方案对比：
┌────────────────┬──────────┬──────────┬──────────┐
│ 方案           │ 实时性    │ 语义理解  │ 决策能力  │
├────────────────┼──────────┼──────────┼──────────┤
│ 点对点接口      │ 中        │ 无       │ 无       │
│ ESB 集成       │ 中-高     │ 弱       │ 无       │
│ 数据仓库       │ 低 (T+1)  │ 弱       │ 有限     │
│ Coomia DIP     │ 高 （秒级） │ 强       │ 内建     │
└────────────────┴──────────┴──────────┴──────────┘

ObjectType: Case
  description: "核心业务实体"
  properties:
    - id: string (PK)
    - name: string
    - type: enum
    - status: enum [Active, Inactive, Pending, Archived]
    - created_at: datetime
    - updated_at: datetime
    - created_by: string
    - priority: enum [Low, Normal, High, Critical]
    - metadata: dict
  computed_properties:
    - risk_score: float
    - health_index: float
    - trend: enum [Improving, Stable, Declining]

ObjectType: Contact
  description: "辅助数据实体"
  properties:
    - id: string (PK)
    - source_system: string
    - timestamp: datetime
    - value: float
    - unit: string
    - quality_flag: enum [Good, Suspect, Bad]
    - dimensions: dict
  time_series: true
  retention: "365d"

ObjectType: Location
  description: "流程/事件实体"
  properties:
    - id: string (PK)
    - type: enum
    - status: enum [Draft, Submitted, InReview, Approved, Rejected, Completed]
    - requester: string
    - start_time: datetime
    - end_time: datetime
    - result: string
    - severity: enum [Low, Medium, High, Critical]

ObjectType: LabTest
  description: "分析/决策实体"
  properties:
    - id: string (PK)
    - analysis_type: string
    - input_data: dict
    - result: dict
    - confidence: float [0-1]
    - model_version: string
    - generated_at: datetime

ObjectType: TransmissionChain
  description: "关联/追踪实体"
  properties:
    - id: string (PK)
    - source_id: string
    - target_id: string
    - relation_type: string
    - weight: float
    - evidence: list[string]
    - discovered_at: datetime

Relations:
  - Case -> generates -> Contact
    cardinality: 1:N
    description: "核心实体产生数据记录"

  - Case -> triggers -> Location
    cardinality: 1:N
    description: "核心实体触发流程/事件"

  - Contact -> analyzedBy -> LabTest
    cardinality: N:1
    description: "数据被分析引擎处理"

  - LabTest -> impacts -> Case
    cardinality: N:M
    description: "分析结果反馈到核心实体"

  - Case -> linkedVia -> TransmissionChain
    cardinality: N:M
    description: "实体间的关联追踪"

  - Location -> resolvedBy -> LabTest
    cardinality: N:1
    description: "事件通过分析得到解决方案"

┌──────────────────────────────────────────────────────┐
│                    应用层                              │
│  ┌──────────┐  ┌──────────┐  ┌──────────┐           │
│  │ 业务看板  │  │ 分析报告  │  │ 移动端   │           │
│  └────┬─────┘  └────┬─────┘  └────┬─────┘           │
│       └─────────────┼─────────────┘                  │
│                     │                                 │
│  ┌──────────────────┴─────────────────────┐          │
│  │         Ontology 语义层                  │          │
│  │   Case --- Contact --- Location          │          │
│  │       |       |           |              │          │
│  │   LabTest --- TransmissionChain          │          │
│  │   统一模型 / 统一查询 / 统一权限            │          │
│  └──────────────────┬─────────────────────┘          │
│                     │                                 │
│  ┌────────┐  ┌─────┴──────┐  ┌──────────┐           │
│  │Plane B │  │  Plane C   │  │ Plane D  │           │
│  │Control │  │  Data      │  │Reasoning │           │
│  └────────┘  └────────────┘  └──────────┘           │
│                     │                                 │
│  ┌──────────────────┴─────────────────────┐          │
│  │   数据接入： CDC | API | Stream | Batch  │          │
│  └────────────────────────────────────────┘          │
└──────────────────────────────────────────────────────┘

from ontology_sdk import OntoPlatform

platform = OntoPlatform()

# 1. 查询高优先级实体及关联数据
entities = (
    platform.ontology
    .object_type("Case")
    .filter(status="Active")
    .filter(priority__in=["High", "Critical"])
    .include("Contact")
    .include("Location")
    .order_by("updated_at", ascending=False)
    .limit(100)
    .execute()
)

for entity in entities:
    print(f"实体： {entity.name} | 风险： {entity.risk_score}")

    recent_bad = [d for d in entity.contacts
                  if d.quality_flag == "Bad"]
    if len(recent_bad) > 5:
        platform.actions.execute(
            "ExecuteLabTest",
            target_id=entity.id,
            analysis_type="anomaly_detection",
            parameters={"window": "24h"}
        )

# 2. 订阅实时事件
def on_event(event):
    if event.severity == "Critical":
        platform.actions.execute(
            "Escalate",
            issue_id=event.entity_id,
            severity="Critical",
            escalate_to="on_call_manager"
        )

platform.subscribe(
    object_type="Location",
    events=["created", "severity_changed"],
    callback=on_event
)

rules:
  - name: "高风险告警"
    trigger: Case.risk_score > 80
    actions:
      - alert: critical
      - action: Escalate(severity=Critical)

  - name: "趋势恶化"
    trigger: Case.trend == "Declining" AND priority in [High, Critical]
    actions:
      - alert: warning
      - action: ExecuteLabTest(type=root_cause)

  - name: "数据质量"
    trigger: Contact.quality_flag == "Bad" count > 10/hour
    actions:
      - alert: warning

  - name: "事件自动升级"
    trigger: Location.severity == "Critical"
    actions:
      - action: Escalate(severity=Critical)
      - notification: sms -> on_call

数据采集 --> 规则评估 --> 决策生成 --> Action执行 --> 反馈闭环
  CDC        Plane D     ML/Rules    自动/人工     效果追踪
  Stream     Ontology查询            通知/告警     模型更新

from intelligence_plane.models import PredictionModel
from datetime import timedelta

class LabTestModel(PredictionModel):
    def __init__(self):
        super().__init__(
            name="labtest_v2",
            input_type="Case",
            output_type="LabTest"
        )

    def predict(self, entity, context):
        history = (
            context.ontology.object_type("Contact")
            .filter(source_id=entity.id)
            .filter(timestamp__gte=context.now - timedelta(days=90))
            .order_by("timestamp")
            .execute()
        )
        features = self.extract_features(history)
        prediction = self.model.predict(features)
        return {
            "level": prediction["level"],
            "confidence": prediction["confidence"],
            "factors": prediction["contributing_factors"],
            "actions": prediction["recommended_actions"]
        }

首年 ROI = (220 - 55) / 55 * 100% = 300%
三年 ROI = (220*3 - 55 - 15*2) / (55 + 15*2) * 100% = 676%