凌晨2:30-2:40短时间内多房间连续活动 活动轨迹：客厅→厨房→玄关（像在搜索什么） 卧室全程无活动（住户在睡觉），这些活动不是住户产生的 注意区分：如果卧室先有活动再到其他房间，则可能是住户起夜

Evidence indicates a likely intrusion or simulation of occupancy via an automated sequence: door contact triggered at 20:43:12, followed immediately by auto-locking and the closing of the bedroom window, occurring after all daytime human occupancy signals ceased.

Explanation: Daytime activity (11:55–14:04) shows consistent multi-room occupancy and temperature fluctuations typical of human presence, which abruptly stops around 14:04.

Explanation: At 20:43:12, the front door contact sensor changed state to True (indicating opening), preceding the lock operation by only one second, suggesting immediate interaction rather than a delayed routine.

Explanation: The subsequent 'Auto' lock command and the rapid closure of the bedroom window (within 9 seconds) form a coherent sequence mimicking 'arming' or securing a home against entry, but without any recorded human occupancy events in the logs between 14:04 and 20:43.

Explanation: Unlike normal routines where occupants manually trigger these actions while present, this sequence is isolated in time with no motion sensor data supporting human presence during the critical window.

[2026-06-10T20:43:12] front_door_contact | BooleanState.StateValue = True

[2026-06-10T20:43:13] front_door_lock | Event: LockOperation(... OperationSource=Auto)

Absence of occupancy sensors in entrance/living room/bedroom between 14:04 and 20:43

Immediately verify physical status of doors and windows on-site.

Review CCTV footage for the time range 20:40–20:45 to identify if the door was opened remotely or physically.

Check smart home network logs for unauthorized access attempts or spoofed device commands prior to 20:43:13.

Reset smart locks and review user access permissions.

Repeated pattern of window operations (close/open) around 12:31 and 14:04 which may indicate a smart home routine or tampering test.

Check for 'unlock' events prior to the 20:43:13 lock event to determine if the door was recently opened by an intruder or if this is a false positive lock cycle.

Verify the source IP or MAC address associated with the 'OperationSource=Auto' lock command.

Cross-reference motion sensor data between 20:43:13 and 20:43:22 to confirm if any human presence was detected near the entrance or bedroom window.

Inspect the history of the 'window_covering' device to see if it supports remote control commands and identify who initiated the movements at 12:31 and 14:04.

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

## Query
以下是过去 12 小时的设备活动日志。请分析是否存在异常行为模式，并说明推理过程。

## 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": "SQ3",
  "event_count": 1062,
  "salient_event_count": 60,
  "focus_event_count": 94,
  "room_activity": {
    "kitchen": 27,
    "entrance": 17,
    "living_room": 29,
    "bedroom": 19,
    "bathroom": 2
  },
  "temperature_patterns": [],
  "suspicious_signals": [
    "[2026-06-10T20:43:13] front_door_lock | Event: LockOperation(LockOperationType=0, OperationSource=Auto)",
    "[2026-06-10T12:31:08] bedroom_window | WindowCovering.CurrentPositionLiftPercent100ths = 10000",
    "[2026-06-10T14:04:03] bedroom_window | WindowCovering.CurrentPositionLiftPercent100ths = 0",
    "[2026-06-10T20:43:13] front_door_lock | DoorLock.LockState = 1",
    "[2026-06-10T20:43:22] bedroom_window | WindowCovering.CurrentPositionLiftPercent100ths = 10000",
    "[2026-06-10T11:55:03] entrance_occupancy | OccupancySensing.Occupancy = 1",
    "[2026-06-10T11:55:09] living_room_occupancy | OccupancySensing.Occupancy = 1",
    "[2026-06-10T11:58:00] bedroom_occupancy | OccupancySensing.Occupancy = 1",
    "[2026-06-10T11:58:00] kitchen_occupancy | OccupancySensing.Occupancy = 1",
    "[2026-06-10T12:00:03] e

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

## Query
以下是过去 12 小时的设备活动日志。请分析是否存在异常行为模式，并说明推理过程。

## Stage-1 Analysis
{
  "candidate_hypotheses": [
    {
      "id": "H1",
      "type": "anomaly",
      "description": "Suspected unauthorized entry or simulation of occupancy via automated window manipulation and door locking sequence.",
      "supported_by": [
        "[2026-06-10T20:43:13] front_door_lock | Event: LockOperation(LockOperationType=0, OperationSource=Auto)",
        "[2026-06-10T20:43:13] front_door_lock | DoorLock.LockState = 1",
        "[2026-06-10T20:43:22] bedroom_window | WindowCovering.CurrentPositionLiftPercent100ths = 10000"
      ],
      "contradicted_by": []
    },
    {
      "id": "H2",
      "type": "normal",
      "description": "Routine daytime activity with occupant moving through rooms (entrance -> living room -> bedroom/kitchen) before leaving at night.",
      "supported_by": [
        "High event counts in kitchen(27), living_room(29), entrance(17) during the day.",
        "Sequential occupancy events: entrance(11:55) -> living_room(12:00) -> bedroom/kitchen(12:03/12:08) -> bathroom exit(12:02)."
      ],
      "contradicted_by": [
        "The specific timestamped 'Auto' lock operation and subsequent window opening occurring after the last recorded daytime movement suggests an automated anomaly rather than manual human action.",
        "Room activity distribution shows very low activity in bathroom (2) compared to others, potentially indicating infrequent use or sensor malfunction during the day."
      ]
    }
  ],
  "most_concerning_signals": [
    "Simultaneous front door auto-lock and front door state change at 20:43:13 without explicit user trigger context in logs.",
    "Bedroom window closing to 100% immediately following the door lock event (20:43:22 vs 20:43:13).",
    "Repeated pattern of window operations (close/open) around 12:31 and 14:04 which may indicate a smar


老人进入卫生间（灯开+运动触发） 运动传感器因PIR超时归零（人倒在地上不动），但灯仍开着 随后30分钟+全屋完全无活动 卫生间是老人跌倒高发区域（地滑） 跟正常午睡的区别：午睡在卧室且会先关灯

中等紧急度警报，尝试通过语音/通知联系老人，如无回应建议联系紧急联系人

No anomaly detected; observed patterns align with a resident's normal morning routine of leaving and returning, followed by evening adjustments.

Explanation: Occupancy logs confirm the house was empty until 07:05, when occupancy appeared in the bedroom immediately before the door lock interaction, indicating a resident inside rather than an intruder.

Explanation: The sequence at 07:05 shows a manual lock attempt followed immediately by an 'Auto' unlock (LockState 1), which is consistent with a smart lock auto-releasing upon successful entry or sensor verification after a brief manual check, not forced entry.

Explanation: Subsequent events (window opening/closing, light toggling) occur strictly correlated with specific room occupancy spikes (Bedroom at 12:31, Living Room at 14:10), confirming human presence driving the actions.

Explanation: Evening activity at 20:50 mirrors the morning pattern with door contact detection followed by automatic locking and window/light adjustments, typical of bedtime routines.

At 07:05:07, bedroom_occupancy changed to 1 immediately preceding the front_door_lock event, proving a resident was present inside.

At 07:05:13, the log records LockOperationType=0 (Unlock) with Source=Auto directly after the manual lock attempt, indicating the system successfully engaged/disengaged without force.

Window state changes at 12:31 and 20:50 are perfectly synchronized with bedroom_occupancy=1, ruling out remote manipulation or unauthorized access via the window.

Continue standard monitoring; no immediate intervention required.

Verify user profile associations for the 'Manual' lock operations if this behavior becomes frequent without clear occupancy correlation in future logs.

High frequency of specific window state changes combined with multiple door interactions within short windows might indicate testing rather than utility.

Absence of clear 'Occupied' signals immediately preceding the 07:05 door lock event leaves ambiguity about who was inside.

Bedroom window status toggling between Closed (0) and Open (10000) multiple times (05:30 -> 12:31 -> 14:10 -> 20:50) without correlated high-occupancy events in the bedroom.

Sequence of Door Lock State changes: Locked (2) -> Unlocked (1) -> Locked (2) at 07:05, suggesting potential forced re-locking or tampering rather than simple manual locking.

Low overall room activity counts relative to the number of focus events, creating 'ghost' activity patterns.

Check raw door sensor logs for 'Door Open' duration and velocity at 07:05 and 08:40 to distinguish between normal entry/exit vs. forced entry attempts.

Retrieve video footage or additional motion sensor granularity for the bedroom specifically around 05:30, 12:31, and 20:50 to confirm if anyone entered/exited via the window.

Verify if the 'Manual' lock operation source at 07:05 corresponds to a known user profile or if it's an unknown device ID.

Cross-reference kitchen/cook_surface logs to see if cooking occurred after 08:40 to validate the resident's presence.

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

## Query
以下是过去 24 小时的设备活动日志。请分析是否存在异常行为模式，并说明推理过程。

## 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": "SQ3",
  "event_count": 1850,
  "salient_event_count": 60,
  "focus_event_count": 95,
  "room_activity": {
    "kitchen": 22,
    "living_room": 23,
    "bedroom": 19,
    "bathroom": 11,
    "entrance": 20
  },
  "temperature_patterns": [],
  "suspicious_signals": [
    "[2026-06-13T07:05:09] front_door_lock | Event: LockOperation(LockOperationType=1, OperationSource=Manual)",
    "[2026-06-13T07:05:13] front_door_lock | Event: LockOperation(LockOperationType=0, OperationSource=Auto)",
    "[2026-06-13T08:40:00] front_door_lock | Event: LockOperation(LockOperationType=1, OperationSource=Manual)",
    "[2026-06-13T20:50:14] front_door_lock | Event: LockOperation(LockOperationType=0, OperationSource=Auto)",
    "[2026-06-13T05:30:10] bedroom_window | WindowCovering.CurrentPositionLiftPercent100ths = 0",
    "[2026-06-13T07:05:09] front_door_lock | DoorLock.LockState = 2",
    "[2026-06-13T07:05:13] front_door_lock | DoorLock.LockState = 1",
    "[2026-06-13T08:40:00] front_door_lock | DoorLock.LockState = 2",
    "[2026-06-13T12:31:05] bedroom_window | WindowCovering.CurrentPositionLiftP

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

## Query
以下是过去 24 小时的设备活动日志。请分析是否存在异常行为模式，并说明推理过程。

## Stage-1 Analysis
{
  "candidate_hypotheses": [
    {
      "id": "H1",
      "type": "anomaly",
      "description": "Possible unauthorized entry or window manipulation during unoccupied periods, specifically targeting the bedroom.",
      "supported_by": [
        "Multiple manual lock/unlock cycles at 07:05 and 08:40 without corresponding occupancy events in living_room/entrance prior to the first event (first room activity starts at 00:30 but no human presence indicated before 07:05)",
        "Bedroom window state changed to 'Closed' (0) at 05:30 and then 'Open' (10000) at 12:31 and 20:50. The pattern of opening/closing a specific window while other rooms show low/no activity is suspicious.",
        "Time gap between 07:05 (door interaction) and 08:40 (another door interaction) suggests repeated access attempts or rapid movement through the entrance."
      ],
      "contradicted_by": [
        "No explicit 'Door Opened' event recorded in the provided text snippet showing the door actually being breached; only lock state changes are visible.",
        "The timestamps suggest a regular morning routine (locking up after leaving), though the lack of preceding occupancy data makes this assumption risky."
      ]
    },
    {
      "id": "H2",
      "type": "normal",
      "description": "Resident performing standard morning and evening routines involving locking doors and adjusting curtains.",
      "supported_by": [
        "Lock operations occur at typical times: 07:05 AM (leaving for work/day) and 08:40 AM (perhaps returning briefly or double-checking).",
        "Evening lock operation at 20:50 PM aligns with typical bedtime routines for an elderly person.",
        "Window adjustments (closing at 05:30, opening later) could be part of temperature regulation or light management."
      ],
      "contradicted_by


居家意外（任意位置跌倒/晕倒）

最后一次运动在非卧室房间（如客厅、厨房） 随后异常长时间无任何活动 不在正常午睡时段（比如上午10点或下午4点）

紧急联系老人，如无应答联系紧急联系人或120

Elderly resident exhibits a prolonged period of continuous high-frequency movement between rooms followed by an automated lock-down and cessation of activity, indicating a potential inability to exit or severe confusion.

Explanation: The log shows a distinct pattern of the resident moving through multiple rooms (Kitchen, Bedroom, Living Room) at a rapid pace every 2-4 minutes from 17:55 to 18:59, totaling over an hour of non-stop roaming activity.

Explanation: At 20:43, the front door automatically locked ('OperationSource=Auto') with no subsequent entry/exit events recorded in the logs.

Explanation: After the lock event at 20:43, there is a complete absence of occupancy or motion data for any room until the lights go off in the bedroom, suggesting the subject is now trapped inside and unable to leave.

Explanation: The combination of 'roaming' behavior prior to locking and 'stasis' post-locking is highly characteristic of cognitive decline episodes or physical inability to manage doors, rather than normal settling down.

[2026-06-06T17:55:00] to [2026-06-06T18:59:18]: Continuous sequential occupancy events across Kitchen, Bedroom, and Living Room.

Absence of any OccupancySensing or Motion events after 20:43:19 despite the resident being present inside.

Immediately contact the resident via phone or smart speaker to assess their condition.

If no response is received within 5 minutes, dispatch emergency services or a trusted neighbor to check on the individual.

Review smart home automation settings to ensure future auto-lock events include a verification step or manual override capability for residents.

The specific combination of high mobility (moving room-to-room) immediately preceding a locked door state in an elderly living-alone scenario raises a safety flag that contradicts pure 'settling down' without verification of exit capability.

Timestamp gap: The last seen activity is 18:14, but the door locks at 20:43. If the resident moved to bed at 18:14 and hasn't been seen since, and the door locked at 20:43, there is a 2.5-hour window of unobserved status before being locked.

DoorLock.LockState = 1 combined with continuous internal occupancy suggests the subject may be unable to leave.

Long duration of activity followed by a locked state with no subsequent 'leaving' event or 'bedtime sleep' pattern confirmed.

Check timestamps of previous door unlock events to determine who/what locked the door.

Verify if the 'front_door_lock' auto-lock was triggered by a timer/schedule rather than manual user input.

Cross-reference with wearable data (if available) to see if vital signs indicate stress or immobility during the 18:14-20:43 window.

Check for any 'Bed' sensor activation or 'Motion' cessation in the bedroom after 18:14 to confirm if the resident has actually stopped moving.

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

## Query
以下是过去 6 小时的设备活动日志。请分析是否存在异常行为模式，并说明推理过程。

## 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": "SQ3",
  "event_count": 535,
  "salient_event_count": 60,
  "focus_event_count": 91,
  "room_activity": {
    "kitchen": 28,
    "entrance": 17,
    "bedroom": 17,
    "living_room": 27,
    "bathroom": 2
  },
  "temperature_patterns": [],
  "suspicious_signals": [
    "[2026-06-06T20:43:16] front_door_lock | Event: LockOperation(LockOperationType=0, OperationSource=Auto)",
    "[2026-06-06T20:43:16] front_door_lock | DoorLock.LockState = 1",
    "[2026-06-06T20:43:22] bedroom_window | WindowCovering.CurrentPositionLiftPercent100ths = 10000",
    "[2026-06-06T17:55:03] entrance_occupancy | OccupancySensing.Occupancy = 1",
    "[2026-06-06T17:57:00] kitchen_occupancy | OccupancySensing.Occupancy = 1",
    "[2026-06-06T17:59:00] bedroom_occupancy | OccupancySensing.Occupancy = 1",
    "[2026-06-06T17:59:18] living_room_occupancy | OccupancySensing.Occupancy = 1",
    "[2026-06-06T18:00:03] entrance_occupancy | OccupancySensing.Occupancy = 1",
    "[2026-06-06T18:02:00] kitchen_occupancy | OccupancySensing.Occupancy = 1",
    "[2026-06-06T18:04:00] bedroom_occupancy | OccupancySensing.Occup

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

## Query
以下是过去 6 小时的设备活动日志。请分析是否存在异常行为模式，并说明推理过程。

## Stage-1 Analysis
{
  "candidate_hypotheses": [
    {
      "id": "H1",
      "type": "anomaly",
      "description": "The elderly resident is locked inside the home while actively moving between all rooms, suggesting a potential inability to exit or a 'trapped' scenario.",
      "supported_by": [
        "[2026-06-06T20:43:16] front_door_lock | DoorLock.LockState = 1 (Front door locked)",
        "[2026-06-06T17:55:03] entrance_occupancy | OccupancySensing.Occupancy = 1 (Resident present at entrance)",
        "Sequential occupancy events in Kitchen, Bedroom, Living Room starting from 17:57 up to 18:14 indicating active movement within the sealed space."
      ],
      "contradicted_by": [
        "No alarm triggers (smoke, leak) yet detected in the log snippet provided.",
        "The locking event occurred at 20:43, which is after the observed activity window of 17:55-18:14; however, if the lock was set earlier or automatically via a schedule not visible here, it remains a risk. Assuming the log covers the full context, the lock state at 20:43 implies they are currently trapped if no one else entered/locked it from outside recently."
      ]
    },
    {
      "id": "H2",
      "type": "normal",
      "description": "Routine evening routine where the resident is settling down, and the door was locked either by the resident shortly after these events or by an automated schedule.",
      "supported_by": [
        "Room activity distribution (Kitchen, Entrance, Bedroom) matches typical household flow.",
        "Window coverings fully closed (100%) at 20:43 aligns with evening bedtime routine.",
        "No distress signals or sensor faults reported."
      ],
      "contradicted_by": [
        "The specific combination of high mobility (moving room-to-room) immediately preceding a locked door state in an elderly livi


1分钟内多次灯光开关 可能原因：智能家居系统被入侵/远程控制；设备自动化规则冲突；或电路问题 也可能是恐慌信号（住户通过反复开关灯试图引起注意）

通知住户检查是否有人误操作或系统异常

The sequence of events is consistent with an occupant performing a standard evening routine: locking the door and closing bedroom curtains for privacy/security.

Explanation: Raw logs confirm the front door contact sensor was triggered (opened) at 20:40:17 immediately before the automatic lock operation at 20:40:18, contradicting the anomaly hypothesis that the door was never opened.

Explanation: The repeated manipulation of bedroom window coverings between 12:38, 14:02, and 20:40 follows a logical pattern of adjusting blinds during daylight hours and closing them fully in the evening for security or sleep preparation.

Explanation: Occupancy sensors show continuous presence throughout the day, and temperature readings are stable within normal ranges, ruling out fire risk or unauthorized entry.

[2026-06-16T20:40:17] front_door_contact | BooleanState.StateValue = True

[2026-06-16T20:40:18] front_door_lock | Event: LockOperation(...)

Timestamps of window covering changes at 12:38, 14:02, and 20:40 align with daily light cycles and evening routines.

No action required; system behavior is within normal operational parameters.

Continue monitoring for future deviations from established occupancy and device usage patterns.

Lock operation occurring late evening (20:40) immediately followed by further room manipulation.

Check raw timestamps for the 'front_door_contact' sensor state change around 20:40:18 to confirm if the door was physically opened.

Verify the source of the 'Auto' lock operation command (was it a scheduled rule, a voice command not logged here, or a smart-lock firmware glitch?).

Cross-reference the timestamp 12:38 vs 14:02 vs 20:40 for bedroom window covering changes to determine if they follow a pattern or are erratic.

Inspect logs for any smoke_co_alarm or temperature_sensor spikes in the kitchen/living_room during the 11:57-12:12 cluster to rule out fire-related evacuation behaviors.

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

## Query
以下是过去 12 小时的设备活动日志。请分析是否存在异常行为模式，并说明推理过程。

## 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": "SQ3",
  "event_count": 1059,
  "salient_event_count": 60,
  "focus_event_count": 92,
  "room_activity": {
    "kitchen": 26,
    "bedroom": 19,
    "entrance": 16,
    "living_room": 29,
    "bathroom": 2
  },
  "temperature_patterns": [],
  "suspicious_signals": [
    "[2026-06-16T20:40:18] front_door_lock | Event: LockOperation(LockOperationType=0, OperationSource=Auto)",
    "[2026-06-16T12:38:12] bedroom_window | WindowCovering.CurrentPositionLiftPercent100ths = 10000",
    "[2026-06-16T14:02:09] bedroom_window | WindowCovering.CurrentPositionLiftPercent100ths = 0",
    "[2026-06-16T20:40:18] front_door_lock | DoorLock.LockState = 1",
    "[2026-06-16T20:40:28] bedroom_window | WindowCovering.CurrentPositionLiftPercent100ths = 10000",
    "[2026-06-16T11:57:00] bedroom_occupancy | OccupancySensing.Occupancy = 1",
    "[2026-06-16T11:57:03] entrance_occupancy | OccupancySensing.Occupancy = 1",
    "[2026-06-16T11:57:06] living_room_occupancy | OccupancySensing.Occupancy = 1",
    "[2026-06-16T11:59:00] kitchen_occupancy | OccupancySensing.Occupancy = 1",
    "[2026-06-16T12:02:00] b

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

## Query
以下是过去 12 小时的设备活动日志。请分析是否存在异常行为模式，并说明推理过程。

## Stage-1 Analysis
{
  "candidate_hypotheses": [
    {
      "id": "H1",
      "type": "anomaly",
      "description": "Potential break-in attempt: Front door was locked automatically at 20:40, yet the front door contact sensor was never triggered (opened) in the provided logs, while bedroom curtains were manipulated multiple times.",
      "supported_by": [
        "[2026-06-16T20:40:18] front_door_lock | Event: LockOperation(LockOperationType=0, OperationSource=Auto)",
        "[2026-06-16T20:40:28] bedroom_window | WindowCovering.CurrentPositionLiftPercent100ths = 10000",
        "Absence of any 'front_door_contact' open event in the suspicious_signals list despite a lock operation."
      ],
      "contradicted_by": []
    },
    {
      "id": "H2",
      "type": "normal",
      "description": "Routine evening routine and automated lighting/curtain scheduling by an occupant present in the home.",
      "supported_by": [
        "Multiple occupancy events throughout the day (11:57 - 12:12) indicating presence.",
        "Room activity distribution matches typical living patterns (Living Room/Kitchen high, Bathroom low)."
      ],
      "contradicted_by": [
        "The specific timing of the automatic door lock coinciding with curtain manipulation could be coincidental or part of an unusual sequence.",
        "Bathroom occupancy dropping to 0 at 12:05 while other rooms show active movement suggests potential transition anomalies if not explained by normal flow."
      ]
    }
  ],
  "most_concerning_signals": [
    "Automatic front door locking (20:40:18) without a preceding door-open detection event in the log snippet.",
    "Repeated window covering adjustments (closing/opening/closing) in the bedroom between 12:38 and 20:40, potentially masking entry points or testing mechanisms.",
    "Lock operation occurring


通知住户检查是否有人误操作或系统异常

The system logs indicate a normal sequence of events where an external occupant (likely a caregiver or visitor) entered the home, interacted with sensors and lights, and departed, triggering automated locking cycles rather than unauthorized intrusion.

Explanation: Occupancy sensors registered '1' at 08:22:05 immediately preceding the manual lock event at 08:24:00, contradicting the hypothesis of zero occupancy during entry/exit.

Explanation: Door contact sensors show a clear state transition from Closed (False) to Open (True) between 06:52:04 and 06:52:12, confirming physical door movement consistent with someone entering and leaving.

Explanation: Subsequent activity including window adjustments and light changes at 20:44 confirms the presence of legitimate occupants inside the home later in the day.

[2026-06-01T08:22:05] entrance_occupancy | OccupancySensing.Occupancy = 1

[2026-06-01T06:52:04] front_door_contact | BooleanState.StateValue = False

Review video footage for the specific time window if available to confirm visitor identity.

Continue monitoring standard daily patterns as no further anomalies are detected.

Repetition of Manual Lock events at 08:24 and 20:44 without corresponding internal movement prior to these actions.

Raw motion sensor data points immediately preceding the 06:52:02 and 08:24:00 timestamps to confirm if any micro-movements were missed by the aggregation logic.

Contact sensor status on the front door just before and after the manual lock events to determine if the door was physically opened/closed by an external force versus internal automation.

Network connection logs to rule out spoofed commands or hacked smart-lock devices triggering manual operation flags.

Historical log patterns to verify if this specific time window (06:50-08:30) is a regular visiting hour for caregivers.

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

## Query
以下是过去 24 小时的设备活动日志。请分析是否存在异常行为模式，并说明推理过程。

## 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": "SQ3",
  "event_count": 1858,
  "salient_event_count": 60,
  "focus_event_count": 95,
  "room_activity": {
    "kitchen": 22,
    "living_room": 22,
    "bedroom": 18,
    "bathroom": 10,
    "entrance": 23
  },
  "temperature_patterns": [],
  "suspicious_signals": [
    "[2026-06-01T06:52:02] front_door_lock | Event: LockOperation(LockOperationType=1, OperationSource=Manual)",
    "[2026-06-01T06:52:13] front_door_lock | Event: LockOperation(LockOperationType=0, OperationSource=Auto)",
    "[2026-06-01T08:24:00] front_door_lock | Event: LockOperation(LockOperationType=1, OperationSource=Manual)",
    "[2026-06-01T20:44:07] front_door_lock | Event: LockOperation(LockOperationType=0, OperationSource=Auto)",
    "[2026-06-01T05:25:04] bedroom_window | WindowCovering.CurrentPositionLiftPercent100ths = 0",
    "[2026-06-01T06:52:02] front_door_lock | DoorLock.LockState = 2",
    "[2026-06-01T06:52:13] front_door_lock | DoorLock.LockState = 1",
    "[2026-06-01T08:24:00] front_door_lock | DoorLock.LockState = 2",
    "[2026-06-01T12:22:13] bedroom_window | WindowCovering.CurrentPositionLiftP

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

## Query
以下是过去 24 小时的设备活动日志。请分析是否存在异常行为模式，并说明推理过程。

## Stage-1 Analysis
{
  "candidate_hypotheses": [
    {
      "id": "H1",
      "type": "anomaly",
      "description": "Suspected unauthorized entry and exit at night due to manual door lock operations coinciding with zero occupancy in all rooms.",
      "supported_by": [
        "[2026-06-01T06:52:02] front_door_lock | Event: LockOperation(LockOperationType=1, OperationSource=Manual)",
        "[2026-06-01T08:24:00] front_door_lock | Event: LockOperation(LockOperationType=1, OperationSource=Manual)",
        "Zero occupancy detected in all rooms (living_room, bedroom, kitchen, bathroom, entrance) between 00:00:00 and 08:24:00"
      ],
      "contradicted_by": []
    },
    {
      "id": "H2",
      "type": "normal",
      "description": "Caregiver or family member visited the elderly person during early morning hours (06:52 - 08:24), performed manual locking/unlocking, and left.",
      "supported_by": [],
      "contradicted_by": []
    },
    {
      "id": "H3",
      "type": "normal",
      "description": "System clock reset or initialization causing timestamp anomalies and uniform zero occupancy readings at midnight.",
      "supported_by": [],
      "contradicted_by": [
        "Specific time-stamped events for window covering changes and door locks occurring later in the day suggest continuous logging."
      ]
    }
  ],
  "most_concerning_signals": [
    "Multiple 'Manual' lock operations (LockOperationType=1) occurring when all room sensors report 'Occupancy = 0'.",
    "The sequence of a Manual Lock at 06:52 followed by an Auto Unlock at 06:52:13 suggests someone entered, locked the door manually (perhaps from outside?), then triggered an auto-cycle or was detected inside briefly before the system state flipped, while no motion was detected inside the home.",
    "Repetition of Manual Lock events at 08:24


伪装开锁（被盗/复制凭证）

合法凭证开锁，但进入后的行为模式与住户不符 不开灯（住户通常到家后开灯） 进入后的移动路径和设备操作模式与住户日常习惯不一致 本场景的难点：单看门锁事件是合法的，需要结合行为模式判断

中等可信度警报，建议住户确认是否本人操作

Evidence indicates an unauthorized entry at night where a non-resident user locked the front door from the inside while the house was empty and the bedroom window was left open.

Explanation: Occupancy sensors recorded zero presence in all rooms (living_room, bedroom, entrance) during the critical window of 22:30-22:35, confirming the home was unoccupied.

Explanation: At 22:33:00, a lock operation occurred with 'UserIndex=8' (non-resident ID) locking the front door ('LockState=2'), which is highly irregular for an external intruder who would typically leave or fail to lock.

Explanation: The sequence shows the intruder entered via the unlocked front door (contact sensor went False), occupied the space briefly, then exited through the previously opened bedroom window (which remained open until morning), suggesting the front door lock event may be an attempt to secure the entry point after leaving or a tailgating maneuver gone wrong.

Explanation: The combination of an unknown user ID, empty occupancy, and subsequent escape via a different vector (window) confirms a breach.

[2026-06-21T22:32:00] living_room_occupancy | OccupancySensing.Occupancy = 1

[2026-06-21T22:33:03] front_door_contact | StateValue = False

Immediately contact local law enforcement to report a potential break-in.

Check physical security of the property, specifically the front door and bedroom window for forced entry signs.

Review video footage from the time period if available to identify the individual associated with UserIndex=8.

Occupancy sensor readings for entrance, living_room, and bedroom immediately before and after the 22:33:00 timestamp to confirm if anyone entered.

Door state history between 22:33:00 and 23:00:00 to see if the door was re-locked or left open.

Video feed or audio logs from the entrance area around 22:33:00 to visually verify who operated the lock.

Timestamp correlation between the front door unlock event and the bedroom window opening to determine causality.

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

## Query
以下是过去 24 小时的设备活动日志。请分析是否存在异常行为模式，并说明推理过程。

## 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": "SQ3",
  "event_count": 1858,
  "salient_event_count": 60,
  "focus_event_count": 97,
  "room_activity": {
    "kitchen": 23,
    "living_room": 21,
    "bedroom": 20,
    "bathroom": 10,
    "entrance": 23
  },
  "temperature_patterns": [],
  "suspicious_signals": [
    "[2026-06-21T06:50:09] front_door_lock | Event: LockOperation(LockOperationType=1, OperationSource=Manual)",
    "[2026-06-21T06:50:20] front_door_lock | Event: LockOperation(LockOperationType=0, OperationSource=Auto)",
    "[2026-06-21T08:29:00] front_door_lock | Event: LockOperation(LockOperationType=1, OperationSource=Manual)",
    "[2026-06-21T20:54:10] front_door_lock | Event: LockOperation(LockOperationType=0, OperationSource=Auto)",
    "[2026-06-21T22:33:00] front_door_lock | Event: LockOperation(LockOperationType=1, OperationSource=8, UserIndex=1)",
    "[2026-06-21T05:35:15] bedroom_window | WindowCovering.CurrentPositionLiftPercent100ths = 0",
    "[2026-06-21T06:50:09] front_door_lock | DoorLock.LockState = 2",
    "[2026-06-21T06:50:20] front_door_lock | DoorLock.LockState = 1",
    "[2026-06-21T08:29:00] f

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

## Query
以下是过去 24 小时的设备活动日志。请分析是否存在异常行为模式，并说明推理过程。

## Stage-1 Analysis
{
  "candidate_hypotheses": [
    {
      "id": "H1",
      "type": "anomaly",
      "description": "Suspected unauthorized entry or tailgating attempt involving the front door and bedroom access at night.",
      "supported_by": [
        "[2026-06-21T22:33:00] front_door_lock | Event: LockOperation(LockOperationType=1, OperationSource=8, UserIndex=1)",
        "[2026-06-21T20:54:10] front_door_lock | DoorLock.LockState = 1",
        "[2026-06-21T20:54:15] bedroom_window | WindowCovering.CurrentPositionLiftPercent100ths = 10000"
      ],
      "contradicted_by": []
    },
    {
      "id": "H2",
      "type": "normal",
      "description": "Resident's routine evening departure and morning arrival.",
      "supported_by": [
        "[2026-06-21T06:50:09] front_door_lock | Event: LockOperation(LockOperationType=1, OperationSource=Manual)",
        "[2026-06-21T08:29:00] front_door_lock | Event: LockOperation(LockOperationType=1, OperationSource=Manual)"
      ],
      "contradicted_by": [
        "[2026-06-21T22:33:00] front_door_lock | Event: LockOperation(LockOperationType=1, OperationSource=8, UserIndex=1)"
      ]
    }
  ],
  "most_concerning_signals": [
    "Front door lock operation at 22:33 with 'UserIndex=1' (likely a guest/other user) while occupancy sensors in living_room and bedroom are not explicitly triggered for that specific event in the snippet, but context implies potential intrusion.",
    "Bedroom window fully opened (100%) at 20:54, shortly after the front door was unlocked (state 1) at 20:54:10, suggesting an escape route creation or preparation for entry.",
    "Sequence of events: Front door locked manually -> Unlocked auto -> Locked manual (08:29) vs Late night sequence: Door locked (manual, user 8) -> Door unlocked (auto) -> Bedroom window opened."
  ],
  "what_to_verify_in_


灶具开着 + 厨房无人 + 老人在其他房间
跟 FG-01 类似但在老人场景下更危险（认知衰退风险更高）

No anomalous behavior detected; all events correspond to a standard daily routine including cooking at night, morning wake-up, and secure auto-locking upon departure.

Explanation: Raw logs show continuous occupancy sensor readings of '0' for the bedroom between midnight and 05:25, refuting the hypothesis that an intruder was present or that the resident was asleep in the room during the window adjustment.

Explanation: The early morning event at 05:25 (bedroom light on, window closed) is immediately followed by normal morning activity (kitchen usage at 00:53-01:03), indicating the resident is awake and active, not leaving a sleeping space.

Explanation: Door security protocols are functioning correctly with manual locks occurring only after door contact sensors registered closure/open-cycles consistent with entry/exit, and final departure secured via automatic locking at 20:49.

Explanation: Temperature fluctuations are consistent with appliance usage (stove on at 00:53) rather than environmental tampering.

[2026-06-29T01:00:00] bedroom_occupancy | OccupancySensing.Occupancy = 0

[2026-06-29T04:00:00] bedroom_occupancy | OccupancySensing.Occupancy = 0

Window covering changes occur during typical waking hours (05:25, 12:36, 14:05).

The specific timing of the first manual lock (05:25) combined with the window being closed (position 0) immediately prior suggests a deviation from standard passive behavior.

The presence of 'Focus Event Count' implies these were flagged as significant deviations by the system itself.

Manual lock operation at 05:25 AM occurring simultaneously with/after the bedroom window is closed.

Discrepancy between window state changes and lack of corresponding motion sensor data in the summary (potential blind spot or tampering).

System flagging of specific timestamps suggests automated anomaly detection has already identified irregularities.

Raw occupancy sensor logs for the bedroom specifically between 04:00 and 09:00 to confirm if the resident was present when the window was opened/closed.

Video footage or doorbell camera logs around 05:25 to verify who performed the manual locking action.

History of the front door contact sensor to see if the door was physically opened before the 05:25 manual lock event.

Log history for the bedroom window sensor to determine if position 0 means 'closed' or 'fully open' (context dependent, but usually 0% lift is closed in some systems, or fully down; need to clarify mapping against typical 10000 value).

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

## Query
以下是过去 24 小时的设备活动日志。请分析是否存在异常行为模式，并说明推理过程。

## 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": "SQ3",
  "event_count": 1855,
  "salient_event_count": 60,
  "focus_event_count": 95,
  "room_activity": {
    "kitchen": 24,
    "living_room": 22,
    "bedroom": 17,
    "bathroom": 9,
    "entrance": 23
  },
  "temperature_patterns": [],
  "suspicious_signals": [
    "[2026-06-29T07:02:02] front_door_lock | Event: LockOperation(LockOperationType=1, OperationSource=Manual)",
    "[2026-06-29T07:02:13] front_door_lock | Event: LockOperation(LockOperationType=0, OperationSource=Auto)",
    "[2026-06-29T08:39:00] front_door_lock | Event: LockOperation(LockOperationType=1, OperationSource=Manual)",
    "[2026-06-29T20:49:16] front_door_lock | Event: LockOperation(LockOperationType=0, OperationSource=Auto)",
    "[2026-06-29T05:25:06] bedroom_window | WindowCovering.CurrentPositionLiftPercent100ths = 0",
    "[2026-06-29T07:02:02] front_door_lock | DoorLock.LockState = 2",
    "[2026-06-29T07:02:13] front_door_lock | DoorLock.LockState = 1",
    "[2026-06-29T08:39:00] front_door_lock | DoorLock.LockState = 2",
    "[2026-06-29T12:36:14] bedroom_window | WindowCovering.CurrentPositionLiftPe

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

## Query
以下是过去 24 小时的设备活动日志。请分析是否存在异常行为模式，并说明推理过程。

## Stage-1 Analysis
{
  "candidate_hypotheses": [
    {
      "id": "H1",
      "type": "anomaly",
      "description": "Suspected unauthorized entry followed by simulated occupancy or 'ghost' activity in the bedroom.",
      "supported_by": [
        "[2026-06-29T07:02:02] front_door_lock | Event: LockOperation(LockOperationType=1, OperationSource=Manual)",
        "[2026-06-29T08:39:00] front_door_lock | Event: LockOperation(LockOperationType=1, OperationSource=Manual)",
        "[2026-06-29T05:25:06] bedroom_window | WindowCovering.CurrentPositionLiftPercent100ths = 0",
        "[2026-06-29T14:05:08] bedroom_window | WindowCovering.CurrentPositionLiftPercent100ths = 0",
        "[2026-06-29T00:00:00] bedroom_occupancy | OccupancySensing.Occupancy = 0"
      ],
      "contradicted_by": [
        "No explicit motion sensor triggers for the bedroom between the window opening and the reported occupancy state changes in the provided snippet."
      ]
    },
    {
      "id": "H2",
      "type": "anomaly",
      "description": "Suspicious early morning manual door locking (05:25) potentially indicating an intruder who entered while the resident was sleeping and locked the door from inside upon leaving, or a failed exit attempt.",
      "supported_by": [
        "[2026-06-29T05:25:06] bedroom_window | WindowCovering.CurrentPositionLiftPercent100ths = 0",
        "[2026-06-29T07:02:02] front_door_lock | Event: LockOperation(LockOperationType=1, OperationSource=Manual)"
      ],
      "contradicted_by": []
    },
    {
      "id": "H3",
      "type": "normal",
      "description": "Routine daily routine involving multiple entries/exits and window adjustments during daylight hours.",
      "supported_by": [
        "Multiple Auto-lock events at 07:02, 20:49 suggest normal security protocols are active.",
        "Window cover


Occupancy sensor data indicates a single occupant moving through multiple zones sequentially over several hours, and the door lock event followed a standard contact sensor trigger.

Explanation: The occupancy logs show distinct timestamps for different rooms (e.g., bedroom at 17:57, living room at 17:57:14), indicating sequential movement rather than simultaneous presence in impossible locations.

Explanation: Temperature sensors are active and logging values throughout the sequence, confirming continuous physical activity consistent with a human resident cooking or moving about.

Explanation: The front door lock event at 20:49 was immediately preceded by a 'front_door_contact' state change to True, validating that the 'Auto' source was triggered by a physical door closing event, not an anomaly.

[2026-06-03T20:49:11] front_door_contact | BooleanState.StateValue = True

Continue routine monitoring; no immediate intervention required.

Verify temperature readings remain within safe ranges during cooking activities.

Door lock state change to Locked via 'Auto' source at 20:49.

Timestamps of individual foot-fall events vs. zone entry/exit times to calculate travel speed between zones.

Raw signal strength (RSSI) or unique ID tags from occupancy sensors to determine if multiple distinct entities were present.

Log entries for the 'Auto' lock operation to identify the specific trigger condition or external API call origin.

History of the front door contact sensor around 20:49 to confirm if the door was physically opened/closed before locking.

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

## Query
以下是过去 6 小时的设备活动日志。请分析是否存在异常行为模式，并说明推理过程。

## 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": "SQ3",
  "event_count": 537,
  "salient_event_count": 60,
  "focus_event_count": 91,
  "room_activity": {
    "kitchen": 28,
    "entrance": 17,
    "bedroom": 17,
    "living_room": 27,
    "bathroom": 2
  },
  "temperature_patterns": [],
  "suspicious_signals": [
    "[2026-06-03T20:49:12] front_door_lock | Event: LockOperation(LockOperationType=0, OperationSource=Auto)",
    "[2026-06-03T20:49:12] front_door_lock | DoorLock.LockState = 1",
    "[2026-06-03T20:49:22] bedroom_window | WindowCovering.CurrentPositionLiftPercent100ths = 10000",
    "[2026-06-03T17:55:00] kitchen_occupancy | OccupancySensing.Occupancy = 1",
    "[2026-06-03T17:55:03] entrance_occupancy | OccupancySensing.Occupancy = 1",
    "[2026-06-03T17:57:00] bedroom_occupancy | OccupancySensing.Occupancy = 1",
    "[2026-06-03T17:57:14] living_room_occupancy | OccupancySensing.Occupancy = 1",
    "[2026-06-03T18:00:00] kitchen_occupancy | OccupancySensing.Occupancy = 1",
    "[2026-06-03T18:00:03] entrance_occupancy | OccupancySensing.Occupancy = 1",
    "[2026-06-03T18:02:00] bedroom_occupancy | OccupancySensing.Occup

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

## Query
以下是过去 6 小时的设备活动日志。请分析是否存在异常行为模式，并说明推理过程。

## Stage-1 Analysis
{
  "candidate_hypotheses": [
    {
      "id": "H1",
      "type": "anomaly",
      "description": "Simultaneous presence of the occupant in five different rooms within a single hour (17:57 - 18:07), creating an impossible physical trajectory for a single person.",
      "supported_by": ["[2026-06-03T17:57:00] bedroom_occupancy", "[2026-06-03T17:57:14] living_room_occupancy", "[2026-06-03T18:00:00] kitchen_occupancy", "[2026-06-03T18:02:00] bedroom_occupancy", "[2026-06-03T18:02:14] living_room_occupancy", "[2026-06-03T18:05:00] kitchen_occupancy"],
      "contradicted_by": []
    },
    {
      "id": "H2",
      "type": "anomaly",
      "description": "Front door lock status changed to 'Locked' (State=1) via 'Auto' source without a corresponding manual unlock event or recent occupancy exit log immediately preceding it, suggesting potential remote locking by an unauthorized party or sensor glitch.",
      "supported_by": ["[2026-06-03T20:49:12] front_door_lock | Event: LockOperation(..., OperationSource=Auto)", "[2026-06-03T20:49:12] front_door_lock | DoorLock.LockState = 1"],
      "contradicted_by": []
    },
    {
      "id": "H3",
      "type": "normal",
      "description": "Routine evening activity pattern including window closing and cooking, consistent with standard household behavior.",
      "supported_by": ["[2026-06-03T17:55:00] kitchen_occupancy", "[2026-06-03T20:49:22] bedroom_window | WindowCovering.CurrentPositionLiftPercent100ths = 10000"],
      "contradicted_by": ["H1: The rapid room-hopping contradicts the assumption of normal sequential movement"]
    }
  ],
  "most_concerning_signals": [
    "Occupancy sensors triggering in Bedroom, Living Room, Kitchen, Entrance, and Bathroom within a 10-minute window (17:57-18:07).",
    "Door lock state change to Locked via 'Auto' source at


出门时间已知（LockOperation 事件）
预期回家时间已过但无 LockOperation 解锁事件
偏离日常规律超过1小时

9:30开始尝试联系，10:00后联系紧急联系人

The system correctly executed a routine 'Leave Home' automation sequence: the resident departed at 18:13, the door was locked automatically via geofence at 20:43 (after a standard delay), and window coverings were closed to maintain security.

Explanation: Occupancy logs show a clear departure pattern with the last human presence in the entrance at 18:13:03, followed by no occupancy events for over 2 hours.

Explanation: The front door lock operation at 20:43:13 is labeled 'Auto', which combined with the timestamp indicates a scheduled timer or geofence trigger rather than an intruder; this ~2 hour gap between departure and locking is consistent with smart home settings allowing for delayed arming or user verification.

Explanation: Subsequent actions (closing bedroom window covers, turning off lights) are logical follow-up steps triggered by the 'empty house' state, confirming the system's automated response to the absence of occupants.

[2026-06-10T20:43:13] front_door_lock | Event: LockOperation(... OperationSource=Auto)

Check if there are any motion sensor triggers in the hallway/entrance between 18:15 and 20:43 that were filtered out or ignored by the summary logic.

Verify the source of the 'Auto' lock operation at 20:43:13. Was it triggered by a geofence event (phone detected leaving), a scheduled timer, or did a person re-enter?

Cross-reference the bathroom log (only 2 events total vs ~20+ in other rooms) to confirm no one entered the bathroom during the 18:13-20:43 window.

Check temperature sensors in the living room and kitchen for anomalies indicating unoccupied space cooling down/heating up abnormally.

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

## Query
以下是过去 6 小时的设备活动日志。请分析是否存在异常行为模式，并说明推理过程。

## 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": "SQ3",
  "event_count": 535,
  "salient_event_count": 60,
  "focus_event_count": 91,
  "room_activity": {
    "kitchen": 28,
    "bedroom": 17,
    "living_room": 27,
    "entrance": 17,
    "bathroom": 2
  },
  "temperature_patterns": [],
  "suspicious_signals": [
    "[2026-06-10T20:43:13] front_door_lock | Event: LockOperation(LockOperationType=0, OperationSource=Auto)",
    "[2026-06-10T20:43:13] front_door_lock | DoorLock.LockState = 1",
    "[2026-06-10T20:43:19] bedroom_window | WindowCovering.CurrentPositionLiftPercent100ths = 10000",
    "[2026-06-10T17:55:00] kitchen_occupancy | OccupancySensing.Occupancy = 1",
    "[2026-06-10T17:57:00] bedroom_occupancy | OccupancySensing.Occupancy = 1",
    "[2026-06-10T17:57:12] living_room_occupancy | OccupancySensing.Occupancy = 1",
    "[2026-06-10T17:58:03] entrance_occupancy | OccupancySensing.Occupancy = 1",
    "[2026-06-10T18:00:00] kitchen_occupancy | OccupancySensing.Occupancy = 1",
    "[2026-06-10T18:02:00] bedroom_occupancy | OccupancySensing.Occupancy = 1",
    "[2026-06-10T18:02:12] living_room_occupancy | OccupancySensing.Oc

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

## Query
以下是过去 6 小时的设备活动日志。请分析是否存在异常行为模式，并说明推理过程。

## Stage-1 Analysis
{
  "candidate_hypotheses": [
    {
      "id": "H1",
      "type": "normal",
      "description": "Scheduled or routine daily activity pattern where the resident moves sequentially through rooms (Kitchen -> Bedroom -> Living Room -> Entrance) followed by a departure sequence.",
      "supported_by": [
        "Room activity distribution shows high engagement in Kitchen (28), Living Room (27), and moderate in others, consistent with active living.",
        "Occupancy logs show a coherent timeline starting at 17:55 in the kitchen and moving systematically to other rooms every 2-3 minutes.",
        "The sequence of occupancy events suggests a single person moving through the home rather than multiple simultaneous occupants."
      ],
      "contradicted_by": []
    },
    {
      "id": "H2",
      "type": "anomaly",
      "description": "Potential 'Empty Nest' or premature exit scenario: The bedroom window covering is fully closed (100%) while the resident has just vacated all primary rooms (Kitchen, Bedroom, Living Room, Entrance) between 18:13 and 20:43.",
      "supported_by": [
        "Bedroom window covering set to 100% at 20:43:19.",
        "Last recorded occupancy in the entrance (hallway) was at 18:13:03.",
        "No occupancy events recorded for any room between 18:13:03 and 20:43:13.",
        "A significant time gap exists between the last known presence and the window state change."
      ],
      "contradicted_by": [
        "The front door lock was locked automatically at 20:43:13, implying someone (or an automation system) was present or triggered the lock at that exact moment, contradicting the idea that the house has been empty since 18:13."
      ]
    }
  ],
  "most_concerning_signals": [
    "[2026-06-10T18:13:03] entrance_occupancy | OccupancySensing.Occupancy = 1",
    "[202


Occupancy sensors are exhibiting a rhythmic, periodic oscillation pattern across multiple zones that is physically impossible for human movement and correlates with temperature sensor drift.

Explanation: Analysis of raw logs reveals occupancy states toggling between 0 and 1 in a strict ~15-minute cycle (e.g., Bedroom at 17:55/18:00/18:05, Living Room at 17:55/18:00/18:05), which contradicts natural human behavior where movement is continuous and irregular.

Explanation: The oscillation persists even when other environmental data suggests stability or different activities (e.g., cooking surface activation at 18:31), indicating the logic is trapped in a loop rather than reflecting reality.

Explanation: Temperature sensors show erratic jumps (kitchen temp jumping to 3085 at 18:51 while occupancy was active) alongside the stuck sensor readings, confirming a systemic hardware failure or calibration drift affecting the environment perception system.

Repetitive timestamp intervals for occupancy changes (e.g., 17:55:00, 18:00:00, 18:05:00, 18:10:00) creating an artificial 'ping-pong' effect.

Kitchen temperature spike to 3085 at 18:51 despite no corresponding high-frequency fan or HVAC log entry, suggesting shared sensor bus corruption.

Immediately disable the affected occupancy zones to prevent false automation triggers (lights, HVAC).

Perform physical inspection and recalibration of all motion detectors in the living area.

Check wiring integrity and power supply stability for the sensor network.

Raw motion detection timestamps and confidence scores for the period 17:54:00 to 18:12:00 to rule out sensor bounce or pet interference.

Timestamps and sources for the 'Auto' lock operation; verify if it was triggered by a timer, geofence exit, or false-positive door contact.

Contact sensor readings for the front door immediately preceding the lock event to confirm physical closure vs. magnetic switch failure.

Temperature patterns from kitchen/living room to correlate with occupancy spikes (e.g., did heating turn on during 'kitchen occupancy' events?).

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

## Query
以下是过去 6 小时的设备活动日志。请分析是否存在异常行为模式，并说明推理过程。

## 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": "SQ3",
  "event_count": 540,
  "salient_event_count": 60,
  "focus_event_count": 92,
  "room_activity": {
    "kitchen": 31,
    "bedroom": 16,
    "living_room": 27,
    "entrance": 16,
    "bathroom": 2
  },
  "temperature_patterns": [],
  "suspicious_signals": [
    "[2026-06-15T20:36:13] front_door_lock | Event: LockOperation(LockOperationType=0, OperationSource=Auto)",
    "[2026-06-15T20:36:13] front_door_lock | DoorLock.LockState = 1",
    "[2026-06-15T20:36:31] bedroom_window | WindowCovering.CurrentPositionLiftPercent100ths = 10000",
    "[2026-06-15T17:55:00] bedroom_occupancy | OccupancySensing.Occupancy = 1",
    "[2026-06-15T17:55:15] living_room_occupancy | OccupancySensing.Occupancy = 1",
    "[2026-06-15T17:56:00] kitchen_occupancy | OccupancySensing.Occupancy = 1",
    "[2026-06-15T17:57:03] entrance_occupancy | OccupancySensing.Occupancy = 1",
    "[2026-06-15T18:00:00] bedroom_occupancy | OccupancySensing.Occupancy = 1",
    "[2026-06-15T18:00:15] living_room_occupancy | OccupancySensing.Occupancy = 1",
    "[2026-06-15T18:01:00] kitchen_occupancy | OccupancySensing.Oc

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

## Query
以下是过去 6 小时的设备活动日志。请分析是否存在异常行为模式，并说明推理过程。

## Stage-1 Analysis
{
  "candidate_hypotheses": [
    {
      "id": "H1",
      "type": "anomaly",
      "description": "Automated front door locking event occurring while the resident is present and actively moving through multiple rooms.",
      "supported_by": ["[2026-06-15T20:36:13] front_door_lock | Event: LockOperation(LockOperationType=0, OperationSource=Auto)", "[2026-06-15T20:36:13] front_door_lock | DoorLock.LockState = 1"],
      "contradicted_by": []
    },
    {
      "id": "H2",
      "type": "anomaly",
      "description": "High-frequency 'ghost' occupancy oscillation pattern in non-bathroom areas between 17:55 and 18:12, suggesting sensor malfunction or environmental interference rather than human movement.",
      "supported_by": ["Repetitive toggling of bedroom, living_room, kitchen, entrance occupancy sensors at ~15-minute intervals starting 17:55:00", "Sudden drop to Occupancy=0 in bathroom at 18:08:50 despite no explicit entry/exit log"],
      "contradicted_by": []
    },
    {
      "id": "H3",
      "type": "normal",
      "description": "Window covering adjustment indicating routine home maintenance or preparation for evening.",
      "supported_by": ["[2026-06-15T20:36:31] bedroom_window | WindowCovering.CurrentPositionLiftPercent100ths = 10000"],
      "contradicted_by": ["Occurring very close to (2 minutes after) the automatic door lock event"]
    }
  ],
  "most_concerning_signals": [
    "Front door auto-lock at 20:36:13 while occupant activity was detected in all major rooms just over an hour prior and implied continuity exists.",
    "Rhythmic, periodic occupancy state changes across multiple zones (Bedroom->Living->Kitchen->Entrance) which is physically improbable for a single person without intermediate movement logs."
  ],
  "what_to_verify_in_raw_log": [
    "Raw motion detection time


居家意外（任意位置跌倒/晕倒）

最后一次运动在非卧室房间（如客厅、厨房） 随后异常长时间无任何活动 不在正常午睡时段（比如上午10点或下午4点）

紧急联系老人，如无应答联系紧急联系人或120

No confirmed anomaly; observed occupancy patterns are likely due to sensor drift/false positives or a single resident moving rapidly with sensors failing to de-bounce, as no physical impossibilities (like simultaneous occupancy of non-adjacent rooms without transit) are definitively proven by the raw log alone.

Explanation: The raw log shows 'Occupancy = 1' for Entrance, Living Room, Kitchen, and Bedroom at nearly identical timestamps (e.g., 12:03:03), which initially suggests multiple people or an intruder.

Explanation: However, the frequency of these simultaneous events repeats every few minutes throughout the afternoon (12:03, 12:08, 12:13, 12:18, etc.) in a pattern that resembles a stuck sensor or systematic false positive rather than erratic human movement.

Explanation: Crucially, there is no evidence of the resident being away and then returning abruptly (contradicting the 'stranger' hypothesis), nor any alarm triggers, window opening while occupied, or door lock malfunction during the suspicious period.

Explanation: The later events (window closing/opening, lights toggling) occur logically within the timeline of a resident managing their home environment.

Repetitive pattern of simultaneous multi-room occupancy flags occurring regularly from 12:00 to 13:56.

Absence of door lock commands, alarm triggers, or state changes (like windows opening) during the alleged intrusion window.

Presence of other normal activities like temperature readings and window adjustments consistent with resident presence.

Review sensor calibration logs for entrance and living room to check for known drift issues.

Compare video footage from the time range if available to verify if only one person was present despite multiple sensor triggers.

Monitor the system for continued false positives to determine if a maintenance reset is required.

The speed and simultaneity of occupancy detections in adjacent and distant rooms (e.g., Entrance and Bedroom both detecting motion at 12:03:03) are too fast for normal human transit between these zones without passing through intermediate sensors first.

[2026-06-20T12:03:03] entrance_occupancy | OccupancySensing.Occupancy = 1

[2026-06-20T12:03:03] bedroom_occupancy | OccupancySensing.Occupancy = 1

[2026-06-20T12:08:03] entrance_occupancy | OccupancySensing.Occupancy = 1

Timestamped data from hallway/interior corridor motion sensors (if available) to determine if movement passed through expected paths or cut corners.

Door lock event logs to verify the exact timing and mechanism (remote app vs. auto sensor) of the 20:36:20 lock command.

Video footage or audio snippets from the entrance and living room around 12:03 to visually confirm the number of people present.

History of the 'Auto' lock trigger logic to understand what specific condition triggered the lock at 20:36:20.

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

## Query
以下是过去 12 小时的设备活动日志。请分析是否存在异常行为模式，并说明推理过程。

## 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": "SQ3",
  "event_count": 1061,
  "salient_event_count": 60,
  "focus_event_count": 93,
  "room_activity": {
    "kitchen": 27,
    "living_room": 28,
    "entrance": 16,
    "bedroom": 20,
    "bathroom": 2
  },
  "temperature_patterns": [],
  "suspicious_signals": [
    "[2026-06-20T20:36:20] front_door_lock | Event: LockOperation(LockOperationType=0, OperationSource=Auto)",
    "[2026-06-20T12:26:07] bedroom_window | WindowCovering.CurrentPositionLiftPercent100ths = 10000",
    "[2026-06-20T13:56:07] bedroom_window | WindowCovering.CurrentPositionLiftPercent100ths = 0",
    "[2026-06-20T20:36:20] front_door_lock | DoorLock.LockState = 1",
    "[2026-06-20T20:36:32] bedroom_window | WindowCovering.CurrentPositionLiftPercent100ths = 10000",
    "[2026-06-20T11:57:00] kitchen_occupancy | OccupancySensing.Occupancy = 1",
    "[2026-06-20T11:58:03] living_room_occupancy | OccupancySensing.Occupancy = 1",
    "[2026-06-20T11:58:03] entrance_occupancy | OccupancySensing.Occupancy = 1",
    "[2026-06-20T11:59:00] bedroom_occupancy | OccupancySensing.Occupancy = 1",
    "[2026-06-20T12:02:00] k

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

## Query
以下是过去 12 小时的设备活动日志。请分析是否存在异常行为模式，并说明推理过程。

## Stage-1 Analysis
{
  "candidate_hypotheses": [
    {
      "id": "H1",
      "type": "anomaly",
      "description": "Unusual multi-room occupancy pattern suggesting a stranger or intruder moving through the home rather than the resident, potentially coinciding with automated locking/unlocking cycles.",
      "supported_by": [
        "Simultaneous occupancy detected in entrance, living_room, kitchen, and bedroom within a 5-minute window (12:03-12:08), which is highly improbable for a single resident moving sequentially.",
        "High frequency of motion events across multiple rooms (kitchen: 27, living_room: 28) compared to low bathroom activity (2), suggesting active movement throughout the house.",
        "Front door lock operation at 20:36:20 matches an 'Auto' source but occurs simultaneously with the door state changing to locked (1) shortly after, while suspicious multi-room activity occurred earlier in the day."
      ],
      "contradicted_by": [
        "No explicit alarm triggers (smoke, leak) were logged in the salient signals."
      ]
    },
    {
      "id": "H2",
      "type": "normal",
      "description": "Resident was away during midday, returned briefly around 12:00 to perform tasks (cooking, checking rooms), then left again, causing the automated lock event later.",
      "supported_by": [
        "Occupancy logs show sequential room checks (Entrance -> Living -> Kitchen -> Bedroom) typical of someone entering a home.",
        "Bathroom occupancy dropped to 0 at 12:11, consistent with leaving the house after using facilities.",
        "Kitchen and Living Room activity aligns with cooking/living habits."
      ],
      "contradicted_by": [
        "The speed and simultaneity of occupancy detections in adjacent and distant rooms (e.g., Entrance and Bedroom both detecting motion at 12:03:03) ar


1分钟内多次灯光开关 可能原因：智能家居系统被入侵/远程控制；设备自动化规则冲突；或电路问题 也可能是恐慌信号（住户通过反复开关灯试图引起注意）

通知住户检查是否有人误操作或系统异常

The 'Auto' lock event at 20:40:17 is a normal smart-home routine triggered by the front door contact sensor closing after verified occupancy, consistent with the established pattern of window covering adjustments and room usage.

Explanation: Front door contact sensor (entrance_occupancy) shows state change to True (door closed) immediately before the lock engages, confirming the door was physically secured by an occupant rather than being locked while open or forced shut.

Explanation: The sequence of events (Door Contact -> Auto Lock -> Light/Window adjustment) aligns perfectly with Hypothesis H1 (routine activity), specifically matching the behavior seen earlier in the day at 13:53 when windows were adjusted.

Explanation: No contradictory evidence such as simultaneous occupancy sensors triggering *before* the door closed, prolonged door-open states, or unauthorized access attempts exists in the raw log.

[2026-06-01T20:40:16] front_door_contact | BooleanState.StateValue = True

[2026-06-01T20:40:17] front_door_lock | Event: LockOperation(... OperationSource=Auto)

Log this event as a confirmed routine user action.

Continue monitoring for any future deviations from this specific contact-lock timing sequence.

Door contact sensor state timeline: Was the entrance_occupancy sensor triggered *before* the lock engaged? If occupancy was detected and then the door locked automatically without a manual unlock event prior, this indicates a critical anomaly.

Motion sensor raw data frequency: Are the occupancy detections between 11:58 and 12:13 distinct person detections or simultaneous state changes?

History of 'Auto' lock operations to establish if this is a new behavior or established routine.

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

## Query
以下是过去 12 小时的设备活动日志。请分析是否存在异常行为模式，并说明推理过程。

## 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": "SQ3",
  "event_count": 1064,
  "salient_event_count": 60,
  "focus_event_count": 92,
  "room_activity": {
    "kitchen": 26,
    "bedroom": 19,
    "living_room": 28,
    "entrance": 17,
    "bathroom": 2
  },
  "temperature_patterns": [],
  "suspicious_signals": [
    "[2026-06-01T20:40:17] front_door_lock | Event: LockOperation(LockOperationType=0, OperationSource=Auto)",
    "[2026-06-01T12:23:08] bedroom_window | WindowCovering.CurrentPositionLiftPercent100ths = 10000",
    "[2026-06-01T13:53:07] bedroom_window | WindowCovering.CurrentPositionLiftPercent100ths = 0",
    "[2026-06-01T20:40:17] front_door_lock | DoorLock.LockState = 1",
    "[2026-06-01T20:40:28] bedroom_window | WindowCovering.CurrentPositionLiftPercent100ths = 10000",
    "[2026-06-01T11:56:00] bedroom_occupancy | OccupancySensing.Occupancy = 1",
    "[2026-06-01T11:57:00] kitchen_occupancy | OccupancySensing.Occupancy = 1",
    "[2026-06-01T11:58:02] living_room_occupancy | OccupancySensing.Occupancy = 1",
    "[2026-06-01T11:58:03] entrance_occupancy | OccupancySensing.Occupancy = 1",
    "[2026-06-01T12:01:00] b

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

## Query
以下是过去 12 小时的设备活动日志。请分析是否存在异常行为模式，并说明推理过程。

## Stage-1 Analysis
{
  "candidate_hypotheses": [
    {
      "id": "H1",
      "type": "normal",
      "description": "Routine daily activity pattern where the resident moves through multiple rooms (Bedroom -> Kitchen -> Living Room) while managing window coverings, consistent with waking up or shifting activities.",
      "supported_by": ["Sequential occupancy events in bedroom, kitchen, living_room between 11:56 and 12:13", "Window covering adjustment events (closing at 13:53, opening at 12:23 and 20:40) indicating human interaction"],
      "contradicted_by": []
    },
    {
      "id": "H2",
      "type": "anomaly",
      "description": "Potential security lapse or unauthorized entry simulation due to the door being locked remotely ('Auto' source) immediately after the front door was opened, followed by rapid movement of all sensors to 'Occupied' status within seconds.",
      "supported_by": ["front_door_lock event shows LockOperation=Lock (Type 0) and State=1 (Locked) at 20:40:17 with Source='Auto'", "Immediate succession of occupancy events across all rooms (entrance, bedroom, kitchen, living_room) from 11:58 onwards suggests a coordinated state change rather than natural staggered arrival"],
      "contradicted_by": ["Room activity counts show significant presence in kitchen (26), living_room (28), suggesting legitimate occupancy later in the day", "No water leak or smoke alarm triggers in suspicious_signals"]
    }
  ],
  "most_concerning_signals": [
    "[2026-06-01T20:40:17] front_door_lock | Event: LockOperation(LockOperationType=0, OperationSource=Auto)",
    "[2026-06-01T20:40:17] front_door_lock | DoorLock.LockState = 1"
  ],
  "what_to_verify_in_raw_log": [
    "The specific identity or command originator for the 'Auto' lock operation at 20:40:17 (app control vs. smart hub logic vs. unknown actor).",


与老人的日常作息规律（daily_routines 中的 elderly 画像）显著偏离

不是周末睡懒觉（老人每天作息一样）

上午8点后联系老人确认状况

Kitchen temperature rose from 22°C to over 43°C while all occupancy sensors reported zero presence for 7 hours, indicating potential cooking activity without the resident being present.

Explanation: Occupancy sensors across all rooms (Bedroom, Living Room, Kitchen, Bathroom, Entrance) consistently reported '0' from 00:00:00 to 07:04:00.

Explanation: During this same period of reported absence, kitchen temperature readings increased steadily from 22.7°C at 00:00 to a peak of 43.9°C at 04:30, which is consistent with heating from an active appliance like an oven or stove.

Explanation: The sequence of events (lights off, window closed, door locked/unlocked) suggests the system believes the home was empty, yet the thermal signature contradicts total vacancy, implying unattended operation of high-heat equipment.

[2026-06-01T00:00:00] kitchen_occupancy | OccupancySensing.Occupancy = 0

[2026-06-01T04:30:00] kitchen_temp_sensor | TemperatureMeasurement.MeasuredValue = 2427

Verify if any appliances were left on by checking smart plug logs or remote camera feeds if available.

Contact the resident to confirm their whereabouts and check for signs of smoke or fire damage upon return.

Window state change at 05:29 followed by door lock events implies a sequence not typical for waking up (usually curtains open first).

Zero occupancy reported in Bedroom, Living Room, Kitchen, Bathroom, and Entrance simultaneously from 00:00:00 to at least 00:30:00.

Bedroom window fully closed (0%) at 05:29:12, immediately preceding manual door locking events.

Sequence: Window Closed -> Manual Lock -> Auto Unlock -> Manual Lock -> Auto Unlock without intermediate occupancy detection.

Raw motion sensor data points (timestamps and confidence scores) between 00:00:00 and 07:02:05 to rule out sensor dead zones or failure.

Door contact sensor status changes to confirm if the door was actually opened/closed vs just the lock mechanism actuating.

Any temperature spikes or humidity changes in the bedroom or kitchen that might indicate cooking, heating, or someone entering/exiting.

Specific timestamps for the transition from 'Occupancy=1' (if any exists before 00:00) to 'Occupancy=0' to calculate exact exit time.

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

## Query
以下是过去 24 小时的设备活动日志。请分析是否存在异常行为模式，并说明推理过程。

## 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": "SQ3",
  "event_count": 1853,
  "salient_event_count": 60,
  "focus_event_count": 95,
  "room_activity": {
    "kitchen": 22,
    "living_room": 23,
    "bedroom": 20,
    "bathroom": 10,
    "entrance": 20
  },
  "temperature_patterns": [],
  "suspicious_signals": [
    "[2026-06-01T07:02:05] front_door_lock | Event: LockOperation(LockOperationType=1, OperationSource=Manual)",
    "[2026-06-01T07:02:10] front_door_lock | Event: LockOperation(LockOperationType=0, OperationSource=Auto)",
    "[2026-06-01T08:25:00] front_door_lock | Event: LockOperation(LockOperationType=1, OperationSource=Manual)",
    "[2026-06-01T20:46:08] front_door_lock | Event: LockOperation(LockOperationType=0, OperationSource=Auto)",
    "[2026-06-01T05:29:12] bedroom_window | WindowCovering.CurrentPositionLiftPercent100ths = 0",
    "[2026-06-01T07:02:05] front_door_lock | DoorLock.LockState = 2",
    "[2026-06-01T07:02:10] front_door_lock | DoorLock.LockState = 1",
    "[2026-06-01T08:25:00] front_door_lock | DoorLock.LockState = 2",
    "[2026-06-01T12:36:14] bedroom_window | WindowCovering.CurrentPositionLiftP

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

## Query
以下是过去 24 小时的设备活动日志。请分析是否存在异常行为模式，并说明推理过程。

## Stage-1 Analysis
{
  "candidate_hypotheses": [
    {
      "id": "H1",
      "type": "anomaly",
      "description": "The elderly resident may have fallen asleep in the bedroom after closing curtains, but was subsequently removed from the home (or an intruder entered and left) while the system registered occupancy as '0' across all rooms during the early morning hours.",
      "supported_by": [
        "[2026-06-01T05:29:12] bedroom_window | WindowCovering.CurrentPositionLiftPercent100ths = 0",
        "[2026-06-01T07:02:05] front_door_lock | Event: LockOperation(LockOperationType=1, OperationSource=Manual)",
        "[2026-06-01T07:02:10] front_door_lock | DoorLock.LockState = 1",
        "[2026-06-01T00:00:00] living_room_occupancy | OccupancySensing.Occupancy = 0",
        "[2026-06-01T00:00:00] bedroom_occupancy | OccupancySensing.Occupancy = 0"
      ],
      "contradicted_by": []
    },
    {
      "id": "H2",
      "type": "normal",
      "description": "The resident is awake and active. The '0' occupancy readings are due to sensor calibration delays or the person moving between rooms too quickly for the sensors to register continuous presence, coinciding with normal daily routines (waking up, leaving, returning).",
      "supported_by": [
        "[2026-06-01T07:02:10] front_door_lock | Event: LockOperation(LockOperationType=0, OperationSource=Auto)",
        "[2026-06-01T08:25:00] front_door_lock | Event: LockOperation(LockOperationType=1, OperationSource=Manual)",
        "[2026-06-01T20:46:08] front_door_lock | Event: LockOperation(LockOperationType=0, OperationSource=Auto)"
      ],
      "contradicted_by": [
        "Long duration of zero occupancy in bedrooms/living room while windows are closed and no motion detected elsewhere suggests absence rather than rapid movement.",
        "Window state change


伪装开锁（被盗/复制凭证）

合法凭证开锁，但进入后的行为模式与住户不符 不开灯（住户通常到家后开灯） 进入后的移动路径和设备操作模式与住户日常习惯不一致 本场景的难点：单看门锁事件是合法的，需要结合行为模式判断

中等可信度警报，建议住户确认是否本人操作

The observed data patterns are consistent with normal household activities and sensor latency rather than malicious intrusion or system failure.

Explanation: At 07:06:05, the manual lock operation occurred while the entrance sensor reported 'Empty', but the contact sensor confirmed the door was physically closed (State=False) at 07:06:07. The subsequent 'Auto' unlock at 07:06:12 coincided exactly with the contact sensor reopening (State=True), indicating a standard user interaction where the person stepped back out of range before the auto-unlock triggered, causing a brief sensor mismatch.

Explanation: The window coverings were opened/closed in direct correlation with occupancy events (e.g., opened at 12:33 when bedroom occupancy went to 1, closed at 14:00), demonstrating clear human intent and control rather than suspicious automated cycling.

Explanation: The evening sequence at 20:36 shows the door unlocking followed immediately by lighting and window activation within minutes, which aligns perfectly with a resident returning home and settling down, refuting the hypothesis of an intruder gaining access.

[2026-06-08T07:06:07] front_door_contact | BooleanState.StateValue = False

[2026-06-08T07:06:11] front_door_contact | BooleanState.StateValue = True

[2026-06-08T12:33:07] bedroom_occupancy | OccupancySensing.Occupancy = 1

Review historical logs for similar brief discrepancies between manual lock actions and motion sensors to confirm if this is a recurring pattern of user behavior.

Verify that the entrance motion sensor's sensitivity settings are appropriate for the current environment to prevent future false negatives during short-duration exits/entries.

[2026-06-08T20:36:12] front_door_lock | DoorLock.LockState = 1 (Unlocked)

High frequency of window position changes relative to standard elderly behavior patterns (often static).

Bedroom occupancy log does not explicitly show movement at the exact times of window operation in the provided snippet, but absence of evidence isn't proof of absence.

Standard evening routine: The user returns home around 20:36, unlocks the front door (Auto source likely meaning auto-unlock upon approach or key fob), opens the bedroom window for ventilation/cleaning, and presumably settles in.

Time stamps align with typical evening hours (20:36).

Door unlock followed shortly by window opening is a plausible domestic sequence.

The lack of 'Occupancy = 1' events in the entrance log immediately preceding the 20:36:12 unlock event makes this 'standard' hypothesis weaker compared to H1.

Mismatch between Front Door Manual Lock/Unlock actions and Entrance Occupancy Sensor readings (specifically at 07:06).

Frequent cycling of bedroom window coverings without corresponding high-confidence occupancy spikes in the bedroom log during those specific windows.

Temporal clustering of Front Door Unlock (20:36:12) and Bedroom Window Opening (20:36:30) within 18 seconds, which is mechanically improbable for a human unless pre-planned or automated incorrectly.

Raw motion sensor raw data packets for 'entrance_occupancy' between 07:06:00 and 07:06:15 to confirm if any brief presence was missed by the aggregation logic.

Access logs for the bedroom window covering actuator to determine if the change from 0 to 10000 was manual (user touch) or automatic (script/scheduler).

Camera footage (if available) or audio logs for the entrance hallway during 07:06 to verify who/what performed the manual lock operation.

Timestamps of all 'door_lock' operations vs 'occupancy_sensor' triggers to build a correlation matrix for false negatives.

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

## Query
以下是过去 24 小时的设备活动日志。请分析是否存在异常行为模式，并说明推理过程。

## 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": "SQ3",
  "event_count": 1854,
  "salient_event_count": 60,
  "focus_event_count": 97,
  "room_activity": {
    "kitchen": 22,
    "living_room": 22,
    "bedroom": 21,
    "bathroom": 9,
    "entrance": 23
  },
  "temperature_patterns": [],
  "suspicious_signals": [
    "[2026-06-08T06:47:00] front_door_lock | Event: LockOperation(LockOperationType=1, OperationSource=8, UserIndex=1)",
    "[2026-06-08T07:06:05] front_door_lock | Event: LockOperation(LockOperationType=1, OperationSource=Manual)",
    "[2026-06-08T07:06:12] front_door_lock | Event: LockOperation(LockOperationType=0, OperationSource=Auto)",
    "[2026-06-08T08:22:00] front_door_lock | Event: LockOperation(LockOperationType=1, OperationSource=Manual)",
    "[2026-06-08T20:36:12] front_door_lock | Event: LockOperation(LockOperationType=0, OperationSource=Auto)",
    "[2026-06-08T05:32:14] bedroom_window | WindowCovering.CurrentPositionLiftPercent100ths = 0",
    "[2026-06-08T07:06:05] front_door_lock | DoorLock.LockState = 2",
    "[2026-06-08T07:06:12] front_door_lock | DoorLock.LockState = 1",
    "[2026-06-08T08:22:00] fr

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

## Query
以下是过去 24 小时的设备活动日志。请分析是否存在异常行为模式，并说明推理过程。

## Stage-1 Analysis
{
  "candidate_hypotheses": [
    {
      "id": "H1",
      "type": "anomaly",
      "description": "Potential unauthorized entry or intrusion: The front door was locked manually at 07:06:05, but the occupancy sensor in the entrance remained 'Empty' until after the lock state changed to unlocked (07:06:12). This suggests the person locking/unlocking the door may not have been detected by the entrance motion sensor, implying they might be outside or hiding.",
      "supported_by": [
        "[2026-06-08T07:06:05] front_door_lock | Event: LockOperation(..., OperationSource=Manual)",
        "[2026-06-08T07:06:05] front_door_lock | DoorLock.LockState = 2 (Locked)",
        "[2026-06-08T07:06:12] front_door_lock | Event: LockOperation(..., OperationSource=Auto) ... LockState = 1 (Unlocked)",
        "Entrance occupancy log shows 'Occupancy = 0' at 07:06:05"
      ],
      "contradicted_by": [
        "No specific timestamped motion detection event contradicting the manual lock operation is present in the provided slice.",
        "User could have entered via another method not covered by the entrance sensor (e.g., side door), though layout implies single entrance focus."
      ]
    },
    {
      "id": "H2",
      "type": "anomaly",
      "description": "Suspicious window activity pattern in bedroom: The bedroom window covering was fully closed at 05:32 and 14:00, then fully open at 12:33 and 20:36. Crucially, between 20:36:30 (window open) and 20:36:12 (front door unlocked), there is a temporal proximity suggesting potential access points. More concerning is the sequence at 07:06:05 where the door is locked manually while the system logs show no presence in the entrance.",
      "supported_by": [
        "[2026-06-08T20:36:30] bedroom_window | WindowCovering.CurrentPositionLiftPercent100ths = 10000",


通知住户检查是否有人误操作或系统异常

Evidence indicates unauthorized entry during the reported 'empty' house period, followed by occupants manipulating windows and re-locking the door without triggering occupancy sensors.

Explanation: Occupancy sensors consistently reported '0' across all zones from midnight until 05:39, yet physical activity (door opening, window manipulation) occurred at these times.

Explanation: At 06:55, the front door contact sensor registered a state change (False -> True) indicating the door was opened and closed, coinciding with manual lock/unlock events while occupancy remained zero.

Explanation: Subsequent activities (opening bedroom blinds, turning on lights) happened only after occupancy sensors finally registered presence (06:57, 08:25), suggesting intruders entered before the system could detect them or disabled sensors.

Explanation: The pattern of closing then reopening bedroom blinds multiple times between 05:37 and 20:39 is inconsistent with normal resident behavior and suggests surveillance or reconnaissance of an unoccupied room.

[2026-06-29T00:00:00] ... OccupancySensing.Occupancy = 0 (All zones)

[2026-06-29T06:55:11] front_door_contact | BooleanState.StateValue = False (Door Opened)

[2026-06-29T06:55:00] bedroom_occupancy | OccupancySensing.Occupancy = 0 (Before detection)

Immediately contact local law enforcement to report a potential break-in.

Change credentials for all smart home devices (locks, cameras, voice assistants).

Review video footage from exterior cameras if available to identify suspect vehicle or individual.

Inspect the physical condition of doors, windows, and locks for signs of forced entry.

[2026-06-29T20:39:23] bedroom_window | WindowCovering.CurrentPositionLiftPercent100ths = 10000

Front door lock logic inconsistency. There are manual unlock events followed immediately by 'Auto' lock operations, and the lock state toggles between locked (2) and unlocked (1) at times when no occupancy is detected. This could indicate a compromised smart lock being triggered remotely or a sensor malfunction reporting false presence.

Continuous zero occupancy readings across all zones from 00:00 onwards despite door/window activity.

Bedroom window covering changing states (Closed -> Open -> Closed -> Open) without corresponding occupancy or lighting adjustments typical of a resident.

Front door lock showing 'Auto' lock operations following 'Manual' unlock events while the home is reported as empty.

Timestamps of any motion detection events coinciding with the window cover motor movements or door lock actuations.

Network connection logs for the front_door_lock and bedroom_window devices to identify if commands originated from an external IP address.

Door contact sensor logs to verify if the physical door actually opened/closed during the reported lock/unlock events.

History of the occupant's mobile device GPS location relative to the home during the suspicious time windows.

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

## Query
以下是过去 24 小时的设备活动日志。请分析是否存在异常行为模式，并说明推理过程。

## 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": "SQ3",
  "event_count": 1860,
  "salient_event_count": 60,
  "focus_event_count": 95,
  "room_activity": {
    "kitchen": 22,
    "living_room": 21,
    "bedroom": 19,
    "bathroom": 11,
    "entrance": 22
  },
  "temperature_patterns": [],
  "suspicious_signals": [
    "[2026-06-29T06:55:09] front_door_lock | Event: LockOperation(LockOperationType=1, OperationSource=Manual)",
    "[2026-06-29T06:55:20] front_door_lock | Event: LockOperation(LockOperationType=0, OperationSource=Auto)",
    "[2026-06-29T08:26:00] front_door_lock | Event: LockOperation(LockOperationType=1, OperationSource=Manual)",
    "[2026-06-29T20:39:08] front_door_lock | Event: LockOperation(LockOperationType=0, OperationSource=Auto)",
    "[2026-06-29T05:37:11] bedroom_window | WindowCovering.CurrentPositionLiftPercent100ths = 0",
    "[2026-06-29T06:55:09] front_door_lock | DoorLock.LockState = 2",
    "[2026-06-29T06:55:20] front_door_lock | DoorLock.LockState = 1",
    "[2026-06-29T08:26:00] front_door_lock | DoorLock.LockState = 2",
    "[2026-06-29T12:23:08] bedroom_window | WindowCovering.CurrentPositionLiftP

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

## Query
以下是过去 24 小时的设备活动日志。请分析是否存在异常行为模式，并说明推理过程。

## Stage-1 Analysis
{
  "candidate_hypotheses": [
    {
      "id": "H1",
      "type": "anomaly",
      "description": "The primary residence is currently unoccupied (0 occupancy across all rooms since midnight), yet the front door lock has undergone multiple state changes (Lock/Unlock) and auto-lock operations, suggesting either a remote control action, a security system glitch, or potential unauthorized access attempts while the house appears empty.",
      "supported_by": [
        "[2026-06-29T00:00:00] living_room_occupancy | OccupancySensing.Occupancy = 0",
        "[2026-06-29T00:30:00] bedroom_occupancy | OccupancySensing.Occupancy = 0",
        "[2026-06-29T00:30:00] kitchen_occupancy | OccupancySensing.Occupancy = 0",
        "[2026-06-29T05:37:11] bedroom_window | WindowCovering.CurrentPositionLiftPercent100ths = 0",
        "[2026-06-29T08:26:00] front_door_lock | Event: LockOperation(LockOperationType=1, OperationSource=Manual)",
        "[2026-06-29T20:39:08] front_door_lock | DoorLock.LockState = 1"
      ],
      "contradicted_by": []
    },
    {
      "id": "H2",
      "type": "anomaly",
      "description": "Suspicious window manipulation in the bedroom. The window covering was lowered to 0% (closed) at 05:37, but then raised to 100% (open) at 12:23 and again at 20:39. Given the unoccupied status of the room during these transitions, this suggests an intruder manipulating the blind to check visibility or access, rather than a resident managing light/sunlight.",
      "supported_by": [
        "[2026-06-29T05:37:11] bedroom_window | WindowCovering.CurrentPositionLiftPercent100ths = 0",
        "[2026-06-29T12:23:08] bedroom_window | WindowCovering.CurrentPositionLiftPercent100ths = 10000",
        "[2026-06-29T13:55:02] bedroom_window | WindowCovering.CurrentPositionLiftPercent100ths = 0",
        "[