DF-06-FP: 窗帘正常移动但速度慢

The master_bedroom occupancy sensor is functioning normally with no detected data loss or state anomalies.

Explanation: Analyzed contiguous time windows (C00, C01, C28) surrounding the kitchen anomaly at 18:24:00 to verify continuous operation of the master bedroom sensor.

Explanation: Confirmed that master_bedroom_occupancy consistently reported '1' during the fault window and adjacent periods, indicating robustness against the network transient affecting other sensors.

Explanation: Verified that temperature readings for master_bedroom_temp_sensor remained within plausible ranges and showed normal diurnal variation, ruling out sensor drift or hardware failure.

master_bedroom_occupancy reported '1' consistently in chunks C00, C01, C22, C28, and C29 without interruption.

No null values or missing timestamps were observed for master_bedroom_occupancy logs during the critical event at 18:24:00.

The 'attribute_frequency' shows low counts for 'OperationalStatus' (2) and 'LockState' (5), suggesting these specific diagnostic attributes are rarely polled, masking underlying device health status.

The master_bedroom_occupancy sensor itself never reported a null value or failure code in the provided chunks; it continued reporting '1'.

Temperature values for master_bedroom remain within plausible ranges and show normal diurnal variation, lacking the erratic jumps often seen in failing thermal sensors.

The simultaneous occurrence of 'kitchen_temp_sensor' returning None at 18:24:00 while house occupancy remains high indicates a systemic transient issue (power blip, gateway reset, or bus collision) rather than isolated master_bedroom hardware failure.

Contrast between 'master_bedroom_occupancy' (consistent 1s) and 'kitchen_temp_sensor' (null spike) suggests the occupancy sensor may be more robust but operates in the same vulnerable network segment.

Raw log entries for 'master_bedroom_temp_sensor' OperationalStatus or BatteryLevel to rule out power-induced silent failures.

Timestamped network latency or packet loss metrics around 18:24:00 to confirm if the kitchen null was a local event or a network partition affecting the zone.

History of 'lock_state_flips' specifically tied to the master bedroom door vs. front door to assess actuator stability.

Verify 'master_bedroom_occupancy' status immediately before (C00/C01) and after (C28) the anomaly window to confirm continuous operation.

Check for correlated network latency spikes or gateway resets in chunks surrounding C22 to distinguish local sensor failure from systemic outage.

The analysis correctly identified that the master_bedroom_occupancy sensor did not report null values during the anomaly, suggesting it may be functioning. However, the evidence is insufficient because the current focus is heavily skewed towards the kitchen anomaly and specific chunks (C22, C29). To definitively rule out intermittent failures or data masking, we need to check contiguous timestamps (C00, C01, C28) around the fault event to ensure no silent gaps occurred.

The investigator's hypothesis relies on the absence of errors in the provided logs; without checking adjacent time windows, a brief communication drop that didn't trigger an error code but caused a data gap cannot be ruled out.

## Query
请检查以下设备状态历史，判断 master_bedroom 区域的传感器 是否工作正常。如果异常，请说明可能的故障类型。

## Layout
Layout: 三室两厅 (3BR)
- balcony: 洗衣机<laundry_washer>, 阳台灯<onoff_light>, 阳台门传感器<contact_sensor>
- dining_room: 餐厅灯<dimmable_light>, 餐厅运动传感器<occupancy_sensor>
- entrance: 入户门传感器<contact_sensor>, 入户门锁<door_lock>, 玄关灯<onoff_light>, 玄关运动传感器<occupancy_sensor>
- kids_room: 儿童房温度传感器<temperature_sensor>, 儿童房灯<dimmable_light>, 儿童房窗帘<window_covering>, 儿童房窗户传感器<contact_sensor>, 儿童房运动传感器<occupancy_sensor>
- kitchen: 厨房温度传感器<temperature_sensor>, 厨房漏水传感器<water_leak_detector>, 厨房灯<onoff_light>, 厨房灶具<cook_surface>, 厨房烟雾报警器<smoke_co_alarm>, 厨房窗户传感器<contact_sensor>, 洗碗机<dishwasher>
- living_room: 客厅主灯<dimmable_light>, 客厅副灯<dimmable_light>, 客厅氛围灯<onoff_light>, 客厅温度传感器<temperature_sensor>, 客厅烟雾报警器<smoke_co_alarm>, 客厅空调<air_conditioner>, 客厅窗帘1<window_covering>, 客厅窗户传感器1<contact_sensor>, 客厅窗户传感器2<contact_sensor>, 客厅运动传感器<occupancy_sensor>
- master_bathroom: 主卫漏水传感器<water_leak_detector>, 主卫灯<onoff_light>, 主卫运动传感器<occupancy_sensor>
- master_bedroom: 主卧床头灯<onoff_light>, 主卧温度传感器<temperature_sensor>, 主卧灯<dimmable_light>, 主卧空调<air_conditioner>, 主卧窗帘<window_covering>, 主卧窗户传感器<contact_sensor>, 主卧运动传感器<occupancy_sensor>
- second_bathroom: 次卫漏水传感器<water_leak_detector>, 次卫灯<onoff_light>
- second_bedroom: 次卧灯<dimmable_light>, 次卧空调<air_conditioner>, 次卧窗帘<window_covering>, 次卧窗户传感器<contact_sensor>, 次卧运动传感器<occupancy_sensor>

## Deterministic Signals
{
  "room_activity": {
    "living_room": 403,
    "master_bedroom": 526,
    "kids_room": 517,
    "kitchen": 303,
    "dining_room": 219,
    "second_bedroom": 48,
    "master_bathroom": 222,
    "entrance": 225,
    "second_bathroom": 1
  },
  "attribute_frequency": {
    "Occupancy": 1254,
    "MeasuredValue": 1156,
    "OnOff": 26,
    "StateValue": 7,
    "CurrentPositionLiftPercent100ths": 6,
    "LockState": 5,
    "OperationalStatus": 2,
    "SystemMode": 1,
    "OperationalState": 1
  },
  "lock_state_flips": 5,
  "occupancy_events": 1254,
  "temperature_patterns": []
}

## Chunk Index
[
  {
    "chunk_id": "C00",
    "start_ts": "2026-06-22T00:00:00",
    "end_ts": "2026-06-22T01:10:00",
    "event_count": 80,
    "rooms": {
      "living_room": 18,
      "master_bedroom": 18,
      "kids_room": 18,
      "kitchen": 14,
      "dining_room": 3,
      "second_bedroom": 3,
      "master_bathroom": 3,
      "entrance": 3
    },
    "top_devices": [
      "living_room_temp_sensor",
      "master_bedroom_temp_sensor",
      "kids_room_temp_sensor",
      "kitchen_tem

## Query
请检查以下设备状态历史，判断 master_bedroom 区域的传感器 是否工作正常。如果异常，请说明可能的故障类型。

## Triage
{
  "latent_task_profile": "device-health | single-event-safety",
  "focus_rooms": ["kitchen"],
  "focus_devices": ["kitchen_temp_sensor", "master_bedroom_occupancy", "kids_room_occupancy", "entrance_occupancy"],
  "focus_chunk_ids": ["C22", "C29"],
  "suspected_patterns": [
    "Sensor data loss (Null value)",
    "Potential false negative occupancy due to sensor failure"
  ],
  "why_these_chunks": [
    "Chunk C22 contains a critical anomaly: 'kitchen_temp_sensor' returns 'None' at 18:24:00, indicating a hardware or communication failure.",
    "Chunk C29 shows 'master_bedroom_window' reporting fully closed (100%) while the house is occupied and lights are off, which might indicate a stuck actuator or sensor misalignment if manual intervention wasn't performed, though less critical than the null sensor reading.",
    "Verifying master_bedroom/kids_room occupancy in these chunks helps determine if the temperature drop in C22 was real (house empty) or if the sensor failed to report while people were present."
  ]
}

## Investigator
{
  "normal_hypotheses": [
    {
      "id": "N1",
      "description": "Master bedroom occupancy sensor is functioning normally, correctly detecting continuous presence.",
      "evidence": ["Occupancy reported as 1 consistently throughout C22 (18:20-18:55) and C29 (22:46-23:25).", "No false negatives or erratic state flips observed in the master_bedroom_occupancy logs."],
      "weaknesses": ["Does not account for potential data loss masked by other sensors' activity; relies on the assumption that a single '1' reading implies valid operation despite system-wide noise."]
    }
  ],
  "anomaly_hypotheses": [
    {
      "id": "A1",
      "description": "Intermittent communication failure or sensor glitch in the master bedroom ecosystem, though not explicitly logged as an error code.",
      "threat_type": "Data Integrity / False Negative Risk",
      "evidence": [
        "At 18:24:00, the kitchen_temp_sensor returns None while the system is active (lights/cookers on), indicating a transient network or hardware fault affecting the local gateway cluster.",
        "While master_bedroom_temp_sensor does not return None at 18:24:00, its value (2316) deviates slightly from neighbors, suggesting potential synchronization issues or environmental variance during the same fault window.",
        "The 'attribute_frequency' shows low counts for 'OperationalSt


触发间隔不规律（不是周期性的），且频率较低（几分钟一次不是几十秒一次）

The second_bedroom sensor is functioning normally with consistent temperature readings, despite a separate anomaly detected in the kitchen sensor.

Explanation: Analyzed data for 'second_bedroom_temp_sensor' across both provided chunks (C17 and C18).

Explanation: Observed that the second_bedroom sensor consistently returns valid integer values (ranging from ~2260 to ~2300) at every timestamp where it was recorded.

Explanation: Confirmed that occupancy events were successfully recorded for the second_bedroom, indicating the device is actively communicating with the system.

In Chunk C17, at timestamps like 19:00:00 and 19:55:00, second_bedroom_temp_sensor reports valid values (2288, 2297) while the kitchen sensor reports None or anomalous values.

In Chunk C18, at timestamps like 19:42:00 (when kitchen sensor is None) and 20:00:00, the second_bedroom sensor continues to report valid integers (2271, 2268).

Investigate and replace/maintain the kitchen_temp_sensor which is showing data loss and null values.

No immediate action required for the second_bedroom sensor as it appears healthy.

In Chunk C17, kitchen_temp_sensor returns 'None' at 18:52:00 after cooking starts, while second_bedroom continues reporting valid data (2299).

In Chunk C18, kitchen_temp_sensor returns 'None' again at 19:42:00 despite temperatures dropping, while second_bedroom reports valid data (2271).

Structured Signals show 'temperature_patterns': [] (empty), suggesting no aggregate pattern analysis succeeded likely due to the missing kitchen data.

Relies on the assumption that the Triage Output correctly identified the root cause. If the user query strictly demands an evaluation of the *second_bedroom* only based on raw logs, this hypothesis argues the premise of the question contains a false positive regarding the target device.

Comparison of timestamps 18:52:00 and 19:42:00 shows kitchen_temp_sensor = None while second_bedroom_temp_sensor holds valid integer values, proving the fault is localized to the kitchen sensor.

Triage Output selection of 'kitchen' as focus_room and 'kitchen_temp_sensor' as focus_device contradicts the user's request to check 'second_bedroom', implying the second_bedroom data is actually healthy.

Device firmware version and battery status for all sensors involved.

Historical data prior to 2026-06-10 to determine if the kitchen sensor failure is intermittent or permanent.

Verify if 'second_bedroom' is explicitly mentioned in the Triage Output's focus_rooms or focus_devices fields to confirm scope drift.

Re-evaluate the Investigator's conclusion that the user query premise might be flawed due to the mismatch between the requested device ('second_bedroom') and the triaged anomaly ('kitchen').

The analysis has drifted significantly from the user's specific query. The user asked to check the 'second_bedroom' sensor status, but the Triage Output explicitly focused on the 'kitchen' sensor (focus_room: 'kitchen', focus_devices: 'kitchen_temp_sensor').

While the Investigator correctly identified that the second_bedroom data appears healthy based on the provided chunks, their reasoning relies heavily on assuming the Triage Output was misdirected rather than directly answering the prompt by confirming the health of the requested room.

The evidence regarding 'second_bedroom' validity is sufficient for the current chunks, but the investigation should have started by explicitly validating the 'second_bedroom' sensors across all relevant timeframes before dismissing the query in favor of the 'kitchen' anomaly.

## Query
请检查以下设备状态历史，判断 second_bedroom 区域的传感器 是否工作正常。如果异常，请说明可能的故障类型。

## Layout
Layout: 两室一厅 (2BR)
- balcony: 洗衣机<laundry_washer>, 阳台灯<onoff_light>, 阳台门传感器<contact_sensor>
- bathroom: 卫生间漏水传感器<water_leak_detector>, 卫生间灯<onoff_light>, 卫生间运动传感器<occupancy_sensor>
- entrance: 入户门传感器<contact_sensor>, 入户门锁<door_lock>, 玄关灯<onoff_light>, 玄关运动传感器<occupancy_sensor>
- kitchen: 厨房温度传感器<temperature_sensor>, 厨房灯<onoff_light>, 厨房灶具<cook_surface>, 厨房烟雾报警器<smoke_co_alarm>, 厨房窗户传感器<contact_sensor>, 洗碗机<dishwasher>
- living_room: 客厅主灯<dimmable_light>, 客厅温度传感器<temperature_sensor>, 客厅空调<air_conditioner>, 客厅窗帘<window_covering>, 客厅窗户传感器<contact_sensor>, 客厅落地灯<onoff_light>, 客厅运动传感器<occupancy_sensor>
- master_bedroom: 主卧温度传感器<temperature_sensor>, 主卧灯<dimmable_light>, 主卧空调<air_conditioner>, 主卧窗帘<window_covering>, 主卧窗户传感器<contact_sensor>, 主卧运动传感器<occupancy_sensor>
- second_bedroom: 次卧温度传感器<temperature_sensor>, 次卧灯<dimmable_light>, 次卧窗帘<window_covering>, 次卧窗户传感器<contact_sensor>, 次卧运动传感器<occupancy_sensor>

## Deterministic Signals
{
  "room_activity": {
    "living_room": 401,
    "master_bedroom": 336,
    "second_bedroom": 340,
    "kitchen": 298,
    "bathroom": 222,
    "entrance": 233
  },
  "attribute_frequency": {
    "MeasuredValue": 1154,
    "Occupancy": 647,
    "OnOff": 14,
    "StateValue": 5,
    "LockState": 4,
    "SystemMode": 1,
    "OperationalState": 1
  },
  "lock_state_flips": 4,
  "occupancy_events": 647,
  "temperature_patterns": []
}

## Chunk Index
[
  {
    "chunk_id": "C00",
    "start_ts": "2026-06-10T00:00:00",
    "end_ts": "2026-06-10T01:20:00",
    "event_count": 80,
    "rooms": {
      "living_room": 20,
      "master_bedroom": 19,
      "second_bedroom": 19,
      "kitchen": 16,
      "bathroom": 3,
      "entrance": 3
    },
    "top_devices": [
      "living_room_temp_sensor",
      "master_bedroom_temp_sensor",
      "second_bedroom_temp_sensor",
      "kitchen_temp_sensor",
      "living_room_occupancy",
      "master_bedroom_occupancy"
    ],
    "alarmish_count": 15,
    "preview_lines": [
      "[2026-06-10T00:00:00] living_room_temp_sensor | TemperatureMeasurement.MeasuredValue = 2276",
      "[2026-06-10T00:00:00] master_bedroom_temp_sensor | TemperatureMeasurement.MeasuredValue = 2267",
      "[2026-06-10T00:00:00] second_bedroom_temp_sensor | TemperatureMeasurement.MeasuredValue = 2272",
      "[2026-06-10T00:00:00] kitchen_temp_sensor | TemperatureMeasurement.MeasuredValue = 2279",
      "[2026-06-10T00:00:00] living_room_occupancy | Oc

## Query
请检查以下设备状态历史，判断 second_bedroom 区域的传感器 是否工作正常。如果异常，请说明可能的故障类型。

## Triage
{
  "latent_task_profile": "device-health",
  "focus_rooms": ["kitchen"],
  "focus_devices": ["kitchen_temp_sensor"],
  "focus_chunk_ids": ["C17", "C18"],
  "suspected_patterns": [
    "Temperature sensor returning null/None values during active heating events",
    "High temperature readings followed by data loss indicating sensor failure or battery drain"
  ],
  "why_these_chunks": "Chunks C17 and C18 contain the critical failure evidence. In C17, the kitchen temperature spikes to ~2665 (anomalous high) just before the cook surface is turned on, and at the end of the chunk, the sensor explicitly returns 'None'. In C18, despite the cook surface being off and temperatures dropping to normal ranges (~2270), the sensor continues to return 'None', confirming a persistent hardware fault rather than a transient glitch."
}

## Investigator
{
  "normal_hypotheses": [
    {
      "id": "N1",
      "description": "The second_bedroom sensor is functioning normally, reporting consistent temperature values (~2270-2300) while the kitchen sensor experiences a transient glitch or communication error specific to that device.",
      "evidence": [
        "second_bedroom_temp_sensor consistently returns valid MeasuredValue integers (e.g., 2297, 2288, 2275) throughout both C17 and C18 chunks.",
        "Occupancy events for second_bedroom are recorded (e.g., at 19:00:00 and 20:00:00), indicating the room's IoT gateway is actively polling devices in this zone."
      ],
      "weaknesses": [
        "Fails to explain why the 'kitchen' chunk was selected as the primary focus by the Triage Output if the anomaly were isolated to the kitchen. However, this hypothesis assumes the triage logic might be overly broad or that the kitchen failure triggered a system-wide alert."
      ]
    }
  ],
  "anomaly_hypotheses": [
    {
      "id": "A1",
      "description": "The investigation scope has been misdirected; the actual anomaly lies with the kitchen sensor, not the second bedroom. The second bedroom sensor is normal, but the system health check flagged a critical failure elsewhere.",
      "threat_type": "Hardware Failure / Sensor Data Loss",
      "evidence": [
        "Triage Output explicitly identifies focus_room as 'kitchen' and suspected_patterns involve 'Temperature sensor returning null/None values'.",
        "In Chunk C17, kitchen_temp_sensor returns 'None' at 18:52:00 after cooking starts, w


DF-04-FP: 门锁响应延迟但最终成功锁定

The door contact sensor malfunctioned by reporting the door as closed immediately after it was opened, triggering an erroneous automatic lock command.

Explanation: At 07:15:20, the front_door_contact sensor reported StateValue = False (Open).

Explanation: Only 3 seconds later at 07:15:22, the sensor reported StateValue = True (Closed), indicating a rapid closure or sensor glitch.

Explanation: Critically, at 07:15:23, the system executed a LockOperation (Auto) while the sensor had only just flipped to 'Closed', and no human occupancy was recorded during this transition window.

Explanation: This sequence suggests the automation logic locked the door based on a potentially false-positive 'closed' signal appearing too quickly after the door was physically opened, or the door remained open the entire time and the sensor failed to report the open state correctly until the lock attempt occurred.

Sequence: Open Report (07:15:20) -> Closed Report (07:15:22) -> Auto Lock (07:15:23)

Absence of Occupancy events in Entrance/Living Room between 07:15:18 and 07:15:23

Physically verify if the door was actually closed when the lock command was issued.

Inspect and recalibrate the front_door_contact sensor for intermittent failures or misalignment.

Review automation rules to add a hysteresis delay before auto-locking after a state change from Open to Closed.

```json
{
  "normal_hypotheses": [
    {
      "id": "N1",
      "description": "The door contact sensor is functioning correctly, and the locking event was a standard automation routine triggered by an external signal or timer, not necessarily linked to the immediate physical state of the door at that exact second.",
      "evidence": [
        "Standard lock operation events (LockOperationType=0) often occur automatically based on schedules or external triggers independent of real-time contact verification.",
        "The system successfully reported the LockState as 'Locked' (1) immediately after the operation."
      ],
      "weaknesses": [
        "Fails to explain why the automation triggered while the contact sensor explicitly reported the door was open ('False') just moments prior.",
        "Does not account for the lack of occupancy data in the entrance area to justify an auto-lock if the door was physically open."
      ]
    }
  ],
  "anomaly_hypotheses": [
    {
      "id": "A1",
      "description": "The front_door_contact sensor is experiencing a false negative or intermittent failure, reporting the door as closed when it was actually left open, triggering an erroneous auto-lock command.",
      "threat_type": "Sensor Fault / Logic Error",
      "evidence": [
        "At 07:15:20, the sensor reports StateValue = False (Open).",
        "At 07:15:23 (3 seconds later), the system executes LockOperation despite the door being reported as Open.",
        "Logical inconsistency: A standard safety protocol would prevent locking an open door; doing so implies the system believes the door is closed based on the faulty sensor reading.",
        "No subsequent occupancy events or door closure confirmations are visible in the provided chunk to correct this state before the lock attempt."
      ],
      "weaknesses": [
        "Cannot definitively prove the physical state of the door without video confirmation; the door could have been opened, then quickly closed by an unseen agent (though no occupancy spike is recorded)."
      ]
    },
    {
      "id": "A2",
      "description": "Data synchronization lag between the contact sensor and the lock controller caused a race condition where the lock command executed before the final 'closed' state was registered.",
      "threat_type": "System Latency",
      "evidence": [
        "Short temporal gap (3 seconds) between the 'Open' report and the 'Lock' command.",
        "Potential network latency in transmitting the final 'Closed' state update."
      ],
      "weaknesses": [
        "Highly unlikely that a 3-second lag would cause a security vulnerability unless the door remained open the entire time, which contradicts the eventual locked state assumption.",
        "Less severe than a hard sensor failure but still indicates system inefficiency."
      ]
    }
  ],
  "most_discriminative_evidence": [
    "The specific sequence: 07:15:20 (Contact=False/Open) -> 07:15:23 (LockOperation/Auto). The execution of a lock command while the primary sensor indicates the path is open is the strongest indicator of sensor failure or flawed logic.",
    "The absence of any 'Occupancy' events for the entrance room during this critical window suggests no human presence to manually verify the door state or override the automated action."
  ],
  "missing_information": [
    "Physical verification log or camera footage of the entrance area around 07:15:20-07:15:25 to confirm if the door was actually open.",
    "Configuration details of the 'Auto' lock source to determine if it ignores contact status or requires a specific condition that might be misinterpreted.",
    "History of similar events (lock operations with open doors) to distinguish between a one-time glitch and a systemic pattern."
  ]
}
```

Verify if the door physically closed between 07:15:20 and 07:15:23 in chunks C03 (pre-event) or C05 (post-event) to rule out rapid closure.

Check for occupancy events in surrounding chunks to determine if a user was present to manually override or close the door.

Review configuration logs to see if the lock automation logic explicitly ignores contact sensor status.

The investigation correctly identified the anomaly in C04 (locking while sensor reports open).

However, the evidence is currently insufficient to distinguish between 'Sensor Fault' and 'Rapid Closure followed by Latency'.

Chunk C03 is needed to see if the door was closing/opening just before the event, which might explain the state flip at 07:15:20.

Chunk C05 is needed to verify if the door remained open or closed immediately after the lock command, confirming the final physical state.

Without checking these adjacent time windows, the conclusion remains speculative regarding the root cause.

## Query
请检查以下设备状态历史，判断 entrance 区域的传感器 是否工作正常。如果异常，请说明可能的故障类型。

## Layout
Layout: 一室一厅 (Studio/1BR)
- bathroom: 卫生间漏水传感器<water_leak_detector>, 卫生间灯<onoff_light>, 卫生间运动传感器<occupancy_sensor>
- bedroom: 卧室温度传感器<temperature_sensor>, 卧室灯<dimmable_light>, 卧室窗帘<window_covering>, 卧室窗户传感器<contact_sensor>, 卧室运动传感器<occupancy_sensor>
- entrance: 入户门传感器<contact_sensor>, 入户门锁<door_lock>, 玄关灯<onoff_light>
- kitchen: 厨房温度传感器<temperature_sensor>, 厨房灯<onoff_light>, 厨房灶具<cook_surface>, 厨房烟雾报警器<smoke_co_alarm>, 厨房窗户传感器<contact_sensor>, 洗碗机<dishwasher>
- living_room: 客厅主灯<dimmable_light>, 客厅台灯<onoff_light>, 客厅温度传感器<temperature_sensor>, 客厅空调<air_conditioner>, 客厅窗帘<window_covering>, 客厅窗户传感器<contact_sensor>, 客厅运动传感器<occupancy_sensor>

## Deterministic Signals
{
  "room_activity": {
    "living_room": 397,
    "bedroom": 353,
    "kitchen": 298,
    "bathroom": 52,
    "entrance": 17
  },
  "attribute_frequency": {
    "MeasuredValue": 866,
    "Occupancy": 212,
    "OnOff": 18,
    "LockState": 6,
    "StateValue": 5,
    "CurrentPositionLiftPercent100ths": 2,
    "SystemMode": 1,
    "OperationalState": 1,
    "CurrentLevel": 1
  },
  "lock_state_flips": 6,
  "occupancy_events": 212,
  "temperature_patterns": []
}

## Chunk Index
[
  {
    "chunk_id": "C00",
    "start_ts": "2026-06-12T00:00:00",
    "end_ts": "2026-06-12T01:50:00",
    "event_count": 80,
    "rooms": {
      "living_room": 27,
      "bedroom": 27,
      "kitchen": 22,
      "bathroom": 4
    },
    "top_devices": [
      "living_room_temp_sensor",
      "bedroom_temp_sensor",
      "kitchen_temp_sensor",
      "living_room_occupancy",
      "bedroom_occupancy",
      "bathroom_occupancy"
    ],
    "alarmish_count": 12,
    "preview_lines": [
      "[2026-06-12T00:00:00] living_room_temp_sensor | TemperatureMeasurement.MeasuredValue = 2275",
      "[2026-06-12T00:00:00] bedroom_temp_sensor | TemperatureMeasurement.MeasuredValue = 2169",
      "[2026-06-12T00:00:00] kitchen_temp_sensor | TemperatureMeasurement.MeasuredValue = 2279",
      "[2026-06-12T00:00:00] living_room_occupancy | OccupancySensing.Occupancy = 0",
      "[2026-06-12T00:00:00] bedroom_occupancy | OccupancySensing.Occupancy = 0",
      "[2026-06-12T00:00:00] bathroom_occupancy | OccupancySensing.Occupancy = 0",
      "[2026-06-12T00:05:00] living_room_temp_sensor | TemperatureMeasurement.MeasuredValue = 2271",
      "[2026-06-12T00:05:00] bedroom_temp_sensor | TemperatureMeasurement.MeasuredValue = 2165",
      "[2026-06-12T00:05:00] kitchen_t

## Query
请检查以下设备状态历史，判断 entrance 区域的传感器 是否工作正常。如果异常，请说明可能的故障类型。

## Triage
{
  "latent_task_profile": "single-event-safety",
  "focus_rooms": ["entrance"],
  "focus_devices": ["front_door_contact", "front_door_lock"],
  "focus_chunk_ids": ["C04"],
  "suspected_patterns": [
    "Door open immediately followed by lock operation without clear occupancy correlation",
    "Potential sensor lag or false negative on door contact state relative to lock state change"
  ],
  "why_these_chunks": [
    "Chunk C04 is the only segment containing explicit events for 'front_door_contact' and 'front_door_lock'.",
    "The sequence shows the door contact flipping to False (open) at 07:15:20, then immediately locking at 07:15:23 while the door was reportedly open.",
    "This specific temporal proximity suggests a potential fault in the contact sensor reporting the door as closed when it was actually open, or a logic error in the automation routine triggered by the faulty signal."
  ]
}

## Investigator
```json
{
  "normal_hypotheses": [
    {
      "id": "N1",
      "description": "The door contact sensor is functioning correctly, and the locking event was a standard automation routine triggered by an external signal or timer, not necessarily linked to the immediate physical state of the door at that exact second.",
      "evidence": [
        "Standard lock operation events (LockOperationType=0) often occur automatically based on schedules or external triggers independent of real-time contact verification.",
        "The system successfully reported the LockState as 'Locked' (1) immediately after the operation."
      ],
      "weaknesses": [
        "Fails to explain why the automation triggered while the contact sensor explicitly reported the door was open ('False') just moments prior.",
        "Does not account for the lack of occupancy data in the entrance area to justify an auto-lock if the door was physically open."
      ]
    }
  ],
  "anomaly_hypotheses": [
    {
      "id": "A1",
      "description": "The front_door_contact sensor is experiencing a false negative or intermittent failure, reporting the door as closed when it was actually left open, triggering an erroneous auto-lock command.",
      "threat_type": "Sensor Fault / Logic Error",
      "evidence": [
        "At 07:15:20, the sensor reports StateValue = False (Open).",
        "At 07:15:23 (3 seconds later), the system executes LockOperation despite the door being reported as Open.",
        "Logical


DF-05-FP: 烟雾报警器正常自检

有完整的自检流程（TestInProgress=true → SelfTestComplete → TestInProgress=false），不是HardwareFault

The kitchen temperature sensor exhibits significant data drift and erratic oscillations compared to stable neighboring sensors, indicating potential hardware degradation or calibration failure.

Explanation: Analysis of C12 shows the kitchen temperature sensor tracking closely with other rooms (e.g., dropping from 2391 to 2337) with smooth, consistent intervals.

Explanation: In C13, the kitchen sensor displays abnormal behavior: values oscillate rapidly between 2315, 2310, 2308, and back up, unlike the smooth trends in living_room and bedroom sensors.

Explanation: At 18:45:00, the kitchen sensor drops precipitously from 2310 to 2295 immediately before the smoke alarm self-test, while other rooms show much smaller relative shifts, suggesting a malfunction rather than environmental noise.

Explanation: Following the test in C13/C14, the readings remain unstable (fluctuating between 2290-2288), failing to settle into the steady cooling trend observed in adjacent rooms.

Kitchen sensor oscillation pattern in C13 (2315->2310->2308->2315) contrasts sharply with linear cooling in other rooms.

Abnormal sharp drop from 2310 to 2295 at 18:45:00 coinciding with the start of the smoke alarm self-test.

Persistent instability in post-test readings (C14) where kitchen sensor jumps between 2280-2288 while others stabilize around 2260-2270.

Calibrate the kitchen temperature sensor immediately to correct offset errors.

Inspect the sensor for physical obstructions or interference from nearby cooking appliances.

If calibration fails, replace the sensor as it likely suffers from internal component degradation.

```json
{
  "normal_hypotheses": [
    {
      "id": "N1",
      "description": "Kitchen sensors are functioning normally, reporting stable temperature readings consistent with the rest of the house during a cooling trend.",
      "evidence": [
        "kitchen_temp_sensor reports values decreasing from 2331 to 2295 over time, similar to other rooms (e.g., living_room dropping from 2325 to 2297).",
        "No explicit alarm triggers or 'TestInProgress' flags appear for the kitchen smoke alarm until a scheduled self-test at 18:45:00."
      ],
      "weaknesses": [
        "The magnitude of temperature fluctuation in the kitchen sensor appears slightly higher than neighbors at certain intervals (e.g., 2315 -> 2295 drop vs others), though this could be normal variance.",
        "Does not account for the specific spike in event count noted in the triage output if that metric is derived from raw log frequency rather than just visible lines."
      ]
    }
  ],
  "anomaly_hypotheses": [
    {
      "id": "A1",
      "description": "Kitchen temperature sensor exhibits data drift or instability, potentially indicating hardware degradation or calibration failure.",
      "threat_type": "Sensor_Data_Drift",
      "evidence": [
        "Between 18:10 and 18:45, the kitchen sensor shows erratic jumps: 2315 -> 2310 -> 2308 -> 2315 -> 2310 -> 2295. While other sensors show smooth trends, the kitchen sensor oscillates more frequently within short windows.",
        "At 18:45, immediately before the smoke alarm self-test, the kitchen temperature reads 2295, which is significantly lower than the previous reading of 2310 (a ~1.5 degree drop in 5 mins) while other rooms showed less dramatic shifts relative to their own baselines.",
        "The 'Event_Count_Anomaly' suspected by triage suggests a higher frequency of state changes or retries not fully explained by simple cooling."
      ],
      "weaknesses": [
        "Temperature fluctuations can occur due to environmental factors (opening doors, cooking residual heat) even if the sensor itself is healthy; without external context, it's hard to rule out environmental noise.",
        "The absolute difference between sensors (e.g., Kitchen 2295 vs Living Room 2307) is small enough to be within normal device tolerance ranges."
      ]
    },
    {
      "id": "A2",
      "description": "Kitchen smoke alarm behavior indicates a potential false positive trigger or sensitivity issue during its self-test cycle.",
      "threat_type": "Missing_Safety_Alarm_Event | False Positive Risk",
      "evidence": [
        "Triage output notes an 'elevated alarmish_count (19)' for C13, yet the provided logs only show one clear event sequence: TestInProgress=True followed by SelfTestComplete(). The discrepancy between the high internal count and low visible events suggests hidden transient alarms.",
        "At 18:45:00, the sensor enters TestInProgress mode with ExpressedState=4, then completes at 18:45:30. If 'alarmish_count' includes near-misses or threshold crossings during this test, the sensor might be overly sensitive.",
        "The presence of 'TestInProgress' and 'ExpressedState' flags confirms the device is active, but the gap between the expected number of safety events and observed ones raises questions about filtering logic or sensor stability."
      ],
      "weaknesses": [
        "The logs explicitly show a successful completion of the self-test ('SelfTestComplete'), which usually validates the sensor's health. An anomaly might only exist in metadata not present in these specific text lines.",
        "Without access to the full definition of 'alarmish_count', we cannot confirm if those 19 counts represent actual fire hazards, false positives, or internal diagnostic pings."
      ]
    }
  ],
  "most_discriminative_evidence": [
    "Discrepancy between Triaged 'alarmish_count' of 19 and the visible single self-test event sequence in Chunk C13.",
    "Higher frequency of temperature value changes in the kitchen sensor compared to the smoother trajectories of living_room and bedroom sensors in the same timestamp window.",
    "Sudden temperature drop in kitchen sensor (2310 to 2295) coinciding with the start of the smoke alarm self-test, suggesting potential cross-sensitivity or timing correlation."
  ],
  "missing_information": [
    "Definition of 'alarmish_count': Does it include near-threshold readings, retries, or only confirmed alarms?",
    "Historical baseline data for kitchen temperature variance to determine if the current fluctuations are abnormal.",
    "Full event log for the smoke alarm prior to 18:45:00 to verify the source of the reported 19 'alarmish' events.",
    "Environmental context (e.g., was cooking occurring?) to explain rapid temperature shifts."
  ]
}
```

Retrieve full event logs for C13 to verify the source of the 19 'alarmish_count' entries.

Compare kitchen temperature variance trends in adjacent chunks (C12, C14) to establish a baseline for normal fluctuation.

The current analysis correctly identifies potential data drift and alarm count discrepancies but relies heavily on limited visibility within Chunk C13.

To confirm if the temperature oscillations are anomalies or environmental noise, direct comparison with the immediate preceding (C12) and succeeding (C14) time windows is required to establish a temporal baseline.

The definition of 'alarmish_count' remains unknown; without retrieving the full log for C13, we cannot distinguish between hardware faults and benign near-misses.

## Query
请检查以下设备状态历史，判断 kitchen 区域的传感器 是否工作正常。如果异常，请说明可能的故障类型。

## Layout
Layout: 两室一厅 (2BR)
- balcony: 洗衣机<laundry_washer>, 阳台灯<onoff_light>, 阳台门传感器<contact_sensor>
- bathroom: 卫生间漏水传感器<water_leak_detector>, 卫生间灯<onoff_light>, 卫生间运动传感器<occupancy_sensor>
- entrance: 入户门传感器<contact_sensor>, 入户门锁<door_lock>, 玄关灯<onoff_light>, 玄关运动传感器<occupancy_sensor>
- kitchen: 厨房温度传感器<temperature_sensor>, 厨房灯<onoff_light>, 厨房灶具<cook_surface>, 厨房烟雾报警器<smoke_co_alarm>, 厨房窗户传感器<contact_sensor>, 洗碗机<dishwasher>
- living_room: 客厅主灯<dimmable_light>, 客厅温度传感器<temperature_sensor>, 客厅空调<air_conditioner>, 客厅窗帘<window_covering>, 客厅窗户传感器<contact_sensor>, 客厅落地灯<onoff_light>, 客厅运动传感器<occupancy_sensor>
- master_bedroom: 主卧温度传感器<temperature_sensor>, 主卧灯<dimmable_light>, 主卧空调<air_conditioner>, 主卧窗帘<window_covering>, 主卧窗户传感器<contact_sensor>, 主卧运动传感器<occupancy_sensor>
- second_bedroom: 次卧温度传感器<temperature_sensor>, 次卧灯<dimmable_light>, 次卧窗帘<window_covering>, 次卧窗户传感器<contact_sensor>, 次卧运动传感器<occupancy_sensor>

## Deterministic Signals
{
  "room_activity": {
    "living_room": 336,
    "master_bedroom": 336,
    "second_bedroom": 336,
    "kitchen": 293,
    "bathroom": 48,
    "entrance": 48
  },
  "attribute_frequency": {
    "MeasuredValue": 1152,
    "Occupancy": 240,
    "TestInProgress": 2,
    "ExpressedState": 2
  },
  "lock_state_flips": 0,
  "occupancy_events": 240,
  "temperature_patterns": []
}

## Chunk Index
[
  {
    "chunk_id": "C00",
    "start_ts": "2026-06-06T00:00:00",
    "end_ts": "2026-06-06T01:20:00",
    "event_count": 80,
    "rooms": {
      "living_room": 20,
      "master_bedroom": 19,
      "second_bedroom": 19,
      "kitchen": 16,
      "bathroom": 3,
      "entrance": 3
    },
    "top_devices": [
      "living_room_temp_sensor",
      "master_bedroom_temp_sensor",
      "second_bedroom_temp_sensor",
      "kitchen_temp_sensor",
      "living_room_occupancy",
      "master_bedroom_occupancy"
    ],
    "alarmish_count": 15,
    "preview_lines": [
      "[2026-06-06T00:00:00] living_room_temp_sensor | TemperatureMeasurement.MeasuredValue = 2280",
      "[2026-06-06T00:00:00] master_bedroom_temp_sensor | TemperatureMeasurement.MeasuredValue = 2266",
      "[2026-06-06T00:00:00] second_bedroom_temp_sensor | TemperatureMeasurement.MeasuredValue = 2281",
      "[2026-06-06T00:00:00] kitchen_temp_sensor | TemperatureMeasurement.MeasuredValue = 2268",
      "[2026-06-06T00:00:00] living_room_occupancy | OccupancySensing.Occupancy = 0",
      "[2026-06-06T00:00:00] maste

## Query
请检查以下设备状态历史，判断 kitchen 区域的传感器 是否工作正常。如果异常，请说明可能的故障类型。

## Triage
{
  "latent_task_profile": "device-health | single-event-safety | composite-safety",
  "focus_rooms": ["kitchen"],
  "focus_devices": ["kitchen_temp_sensor", "kitchen_smoke_alarm"],
  "focus_chunk_ids": ["C13"],
  "suspected_patterns": [
    "Sensor_Data_Drift",
    "Missing_Safety_Alarm_Event",
    "Event_Count_Anomaly"
  ],
  "why_these_chunks": [
    "Chunk C13 is the only chunk where 'kitchen_smoke_alarm' appears in top devices and has an elevated 'alarmish_count' (19), yet no explicit alarm event is visible in the preview lines, suggesting a potential false positive or sensor malfunction.",
    "The kitchen temperature sensor shows significant fluctuations in C13 compared to other chunks, which could trigger safety alerts if the threshold is low.",
    "The 'event_count' for kitchen in C13 (20) is notably higher than the typical ~16-17 seen in other chunks, indicating unusual activity or stuck reporting."
  ]
}

## Investigator
```json
{
  "normal_hypotheses": [
    {
      "id": "N1",
      "description": "Kitchen sensors are functioning normally, reporting stable temperature readings consistent with the rest of the house during a cooling trend.",
      "evidence": [
        "kitchen_temp_sensor reports values decreasing from 2331 to 2295 over time, similar to other rooms (e.g., living_room dropping from 2325 to 2297).",
        "No explicit alarm triggers or 'TestInProgress' flags appear for the kitchen smoke alarm until a scheduled self-test at 18:45:00."
      ],
      "weaknesses": [
        "The magnitude of temperature fluctuation in the kitchen sensor appears slightly higher than neighbors at certain intervals (e.g., 2315 -> 2295 drop vs others), though this could be normal variance.",
        "Does not account for the specific spike in event count noted in the triage output if that metric is derived from raw log frequency rather than just visible lines."
      ]
    }
  ],
  "anomaly_hypotheses": [
    {
      "id": "A1",
      "description": "Kitchen temperature sensor exhibits data drift or instability, potentially indicating hardware degradation or calibration failure.",
      "threat_type": "Sensor_Data_Drift",
      "evidence": [
        "Between 18:10 and 18:45, the kitchen sensor shows erratic jumps: 2315 -> 2310 -> 2308 -> 2315 -> 2310 -> 2295. While other sensors show smooth trends, the kitchen sensor oscillates more frequently within short windows.",
        "


DF-01-FP: 空调恒温控制导致温度稳定

有微小波动（±0.2°C），不是完全固定值；且空调处于开启状态

The living_room_temperature_sensor exhibits a continuous, non-physical linear drift from ~22.69°C to ~24.75°C over 6 hours despite the room being unoccupied and no external heat sources being active.

Explanation: Data analysis reveals a deterministic upward trend in `living_room_temp_sensor` readings (e.g., 2269 at 00:00 to 2475 at 05:45), increasing by approximately 1 unit every 5-10 minutes.

Explanation: This rate of change (~0.8°C per hour) is inconsistent with natural thermal inertia or ambient temperature fluctuations for a residential environment without an active heater.

Explanation: The anomaly persists regardless of occupancy status (Occupancy=0 throughout the window) and diverges significantly from other room sensors which show erratic but non-linear patterns, isolating the fault to this specific device's firmware or calibration logic.

Explanation: No 'OutOfRange' flags or error bits are present in the logs, suggesting the sensor is not detecting its own failure but rather misreporting values due to software drift.

[2026-06-03T00:00:00] living_room_temp_sensor | TemperatureMeasurement.MeasuredValue = 2269 vs [2026-06-03T05:45:00] ... = 2475

Continuous increase observed even when [2026-06-03T00:30:00] living_room_occupancy | OccupancySensing.Occupancy = 0

Re-calibrate or replace the living_room_temperature_sensor hardware immediately.

Inspect the sensor's firmware version and update if a known bug causes linear drift has been reported.

Cross-reference physical thermometer readings at the sensor location to confirm the data integrity issue.

Hardware failure or calibration drift in the living_room_temp_sensor causing non-physical temperature readings.

Deterministic linear drift: Temperature rises steadily from 2269 to >2470 despite no occupancy events ('Occupancy = 0' for the entire duration).

Magnitude anomaly: A rise of ~2 degrees Celsius in one hour is physically improbable for a static environment without an active heater.

Correlation failure: The sensor continues to report rising values even when the occupancy sensor confirms the room is empty, ruling out human body heat as the cause.

Inconsistency: Kitchen and other room sensors show different fluctuation patterns, isolating the anomaly to specific sensors rather than a network-wide issue.

Data logging corruption or unit conversion error in the living_room_temp_sensor data stream.

The raw values (e.g., 2269, 2355) are integers that lack decimal precision often found in real-world temperature logs, though they could represent milli-degrees.

If these were milli-degrees, the range would be 22.69°C to 24.70°C, which is plausible. However, if the system interprets them as whole degrees (or if the log format implies a fault), the jump is anomalous.

No alarm flags or 'OutOfRange' states are triggered in the logs despite values exceeding typical comfort ranges if interpreted as single digits.

The strict correlation between 'Occupancy = 0' and the continuous, rapid rise in temperature suggests the source of the heat signal is not biological but instrumental or erroneous.

The divergence between the living_room sensor trend and the more erratic trends of master_bedroom and kitchen sensors indicates a localized device fault rather than a global HVAC event.

Raw binary logs or manufacturer-specific diagnostic status bits to confirm if the sensor reports 'calibration error' or 'battery low'.

Historical baseline data from previous days to determine if this linear drift is a recurring pattern or a new onset.

Physical location of the sensor relative to potential external heat sources (windows, exterior walls) to rule out solar gain during the observed hours.

Verify raw data units (check if values like 2269 represent milli-degrees or actual degrees) across all chunks.

Check for 'OutOfRange' flags or calibration error bits in the metadata of C00-C28.

Cross-reference with historical baseline data from previous days to distinguish between recurring drift and new failure.

The investigation is currently on-topic and has correctly identified a potential sensor anomaly based on the provided chunks.

However, the evidence regarding the 'Data logging corruption or unit conversion error' hypothesis (A2) remains theoretical. The Investigator Output notes that raw values lack decimal precision, but without examining specific chunks to confirm the log format interpretation, this cannot be ruled out as the primary cause.

The current chunk selection covers the full observation window, so no additional time-series chunks are needed. Instead, deeper inspection of the existing chunks' metadata and cross-referencing with external historical data is required.

Before finalizing the diagnosis, it is crucial to rule out the data integrity issue before concluding hardware failure.

## Query
请检查以下设备状态历史，判断 living_room 区域的传感器 是否工作正常。如果异常，请说明可能的故障类型。

## Layout
Layout: 三室两厅 (3BR)
- balcony: 洗衣机<laundry_washer>, 阳台灯<onoff_light>, 阳台门传感器<contact_sensor>
- dining_room: 餐厅灯<dimmable_light>, 餐厅运动传感器<occupancy_sensor>
- entrance: 入户门传感器<contact_sensor>, 入户门锁<door_lock>, 玄关灯<onoff_light>, 玄关运动传感器<occupancy_sensor>
- kids_room: 儿童房温度传感器<temperature_sensor>, 儿童房灯<dimmable_light>, 儿童房窗帘<window_covering>, 儿童房窗户传感器<contact_sensor>, 儿童房运动传感器<occupancy_sensor>
- kitchen: 厨房温度传感器<temperature_sensor>, 厨房漏水传感器<water_leak_detector>, 厨房灯<onoff_light>, 厨房灶具<cook_surface>, 厨房烟雾报警器<smoke_co_alarm>, 厨房窗户传感器<contact_sensor>, 洗碗机<dishwasher>
- living_room: 客厅主灯<dimmable_light>, 客厅副灯<dimmable_light>, 客厅氛围灯<onoff_light>, 客厅温度传感器<temperature_sensor>, 客厅烟雾报警器<smoke_co_alarm>, 客厅空调<air_conditioner>, 客厅窗帘1<window_covering>, 客厅窗户传感器1<contact_sensor>, 客厅窗户传感器2<contact_sensor>, 客厅运动传感器<occupancy_sensor>
- master_bathroom: 主卫漏水传感器<water_leak_detector>, 主卫灯<onoff_light>, 主卫运动传感器<occupancy_sensor>
- master_bedroom: 主卧床头灯<onoff_light>, 主卧温度传感器<temperature_sensor>, 主卧灯<dimmable_light>, 主卧空调<air_conditioner>, 主卧窗帘<window_covering>, 主卧窗户传感器<contact_sensor>, 主卧运动传感器<occupancy_sensor>
- second_bathroom: 次卫漏水传感器<water_leak_detector>, 次卫灯<onoff_light>
- second_bedroom: 次卧灯<dimmable_light>, 次卧空调<air_conditioner>, 次卧窗帘<window_covering>, 次卧窗户传感器<contact_sensor>, 次卧运动传感器<occupancy_sensor>

## Deterministic Signals
{
  "room_activity": {
    "living_room": 403,
    "master_bedroom": 520,
    "kids_room": 521,
    "kitchen": 303,
    "dining_room": 224,
    "second_bedroom": 48,
    "master_bathroom": 60,
    "entrance": 227,
    "second_bathroom": 1
  },
  "attribute_frequency": {
    "MeasuredValue": 1160,
    "Occupancy": 1098,
    "OnOff": 27,
    "StateValue": 7,
    "LockState": 5,
    "CurrentPositionLiftPercent100ths": 3,
    "SystemMode": 1,
    "OperationalState": 1
  },
  "lock_state_flips": 5,
  "occupancy_events": 1098,
  "temperature_patterns": []
}

## Chunk Index
[
  {
    "chunk_id": "C00",
    "start_ts": "2026-06-03T00:00:00",
    "end_ts": "2026-06-03T01:10:00",
    "event_count": 80,
    "rooms": {
      "living_room": 18,
      "master_bedroom": 18,
      "kids_room": 18,
      "kitchen": 14,
      "dining_room": 3,
      "second_bedroom": 3,
      "master_bathroom": 3,
      "entrance": 3
    },
    "top_devices": [
      "living_room_temp_sensor",
      "master_bedroom_temp_sensor",
      "kids_room_temp_sensor",
      "kitchen_temp_sensor",
      "living_room_oc

## Query
请检查以下设备状态历史，判断 living_room 区域的传感器 是否工作正常。如果异常，请说明可能的故障类型。

## Triage
{
  "latent_task_profile": "device-health",
  "focus_rooms": ["living_room"],
  "focus_devices": [
    "living_room_temperature_sensor",
    "living_room_occupancy_sensor"
  ],
  "focus_chunk_ids": [
    "C00",
    "C01",
    "C02",
    "C03",
    "C04",
    "C05",
    "C06",
    "C07",
    "C08",
    "C09",
    "C10",
    "C11",
    "C12",
    "C13",
    "C14",
    "C15",
    "C16",
    "C17",
    "C18",
    "C19",
    "C20",
    "C21",
    "C22",
    "C23",
    "C24",
    "C25",
    "C26",
    "C27",
    "C28"
  ],
  "suspected_patterns": [
    "Temperature drift anomaly: Sensor readings start at ~22°C and linearly increase to >25°C over time, deviating from expected ambient stability.",
    "Occupancy-temperature correlation failure: Temperature continues to rise even when occupancy sensor reports '0' (e.g., C00, C08, C15), suggesting the temperature reading may be erroneous or influenced by non-occupant factors not captured by the logic."
  ],
  "why_these_chunks": [
    "All chunks contain the target room's data.",
    "Chunks C00-C04 show the initial baseline and the onset of significant temperature deviation.",
    "Chunks C05-C12 illustrate the sustained drift despite varying occupancy states.",
    "Chunks C13-C28 confirm the persistence of the anomaly throughout the observation window.",
    "No specific event logs (alarms, lock flips) are visible in previews that would indicate an emergency; the primary signal is the deterministic trend in sensor values."
  ]
}

## Investigator
{
  "normal_hypotheses": [
    {
      "id": "N1",
      "description": "The living_room_temp_sensor is functioning correctly, reporting a gradual increase in ambient temperature due to external factors (e.g., sunrise heating the house) or internal heat gain before occupancy.",
      "evidence": ["All sensors across all rooms show a consistent upward trend from ~22.7°C to ~24.8°C over the observation window.", "The reading at 00:00 (22.69) aligns with typical overnight indoor temperatures."],
      "weaknesses": ["The rate of increase (~2.0°C per hour) is significantly faster than typical passive thermal diffusion for a standard home without active heating.", "Other room sensors do not exhibit this exact synchronized drift pattern; some fluctuate differently, suggesting localized sensor issues rather than global environmental changes."]
    }
  ],
  "anomaly_hypotheses": [
    {
      "id": "A1"


DF-02-FP: 夏季午后持续升温（正常日照效应）

温度上升后在午后到达峰值并回落，符合日间正弦曲线；而真正的漂移是不回落的单向偏移

The living room occupancy sensor is malfunctioning and stuck reporting '0' (empty) despite the house being active, while temperature data shows a likely unit/formatting discrepancy but remains physically plausible when corrected.

Explanation: The living_room_occupancy sensor consistently reports a value of 0 across all provided chunks (C00-C06), spanning over 12 hours of continuous monitoring.

Explanation: This reading contradicts observed activity in other rooms (e.g., bedroom lights turning on, bathroom occupancy changing to 1, door locks operating), indicating the living room was not actually empty or the sensor failed to detect presence.

Explanation: The temperature readings (e.g., 2267) are significantly higher than typical Celsius values, suggesting a unit mismatch (likely millidegrees) or missing decimal point, but the trend relative to other rooms remains stable, making this a data formatting issue rather than a critical hardware failure compared to the stuck occupancy sensor.

Timestamped logs from C03 show bedroom_occupancy switching to 1 at 06:19:00 and 06:24:00, while living_room_occupancy remains 0 simultaneously.

Every single occupancy log entry for living_room_temp_sensor's companion device (living_room_occupancy) in chunks C00 through C06 reads exactly 'OccupancySensing.Occupancy = 0'.

Inspect the living room occupancy sensor hardware and its power supply/circuit connection immediately.

Verify sensor placement to ensure it is not obstructed by furniture or positioned in a dead zone, though the persistent '0' suggests a hard fault.

Firmware scaling error or data transmission artifact: The living_room_temp_sensor (and others) are reporting temperature values scaled by a factor of 10 or 100 incorrectly, likely due to a configuration mismatch or firmware bug.

Raw values start at ~2267 and trend upwards to ~2543 over 8 hours. If interpreted as °C, this implies heating from 22.67°C to 25.43°C, which is physically plausible.

However, the Triage Output explicitly notes: 'All chunks contain temperature readings... scaled by a factor of 10 (e.g., 2267 vs expected ~22.6°C)' - wait, 2267/100 = 22.67. The triage suggests a factor of 10, but 2267/10 = 226.7°C which is fire. The most logical anomaly is that the value **2267** is actually meant to be **22.67**, implying a missing decimal point in the logging layer or a specific encoding where the last two digits are tenths/hundredths.

Crucially, the Living Room occupancy sensor reports `Occupancy = 0` continuously throughout all chunks (C00-C04), despite significant activity events occurring in other rooms (Bedroom light on, Door lock flipped, Bathroom occupancy active). This indicates a 'Dead Zone' or power/circuit issue specific to the living room occupancy detector.

Temperature trends are smooth and realistic if divided by 100, making the 'scaling error' less severe than a hard failure; it might just be a display/formatting issue.

Living Room Occupancy Sensor Failure / Dead Zone: The sensor is stuck at '0' (Empty) despite being in a highly active area during morning routines.

Structured Signals show 'living_room': 404 (high activity count) while bedroom is 357, yet the log shows `living_room_occupancy | OccupancySensing.Occupancy = 0` at almost every timestamp.

Activity evidence in C03/C04: Bedroom lights turn on, bathroom occupancy fluctuates, door locks operate, yet the living room occupancy remains stubbornly 0.

In C03, timestamps like `06:19`, `06:24`, `06:29` show bedroom/bathroom activity, but no corresponding living room event triggers a state change to '1'.

It is possible the living room was genuinely empty while other parts of the house were used (e.g., everyone went to bed/lights off in LR while using kitchen/bath), though the high 'room_activity' score contradicts this slightly.

Discrepancy between high 'room_activity' metric (404) for living_room and continuous 'Occupancy=0' logs, indicating a sensor blind spot or failure.

Temperature values (e.g., 2267) appearing as integers with no decimal separator, requiring assumption of a hidden decimal point (22.67) or a unit multiplier to be physically sensible.

Definition of the raw data unit: Is 2267 millidegrees Celsius, or is it a floating-point number truncated in the log?

History of the living_room_occupancy sensor prior to 2026-06-09T00:00:00 to determine if it has always been stuck at 0 or failed recently.

Location of living_room occupants if known via other systems (e.g., mobile phone GPS, wearable tags) to confirm if the sensor is truly failing to detect presence.

Verify if the temperature scaling issue (2267) is consistent across ALL chunks or specific to early/late periods.

Check C05 and C14 to confirm if the 'Occupancy=0' anomaly persists at the end of the monitoring window (to rule out intermittent failure vs permanent stuck state).

Cross-reference with alarm logs in missing chunks to verify the 'high alarmish_count' mentioned in Triage.

The Investigator correctly identified the two main anomalies: potential data formatting/scaling for temperature and a dead sensor for occupancy.

However, the analysis relies heavily on chunks C00-C04. We need to inspect the tail of the dataset (C14) and middle sections (C05+) to determine if these are transient glitches or systemic hardware failures.

Specifically, checking C14 is crucial to see if the occupancy sensor ever recovers or if it remains stuck at 0 until the very end of the log period.

## Query
请检查以下设备状态历史，判断 living_room 区域的传感器 是否工作正常。如果异常，请说明可能的故障类型。

## Layout
Layout: 一室一厅 (Studio/1BR)
- bathroom: 卫生间漏水传感器<water_leak_detector>, 卫生间灯<onoff_light>, 卫生间运动传感器<occupancy_sensor>
- bedroom: 卧室温度传感器<temperature_sensor>, 卧室灯<dimmable_light>, 卧室窗帘<window_covering>, 卧室窗户传感器<contact_sensor>, 卧室运动传感器<occupancy_sensor>
- entrance: 入户门传感器<contact_sensor>, 入户门锁<door_lock>, 玄关灯<onoff_light>
- kitchen: 厨房温度传感器<temperature_sensor>, 厨房灯<onoff_light>, 厨房灶具<cook_surface>, 厨房烟雾报警器<smoke_co_alarm>, 厨房窗户传感器<contact_sensor>, 洗碗机<dishwasher>
- living_room: 客厅主灯<dimmable_light>, 客厅台灯<onoff_light>, 客厅温度传感器<temperature_sensor>, 客厅空调<air_conditioner>, 客厅窗帘<window_covering>, 客厅窗户传感器<contact_sensor>, 客厅运动传感器<occupancy_sensor>

## Deterministic Signals
{
  "room_activity": {
    "living_room": 404,
    "bedroom": 357,
    "kitchen": 298,
    "bathroom": 58,
    "entrance": 14
  },
  "attribute_frequency": {
    "MeasuredValue": 871,
    "Occupancy": 224,
    "OnOff": 18,
    "StateValue": 5,
    "LockState": 4,
    "CurrentPositionLiftPercent100ths": 2,
    "SystemMode": 1,
    "OperationalState": 1,
    "CurrentLevel": 1
  },
  "lock_state_flips": 4,
  "occupancy_events": 224,
  "temperature_patterns": []
}

## Chunk Index
[
  {
    "chunk_id": "C00",
    "start_ts": "2026-06-09T00:00:00",
    "end_ts": "2026-06-09T01:50:00",
    "event_count": 80,
    "rooms": {
      "living_room": 27,
      "bedroom": 27,
      "kitchen": 22,
      "bathroom": 4
    },
    "top_devices": [
      "living_room_temp_sensor",
      "bedroom_temp_sensor",
      "kitchen_temp_sensor",
      "living_room_occupancy",
      "bedroom_occupancy",
      "bathroom_occupancy"
    ],
    "alarmish_count": 12,
    "preview_lines": [
      "[2026-06-09T00:00:00] living_room_temp_sensor | TemperatureMeasurement.MeasuredValue = 2267",
      "[2026-06-09T00:00:00] bedroom_temp_sensor | TemperatureMeasurement.MeasuredValue = 2177",
      "[2026-06-09T00:00:00] kitchen_temp_sensor | TemperatureMeasurement.MeasuredValue = 2280",
      "[2026-06-09T00:00:00] living_room_occupancy | OccupancySensing.Occupancy = 0",
      "[2026-06-09T00:00:00] bedroom_occupancy | OccupancySensing.Occupancy = 0",
      "[2026-06-09T00:00:00] bathroom_occupancy | OccupancySensing.Occupancy = 0",
      "[2026-06-09T00:05:00] living_room_temp_sensor | TemperatureMeasurement.MeasuredValue = 2262",
      "[2026-06-09T00:05:00] bedroom_temp_sensor | TemperatureMeasurement.MeasuredValue = 2168",
      "[2026-06-09T00:05:00] kitche

## Query
请检查以下设备状态历史，判断 living_room 区域的传感器 是否工作正常。如果异常，请说明可能的故障类型。

## Triage
{
  "latent_task_profile": "device-health",
  "focus_rooms": ["living_room"],
  "focus_devices": [
    "living_room_temp_sensor",
    "living_room_occupancy"
  ],
  "focus_chunk_ids": ["C00", "C01", "C02", "C03", "C04", "C05", "C06", "C07", "C08", "C09", "C10", "C11", "C12", "C13", "C14"],
  "suspected_patterns": [
    "TemperatureOffsetAnomaly",
    "SensorCalibrationDrift",
    "OccupancyReportingGap"
  ],
  "why_these_chunks": [
    "All chunks contain temperature readings for the living room that are scaled by a factor of 10 (e.g., 2267 vs expected ~22.6°C), indicating a potential firmware scaling error or unit mismatch.",
    "The 'alarmish_count' is consistently high (12-29) per chunk despite no explicit alarm logs in previews, suggesting the system flags these anomalies automatically.",
    "Occupancy events show frequent gaps where the sensor reports '0' even when other devices (lights/AC) are active, potentially indicating a dead zone or calibration issue.",
    "Every single chunk must be inspected to determine if this is a global data transmission artifact or specific to the living room sensor hardware."
  ]
}

## Investigator
{
  "normal_hypotheses": [
    {
      "id": "N1",
      "description": "All temperature sensors are functioning correctly, reporting raw integer values in millidegrees Celsius (°C * 1000) or a similar high-resolution internal unit.",
      "evidence": [
        "All room sensors report values in the range of 2100-2500, which is consistent across Living Room (~22-23°C), Bedroom (~21-24°C), and Kitchen (~22-25°C).",
        "The relative temperature differences between rooms remain stable over time (e.g., Living Room consistently ~80 units higher than Bedroom), suggesting valid thermal gradients rather than sensor failure."
      ],
      "weaknesses": [
        "Standard HVAC and environmental sensors typically output decimals (e.g., 22.67), not integers ending in 00. A reading of '2267' is statistically unlikely for a standard °C*10 scale unless it represents millidegrees, which would be an unusually high resolution display format.",
        "If the system expects degrees Celsius directly, every reported value is physically impossible (2267°C)."
      ]
    }
  ],
  "anomaly_hypotheses": [
    {
      "id": "A1",
      "description": "Firmware scaling error or data transmission artifact: The living_room_temp_sensor (and others) are reporting tem


状态 vs 实际位置不一致

The bedroom temperature sensor exhibits a persistent and significant calibration offset (~10°C) compared to adjacent rooms, indicating measurement inaccuracy rather than environmental factors.

Explanation: Analysis of C00 through C06 reveals that while all rooms are unoccupied for extended periods (e.g., C00-C02), the living room and kitchen temperatures rise from ~2265 to ~2460 (an increase of ~195 units), whereas the bedroom temperature only rises from ~2172 to ~2360 (an increase of ~188 units).

Explanation: The absolute difference between the bedroom sensor and the living room/kitchen sensors remains remarkably constant at approximately 100-110 units throughout the entire monitoring period, regardless of occupancy changes or thermal dynamics.

Explanation: This consistent delta strongly suggests a hardware calibration error or scale factor mismatch in the bedroom sensor's firmware or hardware, as natural thermal gradients would not maintain such a precise, static offset over 12+ hours of varying ambient conditions.

Calibrate the bedroom temperature sensor against a known reference or the readings from the living room/kitchen sensors.

Check the sensor installation location to ensure it is not affected by localized heat sources or drafts that could cause a permanent bias, though the consistency points to calibration.

Bedroom temperature sensor suffers from significant calibration offset or scale error, reading values ~6-10°C lower than adjacent living areas despite similar ambient conditions when occupied.

Measurement Inaccuracy / Calibration Drift

Persistent Delta: Throughout C10-C13, when both living_room and bedroom are occupied (Occupancy=1), the bedroom temp remains ~2150-2170 while living room is ~2260-2280. Example C13 (22:25): BR=2150 vs LR=2258.

Scale Discrepancy: Bedroom readings (e.g., 2142 in C12) are suspiciously low for a standard Celsius reading; if these are raw millidegrees or scaled integers, they map to ~21.4°C, but the gap persists even when accounting for conversion factors if applied uniformly.

Lack of Convergence: When bedroom occupants enter (C12: 21:59:21 -> Occupancy=1), the temperature does not rise towards the living room baseline as expected from body heat, remaining flat at ~2150 while living room stabilizes higher.

If the house has distinct zones with HVAC setpoints (e.g., bedroom kept cool at night), the delta could be intentional. However, the AC in living room was turned OFF at 21:59:16, same time bedroom light turned ON, weakening the 'cooling zone' theory.

Without historical baseline data, it is difficult to confirm this is a new drift versus a permanent installation setting.

Bedroom occupancy sensor exhibits false negatives (stuck-at-zero behavior) during periods of confirmed human activity.

Contradictory State in C12: At 21:59:26, 'bedroom_light' turns ON, and shortly after at 22:04:21 and 22:09:21, 'bedroom_occupancy' reports 1. However, immediately prior to lights turning on (21:59:21), occupancy jumped to 1, suggesting the sensor detected entry. But note C11->C12 transition: At 21:31:42 occupancy=0, then at 21:59:21 it becomes 1. This sequence seems consistent.

Re-evaluating A2 based on full log: The occupancy sensor actually behaves reasonably well in C12/C13 where it flips 0->1 upon light activation. The primary anomaly remains the temperature discrepancy. False negatives are less supported by the specific timestamps provided where light/occupancy correlate.

Upon closer inspection of C12/C13, the occupancy sensor accurately reflects the state change when lights turn on (21:59:26). The hypothesis that it is stuck at zero is weak because it does report 1s frequently. The primary issue is likely the temperature sensor accuracy.

C12 & C13 Thermal Divergence: At timestamp 22:25:00, Living Room Temp = 2258°C (approx), Bedroom Temp = 2150°C (approx). Both rooms have Occupancy=1. If the home is heated/cooled centrally, the bedroom should track the living room much closer unless there is a massive HVAC failure or intentional override, which contradicts the simultaneous AC OFF event in the living room.

Missing Kitchen Data Context: While kitchen sensor goes 'None' at 18:51:00 and 19:41:00, the bedroom sensor never fails. This isolates the anomaly specifically to the measurement *value* range of the bedroom sensor rather than a complete communication drop.

HVAC System Configuration: What are the target setpoints for the bedroom vs. living room? Is there a dedicated thermostat?

Historical Baseline: What were the bedroom temperatures at the start of the day or previous days to determine if the offset is new or chronic?

Unit Definition: Are the 'MeasuredValue' integers representing raw ADC counts, millidegrees Celsius, or actual degrees Celsius?

Verify if the large temperature delta (~10°C) persists in unoccupied periods to rule out passive cooling/heating effects.

Check historical baseline data (earlier than C10) to determine if this is a new calibration drift or a chronic installation error.

Confirm sensor unit definitions (raw counts vs. scaled degrees) as mentioned in 'missing_information'.

Investigate HVAC zone settings and setpoints for the bedroom specifically.

The analysis correctly identified the primary anomaly: a persistent ~10°C offset between the bedroom and living room sensors when both are occupied.

However, the evidence collection is insufficient because it relies heavily on chunks C10-C13. Without earlier chunks (C00-C09 excluding C09's window context), we cannot establish if this offset is new (drift) or chronic (installation error).

We also lack data from later chunks (C14) to see if the pattern continues or resolves.

Crucially, there is no evidence regarding the bedroom's HVAC status or setpoints in the selected chunks, which is necessary to validate the hypothesis that the low reading is intentional climate control rather than sensor failure.

The investigation of the occupancy sensor was premature; without full temporal coverage across the entire day, confirming false negatives requires more robust correlation with activity logs outside the specific C12/C13 window.

## Query
请检查以下设备状态历史，判断 bedroom 区域的传感器 是否工作正常。如果异常，请说明可能的故障类型。

## Layout
Layout: 一室一厅 (Studio/1BR)
- bathroom: 卫生间漏水传感器<water_leak_detector>, 卫生间灯<onoff_light>, 卫生间运动传感器<occupancy_sensor>
- bedroom: 卧室温度传感器<temperature_sensor>, 卧室灯<dimmable_light>, 卧室窗帘<window_covering>, 卧室窗户传感器<contact_sensor>, 卧室运动传感器<occupancy_sensor>
- entrance: 入户门传感器<contact_sensor>, 入户门锁<door_lock>, 玄关灯<onoff_light>
- kitchen: 厨房温度传感器<temperature_sensor>, 厨房灯<onoff_light>, 厨房灶具<cook_surface>, 厨房烟雾报警器<smoke_co_alarm>, 厨房窗户传感器<contact_sensor>, 洗碗机<dishwasher>
- living_room: 客厅主灯<dimmable_light>, 客厅台灯<onoff_light>, 客厅温度传感器<temperature_sensor>, 客厅空调<air_conditioner>, 客厅窗帘<window_covering>, 客厅窗户传感器<contact_sensor>, 客厅运动传感器<occupancy_sensor>

## Deterministic Signals
{
  "room_activity": {
    "living_room": 401,
    "bedroom": 357,
    "kitchen": 298,
    "bathroom": 59,
    "entrance": 14
  },
  "attribute_frequency": {
    "MeasuredValue": 866,
    "Occupancy": 222,
    "OnOff": 18,
    "StateValue": 5,
    "LockState": 4,
    "CurrentPositionLiftPercent100ths": 3,
    "OperationalStatus": 3,
    "SystemMode": 1,
    "OperationalState": 1,
    "CurrentLevel": 1
  },
  "lock_state_flips": 4,
  "occupancy_events": 222,
  "temperature_patterns": []
}

## Chunk Index
[
  {
    "chunk_id": "C00",
    "start_ts": "2026-06-19T00:00:00",
    "end_ts": "2026-06-19T01:50:00",
    "event_count": 80,
    "rooms": {
      "living_room": 27,
      "bedroom": 27,
      "kitchen": 22,
      "bathroom": 4
    },
    "top_devices": [
      "living_room_temp_sensor",
      "bedroom_temp_sensor",
      "kitchen_temp_sensor",
      "living_room_occupancy",
      "bedroom_occupancy",
      "bathroom_occupancy"
    ],
    "alarmish_count": 12,
    "preview_lines": [
      "[2026-06-19T00:00:00] living_room_temp_sensor | TemperatureMeasurement.MeasuredValue = 2265",
      "[2026-06-19T00:00:00] bedroom_temp_sensor | TemperatureMeasurement.MeasuredValue = 2172",
      "[2026-06-19T00:00:00] kitchen_temp_sensor | TemperatureMeasurement.MeasuredValue = 2264",
      "[2026-06-19T00:00:00] living_room_occupancy | OccupancySensing.Occupancy = 0",
      "[2026-06-19T00:00:00] bedroom_occupancy | OccupancySensing.Occupancy = 0",
      "[2026-06-19T00:00:00] bathroom_occupancy | OccupancySensing.Occupancy = 0",
      "[2026-06-19T00:05:00] living_room_temp_sensor | TemperatureMeasurement.MeasuredValue = 2265",
      "[2026-06-19T00:05:00] bedroom_temp_sensor | TemperatureMeasurement.MeasuredValue = 2169",
      "[202

## Query
请检查以下设备状态历史，判断 bedroom 区域的传感器 是否工作正常。如果异常，请说明可能的故障类型。

## Triage
{
  "latent_task_profile": "device-health",
  "focus_rooms": ["bedroom"],
  "focus_devices": ["bedroom_temp_sensor", "bedroom_occupancy", "bedroom_window"],
  "focus_chunk_ids": ["C09", "C10", "C11", "C12", "C13"],
  "suspected_patterns": [
    "Temperature drift or calibration offset in bedroom sensor compared to other rooms",
    "Occupancy sensor false negatives (reported empty while activity occurs)",
    "Window contact sensor reporting open when likely closed"
  ],
  "why_these_chunks": [
    "C09: First appearance of 'bedroom_window' events, potential correlation with temperature drops.",
    "C10-C13: Contains critical anomalies including a 'None' value for kitchen sensor (system health indicator), specific window contact state changes, and occupancy reports that may conflict with room activity levels observed in layout analysis.",
    "C12-C13: High alarm counts correlate with the suspected sensor issues in the bedroom context."
  ]
}

## Investigator
{
  "normal_hypotheses": [
    {
      "id": "N1",
      "description": "Bedroom sensors are functioning correctly, reporting a stable low temperature due to the room being unoccupied and insulated from external heat sources compared to the kitchen.",
      "evidence": [
        "Temperature values in bedroom (2142-2284) are consistently lower than living room (2245-2383) and kitchen (2247-2715), which is physically plausible for an unoccupied secondary room.",
        "Bedroom occupancy sensor alternates between 0 and 1, matching events like lights turning on/off (C12: 'bedroom_light | OnOff.OnOff = True' at 21:59:26 coincides with occupancy=1).",
        "No missing values ('None') reported for bedroom_temp_sensor or bedroom_occupancy throughout the entire log."
      ],
      "weaknesses": [
        "The absolute temperature difference (~6-10 degrees Celsius) between bedroom and living room is unusually large for typical residential insulation without active cooling/heating differences, suggesting potential calibration drift rather than just environmental isolation.",
        "The window coverings were opened (position 10000) while occupancy was 1, yet no significant temperature rise relative to other rooms occurred, making the thermal dynamics slightly ambiguous."
      ]
    }
  ],
  "anomaly_hypotheses": [
    {
      "id": "A1",
      "description": "Bedroom temperature sensor suffers from significant calibration offset


The bathroom occupancy sensor is functioning normally; the absence of 'Occupancy=1' events in later logs corresponds to periods when no one was using the bathroom, confirmed by normal activity patterns in C00-C04.

Explanation: Analysis of chunks C00 through C04 confirms the sensor reported 'Occupancy=0' consistently during hours of inactivity (00:00-09:10) and correctly transitioned to 'Occupancy=1' at 08:55 and 09:00 when users were active.

Explanation: In chunk C05, the sensor correctly detected presence ('Occupancy=1') from 08:55 until 09:22, after which it switched to '0'. This switch coincides with the light turning off at 09:18 and subsequent user movement out of the room's detection range, indicating proper state management rather than failure.

Explanation: Chunks C12 and C16 show the sensor reporting 'Occupancy=0', which aligns with the timeline suggesting the household had left or the bathroom was unused. The Triage hypothesis regarding system-wide data loss is refuted because the raw logs for `bathroom_occupancy` contain valid values throughout the entire period, unlike the `kitchen_temp_sensor` which shows multiple 'None' errors.

Explanation: The initial investigator concern about 'missing' Occupancy=1 events is resolved by recognizing that a lack of positive detections simply indicates an empty room, not a malfunctioning sensor.

C05 Log: '2026-06-07T09:00:00] bathroom_occupancy | OccupancySensing.Occupancy = 1' followed by '2026-06-07T09:22:00] bathroom_occupancy | OccupancySensing.Occupancy = 0', showing correct state transitions.

C05 Log: Presence of '2026-06-07T09:19:10] kitchen_temp_sensor | TemperatureMeasurement.MeasuredValue = None' proves sensor failures exist in the system but are isolated to the temperature sensor, not the occupancy sensor.

C00-C04 Logs: Consistent pattern of 'Occupancy=0' during morning hours prior to 08:55, demonstrating the sensor successfully reports emptiness without generating false positives.

No immediate action required as the device is operating within expected parameters.

Monitor the kitchen_temp_sensor separately due to its recurring 'None' readings which may indicate power or connectivity issues.

Triage Output explicitly flags 'Sensor data loss (Null/None values)' as a suspected pattern. While the provided chunks show kitchen_temp_sensor returning 'None', the Triage system aggregates this as a system-wide reliability indicator affecting the bathroom focus device.

C05: The bathroom occupancy state toggles from 1 to 0 at 09:22 and back to 0 at 09:49. If the sensor were fully functional, one might expect a stable '1' if someone was using the room, or a clear transition. However, the critical evidence lies in the *absence* of any 'Occupancy=1' events for the bathroom after 09:49 in C05, despite other rooms having frequent updates. This suggests the sensor may have stopped reporting (dormant) rather than just changing state.

Comparison with other devices: Temperature sensors report values every ~5 minutes. Bathroom occupancy only reports specific state changes. The lack of 'Occupancy=1' readings in the later parts of C05 and C12/C16 (where activity logs show people in other rooms) suggests the sensor is either offline or dead silent, not just reporting 'empty'.

Note: The raw chunks do not explicitly contain a string 'MeasuredValue = None' for the *bathroom_occupancy* sensor itself, only for kitchen_temp_sensor. The anomaly hypothesis relies on the Triage Output's assertion that these failures are linked or indicative of a broader issue affecting the bathroom device mentioned in the `focus_devices` list.

The provided raw chunks (C05, C12, C16) do not explicitly display a 'null' or 'None' value for the `bathroom_occupancy` sensor; they only show it switching to 0. The strong evidence for 'data loss' comes from the `kitchen_temp_sensor` nulls and the Triage summary, requiring an inference that the bathroom sensor shares the same fate or has similar unlogged gaps.

It is possible the bathroom was genuinely empty for long periods, making the lack of '1' events normal behavior, though the frequency of '0' events without corresponding '1' events in active periods suggests otherwise.

High Probability due to system-wide correlation

Lighting control logic mismatch: The bathroom light turned OFF while the occupancy sensor still reported OCCUPIED, indicating a potential sensor-to-actuator lag or false positive occupancy detection.

C05: At 09:18:00, `bathroom_light` reports `OnOff.OnOff = False`. Shortly before this, at 09:15:00 and 09:18:00 (just prior to the off command), `bathroom_occupancy` reported `OccupancySensing.Occupancy = 1`.

In many smart home systems, lights turn ON when occupied and OFF when unoccupied. If the light turned OFF at 09:18 while occupancy was still 1 (or had just been 1 moments ago), it implies the light controller received a 'leave' signal before the occupancy sensor did, OR the occupancy sensor falsely detected presence causing the light to stay on longer than intended (though here it went off). More critically, if the light goes off, the system expects occupancy to go to 0. The sequence is tight, suggesting a race condition or misalignment.

However, looking closer at C05: Light OFF at 09:18. Occupancy 1 at 09:15 and 09:18. Occupancy 0 at 09:22. This looks like a standard 'person leaves, light turns off manually or via timer, person walks out of range'. It is less definitive than A1.

The timing (1 min gap between occupancy=1 and occupancy=0 after light off) is within acceptable human movement tolerance.

No explicit 'Occupancy=1' reading exists *after* the light turns off in the log until 09:49 (which is 0), so we cannot prove a conflict easily.

Low Confidence - likely normal operational sequence

The Triage Output's explicit identification of 'Sensor data loss (Null/None values)' and 'Network disconnection' as key issues, coupled with the high frequency of 'None' values in the kitchen sensor (a common failure mode often systemic) and the total absence of 'Occupancy=1' events for the bathroom in the latter half of the logs despite general household activity.

The discrepancy between the structured signal showing low bathroom activity (55 vs 482 living room) and the complete silence of the bathroom occupancy sensor in the detailed logs after the initial few entries in C05.

Raw log data containing explicit 'Occupancy = None' or 'Occupancy = Error' events for the bathroom sensor to definitively confirm hardware failure versus software filtering.

Logs covering the period immediately following the last 'Occupancy=1' read to see if there is a prolonged gap before the next update.

Configuration settings for the bathroom light automation (delay timers, sensitivity) to rule out intentional behavior mimicking faults.

Verify if 'Occupancy=1' events exist in earlier chunks (C00-C04) to establish a baseline of normal activity patterns before the potential failure window.

Check raw logs in all selected chunks for explicit 'MeasuredValue = None' or 'Error' strings specifically associated with 'bathroom_occupancy', not just kitchen_temp_sensor.

Analyze timestamp continuity between chunks C05 and C12 to rule out long gaps caused by log rotation rather than device dormancy.

The current investigation relies heavily on Triage output assumptions that kitchen sensor failures affect the bathroom sensor. This is an inference, not direct evidence.

Chunks C05, C12, and C16 are insufficient to determine the *start* or *end* of the anomaly. We need the beginning of the log (C00-C04) to see if the bathroom was working normally before stopping reporting at 09:49 in C05.

The absence of 'Occupancy=1' readings could be due to the room actually being empty for extended periods. Without seeing activity in the first few chunks, we cannot confirm a 'device death' vs 'room usage pattern'.

The investigator correctly identified missing explicit null values for the bathroom sensor; additional chunks must be examined to either find these errors or prove their absence throughout the entire timeline.

## Query
请检查以下设备状态历史，判断 bathroom 区域的传感器 是否工作正常。如果异常，请说明可能的故障类型。

## Layout
Layout: 一室一厅 (Studio/1BR)
- bathroom: 卫生间漏水传感器<water_leak_detector>, 卫生间灯<onoff_light>, 卫生间运动传感器<occupancy_sensor>
- bedroom: 卧室温度传感器<temperature_sensor>, 卧室灯<dimmable_light>, 卧室窗帘<window_covering>, 卧室窗户传感器<contact_sensor>, 卧室运动传感器<occupancy_sensor>
- entrance: 入户门传感器<contact_sensor>, 入户门锁<door_lock>, 玄关灯<onoff_light>
- kitchen: 厨房温度传感器<temperature_sensor>, 厨房灯<onoff_light>, 厨房灶具<cook_surface>, 厨房烟雾报警器<smoke_co_alarm>, 厨房窗户传感器<contact_sensor>, 洗碗机<dishwasher>
- living_room: 客厅主灯<dimmable_light>, 客厅台灯<onoff_light>, 客厅温度传感器<temperature_sensor>, 客厅空调<air_conditioner>, 客厅窗帘<window_covering>, 客厅窗户传感器<contact_sensor>, 客厅运动传感器<occupancy_sensor>

## Deterministic Signals
{
  "room_activity": {
    "living_room": 482,
    "bedroom": 493,
    "kitchen": 308,
    "bathroom": 55,
    "entrance": 3
  },
  "attribute_frequency": {
    "MeasuredValue": 870,
    "Occupancy": 442,
    "OnOff": 18,
    "StateValue": 4,
    "CurrentPositionLiftPercent100ths": 3,
    "SystemMode": 1,
    "OperationalState": 1,
    "LockState": 1
  },
  "lock_state_flips": 1,
  "occupancy_events": 442,
  "temperature_patterns": []
}

## Chunk Index
[
  {
    "chunk_id": "C00",
    "start_ts": "2026-06-07T00:00:00",
    "end_ts": "2026-06-07T01:50:00",
    "event_count": 80,
    "rooms": {
      "living_room": 27,
      "bedroom": 27,
      "kitchen": 22,
      "bathroom": 4
    },
    "top_devices": [
      "living_room_temp_sensor",
      "bedroom_temp_sensor",
      "kitchen_temp_sensor",
      "living_room_occupancy",
      "bedroom_occupancy",
      "bathroom_occupancy"
    ],
    "alarmish_count": 12,
    "preview_lines": [
      "[2026-06-07T00:00:00] living_room_temp_sensor | TemperatureMeasurement.MeasuredValue = 2277",
      "[2026-06-07T00:00:00] bedroom_temp_sensor | TemperatureMeasurement.MeasuredValue = 2163",
      "[2026-06-07T00:00:00] kitchen_temp_sensor | TemperatureMeasurement.MeasuredValue = 2269",
      "[2026-06-07T00:00:00] living_room_occupancy | OccupancySensing.Occupancy = 0",
      "[2026-06-07T00:00:00] bedroom_occupancy | OccupancySensing.Occupancy = 0",
      "[2026-06-07T00:00:00] bathroom_occupancy | OccupancySensing.Occupancy = 0",
      "[2026-06-07T00:05:00] living_room_temp_sensor | TemperatureMeasurement.MeasuredValue = 2266",
      "[2026-06-07T00:05:00] bedroom_temp_sensor | TemperatureMeasurement.MeasuredValue = 2164",
      "[2026-06-07T00:05:00] kitchen_temp_sensor | Temperature

## Query
请检查以下设备状态历史，判断 bathroom 区域的传感器 是否工作正常。如果异常，请说明可能的故障类型。

## Triage
{
  "latent_task_profile": "device-health | single-event-safety",
  "focus_rooms": ["bathroom"],
  "focus_devices": ["bathroom_occupancy", "bathroom_light", "water_leak_detector"],
  "focus_chunk_ids": ["C05", "C12", "C16"],
  "suspected_patterns": [
    "Sensor data loss (Null/None values)",
    "Occupancy state inconsistency with activity logs",
    "Light state mismatch with occupancy events"
  ],
  "why_these_chunks": [
    "C05: Contains a critical 'MeasuredValue = None' event for kitchen_temp_sensor, indicating potential sensor communication failures or power issues that may affect the entire system's reliability. Also shows bathroom occupancy toggling to 1 while no other bathroom device activity is recorded.",
    "C12: Shows 'kitchen_temp_sensor' returning 'None' again shortly after an event, reinforcing a pattern of sporadic sensor data loss.",
    "C16: The final chunk has significantly fewer total events and low bathroom event count compared to previous chunks, suggesting possible network disconnection or device dormancy/death at the end of the log period."
  ]
}

## Investigator
{
  "normal_hypotheses": [
    {
      "id": "N1",
      "description": "Bathroom occupancy sensor is functioning correctly, accurately detecting presence and absence based on user activity patterns.",
      "evidence": ["C05: Sensor reports Occupancy=1 at 09:10 and 09:15, then switches to 0 at 09:22 and 09:49, consistent with short usage intervals.", "C12: Sensor reports Occupancy=0 consistently between 18:19 and 18:49 during evening hours.", "C16: Sensor reports Occupancy=0 consistently between 23:19 and 23:49 during night hours."],
      "weaknesses": ["The sensor shows a single event of 'Occupancy=1' in C05 at the very beginning (09:10) without a preceding 'Occupancy=0' or duration data to confirm entry, though this could be a log start artifact."],
      "status": "Plausible but incomplete context"
    }
  ],
  "anomaly_hypotheses": [
    {
      "id": "A1",
      "description": "Bathroom occupancy sensor exhibits intermittent communication failure or power loss, indicated by sporadic 'null' values or missing state transitions that contradict expected continuous monitoring.",
      "threat_type": "Data Integrity Loss / Device Dormancy",
      "evidence": ["Triage Output explicitly flags 'Sensor data loss (Null/None values)' as a suspected pattern. While the provided chunks show kitchen_temp_


温度传感器读数漂移（drift）

温度读数持续单向偏移，不符合正常日间温度曲线 其他传感器或空调自带温度显示不一致（如果可以交叉验证）

The living_room_temp_sensor is experiencing intermittent hardware failure or communication loss, resulting in periodic null data readings at regular intervals while other sensors function normally.

Explanation: Evidence from Chunk C04 shows the sensor reporting valid values (e.g., 2552) immediately before the anomaly window.

Explanation: At exactly 09:00:00 and 10:00:00 in Chunk C05, the log entry for living_room_temp_sensor contains no numeric value ('MeasuredValue = '), whereas all other room sensors report valid numbers simultaneously.

Explanation: This pattern repeats at 20:00:00 in Chunk C11, indicating a systematic fault rather than random noise or network latency.

Explanation: The consistent recurrence of 'null' events at the same hourly timestamp suggests a firmware crash, power cycle issue, or specific communication timeout inherent to this device.

[2026-06-01T09:00:00] living_room_temp_sensor | TemperatureMeasurement.MeasuredValue = 2548 ... [2026-06-01T09:00:00] living_room_temp_sensor |

[2026-06-01T10:00:00] living_room_temp_sensor | TemperatureMeasurement.MeasuredValue = 2555 ... [2026-06-01T10:00:00] living_room_temp_sensor |

[2026-06-01T20:00:00] living_room_temp_sensor | TemperatureMeasurement.MeasuredValue = 2264 ... [2026-06-01T20:00:00] living_room_temp_sensor |

Inspect the physical connections and power supply to the living_room_temp_sensor.

Check the device logs on the sensor itself for error codes related to memory overflow, watchdog resets, or bus timeouts.

Consider replacing the sensor if the nulling persists after power cycling and connection checks.

Contrast with other sensors: At both 09:00:00 and 10:00:00, bedroom, kitchen, and bathroom sensors reported valid numeric values, isolating the fault to the living room device.

The anomaly occurs during periods of high activity (living_room occupancy events present in C11 around the time of the C05 gap context).

The exact trigger mechanism (e.g., power cycle vs. firmware crash) is not visible in the provided text logs.

The living_room_temp_sensor exhibits a calibration drift or offset error, reading significantly higher values than actual temperature compared to other rooms.

In Chunk C05, Living Room reads ~2550 while Bedroom reads ~2440 (a difference of ~110 raw units).

In Chunk C11, Living Room reads ~2260-2280 while Bedroom reads ~2160-2180 (a difference of ~100 raw units).

This relative difference remains constant across different times and environmental states (day/evening).

A constant offset could be normal if the living room thermostat is set significantly higher than bedrooms. However, combined with the hard 'null' errors in A1, this hypothesis is less likely to be the primary issue.

Cannot rule out that the 'high' reading is actually correct and the bedroom sensor is cold.

The literal absence of a numeric value in the log entry '[2026-06-01T09:00:00] living_room_temp_sensor | ' at two distinct hourly intervals (09:00 and 10:00) while all other room sensors report valid numbers simultaneously.

The recurrence of the empty log line suggests a systematic hardware reset or communication timeout specific to the living room sensor module, rather than random noise.

System configuration: What is the expected 'raw unit' conversion factor for temperature? (e.g., are these Kelvin, scaled Celsius, or proprietary units?)

Network topology: Is the living_room sensor on a separate bus or VLAN that might have experienced a periodic outage?

Previous history: Did similar null events occur before 08:57:18 in Chunk C05?

Verify if the null event in C05 was an isolated incident or part of a recurring pattern by checking the immediate preceding chunk (C04) for prior occurrences.

Confirm whether the null event persists or resolves immediately after the timestamp by checking the immediate succeeding chunk (C06).

Cross-reference 'living_room_occupancy' status specifically at the exact timestamps 09:00:00 and 10:00:00 to definitively rule out occupancy-triggered communication drops.

The current analysis relies heavily on Chunk C05 and C11. To confirm if the sensor failure is intermittent or permanent, evidence from the chunks immediately surrounding C05 (C04 and C06) is required to establish the temporal boundaries of the fault.

While the investigator noted high activity during the anomaly, explicit confirmation of the occupancy state at the exact moment of the null reading is missing; this requires reviewing the logs in C04/C05/C06 context where occupancy events are logged alongside temperature data.

The hypothesis regarding calibration drift (A2) is plausible but less likely given the hard nulls; however, without seeing the trend before and after the nulls in adjacent chunks, the magnitude of drift cannot be fully quantified.

## Query
请检查以下设备状态历史，判断 living_room 区域的传感器 是否工作正常。如果异常，请说明可能的故障类型。

## Layout
Layout: 一室一厅 (Studio/1BR)
- bathroom: 卫生间漏水传感器<water_leak_detector>, 卫生间灯<onoff_light>, 卫生间运动传感器<occupancy_sensor>
- bedroom: 卧室温度传感器<temperature_sensor>, 卧室灯<dimmable_light>, 卧室窗帘<window_covering>, 卧室窗户传感器<contact_sensor>, 卧室运动传感器<occupancy_sensor>
- entrance: 入户门传感器<contact_sensor>, 入户门锁<door_lock>, 玄关灯<onoff_light>
- kitchen: 厨房温度传感器<temperature_sensor>, 厨房灯<onoff_light>, 厨房灶具<cook_surface>, 厨房烟雾报警器<smoke_co_alarm>, 厨房窗户传感器<contact_sensor>, 洗碗机<dishwasher>
- living_room: 客厅主灯<dimmable_light>, 客厅台灯<onoff_light>, 客厅温度传感器<temperature_sensor>, 客厅空调<air_conditioner>, 客厅窗帘<window_covering>, 客厅窗户传感器<contact_sensor>, 客厅运动传感器<occupancy_sensor>

## Deterministic Signals
{
  "room_activity": {
    "living_room": 407,
    "bedroom": 357,
    "kitchen": 298,
    "bathroom": 56,
    "entrance": 14
  },
  "attribute_frequency": {
    "MeasuredValue": 866,
    "Occupancy": 225,
    "OnOff": 18,
    "StateValue": 5,
    "LockState": 4,
    "CurrentPositionLiftPercent100ths": 2,
    "SystemMode": 1,
    "OperationalState": 1,
    "CurrentLevel": 1
  },
  "lock_state_flips": 4,
  "occupancy_events": 225,
  "temperature_patterns": []
}

## Chunk Index
[
  {
    "chunk_id": "C00",
    "start_ts": "2026-06-01T00:00:00",
    "end_ts": "2026-06-01T01:50:00",
    "event_count": 80,
    "rooms": {
      "living_room": 27,
      "bedroom": 27,
      "kitchen": 22,
      "bathroom": 4
    },
    "top_devices": [
      "living_room_temp_sensor",
      "bedroom_temp_sensor",
      "kitchen_temp_sensor",
      "living_room_occupancy",
      "bedroom_occupancy",
      "bathroom_occupancy"
    ],
    "alarmish_count": 12,
    "preview_lines": [
      "[2026-06-01T00:00:00] living_room_temp_sensor | TemperatureMeasurement.MeasuredValue = 2270",
      "[2026-06-01T00:00:00] bedroom_temp_sensor | TemperatureMeasurement.MeasuredValue = 2176",
      "[2026-06-01T00:00:00] kitchen_temp_sensor | TemperatureMeasurement.MeasuredValue = 2276",
      "[2026-06-01T00:00:00] living_room_occupancy | OccupancySensing.Occupancy = 0",
      "[2026-06-01T00:00:00] bedroom_occupancy | OccupancySensing.Occupancy = 0",
      "[2026-06-01T00:00:00] bathroom_occupancy | OccupancySensing.Occupancy = 0",
      "[2026-06-01T00:05:00] living_room_temp_sensor | TemperatureMeasurement.MeasuredValue = 2265",
      "[2026-06-01T00:05:00] bedroom_temp_sensor | TemperatureMeasurement.MeasuredValue = 2182",
      "[2026-06-01T00:05:00] kitche

## Query
请检查以下设备状态历史，判断 living_room 区域的传感器 是否工作正常。如果异常，请说明可能的故障类型。

## Triage
{
  "latent_task_profile": "device-health | single-event-safety",
  "focus_rooms": ["living_room"],
  "focus_devices": ["living_room_temp_sensor", "living_room_occupancy"],
  "focus_chunk_ids": ["C05", "C11"],
  "suspected_patterns": [
    "Temperature sensor reading missing/null (Data Loss)",
    "High frequency alarm events correlated with occupancy changes"
  ],
  "why_these_chunks": [
    "Chunk C05 contains a log entry '[2026-06-01T09:00:00] living_room_temp_sensor | TemperatureMeasurement.MeasuredValue = ' indicating a null or missing value, suggesting sensor failure or communication loss.",
    "Chunk C11 shows multiple alarm counts and potential environmental anomalies (kitchen window open followed by temp sensor null), requiring verification of the living room's correlation during active occupancy periods to rule out systemic issues vs. isolated sensor faults."
  ]
}

## Investigator
{
  "normal_hypotheses": [
    {
      "id": "N1",
      "description": "The living_room_temp_sensor is functioning correctly, reporting valid temperature data within the expected range (~24-25°C or ~2270-2550 raw units), and gaps in logs are due to temporary network latency or clock synchronization issues.",
      "evidence": [
        "In Chunk C11, readings like 2271, 2281, 2275, 2264, 2273 show a stable, physically plausible progression of temperatures.",
        "In Chunk C05, after a gap at 09:00:00, the sensor resumes reporting values (2536, 2550) that are consistent with neighboring sensors (bedroom/kitchen)."
      ],
      "weaknesses": [
        "Fails to explain the specific timestamp 2026-06-01T09:00:00 where the log explicitly shows an empty value field instead of a number, which indicates a failure event rather than just missing data."
      ]
    }
  ],
  "anomaly_hypotheses": [
    {
      "id": "A1",
      "description": "The living_room_temp_sensor has a hardware fault causing periodic total readout failures (Data Loss/Nulling).",
      "threat_type": "Sensor Failure / Data Integrity Loss",
      "evidence": [
        "Chunk C05 Log Line: '[2026-06-01T09:00:00] living_room_temp_sensor | ' contains no value, whereas adjacent lines for other sensors contain numbers.",
        "Chunk C05 Log Line: '[2026-06-01T10:00:00] living_room_temp_sensor | ' repeats this exact pattern exactly one hour later.",
        "Contrast with other sensors: At both 09:00:00 and 10:00:00, bedroom,