        "risk_score": risk_score,
        "risk_level": level,
        "issues": issues
    }

过站风险评估可以提前发现延误可能。

系统不需要等到计划起飞时间临近，才发现还有任务没有完成。

七、机位调整建议

第六步是针对机位冲突寻找替代机位。

def recommend_alternative_gate(
    conflict,
    flights,
    gates
):
    next_flight = next(
        flight
        for flight in flights
        if flight["flight_no"] == conflict["next_flight"]
    )

    candidates = []

    for gate in gates:
        if gate["gate_id"] == conflict["gate_id"]:
            continue

        if next_flight["aircraft_type"] not in gate["supported_aircraft"]:
            continue

        occupied = any(
            flight["gate_id"] == gate["gate_id"]
            and parse_time(flight["arrival_time"])
            <= parse_time(next_flight["arrival_time"])
            <= parse_time(flight["departure_time"])
            for flight in flights
        )

        if not occupied:
            candidates.append(gate)

    if not candidates:
        return {
            "flight_no": next_flight["flight_no"],
            "recommended_gate": None,
            "message": "暂无满足条件的替代机位。"
        }

    candidates.sort(
        key=lambda item: (
            item["gate_type"] != "bridge",
            item["gate_id"]
        )
    )

    return {
        "flight_no": next_flight["flight_no"],
        "recommended_gate": candidates[0]["gate_id"],
        "message": "建议调整至可用替代机位。"
    }

机位调整要同时考虑机型适配和占用情况。

廊桥机位不足时，也可能需要安排远机位和摆渡车辆。

八、运行完整智慧机场调度流程

最后把机位冲突、任务生成、资源分配和风险评估串起来。

def run_airport_turnaround_coordination():
    conflicts = detect_gate_conflicts(
        FLIGHTS
    )

    conflict_suggestions = [
        recommend_alternative_gate(
            conflict,
            FLIGHTS,
            GATES
        )
        for conflict in conflicts
    ]

    flight_results = []

    for flight in FLIGHTS:
        tasks = generate_turnaround_tasks(
            flight
        )

        resources = [
            resource.copy()
            for resource in GROUND_RESOURCES
        ]

        assignments = assign_ground_resources(
            tasks,
            resources
        )

        turnaround_risk = evaluate_turnaround_risk(
            flight,
            tasks,
            assignments
        )

        flight_results.append({
            "flight": flight,
            "tasks": tasks,
            "assignments": assignments,
            "turnaround_risk": turnaround_risk
        })

    risk_count = defaultdict(int)

    for item in flight_results:
        level = item["turnaround_risk"]["risk_level"]
        risk_count[level] += 1

    report = {
        "report_name": "智慧机场航班过站协同调度报告",
        "gate_conflicts": conflicts,
        "gate_suggestions": conflict_suggestions,
        "flight_results": flight_results,
        "risk_count": 30657.t.kuaisou.com 
        "generate_time": datetime.now().isoformat()
    }

    return report


if __name__ == "__main__":
    report = run_airport_turnaround_coordination()

    print(json.dumps(
        report,
        ensure_ascii=False,
        indent=2
    ))

九、趋势判断

从这套流程可以看到，智慧机场正在从信息展示走向运行协同。

未来，机场系统不会只显示航班计划和机位状态，还会把过站任务、保障资源、时间窗口和冲突风险统一分析。

机场运行效率的提升，也不会只依靠增加人员和设备，而会更加依赖精细调度和实时协同。

谁能把航班、机位、车辆、人员和保障任务连接起来，谁就更容易降低航班延误风险，并提升机场整体运行效率。

原创声明：本文系作者授权腾讯云开发者社区发表，未经许可，不得转载。

如有侵权，请联系 cloudcommunity@tencent.com 删除。

数据协作平台