DF-04-FP: 门锁响应延迟但最终成功锁定

The entrance sensor and lock system are functioning correctly; observed state changes correspond to physical door events (contact sensor toggles) and explicit operation logs.

Explanation: Analysis of the 07:05 sequence shows a transition from LockState=2 to LockState=1. Crucially, at 07:06:00, occupancy is recorded in master_bedroom, indicating someone was likely moving through or near the entrance area.

Explanation: Correlating this with the 'front_door_contact' sensor logs reveals a toggle at 07:46:27 (False/Open) immediately after a Manual Lock event, and a subsequent toggle back to True (Closed) before the Auto Unlock event at 07:46:34.

Explanation: The apparent missing 'Unlock' event for the 07:05 state change is explained by the absence of a contact sensor toggle log entry for that specific moment in the provided snippet, while other transitions (07:46, 16:33, 18:01, 22:34) show perfect synchronization between LockOperation events, Contact Sensor state flips, and LockState updates. The system behavior is consistent with normal operation where the door was unlocked manually or automatically off-log, but the lack of conflicting data (like an open door while locked) rules out malfunction.

[2026-06-02T07:05:03] front_door_lock | DoorLock.LockState = 2

[2026-06-02T07:46:27] front_door_contact | BooleanState.StateValue = False

No action required; system status is normal.

Continue monitoring for any future discrepancies between LockState and ContactSensor readings.

[2026-06-02T16:33:00] front_door_lock | Event: LockOperation(LockOperationType=1, OperationSource=Manual)

[2026-06-02T22:34:23] front_door_lock | Event: LockOperation(LockOperationType=0, OperationSource=Auto)

[2026-06-02T06:37:12] kids_room_window | WindowCovering.CurrentPositionLiftPercent100ths = 0

Time gap between Manual Lock command (07:46:25) and LockState change (07:46:25) vs Time gap between Auto Unlock command (07:46:34) and LockState change (07:46:34) appears tight, BUT the LockState at 07:05:08 is UNLOCKED (1) while no unlock event precedes it immediately; more critically, at 18:01:00, a Manual Lock event occurs, followed by LockState=2 at 18:01:00, but earlier at 07:46:25 a Manual Lock event occurred with immediate state change, whereas at 07:05 there is a state change to LOCKED (2) at 07:05:07 (implied by sequence) but the log shows 07:05:08 as UNLOCKED (1). Re-evaluating: The critical anomaly is the sequence at 07:05. Log shows 07:05:07 (implied before 08) state 2? No. Let's look closer.
1. 07:05:07 -> State 2 (Locked)? No, log says 07:05:08 State 1 (Unlocked). Where did State 2 come from? It likely happened off-log or the sensor missed the lock event.
2. 07:46:25 Manual Lock -> State 2 (Immediate match).
3. 07:46:34 Auto Unlock -> State 1 (Immediate match).
4. 16:33:00 Manual Lock -> State 2 (Immediate match).
5. 18:01:00 Manual Lock -> State 2 (Immediate match).
The only inconsistency is the transition to Unlocked at 07:05:08. If the door was locked previously, where is the unlock event? Or if it was unlocked, why did it briefly show Locked (State 2) before 07:05:08? Wait, the log snippet jumps.
Actually, the strongest signal is the **missing 'Unlock' event** for the state change at 07:05:08 (State=1). Usually, manual/auto events are logged. If the door went from Locked to Unlocked without an event record, the sensor or hub failed to report the state transition trigger.

Inconsistent event logging: A state transition to UNLOCKED (State=1) at 07:05:08 lacks a preceding 'LockOperation' event in the provided window, unlike all other transitions which have matching events.

Check raw timestamps for front_door_lock between 07:05:00 and 07:05:10 to confirm if an Unlock operation event exists but was filtered out, or if the state change is spurious noise.

Verify if the 'contact_sensor' (entrance door sensor) reports Open/Closed states that correlate with the LockState changes, specifically around 07:05:08.

Check system logs for communication timeouts or packet loss between the entrance lock device and the hub during the morning timeframe.

## Task
Analyze the following evidence packet for a smart-home anomaly query.

## 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: 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>

## Evidence Packet
{
  "sq_type": "SQ1",
  "event_count": 2286,
  "salient_event_count": 60,
  "focus_event_count": 91,
  "room_activity": {
    "kitchen": 10,
    "living_room": 13,
    "dining_room": 8,
    "master_bedroom": 13,
    "second_bedroom": 8,
    "kids_room": 10,
    "master_bathroom": 6,
    "entrance": 23
  },
  "temperature_patterns": [],
  "suspicious_signals": [
    "[2026-06-02T07:46:25] front_door_lock | Event: LockOperation(LockOperationType=1, OperationSource=Manual)",
    "[2026-06-02T07:46:34] front_door_lock |

## Task
Make the final anomaly decision from compressed evidence plus a focused raw-log excerpt.

## 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。

## Stage-1 Analysis
{
  "candidate_hypotheses": [
    {
      "id": "H1",
      "type": "anomaly",
      "description": "Entrance area exhibits a state mismatch where the door is physically locked (LockState=2) without a corresponding 'Lock' operation event in the log, suggesting a sensor or actuator communication failure.",
      "supported_by": [
        "[2026-06-02T07:05:08] front_door_lock | DoorLock.LockState = 1",
        "[2026-06-02T07:46:25] front_door_lock | DoorLock.LockState = 2",
        "[2026-06-02T07:46:34] front_door_lock | Event: LockOperation(LockOperationType=0, OperationSource=Auto)",
        "[2026-06-02T18:01:00] front_door_lock | Event: LockOperation(LockOperationType=1, OperationSource=Manual)",
        "[2026-06-02T18:01:00] front_door_lock | DoorLock.LockState = 2"
      ],
      "contradicted_by": []
    },
    {
      "id": "H2",
      "type": "normal",
      "description": "The entrance motion sensor and occupancy logic are functioning correctly, showing consistent zero occupancy at midnight and normal activity counts during the day.",
      "supported_by": [
        "[2026-06-02T00:00:00] living_room_occupancy | OccupancySensing.Occupancy = 0",
        "room_activity.entrance: 23"
      ],
      "contradicted_by": []
    }
  ],
  "most_concerning_signals": [
    "Time gap between Manual Lock command (07:46:25) and LockState change (07:46:25) vs Time gap between Auto Unlock command (07:46:34) and LockState change (07:46:34) appears tight, BUT the LockState at 07:05:08 is UNLOCKED (1) while no unlock event precedes it immediately; more critically, at 18:01:00, a Manual Lock event occurs, followed by LockState=2 at 18:01:00, but earlier at 07:46:25 a Manual Lock event occurred with immediate state change, whereas at 07:05 there is a state change to LOCKED (2) at 07:05:07 (impli


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 is reporting physically impossible values (ranging from 2264 to 2532) that indicate a severe calibration failure or hardware malfunction, rendering the data invalid for safety monitoring.

Explanation: Analysis of raw logs reveals 'kitchen_temp_sensor' readings escalating from 2264 to 2532 over several hours.

Explanation: These values are physically impossible for a residential environment (normal range ~18-30°C), indicating the sensor is stuck at an offset or returning raw/binary garbage instead of calibrated degrees.

Explanation: Despite this critical error, the system does not flag an alert, and other sensors (like living_room_temp_sensor showing realistic values like 2282, 2345) function correctly, isolating the fault specifically to the kitchen unit.

[2026-06-25T00:00:00] kitchen_temp_sensor | TemperatureMeasurement.MeasuredValue = 2264

[2026-06-25T08:35:00] kitchen_temp_sensor | TemperatureMeasurement.MeasuredValue = 2532

Immediately replace the kitchen temperature sensor hardware.

Review historical data to determine if fire/heat warnings were missed due to the drifted readings before replacement.

Verify if the kitchen_occupancy sensor reports actual presence duration vs. the aggregated count of 26.

Cross-reference kitchen_temperature_sensor and kitchen_water_leak_detector raw streams for any out-of-range values that were filtered out by the anomaly detector but didn't trigger an alert.

Confirm the status of kitchen_light (onoff_light) and cook_surface (cook_surface) during periods of high occupancy to ensure they are responding to commands.

## Task
Analyze the following evidence packet for a smart-home anomaly query.

## 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: 独居老人 (Elderly Living Alone)
- 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>, 床头灯<onoff_light>
- 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>, 厨房运动传感器<occupancy_sensor>
- living_room: 客厅温度传感器<temperature_sensor>, 客厅灯<dimmable_light>, 客厅烟雾报警器<smoke_co_alarm>, 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>

## Evidence Packet
{
  "sq_type": "SQ1",
  "event_count": 1853,
  "salient_event_count": 60,
  "focus_event_count": 95,
  "room_activity": {
    "kitchen": 26,
    "living_room": 21,
    "bedroom": 19,
    "bathroom": 9,
    "entrance": 20
  },
  "temperature_patterns": [],
  "suspicious_signals": [
    "[2026-06-25T08:25:00] kitchen_smoke_alarm | SmokeCoAlarm.TestInProgress = True",
    "[2026-06-25T08:25:00] kitchen_smoke_alarm | SmokeCoAlarm.ExpressedState = 4",
    "[2026-06-25T08:25:30] kitchen_smoke_alarm | SmokeCoAlarm.TestInProgress = False",
    "[2026-06-25T08:25:30] kitchen_smoke_alarm | SmokeCoAlarm.ExpressedState = 0",
    "[2026-06-25T07:03:06] front_door_lock | Event: LockOperation(LockOperationType=1, OperationSource=Manual)",
    "[2026-06-25T07:03:11] front_door_lock | Event: LockOperation(LockOperationType=0, OperationSource=Auto)",
    "[2026-06-25T08:25:30] kitchen_smoke_alarm | Event: SelfTestComplete()",
    "[2026-06-25T08:36:00] front_door_lock | Event: LockOperation(LockOperationType=1, OperationSource=Manual)",
    "[2026-06-25T20:45:15] front_door_lock | Event: LockOperation(LockO

## Task
Make the final anomaly decision from compressed evidence plus a focused raw-log excerpt.

## 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。

## Stage-1 Analysis
{
  "candidate_hypotheses": [
    {
      "id": "H1",
      "type": "normal",
      "description": "Kitchen sensors are functioning normally; the smoke alarm test was a routine scheduled operation, and no environmental anomalies (leaks, heat spikes) were detected.",
      "supported_by": [
        "[2026-06-25T08:25:00] kitchen_smoke_alarm | SmokeCoAlarm.TestInProgress = True",
        "[2026-06-25T08:25:30] kitchen_smoke_alarm | Event: SelfTestComplete()",
        "Absence of water_leak_detector or temperature_sensor alerts in suspicious_signals"
      ],
      "contradicted_by": []
    },
    {
      "id": "H2",
      "type": "anomaly",
      "description": "Potential sensor malfunction due to lack of activity data correlation; while not explicitly error-flagged, the 'room_activity' count for kitchen is high (26), yet specific state changes (like light on/off or cook surface usage) are missing from 'suspicious_signals', potentially indicating logging gaps or silent failures.",
      "supported_by": [
        "\"kitchen\": 26 (High activity count)",
        "Missing specific event logs for kitchen_light or cook_surface in suspicious_signals despite high activity"
      ],
      "contradicted_by": [
        "No explicit error codes or 'Offline' states in the provided log snippet",
        "Smoke alarm completed self-test successfully without timeout errors"
      ]
    }
  ],
  "most_concerning_signals": [
    "High room_activity count (26) in Kitchen combined with sparse specific state-change events in the summary log may indicate unlogged interactions or filter thresholds hiding minor faults."
  ],
  "what_to_verify_in_raw_log": [
    "Check raw timestamps between 08:25:00 and 08:25:30 for any transient error codes during the smoke alarm self-test.",
    "Verify if the kitchen_occupanc


DF-05-FP: 烟雾报警器正常自检

The kitchen sensors are functioning correctly; the 'empty' occupancy reading is due to legitimate absence during a scheduled smoke alarm self-test and consistent temperature drift indicating normal idle state.

Explanation: The kitchen smoke alarm executed a valid scheduled self-test between 19:42:00 and 19:42:30, which explains why it may have reported non-operational states or triggered specific test events during that window.

Explanation: The persistent 'Occupancy = 0' reading from 00:00:00 until morning activity (05:39) indicates the kitchen was genuinely unoccupied for an extended period, consistent with nighttime hours.

Explanation: Temperature readings in the kitchen show a logical, gradual increase (2279 -> 2393) over time without erratic jumps, confirming the temperature sensor is working and not drifting or stuck.

Explanation: No evidence of intruder entry, fire, gas leaks, or sensor communication failures (e.g., missed timestamps or error codes) was found in the raw log.

[2026-06-29T00:00:00] kitchen_temp_sensor | TemperatureMeasurement.MeasuredValue = 2279

Continue standard monitoring; no immediate intervention required.

Verify that the scheduled self-test completed successfully as indicated by the SelfTestComplete event.

## Task
Analyze the following evidence packet for a smart-home anomaly query.

## 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: 独居老人 (Elderly Living Alone)
- 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>, 床头灯<onoff_light>
- 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>, 厨房运动传感器<occupancy_sensor>
- living_room: 客厅温度传感器<temperature_sensor>, 客厅灯<dimmable_light>, 客厅烟雾报警器<smoke_co_alarm>, 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>

## Evidence Packet
{
  "sq_type": "SQ1",
  "event_count": 1850,
  "salient_event_count": 60,
  "focus_event_count": 95,
  "room_activity": {
    "kitchen": 24,
    "living_room": 21,
    "bedroom": 21,
    "bathroom": 9,
    "entrance": 20
  },
  "temperature_patterns": [],
  "suspicious_signals": [
    "[2026-06-29T19:42:00] kitchen_smoke_alarm | SmokeCoAlarm.TestInProgress = True",
    "[2026-06-29T19:42:00] kitchen_smoke_alarm | SmokeCoAlarm.ExpressedState = 4",
    "[2026-06-29T19:42:30] kitchen_smoke_alarm | SmokeCoAlarm.TestInProgress = False",
    "[2026-06-29T19:42:30] kitchen_smoke_alarm | SmokeCoAlarm.ExpressedState = 0",
    "[2026-06-29T07:07:06] front_door_lock | Event: LockOperation(LockOperationType=1, OperationSource=Manual)",
    "[2026-06-29T07:07:12] front_door_lock | Event: LockOperation(LockOperationType=0, OperationSource=Auto)",
    "[2026-06-29T08:34:00] front_door_lock | Event: LockOperation(LockOperationType=1, OperationSource=Manual)",
    "[2026-06-29T19:42:30] kitchen_smoke_alarm | Event: SelfTestComplete()",
    "[2026-06-29T20:36:15] front_door_lock | Event: LockOperation(LockO

## Task
Make the final anomaly decision from compressed evidence plus a focused raw-log excerpt.

## 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。

## Stage-1 Analysis
{
  "candidate_hypotheses": [
    {
      "id": "H1",
      "type": "normal",
      "description": "Kitchen smoke alarm is functioning correctly and performing a scheduled self-test.",
      "supported_by": ["[2026-06-29T19:42:00] kitchen_smoke_alarm | SmokeCoAlarm.TestInProgress = True", "[2026-06-29T19:42:30] kitchen_smoke_alarm | Event: SelfTestComplete()", "[2026-06-29T19:42:30] kitchen_smoke_alarm | SmokeCoAlarm.ExpressedState = 0"],
      "contradicted_by": []
    },
    {
      "id": "H2",
      "type": "anomaly",
      "description": "Potential sensor malfunction or data logging error in the kitchen occupancy sensor, indicated by persistent 'empty' state at midnight despite high activity counts elsewhere.",
      "supported_by": ["[2026-06-29T00:00:00] kitchen_occupancy | OccupancySensing.Occupancy = 0", "focus_event_count (kitchen): 24 vs salient_event_count total: 60"],
      "contradicted_by": ["room_activity shows significant kitchen events (24), suggesting user presence or activity that contradicts the midnight sensor reading if correlated with other room activities"]
    }
  ],
  "most_concerning_signals": [
    "[2026-06-29T19:42:00] kitchen_smoke_alarm | SmokeCoAlarm.TestInProgress = True",
    "[2026-06-29T00:00:00] kitchen_occupancy | OccupancySensing.Occupancy = 0"
  ],
  "what_to_verify_in_raw_log": [
    "Timestamps of raw PIR/Microwave Doppler pulses for kitchen_occupancy around 00:00:00 to distinguish between no motion and sensor timeout/battery failure.",
    "Battery voltage levels for kitchen_smoke_alarm and kitchen_occupancy_sensor immediately following the test event.",
    "Network packet integrity or drop rates specifically for the kitchen MQTT/HTTP topic during the midnight hour."
  ],
  "provisional_risk": "low"
}

## Focused Raw Log
[2026-06-29T00:00:00]


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 functioning correctly, consistently reporting '0' during the night and early morning when no residents are present in that specific area.

Explanation: Raw logs show consistent 'Occupancy = 0' reports from the living_room_occupancy sensor throughout the entire night shift (00:00 to 04:30).

Explanation: Household activity (bedroom light on, door locking, kitchen occupancy) begins later in the day (around 05:26 - 06:56), confirming the house was empty before this time.

Explanation: When the resident eventually enters the living room at 13:58, the sensor correctly updates to 'Occupancy = 1', proving the device is responsive and not stuck.

Explanation: Temperature readings in the living room also increase logically as the day progresses, indicating other sensors are working normally without interference.

[2026-06-06T04:30:00] living_room_occupancy | OccupancySensing.Occupancy = 0

[2026-06-06T13:58:14] living_room_occupancy | OccupancySensing.Occupancy = 1

No action required; system is operating as expected.

Continue standard monitoring of the home automation system.

Cross-reference the timestamp of the first living room occupancy report (00:00:00) to see if the resident was present earlier in the day (e.g., during the kitchen activities noted at 05:26 or 06:56).

## Task
Analyze the following evidence packet for a smart-home anomaly query.

## 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: 独居老人 (Elderly Living Alone)
- 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>, 床头灯<onoff_light>
- 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>, 厨房运动传感器<occupancy_sensor>
- living_room: 客厅温度传感器<temperature_sensor>, 客厅灯<dimmable_light>, 客厅烟雾报警器<smoke_co_alarm>, 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>

## Evidence Packet
{
  "sq_type": "SQ1",
  "event_count": 1860,
  "salient_event_count": 60,
  "focus_event_count": 95,
  "room_activity": {
    "kitchen": 23,
    "living_room": 22,
    "bedroom": 20,
    "bathroom": 10,
    "entrance": 20
  },
  "temperature_patterns": [],
  "suspicious_signals": [
    "[2026-06-06T06:56:09] front_door_lock | Event: LockOperation(LockOperationType=1, OperationSource=Manual)",
    "[2026-06-06T06:56:18] front_door_lock | Event: LockOperation(LockOperationType=0, OperationSource=Auto)",
    "[2026-06-06T08:20:00] front_door_lock | Event: LockOperation(LockOperationType=1, OperationSource=Manual)",
    "[2026-06-06T20:46:06] front_door_lock | Event: LockOperation(LockOperationType=0, OperationSource=Auto)",
    "[2026-06-06T05:26:12] bedroom_window | WindowCovering.CurrentPositionLiftPercent100ths = 0",
    "[2026-06-06T06:56:09] front_door_lock | DoorLock.LockState = 2",
    "[2026-06-06T06:56:18] front_door_lock | DoorLock.LockState = 1",
    "[2026-06-06T08:20:00] front_door_lock | DoorLock.LockState = 2",
    "[2026-06-06T12:36:20] bedroom_window | WindowCovering.Curr

## Task
Make the final anomaly decision from compressed evidence plus a focused raw-log excerpt.

## 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。

## Stage-1 Analysis
{
  "candidate_hypotheses": [
    {
      "id": "H1",
      "type": "normal",
      "description": "Living room occupancy sensor is functioning correctly, reporting '0' (empty) during early morning and late night hours when no activity is expected.",
      "supported_by": ["[2026-06-06T00:00:00] living_room_occupancy | OccupancySensing.Occupancy = 0", "[2026-06-06T00:30:00] living_room_occupancy | OccupancySensing.Occupancy = 0"],
      "contradicted_by": []
    },
    {
      "id": "H2",
      "type": "anomaly",
      "description": "Potential sensor failure or logical disconnect in the living room: While the system reports the living room as empty at 00:00 and 00:30, there are conflicting signals regarding window states in other rooms that might imply household presence or automated routines not reflected in the living room log snippet provided for that specific time window. However, strictly based on the provided evidence for the living room itself, it appears consistent with an empty state.",
      "supported_by": [],
      "contradicted_by": []
    }
  ],
  "most_concerning_signals": [
    "[2026-06-06T00:00:00] living_room_occupancy | OccupancySensing.Occupancy = 0",
    "[2026-06-06T00:30:00] living_room_occupancy | OccupancySensing.Occupancy = 0"
  ],
  "what_to_verify_in_raw_log": [
    "Check if any motion events were generated by the living_room_occupancy sensor between T00:00:00 and T08:20:00 despite the summary showing '0'.",
    "Verify if the 'living_room_activity' count of 22 includes any false negatives or if the sensor simply missed detecting movement during active hours.",
    "Cross-reference the timestamp of the first living room occupancy report (00:00:00) to see if the resident was present earlier in the day (e.g., during the kitchen activities noted at 05:26 o


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）

Kitchen occupancy sensor is functioning correctly; it accurately reported '0' (empty) during the overnight period and successfully triggered to '1' when activity was confirmed later in the day.

Explanation: The raw log shows consistent 'OccupancySensing.Occupancy = 0' readings for the kitchen between 00:00 and 04:30, which aligns with the house being empty during those times.

Explanation: At 13:54:00, the sensor correctly transitioned to 'OccupancySensing.Occupancy = 1', corroborated by simultaneous 'entrance_occupancy = 1' events, proving the sensor is operational and not stuck or malfunctioning.

Explanation: There is no evidence of sensor failure; the absence of '1' states during the early morning hours simply reflects the absence of occupants.

[2026-06-08T13:54:00] kitchen_occupancy | OccupancySensing.Occupancy = 1

Search for any 'OccupancySensing.Occupancy = 1' events associated with 'kitchen_occupancy' between 06:52 and 20:53 to correlate with the 'front_door_lock' manual entry at 08:29.

Check status updates from 'kitchen_cook_surface' and 'kitchen_temperature_sensor' around 08:29 (when the front door was manually unlocked) to see if they indicate usage while occupancy sensors remained silent.

Verify the timestamp granularity of the occupancy sensor logs to ensure gaps aren't masking short-duration visits.

## Task
Analyze the following evidence packet for a smart-home anomaly query.

## 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: 独居老人 (Elderly Living Alone)
- 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>, 床头灯<onoff_light>
- 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>, 厨房运动传感器<occupancy_sensor>
- living_room: 客厅温度传感器<temperature_sensor>, 客厅灯<dimmable_light>, 客厅烟雾报警器<smoke_co_alarm>, 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>

## Evidence Packet
{
  "sq_type": "SQ1",
  "event_count": 1855,
  "salient_event_count": 60,
  "focus_event_count": 95,
  "room_activity": {
    "kitchen": 22,
    "living_room": 23,
    "bedroom": 17,
    "bathroom": 10,
    "entrance": 23
  },
  "temperature_patterns": [],
  "suspicious_signals": [
    "[2026-06-08T06:52:02] front_door_lock | Event: LockOperation(LockOperationType=1, OperationSource=Manual)",
    "[2026-06-08T06:52:09] front_door_lock | Event: LockOperation(LockOperationType=0, OperationSource=Auto)",
    "[2026-06-08T08:29:00] front_door_lock | Event: LockOperation(LockOperationType=1, OperationSource=Manual)",
    "[2026-06-08T20:53:17] front_door_lock | Event: LockOperation(LockOperationType=0, OperationSource=Auto)",
    "[2026-06-08T05:31:09] bedroom_window | WindowCovering.CurrentPositionLiftPercent100ths = 0",
    "[2026-06-08T06:52:02] front_door_lock | DoorLock.LockState = 2",
    "[2026-06-08T06:52:09] front_door_lock | DoorLock.LockState = 1",
    "[2026-06-08T08:29:00] front_door_lock | DoorLock.LockState = 2",
    "[2026-06-08T12:34:11] bedroom_window | WindowCovering.CurrentP

## Task
Make the final anomaly decision from compressed evidence plus a focused raw-log excerpt.

## 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。

## Stage-1 Analysis
{
  "candidate_hypotheses": [
    {
      "id": "H1",
      "type": "normal",
      "description": "Kitchen occupancy sensor is functioning normally, correctly reporting '0' (empty) during the observed time windows where no kitchen activity is logged.",
      "supported_by": ["room_activity.kitchen = 22 indicates low activity but not zero", "[2026-06-08T00:00:00] kitchen_occupancy | OccupancySensing.Occupancy = 0", "[2026-06-08T00:30:00] kitchen_occupancy | OccupancySensing.Occupancy = 0"],
      "contradicted_by": []
    },
    {
      "id": "H2",
      "type": "anomaly",
      "description": "Potential false positive or sensor failure in kitchen occupancy detection if the resident was present cooking but the sensor failed to trigger, however, this cannot be confirmed without raw logs correlating with other devices like cook_surface.",
      "supported_by": [],
      "contradicted_by": ["The evidence packet explicitly lists 22 'salient events' in the kitchen. If the user were present and active enough to generate salient events, the occupancy sensor should have triggered. The lack of explicit 'Occupancy=1' entries for non-midnight times suggests either no presence or a specific data filtering issue rather than total sensor death."]
    }
  ],
  "most_concerning_signals": [
    "Absence of explicit 'OccupancySensing.Occupancy = 1' events for the kitchen in the provided suspicious signals list, despite 22 reported room activities.",
    "Data truncation or sampling issue: Only midnight snapshots are shown for occupancy; continuous stream might reveal intermittent failures."
  ],
  "what_to_verify_in_raw_log": [
    "Search for any 'OccupancySensing.Occupancy = 1' events associated with 'kitchen_occupancy' between 06:52 and 20:53 to correlate with the 'front_door_lock' manual entry at 08:2


DF-04-FP: 门锁响应延迟但最终成功锁定

The entrance occupancy sensor is functioning correctly; the 'Occupancy = 0' readings during early morning timestamps are accurate because no human entry occurred, while later log entries confirm the sensor successfully detected occupancy when people entered.

Explanation: Initial hypothesis of sensor failure was contradicted by raw logs showing Occupancy=1 at 08:39:11 and 14:04:19, proving the sensor detects motion.

Explanation: During the suspected anomaly window (e.g., 07:04), the front door contact sensor remained closed (False/True state changes indicate door operation) but no occupancy change was logged, implying the user never physically crossed the entrance threshold where the sensor is mounted.

Explanation: The system accurately reflected the house's actual occupancy status: empty in the early morning hours and occupied only after confirmed entry events later in the day.

[2026-06-20T08:39:11] entrance_occupancy | OccupancySensing.Occupancy = 1

[2026-06-20T14:04:19] living_room_occupancy | OccupancySensing.Occupancy = 1

No action required; system is operating as designed.

Verify physical placement of PIR sensors to ensure they cover all likely entry paths if future incidents occur without detection.

```json
{
  "candidate_hypotheses": [
    {
      "id": "H1",
      "type": "anomaly",
      "description": "Entrance occupancy sensor is non-functional or misconfigured, failing to detect human presence despite multiple lock interactions and door state changes.",
      "supported_by": [
        "[2026-06-20T07:04:08] front_door_lock | Event: LockOperation(LockOperationType=1, OperationSource=Manual)",
        "[2026-06-20T07:04:13] front_door_lock | Event: LockOperation(LockOperationType=0, OperationSource=Auto)",
        "[2026-06-20T20:43:11] front_door_lock | Event: LockOperation(LockOperationType=0, OperationSource=Auto)",
        "[2026-06-20T06:42:03] front_door_lock | DoorLock.LockState = 2",
        "[2026-06-20T06:42:08] front_door_lock | DoorLock.LockState = 1",
        "[2026-06-20T07:04:08] front_door_lock | DoorLock.LockState = 2",
        "[2026-06-20T07:04:13] front_door_lock | DoorLock.LockState = 1",
        "[2026-06-20T08:40:00] front_door_lock | DoorLock.LockState = 2",
        "[2026-06-20T20:43:11] front_door_lock | DoorLock.LockState = 1"
      ],
      "contradicted_by": []
    },
    {
      "id": "H2",
      "type": "normal",
      "description": "The entrance area was indeed empty during the logged periods, and the occupancy sensor readings are accurate (e.g., early morning/late night when no one entered).",
      "supported_by": [
        "[2026-06-20T00:00:00] entrance_occupancy | OccupancySensing.Occupancy = 0",
        "[2026-06-20T00:30:00] living_room_occupancy | OccupancySensing.Occupancy = 0"
      ],
      "contradicted_by": [
        "High frequency of manual and auto lock operations at 07:04, 08:40, and 20:43 implies human entry/exit events that should trigger the entrance motion sensor."
      ]
    }
  ],
  "most_concerning_signals": [
    "Discrepancy between active door locking/unlocking events (implying user presence) and persistent 'Occupancy = 0' status for entrance_occupancy.",
    "Timestamp 2026-06-20T07:04:08 shows a Manual Lock followed immediately by Auto Unlock, yet no occupancy change is recorded in the entrance zone."
  ],
  "what_to_verify_in_raw_log": [
    "Raw event stream from entrance_occupancy sensor around timestamps 07:04:08, 08:40:00, and 20:43:11 to confirm if any 'motion_detected' or 'presence_confirmed' flags exist but were suppressed.",
    "Check hardware status logs for entrance_occupancy sensor to rule out battery failure, calibration error, or physical obstruction (e.g., furniture blocking the PIR lens).",
    "Verify if the door contact sensor reports 'Open' state consistently during these times; sometimes occupancy sensors rely on door state transitions rather than independent motion detection."
  ],
  "provisional_risk": "medium"
}
```

## Task
Analyze the following evidence packet for a smart-home anomaly query.

## 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: 独居老人 (Elderly Living Alone)
- 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>, 床头灯<onoff_light>
- 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>, 厨房运动传感器<occupancy_sensor>
- living_room: 客厅温度传感器<temperature_sensor>, 客厅灯<dimmable_light>, 客厅烟雾报警器<smoke_co_alarm>, 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>

## Evidence Packet
{
  "sq_type": "SQ1",
  "event_count": 1853,
  "salient_event_count": 60,
  "focus_event_count": 98,
  "room_activity": {
    "kitchen": 21,
    "living_room": 24,
    "bedroom": 20,
    "bathroom": 9,
    "entrance": 24
  },
  "temperature_patterns": [],
  "suspicious_signals": [
    "[2026-06-20T07:04:08] front_door_lock | Event: LockOperation(LockOperationType=1, OperationSource=Manual)",
    "[2026-06-20T07:04:13] front_door_lock | Event: LockOperation(LockOperationType=0, OperationSource=Auto)",
    "[2026-06-20T08:40:00] front_door_lock | Event: LockOperation(LockOperationType=1, OperationSource=Manual)",
    "[2026-06-20T20:43:11] front_door_lock | Event: LockOperation(LockOperationType=0, OperationSource=Auto)",
    "[2026-06-20T05:33:06] bedroom_window | WindowCovering.CurrentPositionLiftPercent100ths = 0",
    "[2026-06-20T06:42:03] front_door_lock | DoorLock.LockState = 2",
    "[2026-06-20T06:42:08] front_door_lock | DoorLock.LockState = 1",
    "[2026-06-20T07:04:08] front_door_lock | DoorLock.LockState = 2",
    "[2026-06-20T07:04:13] front_door_lock | DoorLock.LockState = 1

## Task
Make the final anomaly decision from compressed evidence plus a focused raw-log excerpt.

## 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。

## Stage-1 Analysis
```json
{
  "candidate_hypotheses": [
    {
      "id": "H1",
      "type": "anomaly",
      "description": "Entrance occupancy sensor is non-functional or misconfigured, failing to detect human presence despite multiple lock interactions and door state changes.",
      "supported_by": [
        "[2026-06-20T07:04:08] front_door_lock | Event: LockOperation(LockOperationType=1, OperationSource=Manual)",
        "[2026-06-20T07:04:13] front_door_lock | Event: LockOperation(LockOperationType=0, OperationSource=Auto)",
        "[2026-06-20T20:43:11] front_door_lock | Event: LockOperation(LockOperationType=0, OperationSource=Auto)",
        "[2026-06-20T06:42:03] front_door_lock | DoorLock.LockState = 2",
        "[2026-06-20T06:42:08] front_door_lock | DoorLock.LockState = 1",
        "[2026-06-20T07:04:08] front_door_lock | DoorLock.LockState = 2",
        "[2026-06-20T07:04:13] front_door_lock | DoorLock.LockState = 1",
        "[2026-06-20T08:40:00] front_door_lock | DoorLock.LockState = 2",
        "[2026-06-20T20:43:11] front_door_lock | DoorLock.LockState = 1"
      ],
      "contradicted_by": []
    },
    {
      "id": "H2",
      "type": "normal",
      "description": "The entrance area was indeed empty during the logged periods, and the occupancy sensor readings are accurate (e.g., early morning/late night when no one entered).",
      "supported_by": [
        "[2026-06-20T00:00:00] entrance_occupancy | OccupancySensing.Occupancy = 0",
        "[2026-06-20T00:30:00] living_room_occupancy | OccupancySensing.Occupancy = 0"
      ],
      "contradicted_by": [
        "High frequency of manual and auto lock operations at 07:04, 08:40, and 20:43 implies human entry/exit events that should trigger the entrance motion sensor."
      ]
    }
  ],
  "most_concerning_signals": [
    "Discr


DF-04-FP: 门锁响应延迟但最终成功锁定

The entrance occupancy sensor reports 'empty' during active hours while other sensors detect presence, likely due to environmental interference or calibration drift rather than a critical system failure.

Explanation: Raw logs show multiple residents moving between rooms (kitchen, bedroom, living room) with occupancy=1 between 07:11 and 16:59, confirming the house is occupied.

Explanation: However, the specific 'entrance_occupancy' sensor consistently reports 0 during these same timestamps, creating a data inconsistency.

Explanation: Crucially, at 16:59:03, the entrance sensor finally transitions to Occupancy=1 immediately after a door unlock event at 16:58:08, suggesting the sensor is functional but suffers from lag, blind spots, or sensitivity issues in that specific zone.

[2026-06-11T07:11:02] kitchen_occupancy | OccupancySensing.Occupancy = 1

[2026-06-11T16:59:03] entrance_occupancy | OccupancySensing.Occupancy = 1

Calibrate the entrance PIR sensor to adjust sensitivity thresholds.

Inspect the entrance sensor's mounting angle for obstructions or direct sunlight interference.

Raw PIR (Passive Infrared) signal strength and raw motion vectors around 07:10 and 08:34 to check for micro-movements that may have been filtered out.

Sensor calibration history and last maintenance timestamp for entrance_occupancy.

Environmental data (temperature/humidity) at the entrance location during active hours to rule out interference from drafts causing false negatives.

Timestamp correlation between door lock state changes and any intermittent occupancy spikes.

## Task
Analyze the following evidence packet for a smart-home anomaly query.

## 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: 独居老人 (Elderly Living Alone)
- 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>, 床头灯<onoff_light>
- 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>, 厨房运动传感器<occupancy_sensor>
- living_room: 客厅温度传感器<temperature_sensor>, 客厅灯<dimmable_light>, 客厅烟雾报警器<smoke_co_alarm>, 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>

## Evidence Packet
{
  "sq_type": "SQ1",
  "event_count": 1851,
  "salient_event_count": 60,
  "focus_event_count": 97,
  "room_activity": {
    "kitchen": 23,
    "living_room": 21,
    "bedroom": 20,
    "bathroom": 9,
    "entrance": 24
  },
  "temperature_patterns": [],
  "suspicious_signals": [
    "[2026-06-11T07:10:09] front_door_lock | Event: LockOperation(LockOperationType=1, OperationSource=Manual)",
    "[2026-06-11T07:10:15] front_door_lock | Event: LockOperation(LockOperationType=0, OperationSource=Auto)",
    "[2026-06-11T08:34:00] front_door_lock | Event: LockOperation(LockOperationType=1, OperationSource=Manual)",
    "[2026-06-11T20:46:17] front_door_lock | Event: LockOperation(LockOperationType=0, OperationSource=Auto)",
    "[2026-06-11T05:34:17] bedroom_window | WindowCovering.CurrentPositionLiftPercent100ths = 0",
    "[2026-06-11T07:10:09] front_door_lock | DoorLock.LockState = 2",
    "[2026-06-11T07:10:15] front_door_lock | DoorLock.LockState = 1",
    "[2026-06-11T08:34:00] front_door_lock | DoorLock.LockState = 2",
    "[2026-06-11T12:31:11] bedroom_window | WindowCovering.CurrentP

## Task
Make the final anomaly decision from compressed evidence plus a focused raw-log excerpt.

## 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。

## Stage-1 Analysis
{
  "candidate_hypotheses": [
    {
      "id": "H1",
      "type": "anomaly",
      "description": "Entrance occupancy sensor reports continuous 'empty' state despite multiple door unlock events and manual lock operations occurring within the residence's active hours, suggesting potential sensor failure or misalignment.",
      "supported_by": [
        "[2026-06-11T07:10:09] front_door_lock | Event: LockOperation(LockOperationType=1, OperationSource=Manual)",
        "[2026-06-11T08:34:00] front_door_lock | Event: LockOperation(LockOperationType=1, OperationSource=Manual)",
        "[2026-06-11T05:34:17] bedroom_window | WindowCovering.CurrentPositionLiftPercent100ths = 0",
        "[2026-06-11T12:31:11] bedroom_window | WindowCovering.CurrentPositionLiftPercent100ths = 10000",
        "[2026-06-11T14:06:04] bedroom_window | WindowCovering.CurrentPositionLiftPercent100ths = 0",
        "[2026-06-11T16:58:03] front_door_lock | DoorLock.LockState = 2",
        "[2026-06-11T20:46:17] front_door_lock | DoorLock.LockState = 1"
      ],
      "contradicted_by": []
    },
    {
      "id": "H2",
      "type": "normal",
      "description": "The entrance occupancy sensor is functioning correctly; the resident entered/exited without triggering motion detection due to slow movement, use of a coat rack, or temporary sensor blind spots, while the door lock system operates normally.",
      "supported_by": [],
      "contradicted_by": [
        "[2026-06-11T00:00:00] entrance_occupancy | OccupancySensing.Occupancy = 0",
        "[2026-06-11T00:30:00] living_room_occupancy | OccupancySensing.Occupancy = 0"
      ]
    }
  ],
  "most_concerning_signals": [
    "[2026-06-11T07:10:09] front_door_lock | Event: LockOperation(LockOperationType=1, OperationSource=Manual)",
    "[2026-06-11T08:34:00] front


The bathroom occupancy sensor is functioning correctly, reporting consistent '0' readings during an empty period, matching the behavior of other zones and the overall system state.

Explanation: Analysis of the raw log shows that all zones (living_room, bedroom, bathroom) consistently report Occupancy = 0 throughout the entire night window (00:00 to 08:30).

Explanation: The system exhibits normal periodic telemetry updates for temperature sensors and static occupancy states, indicating the communication link between devices and the hub is healthy.

Explanation: No motion events were triggered in any room during this time frame; the first positive occupancy event occurs at 08:43 when the user enters the bedroom in the morning, confirming the sensors are active and responsive upon entry.

[2026-06-07T08:43:00] bedroom_occupancy | OccupancySensing.Occupancy = 1 (Sensor responds to presence)

No action required; continue standard monitoring.

Verify that the 'room_activity' count mentioned in Stage-1 corresponds to non-motion telemetry data as expected.

The query implies an investigation into anomalies; if this were purely normal, no specific focus on bathroom sensors would typically be triggered in an anomaly packet without accompanying alarm states.

Continuous stream of 'Occupancy = 0' from 00:00 to 02:00 in bathroom despite it being a high-priority safety zone.

Absence of any 'Occupancy = 1' or state change events in the provided suspicious_signals list for bathroom_occupancy.

Timestamps of the 18 'room_activity' events for 'bathroom': Check if they correlate with motion triggers or are merely status updates.

Raw binary payload of bathroom_occupancy sensor messages during the 00:00-02:00 window to verify if the sensor is transmitting data (even if value is 0) or completely silent (packet loss).

Check for any concurrent 'water_leak_detector' trigger events which might have been ignored due to sensor logic errors.

Verify if the 'front_door_lock' entry at 23:07 correlates with a return event that should have registered occupancy upon re-entry (if person entered after 00:00).

## Task
Analyze the following evidence packet for a smart-home anomaly query.

## 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>

## Evidence Packet
{
  "sq_type": "SQ1",
  "event_count": 1341,
  "salient_event_count": 60,
  "focus_event_count": 104,
  "room_activity": {
    "kitchen": 19,
    "living_room": 39,
    "bedroom": 25,
    "bathroom": 18,
    "entrance": 3
  },
  "temperature_patterns": [],
  "suspicious_signals": [
    "[2026-06-07T23:07:21] front_door_lock | Event: LockOperation(LockOperationType=0, OperationSource=Auto)",
    "[2026-06-07T08:43:20] bedroom_window | WindowCovering.CurrentPositionLiftPercent100ths = 0",
    "[2026-06-07T10:00:21] living_room_window | WindowCovering.CurrentPositionLiftPercent100ths = 0",
    "[2026-06-07T23:07:18] bedroom_window | WindowCovering.CurrentPositionLiftPercent100ths = 10000",
    "[2026-06-07T23:07:21] front_door_lock | DoorLock.LockState = 1",
    "[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",
    "[2026-06-07T00:30:00] living_room_occupancy | OccupancySensing.Occupancy = 0",
    "[2026-06-07T00:30:00] bedroom_occupancy | OccupancySensing.Occupancy = 0",
    "

## Task
Make the final anomaly decision from compressed evidence plus a focused raw-log excerpt.

## 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。

## Stage-1 Analysis
{
  "candidate_hypotheses": [
    {
      "id": "H1",
      "type": "anomaly",
      "description": "The bathroom occupancy sensor is exhibiting a potential failure mode where it fails to detect motion events, indicated by continuous '0' readings despite high room activity relative to other empty zones.",
      "supported_by": [
        "[2026-06-07T00:00:00] bathroom_occupancy | OccupancySensing.Occupancy = 0",
        "[2026-06-07T00:30:00] bathroom_occupancy | OccupancySensing.Occupancy = 0",
        "[2026-06-07T01:00:00] bathroom_occupancy | OccupancySensing.Occupancy = 0",
        "[2026-06-07T01:30:00] bathroom_occupancy | OccupancySensing.Occupancy = 0",
        "[2026-06-07T02:00:00] bathroom_occupancy | OccupancySensing.Occupancy = 0"
      ],
      "contradicted_by": [
        "room_activity.bathroom (18) vs living_room (39): While lower, the count of 18 suggests some interaction or background noise, but if these were user events, occupancy should fluctuate. If 18 represents background telemetry, the lack of positive occupancy signals is suspicious."
      ]
    },
    {
      "id": "H2",
      "type": "normal",
      "description": "The bathroom occupancy sensor is functioning correctly as a static presence detector that only reports '0' when no human movement is detected above a specific threshold, and the 'room_activity' count reflects non-motion events (e.g., temperature/humidity probes or leak checks).",
      "supported_by": [
        "Standard PIR behavior often defaults to 'empty' between movements unless configured for long-term presence tracking.",
        "Consistent '0' readings across multiple hourly intervals suggest stable hardware operation rather than erratic failure."
      ],
      "contradicted_by": [
        "The query implies an investigation into anomal


The bedroom occupancy sensor is functioning correctly; the previous 'Empty' readings were due to normal sleep hours, and the sensor accurately detected occupancy upon waking and activity.

Explanation: Initial zero readings (00:00-06:37) align perfectly with standard human sleep patterns and are corroborated by the absence of motion in other rooms during those same timestamps.

Explanation: At 06:38:00, the sensor immediately transitioned from '0' to '1', coinciding exactly with the timestamp of the light turning on and the user's window opening action.

Explanation: The subsequent consistent 'Occupancy = 1' readings confirm the device tracks presence accurately once activity resumes, ruling out a stuck or failed sensor state.

Explanation: No discrepancies exist between the sensor output and actual room events (light usage, window actuation), indicating healthy system behavior.

[2026-06-09T06:38:00] bedroom_occupancy | OccupancySensing.Occupancy = 1

[2026-06-09T06:38:08] bedroom_light | OnOff.OnOff = True

Continue monitoring as routine; no maintenance required.

```json
{
  "candidate_hypotheses": [
    {
      "id": "H1",
      "type": "anomaly",
      "description": "Bedroom occupancy sensor (occupancy_sensor) is stuck reporting 'Empty' (0) despite evidence of room activity and window interaction, suggesting a potential sensor failure or calibration drift.",
      "supported_by": [
        "[2026-06-09T06:38:16] bedroom_window | WindowCovering.CurrentPositionLiftPercent100ths = 0",
        "[2026-06-09T21:48:25] front_door_lock | DoorLock.LockState = 1",
        "room_activity.bedroom = 24"
      ],
      "contradicted_by": [
        "[2026-06-09T00:00:00] bedroom_occupancy | OccupancySensing.Occupancy = 0",
        "[2026-06-09T00:30:00] bedroom_occupancy | OccupancySensing.Occupancy = 0",
        "[2026-06-09T01:00:00] bedroom_occupancy | OccupancySensing.Occupancy = 0"
      ]
    },
    {
      "id": "H2",
      "type": "normal",
      "description": "The system correctly detected no occupants in the bedroom during the early morning hours (00:00 - 01:30), consistent with standard sleep patterns.",
      "supported_by": [
        "[2026-06-09T00:00:00] bedroom_occupancy | OccupancySensing.Occupancy = 0",
        "[2026-06-09T00:30:00] bedroom_occupancy | OccupancySensing.Occupancy = 0",
        "[2026-06-09T01:00:00] bedroom_occupancy | OccupancySensing.Occupancy = 0",
        "[2026-06-09T01:30:00] living_room_occupancy | OccupancySensing.Occupancy = 0"
      ],
      "contradicted_by": []
    }
  ],
  "most_concerning_signals": [
    "Multiple hourly reports of 'Occupancy = 0' for bedroom_occupancy at 00:00, 00:30, 01:00, 01:30 while other rooms show similar silence, but coupled with high room_activity count (24 events) implies either false negatives or a lack of baseline data showing expected presence.",
    "Window covering state change (0 to 10000/100%) occurring without corresponding occupancy detection changes."
  ],
  "what_to_verify_in_raw_log": [
    "Check raw motion detection logs for 'bedroom_occupancy' between 06:38 and 21:48 to see if any transient movement was missed or if the sensor remains permanently low.",
    "Verify if the 'bedroom_window' contact sensor triggered when the curtains were opened/closed; often motion sensors are triggered by the disturbance of opening blinds.",
    "Cross-reference the timestamp of the window actuation (06:38 and 21:48) with the occupancy log to confirm the gap between action and detection.",
    "Check battery voltage or signal strength metadata for 'bedroom_occupancy' if available in the full packet to rule out power-induced static readings."
  ],
  "provisional_risk": "medium"
}
```

## Task
Analyze the following evidence packet for a smart-home anomaly query.

## 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>

## Evidence Packet
{
  "sq_type": "SQ1",
  "event_count": 1270,
  "salient_event_count": 60,
  "focus_event_count": 109,
  "room_activity": {
    "kitchen": 19,
    "living_room": 34,
    "bedroom": 24,
    "bathroom": 21,
    "entrance": 11
  },
  "temperature_patterns": [],
  "suspicious_signals": [
    "[2026-06-09T07:36:08] front_door_lock | Event: LockOperation(LockOperationType=1, OperationSource=Manual)",
    "[2026-06-09T07:36:12] front_door_lock | Event: LockOperation(LockOperationType=0, OperationSource=Auto)",
    "[2026-06-09T18:30:00] front_door_lock | Event: LockOperation(LockOperationType=1, OperationSource=Manual)",
    "[2026-06-09T21:48:25] front_door_lock | Event: LockOperation(LockOperationType=0, OperationSource=Auto)",
    "[2026-06-09T06:38:16] bedroom_window | WindowCovering.CurrentPositionLiftPercent100ths = 0",
    "[2026-06-09T07:36:08] front_door_lock | DoorLock.LockState = 2",
    "[2026-06-09T07:36:12] front_door_lock | DoorLock.LockState = 1",
    "[2026-06-09T18:30:00] front_door_lock | DoorLock.LockState = 2",
    "[2026-06-09T21:48:21] bedroom_window | WindowCovering.CurrentPositionLiftPercent100ths = 10000",
    "[2026-06-09T21:48:25] front_door_l

## Task
Make the final anomaly decision from compressed evidence plus a focused raw-log excerpt.

## 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。

## Stage-1 Analysis
```json
{
  "candidate_hypotheses": [
    {
      "id": "H1",
      "type": "anomaly",
      "description": "Bedroom occupancy sensor (occupancy_sensor) is stuck reporting 'Empty' (0) despite evidence of room activity and window interaction, suggesting a potential sensor failure or calibration drift.",
      "supported_by": [
        "[2026-06-09T06:38:16] bedroom_window | WindowCovering.CurrentPositionLiftPercent100ths = 0",
        "[2026-06-09T21:48:25] front_door_lock | DoorLock.LockState = 1",
        "room_activity.bedroom = 24"
      ],
      "contradicted_by": [
        "[2026-06-09T00:00:00] bedroom_occupancy | OccupancySensing.Occupancy = 0",
        "[2026-06-09T00:30:00] bedroom_occupancy | OccupancySensing.Occupancy = 0",
        "[2026-06-09T01:00:00] bedroom_occupancy | OccupancySensing.Occupancy = 0"
      ]
    },
    {
      "id": "H2",
      "type": "normal",
      "description": "The system correctly detected no occupants in the bedroom during the early morning hours (00:00 - 01:30), consistent with standard sleep patterns.",
      "supported_by": [
        "[2026-06-09T00:00:00] bedroom_occupancy | OccupancySensing.Occupancy = 0",
        "[2026-06-09T00:30:00] bedroom_occupancy | OccupancySensing.Occupancy = 0",
        "[2026-06-09T01:00:00] bedroom_occupancy | OccupancySensing.Occupancy = 0",
        "[2026-06-09T01:30:00] living_room_occupancy | OccupancySensing.Occupancy = 0"
      ],
      "contradicted_by": []
    }
  ],
  "most_concerning_signals": [
    "Multiple hourly reports of 'Occupancy = 0' for bedroom_occupancy at 00:00, 00:30, 01:00, 01:30 while other rooms show similar silence, but coupled with high room_activity count (24 events) implies either false negatives or a lack of baseline data showing expected presence.",
    "Window covering state change


The entrance contact sensor is functioning correctly, successfully reporting door open/close states that align perfectly with the lock operations.

Explanation: Analysis of the focused raw log reveals explicit 'front_door_contact' state transitions (BooleanState.StateValue) occurring at 07:32:19, 07:32:26, 18:18:02, and 22:09:24.

Explanation: These sensor readings correlate logically with the adjacent 'front_door_lock' events: State=False immediately follows Manual Locking (Door Closed), and State=True immediately precedes Auto Unlocking (Door Open).

Explanation: The presence of these specific sensor logs directly contradicts Stage-1 Hypothesis H1 (sensor malfunction/missing data) and confirms Hypothesis H2 (normal operation).

[2026-06-26T07:32:19] front_door_contact | BooleanState.StateValue = False (Following Manual Lock)

[2026-06-26T07:32:26] front_door_contact | BooleanState.StateValue = True (Preceding Auto Unlock)

No action required. The system is operating as designed.

Absence of 'front_door_contact_sensor' state transitions (Open/Close) despite multiple manual and auto lock cycles occurring between 07:32 and 22:09.

Reliance solely on 'LockState' to infer door security status without corroborating physical proximity/contact sensor data.

Search raw logs for topic/prefix 'entrance_front_door_contact' or similar to verify if the sensor exists and what its value stream looks like during the times of lock operations.

Check if the 'front_door_lock' device ID includes the contact sensor functionality combined (some devices merge these), or if they are truly separate devices.

Verify the configuration of the entrance area to confirm whether a dedicated occupancy/contact sensor was physically installed according to the layout definition.

## Task
Analyze the following evidence packet for a smart-home anomaly query.

## 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>

## Evidence Packet
{
  "sq_type": "SQ1",
  "event_count": 1121,
  "salient_event_count": 60,
  "focus_event_count": 113,
  "room_activity": {
    "kitchen": 22,
    "living_room": 36,
    "bedroom": 27,
    "bathroom": 16,
    "entrance": 12
  },
  "temperature_patterns": [],
  "suspicious_signals": [
    "[2026-06-26T07:32:17] front_door_lock | Event: LockOperation(LockOperationType=1, OperationSource=Manual)",
    "[2026-06-26T07:32:27] front_door_lock | Event: LockOperation(LockOperationType=0, OperationSource=Auto)",
    "[2026-06-26T18:18:00] front_door_lock | Event: LockOperation(LockOperationType=1, OperationSource=Manual)",
    "[2026-06-26T22:09:25] front_door_lock | Event: LockOperation(LockOperationType=0, OperationSource=Auto)",
    "[2026-06-26T06:21:09] bedroom_window | WindowCovering.CurrentPositionLiftPercent100ths = 0",
    "[2026-06-26T07:32:17] front_door_lock | DoorLock.LockState = 2",
    "[2026-06-26T07:32:27] front_door_lock | DoorLock.LockState = 1",
    "[2026-06-26T18:18:00] front_door_lock | DoorLock.LockState = 2",
    "[2026-06-26T22:09:20] bedroom_window | WindowCovering.CurrentPositionLiftPercent100ths = 10000",
    "[2026-06-26T22:09:25] front_door_

## Task
Make the final anomaly decision from compressed evidence plus a focused raw-log excerpt.

## 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。

## Stage-1 Analysis
{
  "candidate_hypotheses": [
    {
      "id": "H1",
      "type": "anomaly",
      "description": "Entrance contact sensor (door status) is likely malfunctioning or misconfigured, failing to report door state changes independently of the lock mechanism.",
      "supported_by": [
        "The log contains frequent 'LockOperation' events (manual/auto) which trigger the lock motor, but there are no corresponding independent 'ContactSensor.Open/Close' events for the front door in the provided evidence.",
        "In a healthy system with both a lock and a contact sensor, one would expect distinct timestamped reports for the physical latch position change separate from the electronic locking command, especially given the high event count (1121) vs salient events (60)."
      ],
      "contradicted_by": [
        "None. The absence of explicit contact sensor state logs does not contradict the hypothesis; it supports the idea that the sensor data stream is missing or filtered out."
      ]
    },
    {
      "id": "H2",
      "type": "normal",
      "description": "Door locking sequence is functioning correctly (Manual Lock -> Auto Unlock cycle observed).",
      "supported_by": [
        "Timestamps show logical sequences: e.g., 07:32:17 Manual Lock (State=2) followed by 07:32:27 Auto Unlock (State=1), repeating at night.",
        "The DoorLock.LockState values (2=Latched/Locked, 1=Unlocked/Open) correlate perfectly with the operation types."
      ],
      "contradicted_by": [
        "No evidence contradicts the *lock* mechanics working, only the lack of independent *sensor verification* data for the door's physical state."
      ]
    }
  ],
  "most_concerning_signals": [
    "Absence of 'front_door_contact_sensor' state transitions (Open/Close) despite multiple manual and auto lock cyc


Kitchen temperature sensor reports physically impossible values (ranging from ~23°C to ~49°C) indicating a severe calibration error or hardware failure.

Explanation: Raw log shows kitchen_temp_sensor reporting values like 2281 and 2485, which are inconsistent with standard Celsius readings for indoor environments.

Explanation: The reported temperatures rise monotonically throughout the day to over 2400 (approx 49°C), suggesting the sensor is stuck in a high-gain state or misconfigured unit scaling rather than detecting actual heat.

Explanation: Despite these erroneous readings, the sensor remains active and reporting at regular intervals, ruling out total malfunction/inactivity but confirming data integrity failure.

[2026-06-09T00:00:00] kitchen_temp_sensor | TemperatureMeasurement.MeasuredValue = 2281

[2026-06-09T06:00:00] kitchen_temp_sensor | TemperatureMeasurement.MeasuredValue = 2484

Verify physical sensor reading against an independent thermometer immediately.

Reset or replace the kitchen temperature sensor due to calibration drift.

Review system configuration to ensure temperature units are correctly set to Celsius and not raw ADC counts.

## Task
Analyze the following evidence packet for a smart-home anomaly query.

## 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: 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>

## Evidence Packet
{
  "sq_type": "SQ1",
  "event_count": 1123,
  "salient_event_count": 60,
  "focus_event_count": 111,
  "room_activity": {
    "kitchen": 21,
    "living_room": 36,
    "bedroom": 26,
    "bathroom": 16,
    "entrance": 12
  },
  "temperature_patterns": [],
  "suspicious_signals": [
    "[2026-06-09T07:38:19] front_door_lock | Event: LockOperation(LockOperationType=1, OperationSource=Manual)",
    "[2026-06-09T07:38:26] front_door_lock | Event: LockOperation(LockOperationType=0, OperationSource=Auto)",
    "[2026-06-09T18:17:00] front_door_lock | Event: LockOperation(LockOperationType=1, OperationSource=Manual)",
    "[2026-06-09T22:04:32] front_door_lock | Event: LockOperation(LockOperationType=0, OperationSource=Auto)",
    "[2026-06-09T06:19:13] bedroom_window | WindowCovering.CurrentPositionLiftPercent100ths = 0",
    "[2026-06-09T07:38:19] front_door_lock | DoorLock.LockState = 2",
    "[2026-06-09T07:38:26] front_door_lock | DoorLock.LockState = 1",
    "[2026-06-09T18:17:00] front_door_lock | DoorLock.LockState = 2",
    "[2026-06-09T22:04:29] bedroom_window | WindowCovering.CurrentPositionLiftPercent100ths = 10000",
    "[2026-06-09T22:04:32] front_door_l

## Task
Make the final anomaly decision from compressed evidence plus a focused raw-log excerpt.

## 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。

## Stage-1 Analysis
{
  "candidate_hypotheses": [
    {
      "id": "H1",
      "type": "anomaly",
      "description": "Kitchen sensor suite is non-functional or data is missing, evidenced by the complete absence of any kitchen-related events in the evidence packet despite significant room activity.",
      "supported_by": ["focus_event_count=111 with zero entries for kitchen sensors (temperature, smoke, contact, etc.)", "room_activity.kitchen=21 indicates physical presence/activity but no corresponding telemetry data", "absence of temperature_patterns array which usually aggregates all rooms"],
      "contradicted_by": []
    },
    {
      "id": "H2",
      "type": "anomaly",
      "description": "Smoke alarm may be offline, misconfigured, or failing to report status changes during active kitchen usage.",
      "supported_by": ["No 'smoke_co_alarm' events recorded while kitchen activity count is 21"],
      "contradicted_by": []
    }
  ],
  "most_concerning_signals": [
    "Zero salient/focus events detected for kitchen devices (temperature_sensor, smoke_co_alarm, contact_sensor, cook_surface) despite 21 minutes of reported activity in the kitchen area.",
    "Missing temperature_patterns data implies a failure to aggregate or stream thermal data from the kitchen zone."
  ],
  "what_to_verify_in_raw_log": [
    "Check if raw MQTT/HTTP logs contain published messages for topics prefixed with 'kitchen/' or specific IDs for temperature and smoke sensors.",
    "Verify if the kitchen motion sensor triggered events that should have correlated with other device states.",
    "Inspect system logs for 'device_offline', 'heartbeat_timeout', or 'publish_failed' errors associated with kitchen device IDs."
  ],
  "provisional_risk": "medium"
}

## Focused Raw Log
[2026-06-09T00:00:00] kitchen_temp_sensor | Temperat


The living room occupancy sensor is functioning correctly; the reported '0' values correspond to an empty house state during the recorded night hours, with no contradictory activity evidence found.

Explanation: Raw logs show continuous occupancy readings of 0 for the living room from 00:00 to 06:30, matching the expected 'empty house' state at night.

Explanation: Activity events (light on, window open) first appear in the bedroom and bathroom starting at 06:19, confirming human presence occurred elsewhere but not in the living room.

Explanation: There is no evidence of user presence in the living room (e.g., motion events or appliance usage) that would contradict a reading of 0.

[2026-06-08T06:30:00] living_room_occupancy | OccupancySensing.Occupancy = 0

## Task
Analyze the following evidence packet for a smart-home anomaly query.

## 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>

## Evidence Packet
{
  "sq_type": "SQ1",
  "event_count": 1122,
  "salient_event_count": 60,
  "focus_event_count": 114,
  "room_activity": {
    "kitchen": 23,
    "living_room": 37,
    "bedroom": 26,
    "bathroom": 16,
    "entrance": 12
  },
  "temperature_patterns": [],
  "suspicious_signals": [
    "[2026-06-08T07:22:16] front_door_lock | Event: LockOperation(LockOperationType=1, OperationSource=Manual)",
    "[2026-06-08T07:22:27] front_door_lock | Event: LockOperation(LockOperationType=0, OperationSource=Auto)",
    "[2026-06-08T18:26:00] front_door_lock | Event: LockOperation(LockOperationType=1, OperationSource=Manual)",
    "[2026-06-08T21:54:45] front_door_lock | Event: LockOperation(LockOperationType=0, OperationSource=Auto)",
    "[2026-06-08T06:19:11] bedroom_window | WindowCovering.CurrentPositionLiftPercent100ths = 0",
    "[2026-06-08T07:22:16] front_door_lock | DoorLock.LockState = 2",
    "[2026-06-08T07:22:27] front_door_lock | DoorLock.LockState = 1",
    "[2026-06-08T18:26:00] front_door_lock | DoorLock.LockState = 2",
    "[2026-06-08T21:54:35] bedroom_window | WindowCovering.CurrentPositionLiftPercent100ths = 10000",
    "[2026-06-08T21:54:45] front_do

## Task
Make the final anomaly decision from compressed evidence plus a focused raw-log excerpt.

## 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。

## Stage-1 Analysis
{
  "candidate_hypotheses": [
    {
      "id": "H1",
      "type": "anomaly",
      "description": "Living room occupancy sensor is malfunctioning (stuck/off) because it fails to detect the user's presence in the living room despite confirmed activity events in that area.",
      "supported_by": ["room_activity.living_room = 37"],
      "contradicted_by": []
    },
    {
      "id": "H2",
      "type": "normal",
      "description": "All sensors are functioning correctly, and the low 'occupancy' values represent an empty house state with no actual activity detected by the motion sensors.",
      "supported_by": [],
      "contradicted_by": ["room_activity.living_room = 37"]
    }
  ],
  "most_concerning_signals": [
    "[2026-06-08T00:00:00] living_room_occupancy | OccupancySensing.Occupancy = 0",
    "[2026-06-08T00:30:00] living_room_occupancy | OccupancySensing.Occupancy = 0",
    "[2026-06-08T01:00:00] living_room_occupancy | OccupancySensing.Occupancy = 0"
  ],
  "what_to_verify_in_raw_log": [
    "Timestamped raw event logs for 'living_room_motion' between 00:00:00 and 01:30:00 to check for missed detections or sensor dead time.",
    "Raw configuration data for 'living_room_occupancy' to verify if the sensitivity threshold was manually lowered or if the device firmware version indicates a known bug.",
    "Correlation with 'living_room_light' or 'living_room_air_conditioner' state changes during the reported active hours to see if other devices triggered based on logic that assumes occupancy."
  ],
  "provisional_risk": "medium"
}

## Focused Raw Log
[2026-06-08T00:00:00] kitchen_temp_sensor | TemperatureMeasurement.MeasuredValue = 2262
[2026-06-08T00:00:00] living_room_occupancy | OccupancySensing.Occupancy = 0
[2026-06-08T00:00:00] bedroom_occupancy | OccupancySensing.Occup


The living room occupancy sensor is functioning correctly; the high activity count was a transient spike or error, as confirmed by subsequent consistent zero readings until actual occupancy occurred at night.

Explanation: Initial discrepancy between 'room_activity' (35) and 'Occupancy=0' at T07:39 appears to be a data inconsistency or transient glitch rather than a hardware failure, given the lack of supporting physical events (like lights turning on).

Explanation: Throughout the day (00:00 to 06:50), the sensor consistently reports 0 occupancy while other sensors in different rooms (bedroom, bathroom) report accurate state changes when people enter/leave, proving the system logic is sound.

Explanation: Crucially, at T22:04:06, the sensor correctly detects human presence ('Occupancy = 1') after people return home, confirming the device was not stuck or disconnected.

Explanation: The temperature logs show normal heating patterns without HVAC anomalies that would suggest an unoccupied space being actively heated by mistake.

[2026-06-01T00:00:00] ... [2026-06-01T06:50:00] living_room_occupancy | OccupancySensing.Occupancy = 0 (Consistent reporting during absence)

[2026-06-01T06:33:08] bedroom_window | WindowCovering.CurrentPositionLiftPercent100ths = 0 AND [2026-06-01T06:33:00] bedroom_occupancy | OccupancySensing.Occupancy = 1 (Other sensors working perfectly)

[2026-06-01T22:04:06] living_room_occupancy | OccupancySensing.Occupancy = 1 (Sensor successfully detects presence upon return)

Log the isolated high 'room_activity' value at 07:39 as a potential sensor calibration drift or temporary network packet loss for review.

Continue monitoring; no immediate intervention required as the device has proven reliable upon reactivation.

Status logs for 'living_room_air_conditioner' and 'living_room_dimmable_light' to see if devices are active while occupancy reports 0.

Signal strength or battery level logs for 'living_room_occupancy_sensor' if available.

## Task
Analyze the following evidence packet for a smart-home anomaly query.

## 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>

## Evidence Packet
{
  "sq_type": "SQ1",
  "event_count": 1277,
  "salient_event_count": 60,
  "focus_event_count": 107,
  "room_activity": {
    "kitchen": 18,
    "living_room": 35,
    "bedroom": 24,
    "bathroom": 19,
    "entrance": 11
  },
  "temperature_patterns": [],
  "suspicious_signals": [
    "[2026-06-01T07:39:14] front_door_lock | Event: LockOperation(LockOperationType=1, OperationSource=Manual)",
    "[2026-06-01T07:39:24] front_door_lock | Event: LockOperation(LockOperationType=0, OperationSource=Auto)",
    "[2026-06-01T18:20:00] front_door_lock | Event: LockOperation(LockOperationType=1, OperationSource=Manual)",
    "[2026-06-01T22:01:49] front_door_lock | Event: LockOperation(LockOperationType=0, OperationSource=Auto)",
    "[2026-06-01T06:33:08] bedroom_window | WindowCovering.CurrentPositionLiftPercent100ths = 0",
    "[2026-06-01T07:39:14] front_door_lock | DoorLock.LockState = 2",
    "[2026-06-01T07:39:24] front_door_lock | DoorLock.LockState = 1",
    "[2026-06-01T18:20:00] front_door_lock | DoorLock.LockState = 2",
    "[2026-06-01T22:01:38] bedroom_window | WindowCovering.CurrentPositionLiftPercent100ths = 10000",
    "[2026-06-01T22:01:49] front_do

## Task
Make the final anomaly decision from compressed evidence plus a focused raw-log excerpt.

## 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。

## Stage-1 Analysis
{
  "candidate_hypotheses": [
    {
      "id": "H1",
      "type": "anomaly",
      "description": "Living room occupancy sensor is malfunctioning or disconnected, as it fails to detect human presence despite high activity in the living room and consistent readings from other sensors.",
      "supported_by": [
        "[2026-06-01T07:39:14] ... room_activity: 'living_room': 35 (High activity reported)",
        "[2026-06-01T00:00:00] living_room_occupancy | OccupancySensing.Occupancy = 0",
        "[2026-06-01T00:30:00] living_room_occupancy | OccupancySensing.Occupancy = 0",
        "[2026-06-01T01:00:00] living_room_occupancy | OccupancySensing.Occupancy = 0"
      ],
      "contradicted_by": []
    },
    {
      "id": "H2",
      "type": "normal",
      "description": "The house is currently empty (no one home), and all sensors are reporting correctly.",
      "supported_by": [
        "[2026-06-01T00:00:00] bedroom_occupancy | OccupancySensing.Occupancy = 0",
        "[2026-06-01T00:00:00] bathroom_occupancy | OccupancySensing.Occupancy = 0",
        "[2026-06-01T07:39:14] ... room_activity: 'kitchen': 18, 'bedroom': 24, 'bathroom': 19, 'entrance': 11 (Activity exists elsewhere)"
      ],
      "contradicted_by": [
        "[2026-06-01T07:39:14] ... room_activity: 'living_room': 35 (High activity count implies people should be present if H2 were true, yet occupancy reads 0)"
      ]
    }
  ],
  "most_concerning_signals": [
    "Discrepancy between high 'living_room' activity count (35) and persistent 'Occupancy = 0' readings.",
    "Lack of temperature pattern data which might indicate HVAC interaction with the space."
  ],
  "what_to_verify_in_raw_log": [
    "Raw timestamped events for 'living_room_occupancy' to check for missed detection windows or communication drops.",


The kitchen temperature sensor is reporting physically impossible values (ranging from ~2261 to ~2641) instead of realistic Celsius temperatures (~22-26), indicating a severe calibration error or data corruption.

Explanation: Kitchen temperature readings start at 2261 and steadily increase over time, reaching 2641 by 18:20.

Explanation: Realistic indoor kitchen temperatures should be between 15°C and 30°C; the reported values are 20x higher than physical possibility.

Explanation: The living room sensor reports similar inflated numbers (e.g., 2475), suggesting a systemic unit conversion failure (likely reading in decicelsius or millikelvin but labeled as celsius) rather than localized hardware damage.

Explanation: This pattern constitutes a 'sensor drift' or 'sensor malfunction' where the device output has drifted completely off-scale.

[2026-06-23T18:20:00] kitchen_temp_sensor | TemperatureMeasurement.MeasuredValue = 2641

Immediately isolate the kitchen temperature sensor from automated alerts to prevent false fire/cooking notifications.

Check sensor firmware for known unit conversion bugs or replace the sensor with a calibrated unit.

Verify if other sensors in the house exhibit similar scaling errors to confirm a system-wide software issue.

## Task
Analyze the following evidence packet for a smart-home anomaly query.

## 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: 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>

## Evidence Packet
{
  "sq_type": "SQ1",
  "event_count": 1121,
  "salient_event_count": 60,
  "focus_event_count": 112,
  "room_activity": {
    "kitchen": 21,
    "living_room": 37,
    "bedroom": 23,
    "bathroom": 19,
    "entrance": 12
  },
  "temperature_patterns": [],
  "suspicious_signals": [
    "[2026-06-23T07:18:20] front_door_lock | Event: LockOperation(LockOperationType=1, OperationSource=Manual)",
    "[2026-06-23T07:18:23] front_door_lock | Event: LockOperation(LockOperationType=0, OperationSource=Auto)",
    "[2026-06-23T18:22:00] front_door_lock | Event: LockOperation(LockOperationType=1, OperationSource=Manual)",
    "[2026-06-23T22:08:33] front_door_lock | Event: LockOperation(LockOperationType=0, OperationSource=Auto)",
    "[2026-06-23T06:15:15] bedroom_window | WindowCovering.CurrentPositionLiftPercent100ths = 0",
    "[2026-06-23T07:18:20] front_door_lock | DoorLock.LockState = 2",
    "[2026-06-23T07:18:23] front_door_lock | DoorLock.LockState = 1",
    "[2026-06-23T18:22:00] front_door_lock | DoorLock.LockState = 2",
    "[2026-06-23T22:08:29] bedroom_window | WindowCovering.CurrentPositionLiftPercent100ths = 10000",
    "[2026-06-23T22:08:33] front_door_l

## Task
Make the final anomaly decision from compressed evidence plus a focused raw-log excerpt.

## 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。

## Stage-1 Analysis
{
  "candidate_hypotheses": [
    {
      "id": "H1",
      "type": "normal",
      "description": "Kitchen sensors are functioning normally with no detected anomalies.",
      "supported_by": ["No events in suspicious_signals or salient_event_count relate to kitchen devices (temperature_sensor, onoff_light, cook_surface, smoke_co_alarm, contact_sensor, dishwasher)."],
      "contradicted_by": []
    }
  ],
  "most_concerning_signals": [],
  "what_to_verify_in_raw_log": [],
  "provisional_risk": "none"
}

## Focused Raw Log
[2026-06-23T00:00:00] kitchen_temp_sensor | TemperatureMeasurement.MeasuredValue = 2261
[2026-06-23T00:00:00] living_room_occupancy | OccupancySensing.Occupancy = 0
[2026-06-23T00:00:00] bedroom_occupancy | OccupancySensing.Occupancy = 0
[2026-06-23T00:00:00] bathroom_occupancy | OccupancySensing.Occupancy = 0
[2026-06-23T00:05:00] living_room_temp_sensor | TemperatureMeasurement.MeasuredValue = 2265
[2026-06-23T00:30:00] kitchen_temp_sensor | TemperatureMeasurement.MeasuredValue = 2278
[2026-06-23T00:30:00] living_room_occupancy | OccupancySensing.Occupancy = 0
[2026-06-23T00:30:00] bedroom_occupancy | OccupancySensing.Occupancy = 0
[2026-06-23T00:30:00] bathroom_occupancy | OccupancySensing.Occupancy = 0
[2026-06-23T00:35:00] living_room_temp_sensor | TemperatureMeasurement.MeasuredValue = 2290
[2026-06-23T01:00:00] kitchen_temp_sensor | TemperatureMeasurement.MeasuredValue = 2299
[2026-06-23T01:00:00] living_room_occupancy | OccupancySensing.Occupancy = 0
[2026-06-23T01:00:00] bedroom_occupancy | OccupancySensing.Occupancy = 0
[2026-06-23T01:00:00] bathroom_occupancy | OccupancySensing.Occupancy = 0
[2026-06-23T01:05:00] living_room_temp_sensor | TemperatureMeasurement.MeasuredValue = 2289
[2026-06-23T01:30:00] kitchen_temp_sensor | TemperatureMeasurement.MeasuredV


The bedroom occupancy sensor reports continuous 'empty' status during the night and early morning (00:00–08:28) despite significant activity logs (window adjustments at 23:09) that logically imply human presence or active automation requiring occupancy context.

Explanation: Raw log shows consistent 'Occupancy = 0' for bedroom_occupancy from 00:00 to 08:28.

Explanation: Activity log at 23:09:19 shows a window covering state change in the bedroom; while this could be automated, the prolonged failure to detect the user present until 08:29 suggests the sensor is not functioning correctly rather than just being delayed.

Explanation: The sensor failed to transition to 'Occupied' immediately upon the visible human interaction with the window at 23:09, indicating a false negative.

[2026-06-21T08:29:00] bedroom_occupancy | OccupancySensing.Occupancy = 1

Inspect the physical placement of the bedroom motion sensor for obstruction or misalignment.

Replace the battery of the bedroom occupancy sensor if low power is suspected.

Re-calibrate the sensor sensitivity settings in the home automation app.

Timestamps and source IPs for the 'bedroom_window' state change events to determine if they were triggered by a scheduled task, voice command, or manual app interaction.

Raw signal strength or RSSI values from the bedroom_occupancy sensor around 23:09 to rule out radio interference causing false negatives.

System logs for any 'sensor calibration', 'battery low', or 'firmware update' events affecting the bedroom_occupancy node between 00:00 and 02:00.

## Task
Analyze the following evidence packet for a smart-home anomaly query.

## 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>

## Evidence Packet
{
  "sq_type": "SQ1",
  "event_count": 1347,
  "salient_event_count": 60,
  "focus_event_count": 104,
  "room_activity": {
    "kitchen": 19,
    "living_room": 40,
    "bedroom": 24,
    "bathroom": 18,
    "entrance": 3
  },
  "temperature_patterns": [],
  "suspicious_signals": [
    "[2026-06-21T23:09:26] front_door_lock | Event: LockOperation(LockOperationType=0, OperationSource=Auto)",
    "[2026-06-21T08:29:12] bedroom_window | WindowCovering.CurrentPositionLiftPercent100ths = 0",
    "[2026-06-21T10:04:24] living_room_window | WindowCovering.CurrentPositionLiftPercent100ths = 0",
    "[2026-06-21T23:09:19] bedroom_window | WindowCovering.CurrentPositionLiftPercent100ths = 10000",
    "[2026-06-21T23:09:26] front_door_lock | DoorLock.LockState = 1",
    "[2026-06-21T00:00:00] living_room_occupancy | OccupancySensing.Occupancy = 0",
    "[2026-06-21T00:00:00] bedroom_occupancy | OccupancySensing.Occupancy = 0",
    "[2026-06-21T00:00:00] bathroom_occupancy | OccupancySensing.Occupancy = 0",
    "[2026-06-21T00:30:00] living_room_occupancy | OccupancySensing.Occupancy = 0",
    "[2026-06-21T00:30:00] bedroom_occupancy | OccupancySensing.Occupancy = 0",
    "[

## Task
Make the final anomaly decision from compressed evidence plus a focused raw-log excerpt.

## 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。

## Stage-1 Analysis
{
  "candidate_hypotheses": [
    {
      "id": "H1",
      "type": "anomaly",
      "description": "Bedroom occupancy sensor is malfunctioning or disabled, reporting continuous 'empty' status despite active device interactions (window operation) occurring within the room.",
      "supported_by": [
        "Multiple consecutive readings from 00:00 to 02:00 show bedroom_occupancy as 0.",
        "Activity log shows 'bedroom_window' state changes at 08:29 and 23:09, implying human presence or automated control requiring context."
      ],
      "contradicted_by": []
    },
    {
      "id": "H2",
      "type": "normal",
      "description": "The house was empty overnight (00:00 - 02:00), and the morning activity (08:29 window close/open sequence) occurred while no one was detected by the motion sensor, suggesting delayed detection or a specific automation trigger unrelated to immediate occupancy.",
      "supported_by": [
        "Consistent 0 occupancy reading across all rooms during early morning hours.",
        "Front door unlocked at 23:09, suggesting departure before the reported occupancy check times."
      ],
      "contradicted_by": [
        "Window covering position changed from 0% to 100% at 23:09:24. If the home was truly empty and the user had left at 23:09, an automatic window closure command without prior occupancy confirmation seems unlikely unless pre-programmed, but the lack of any other evening activity makes this suspicious."
      ]
    }
  ],
  "most_concerning_signals": [
    "[2026-06-21T23:09:24] bedroom_window | WindowCovering.CurrentPositionLiftPercent100ths = 10000 (Closing/Opening event)",
    "[2026-06-21T23:09:19] bedroom_window | WindowCovering.CurrentPositionLiftPercent100ths = 0"
  ],
  "what_to_verify_in_raw_log": [
    "Timestamps and source IPs for th


The bathroom occupancy sensor is functioning correctly; it remained 'empty' during the early morning hours and only reported 'occupied' when residents actually entered the room at 06:28.

Explanation: Initial hypothesis suggested a malfunction due to consistent '0' readings, but this was contradicted by raw log data showing valid state changes later in the day.

Explanation: At 06:28:06, the bathroom sensor correctly transitioned from 0 to 1 immediately after the bathroom light turned on, coinciding with bedroom activity indicating resident presence.

Explanation: The period of continuous '0' readings (00:00 - 06:27) aligns perfectly with the absence of any bathroom light activation or door contact events, confirming the bathroom was genuinely unused.

[2026-06-10T06:28:06] bathroom_occupancy | OccupancySensing.Occupancy = 1

[2026-06-10T06:28:06] bathroom_light | OnOff.OnOff = True

Check for missing timestamp entries in the raw log for bathroom_occupancy between 07:32 and 22:06 to see if data transmission was interrupted.

Verify if the bathroom light (onoff_light) or door contact changed states at any point, which might indicate presence without motion detection.

Cross-reference with smart speaker logs or manual override records to confirm no one manually silenced the sensor.

## Task
Analyze the following evidence packet for a smart-home anomaly query.

## 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>

## Evidence Packet
{
  "sq_type": "SQ1",
  "event_count": 1274,
  "salient_event_count": 60,
  "focus_event_count": 107,
  "room_activity": {
    "kitchen": 19,
    "living_room": 34,
    "bedroom": 23,
    "bathroom": 19,
    "entrance": 12
  },
  "temperature_patterns": [],
  "suspicious_signals": [
    "[2026-06-10T07:32:23] front_door_lock | Event: LockOperation(LockOperationType=1, OperationSource=Manual)",
    "[2026-06-10T07:32:30] front_door_lock | Event: LockOperation(LockOperationType=0, OperationSource=Auto)",
    "[2026-06-10T18:11:00] front_door_lock | Event: LockOperation(LockOperationType=1, OperationSource=Manual)",
    "[2026-06-10T22:06:37] front_door_lock | Event: LockOperation(LockOperationType=0, OperationSource=Auto)",
    "[2026-06-10T06:32:16] bedroom_window | WindowCovering.CurrentPositionLiftPercent100ths = 0",
    "[2026-06-10T07:32:23] front_door_lock | DoorLock.LockState = 2",
    "[2026-06-10T07:32:30] front_door_lock | DoorLock.LockState = 1",
    "[2026-06-10T18:11:00] front_door_lock | DoorLock.LockState = 2",
    "[2026-06-10T22:06:27] bedroom_window | WindowCovering.CurrentPositionLiftPercent100ths = 10000",
    "[2026-06-10T22:06:37] front_door_

## Task
Make the final anomaly decision from compressed evidence plus a focused raw-log excerpt.

## 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。

## Stage-1 Analysis
{
  "candidate_hypotheses": [
    {
      "id": "H1",
      "type": "anomaly",
      "description": "Bathroom occupancy sensor is non-functional or disabled, as it consistently reports '0' (empty) even during periods of high household activity (kitchen/living room usage) and potential bathroom use.",
      "supported_by": [
        "[2026-06-10T00:00:00] bathroom_occupancy | OccupancySensing.Occupancy = 0",
        "[2026-06-10T00:30:00] bathroom_occupancy | OccupancySensing.Occupancy = 0",
        "[2026-06-10T01:00:00] bathroom_occupancy | OccupancySensing.Occupancy = 0",
        "Room activity counts show significant activity in kitchen (19) and living_room (34), suggesting residents are awake and moving, making a continuous 'empty' state in the connected bathroom suspicious."
      ],
      "contradicted_by": []
    },
    {
      "id": "H2",
      "type": "normal",
      "description": "The bathroom occupancy sensor is functioning correctly because the bathroom has genuinely been unused throughout the entire monitoring period.",
      "supported_by": [
        "All timestamps for bathroom_occupancy report 0.",
        "No specific water leak events or light on/off events triggered by motion are visible in the provided subset to contradict this directly."
      ],
      "contradicted_by": [
        "High activity in adjacent rooms (kitchen, living_room) while the house seems occupied at other times suggests the bathroom should have seen some activity if the family was present."
      ]
    }
  ],
  "most_concerning_signals": [
    "bathroom_occupancy consistently reporting 0 across multiple hourly intervals despite general home activity",
    "Lack of any water_leak_detector or onoff_light trigger events associated with bathroom sensors in the evidence packet"
  ],
  "what_to_verify