EL-03-FP: 老人在旁边慢慢做饭

虽然老人短暂离开了厨房，但5分钟后回来检查，不是忘了关火

The data indicates a critical sensor malfunction causing nonsensical temperature readings (2100-2500+) and phantom occupancy signals, rather than a physical safety threat or intrusion.

Explanation: Temperature values are physically impossible for ambient indoor environments (ranging from ~2177 to ~2543), indicating a unit conversion error (likely reporting millikelvin instead of Kelvin) or total sensor failure, as no heat source could generate such temperatures without destroying the house instantly.

Explanation: Occupancy sensors report '1' (occupied) for extended periods while lights remain off and appliance usage is minimal or non-existent, which contradicts normal human behavior patterns (e.g., sleeping rooms occupied at 00:00 with lights off, kitchen occupied before stove activation).

Explanation: The sequence of events (manual lock/unlock followed by immediate auto-lock and entrance motion spikes) appears to be a logical response to the system's erroneous state (interpreting noise or drift as presence) rather than evidence of an external intruder.

Temperature readings starting at 2276 at 00:00 and climbing steadily to over 2500 throughout the day, despite all reported occupancy being zero until 05:37.

Stove activation occurred at 06:01, yet temperatures had already exceeded 2400 three hours prior, ruling out cooking as the cause of the heat spike.

Kitchen sensor returned 'None' at 06:02 and 06:33, confirming intermittent hardware connectivity issues typical of sensor failure.

Immediately replace or recalibrate the temperature sensors in all affected rooms (Living Room, Bedroom, Kitchen).

Review and update sensor configuration to ensure correct unit reporting (Celsius vs. Kelvin) or disable affected sensors if calibration is impossible.

Monitor logs for false-positive occupancy triggers that may lead to unnecessary security alerts or automated responses.

Door Lock State flips to Unlocked (State=2) at 07:00:03 with Manual source, followed immediately by Entrance Occupancy = 1 at 07:00:06, while the resident's bedroom occupancy was previously stable (1).

Rapid, unexplained rise in ambient temperature (from 22°C to 2500+ units) starting early morning correlates with no internal heat source activation until much later (06:01 stove turn-on).

Intruder enters at 07:00, interacts with entrance motion repeatedly (07:05, 07:10, 07:15, 07:20), and leaves via Auto-lock at 07:00:13, yet the system still flags interior occupancy, implying a struggle or hidden presence.

Stove activated at 06:01 (before intruder entry time window suggests premeditation or misinterpretation of timeline) but massive heat persists long after stove turns off at 06:32.

The sheer magnitude of temperature readings (2500+) could be a unit error rather than physical reality, weakening the 'heat weaponization' aspect unless the sensor scale is confirmed broken.

Resident Disoriented due to Extreme Heat Exposure leading to Erratic Behavior.

Temperature spikes continuously from 00:00 to 07:22 despite all rooms being empty for the first half of this period.

At 07:00, the door is manually unlocked and re-locked quickly; if the resident were confused by extreme heat, they might attempt to leave or check outside conditions.

The pattern of frequent entrance motion events (07:05 onwards) matches someone pacing or checking the perimeter due to distress.

If the resident was already inside, why did the front door contact show 'False' (open) at 07:00:05? This implies an exit or entry event that contradicts a purely internal heat exposure scenario.

Timestamp 07:00:03 - 07:00:13: A manual lock unlock occurs while the system reports interior occupancy, followed by immediate auto-locking. Combined with the entrance sensor triggering shortly after, this strongly points to an external actor entering or exiting, rather than internal movement.

Temperature Data vs. Appliance Log: Temperature exceeds 2400 units starting at 03:30, but the kitchen stove (primary heat source) is not turned ON until 06:01. The heat precedes the usage by nearly 3 hours, ruling out normal cooking as the cause.

Historical baseline temperature data to confirm if the '22' reading was truly normal or if the sensor has been drifting for days.

Video footage or audio logs from the entrance area between 07:00 and 07:05 to visualize the interaction at the door.

Confirmation of the temperature sensor's unit of measurement (Celsius vs. scaled integer) to validate the severity of the heat reading.

Verify if '2500' temperature readings are a data logging error (unit mismatch) or actual sensor failure.

Cross-reference door lock events with specific timestamps to confirm the sequence of unlock->occupancy spike->relock.

Check stove appliance logs for exact on/off states and duration relative to the temperature anomaly.

The analysis is currently drifting into highly speculative scenarios ('heat weaponization', 'sabotage') based on a single outlier data point (2500 units) which may simply be a calibration drift or unit error.

The evidence relies heavily on C00-C05 but ignores the full 24-hour context. Chunks C06 and C07 are needed to establish the baseline behavior before the suspected anomaly window and to see if the pattern repeats or resolves later in the day.

Without confirming the nature of the temperature spike (malfunction vs real event), the distinction between Hypothesis N1 (Malfunction) and A1/A2 (Intruder/Health) remains ambiguous. Further chunks are required to validate the timeline of the stove activation versus the temperature rise.

## 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>

## Deterministic Signals
{
  "room_activity": {
    "living_room": 497,
    "bedroom": 529,
    "kitchen": 538,
    "bathroom": 57,
    "entrance": 231
  },
  "attribute_frequency": {
    "Occupancy": 943,
    "MeasuredValue": 870,
    "OnOff": 22,
    "CurrentPositionLiftPercent100ths": 4,
    "LockState": 4,
    "StateValue": 4,
    "CurrentLevel": 1
  },
  "lock_state_flips": 4,
  "occupancy_events": 943,
  "temperature_patterns": []
}

## Chunk Index
[
  {
    "chunk_id": "C00",
    "start_ts": "2026-06-12T00:00:00",
    "end_ts": "2026-06-12T01:35:00",
    "event_count": 80,
    "rooms": {
      "living_room": 24,
      "bedroom": 24,
      "kitchen": 24,
      "bathroom": 4,
      "entrance": 4
    },
    "top_devices": [
      "living_room_temp_sensor",
      "bedroom_temp_sensor",
      "kitchen_temp_sensor",
      "living_room_occupancy",
      "bedroom_occupancy",
      "kitchen_occupancy"
    ],
    "alarmish_count": 20,
    "preview_lines": [
      "[2026-06-12T00:00:00] living_room_temp_sensor | TemperatureMeasurement.MeasuredValue = 2276",
      "[2026-06-12T00:00:00] bedroom_temp_sensor | TemperatureMeasurement.MeasuredValue = 2177",
      "[2026-06-12T00:00:00] kitchen_temp_sensor | TemperatureMeasurement.MeasuredValue = 2269",
      "[2026-06-12T00:00:00] living_room_occupancy | OccupancySensing.Occupancy = 0",
      "[2026-06-12T00:00:00] bedroom_occupancy | OccupancySensing.Occupancy = 0",
      "[2026-06-12T00:00:00] kitchen_occupancy | OccupancySensing.Occupancy = 0",
      "[2026-06-12T00:00:00] bathroom_occupancy | OccupancySensing.Occupancy = 0",
      "[2026-06-12T00:00:00] entrance_occupancy | OccupancySensing.Occupancy = 0",
      "[2026-06-12T00:05:00] living_

## 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。

## Triage
{
  "latent_task_profile": "composite-safety",
  "focus_rooms": ["kitchen", "bedroom", "living_room"],
  "focus_devices": ["temperature_sensor", "occupancy_sensor", "door_lock", "contact_sensor"],
  "focus_chunk_ids": ["C00", "C01", "C02", "C03", "C04", "C05"],
  "suspected_patterns": [
    "Continuous high-temperature readings exceeding normal ambient ranges (22°C -> 25°C+)",
    "Sustained occupancy with no apparent activity variation or sleep cycles",
    "Frequent door lock/unlock events without corresponding entry/exit occupancy patterns"
  ],
  "why_these_chunks": [
    "C00-C03: Show a clear, progressive temperature spike across all rooms starting early morning, deviating from typical environmental baselines.",
    "C04-C05: Introduce critical state changes including stove interaction, frequent entrance motion, and manual lock operations that do not align with the sustained internal occupancy signal.",
    "Combined with the user profile 'Elderly Living Alone', these signals suggest either a device malfunction (sensor drift) or a potential safety incident (e.g., heat exposure, unauthorized access simulation)."
  ]
}

## Investigator
{
  "normal_hypotheses": [
    {
      "id": "N1",
      "description": "Sensor Malfunction/Calibration Drift causing falsely high temperature readings and phantom occupancy.",
      "evidence": [
        "Temperature values start at ~22 (00:00) but rapidly climb to >2500 without any recorded appliance activation until 06:01.",
        "Occupancy sensors report '1' in bedrooms and kitchen during times where lights are off or only just turning on, suggesting false triggers or sensor cross-talk.",
        "The kitchen sensor returns 'None' multiple times (06:02, 06:33), indicating intermittent connectivity issues typical of hardware failure."
      ],
      "weaknesses": [
        "Does not explain the specific sequence of manual lock/unlock events occurring while the house is reported as occupied by a single person who appears unaware of the heat spike."
      ]
    }
  ],
  "anomaly_hypotheses": [
    {
      "id": "A1",
      "description": "Unauthorized Intruder Entry and Potential Heat Weaponization/Sabotage.",
      "threat_type": "Burglary / Safety Hazard",
      "evidence": [
        "Door Lock State flips to Unlocked (State=2) at 07:00:03 with Manual source, followed immediately by Entrance Occupancy = 1 at 07:00:06, while the resident's bedroom occupan


EL-07-FP: 老人散步时间稍长但正常回家

虽然比平时晚了30分钟回来，但最终正常解锁回家并有后续活动

The temperature sensors are exhibiting a systematic calibration failure reporting raw integer values that drift upward over time, creating a false reading of extreme heat while simultaneously losing telemetry during active cooking events.

Explanation: All temperature sensors across the house report values between ~2160 and ~2550, which is physically impossible for ambient room temperature in standard units (Celsius/Fahrenheit), indicating a firmware unit conversion error or raw ADC count leak.

Explanation: The integer values show a consistent monotonic increase throughout the entire 24-hour log (e.g., kitchen rising from 2264 to 2553) without corresponding environmental changes or HVAC cycles, confirming a systemic software bug rather than external overheating.

Explanation: Critical safety blind spots were identified where `kitchen_temp_sensor` returned `None` at T06:10:03 and T06:29:00, which coincided exactly with the activation and deactivation of the `kitchen_cook_surface`, meaning the system failed to monitor high-risk thermal events due to intermittent disconnection or power fluctuation.

Monotonic rise of erroneous integers: Kitchen sensor increased from 2264 (00:00) to 2553 (09:17) despite no occupancy correlation.

Temporal correlation of data loss: Sensor returned 'None' precisely when the cook surface was active (T06:09:33) and immediately after (T06:28:00).

Universal pattern: The same non-physical value range appears in living_room, bedroom, and kitchen sensors simultaneously, ruling out isolated hardware damage.

Immediately disable the faulty temperature sensors in the smart home app to prevent incorrect fire alerts based on raw integer data.

Contact the device manufacturer to apply a firmware update addressing the missing unit conversion logic and investigate the cause of intermittent sensor dropouts during high-load periods.

Sensor Communication Failure with Data Masking: The kitchen temperature sensor experiences intermittent disconnection or power loss. When the sensor is offline, it returns `None`. The system logic erroneously continues reporting stale or extrapolated integer data from another source (or a backup sensor mislabeled as 'kitchen') when the primary is down, creating a false sense of security regarding actual temperatures.

At T06:09:33, `kitchen_cook_surface` turns ON. Shortly after, at T06:10:03, `kitchen_temp_sensor` reports `None`, yet a valid integer `2485` is reported immediately after (likely from a different physical sensor or cached value).

At T06:28:00, `kitchen_cook_surface` turns OFF. Immediately at T06:29:00, `kitchen_temp_sensor` reports `None` again, followed by a jump back to integer `2487`.

The integer values continue their upward drift even when the sensor explicitly reports `None`, implying the 'integer' stream is decoupled from the actual cooking surface's thermal state.

High-risk window identified: T06:09:33 to T06:14:03 and T06:28:00 to T06:30:00 where cook surface was active/just deactivated but temperature telemetry was lost.

No direct evidence confirms the source of the integer values during the `None` gaps; they could theoretically be valid readings from a separate, unaffected sensor if dual sensors exist.

Occupancy logs show people present in the kitchen during these times, increasing the likelihood of unmonitored hazards.

Thermal Runaway / Overheating Event: The kitchen environment is genuinely overheating due to equipment failure (e.g., oven malfunction), and the sensor calibration drift (Hypothesis N1) combined with data loss is masking the severity of the rise.

Raw values reach ~2500 (if interpreted as Kelvin, this is >2200°C, which is impossible for a room; if Fahrenheit, ~1300°C, also impossible; if raw millivolts or arbitrary units, it indicates extreme deviation).

Even assuming the 'drift' is just bad calibration, the absolute magnitude suggests the sensor is saturated or measuring something completely wrong.

The `None` events coincide with high thermal load periods (cooking), suggesting the sensor may be physically damaged or melting off its mounting point.

Without knowing the original unit scale, it is hard to definitively claim 'runaway' without the `None` gap explanation.

Other rooms show similar drift patterns, making an isolated kitchen hardware failure less likely unless there is a shared power/grid issue.

The temporal correlation between `kitchen_cook_surface` On/Off events and the exact timestamps where `kitchen_temp_sensor` returns `None` (T06:10:03, T06:29:00).

The persistence of integer temperature values (e.g., 2485, 2487) immediately following `None` states, indicating a fallback mechanism or multi-sensor redundancy where one sensor failed silently.

The monotonic increase of integer values throughout the log (C00-C04) despite no user interaction, contrasting with typical HVAC cycles.

The defined unit of measurement for `TemperatureMeasurement.MeasuredValue` (e.g., Celsius, Fahrenheit, Kelvin, mV, or Raw ADC count).

Hardware specifications: Are there multiple temperature sensors per room? If so, which ID corresponds to the 'kitchen' reading when `None` occurs?

Power supply logs for the kitchen devices to correlate `None` events with potential brownouts.

Historical baseline data to determine if the integer range (2200-2500) is the normal operating range for this specific sensor model under current conditions.

Verify power supply logs during T06:10:03 and T06:29:00 to confirm if 'None' readings correlate with brownouts.

Cross-reference occupancy data with the specific timestamps of sensor failure to assess risk exposure.

Check historical baseline for this specific device model to determine if 2200-2500 is a known firmware artifact or actual saturation.

The analysis correctly identified the correlation between cooking events and sensor failures (Hypothesis A1), which is critical.

However, evidence is currently insufficient to rule out global system issues vs. isolated kitchen hardware. The investigator noted that other rooms show similar drift; chunks C05-C06 (likely covering mid-day/other zones) are needed to confirm if this is a universal firmware bug or localized.

Chunks C17-C18 should be examined to see if the pattern repeats in the evening cycle or resolves, determining if it's an intermittent glitch or sustained failure.

Without confirming the unit of measurement from the raw log data (which isn't explicitly visible in the summary provided by the investigator), the 'thermal runaway' claim remains speculative until verified against known sensor specs in additional context windows.

## 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>

## Deterministic Signals
{
  "room_activity": {
    "living_room": 499,
    "bedroom": 530,
    "kitchen": 532,
    "bathroom": 64,
    "entrance": 232
  },
  "attribute_frequency": {
    "Occupancy": 947,
    "MeasuredValue": 870,
    "OnOff": 21,
    "CurrentPositionLiftPercent100ths": 4,
    "LockState": 4,
    "StateValue": 4,
    "CurrentLevel": 1
  },
  "lock_state_flips": 4,
  "occupancy_events": 947,
  "temperature_patterns": []
}

## Chunk Index
[
  {
    "chunk_id": "C00",
    "start_ts": "2026-06-15T00:00:00",
    "end_ts": "2026-06-15T01:35:00",
    "event_count": 80,
    "rooms": {
      "living_room": 24,
      "bedroom": 24,
      "kitchen": 24,
      "bathroom": 4,
      "entrance": 4
    },
    "top_devices": [
      "living_room_temp_sensor",
      "bedroom_temp_sensor",
      "kitchen_temp_sensor",
      "living_room_occupancy",
      "bedroom_occupancy",
      "kitchen_occupancy"
    ],
    "alarmish_count": 20,
    "preview_lines": [
      "[2026-06-15T00:00:00] living_room_temp_sensor | TemperatureMeasurement.MeasuredValue = 2279",
      "[2026-06-15T00:00:00] bedroom_temp_sensor | TemperatureMeasurement.MeasuredValue = 2161",
      "[2026-06-15T00:00:00] kitchen_temp_sensor | TemperatureMeasurement.MeasuredValue = 2264",
      "[2026-06-15T00:00:00] living_room_occupancy | OccupancySensing.Occupancy = 0",
      "[2026-06-15T00:00:00] bedroom_occupancy | OccupancySensing.Occupancy = 0",
      "[2026-06-15T00:00:00] kitchen_occupancy | OccupancySensing.Occupancy = 0",
      "[2026-06-15T00:00:00] bathroom_occupancy | OccupancySensing.Occupancy = 0",
      "[2026-06-15T00:00:00] entrance_occupancy | OccupancySensing.Occupancy = 0",
      "[2026-06-15T00:05:00] living_

## 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。

## Triage
{
  "latent_task_profile": "single-event-safety | composite-safety",
  "focus_rooms": ["kitchen"],
  "focus_devices": ["kitchen_temp_sensor", "kitchen_cook_surface", "living_room_temp_sensor"],
  "focus_chunk_ids": ["C00", "C01", "C02", "C03", "C04", "C16"],
  "suspected_patterns": [
    "Sensor calibration failure: Temperature readings are in raw integer format (e.g., 2279, 2504) instead of Celsius/Fahrenheit, suggesting a firmware or unit conversion error.",
    "Data loss event: 'MeasuredValue' becomes 'None' during active cooking periods, indicating sensor disconnection or power fluctuation.",
    "Thermal runaway risk: Despite 'None' readings, the reported integer values continue to trend upwards from ~2279 to ~2550 over several hours, implying unmonitored overheating if the sensor is functional but misreported."
  ],
  "why_these_chunks": [
    "C00-C03 show the initial onset of anomalous temperature values (2279-2459) with no occupancy correlation, establishing the baseline anomaly.",
    "C04 and C16 contain critical safety events where `kitchen_cook_surface` activity coincides with `kitchen_temp_sensor` returning `None`, creating a high-risk blind spot for fire detection.",
    "C01 and C02 demonstrate the continuous upward drift of the erroneous temperature values, confirming a systemic issue rather than a transient glitch."
  ]
}

## Investigator
{
  "normal_hypotheses": [
    {
      "id": "N1",
      "description": "Systematic Sensor Calibration Drift: The temperature sensors are functioning but suffering from a firmware bug where the unit conversion (e.g., Kelvin to Celsius) is missing or incorrect, causing raw integer values that linearly drift over time.",
      "evidence": ["All sensors report values between ~2160 and ~2500 consistently across C00-C04.", "Values increase monotonically in all rooms as ambient temperature naturally rises during the day (morning to afternoon).", "The pattern of increase is uniform across living_room, bedroom, and kitchen, suggesting a global system issue rather than isolated device failure."],
      "weaknesses": ["This hypothesis fails to explain the sudden appearance of `None` values at T06:10:03 and T06:29:00 while other readings remain valid integers.", "It does not account for the correlation between `None` readings and specific high-heat events (cook surface activation)."],
      "threat_type": "Low"
    }
  ],
  "anomaly_hypotheses": [
    {


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

System-wide sensor data corruption and calibration failure causing implausible temperature readings (e.g., 2700+) and inconsistent door lock state reporting.

Explanation: Temperature sensors report values between 2100-2700 throughout the log, which is physically impossible for ambient room temperature in standard units; this indicates a severe sensor drift or unit conversion failure.

Explanation: The sequence of events at 22:04 appears deterministic (Lights Off -> AC Off -> Door Contact True -> Lock Auto), mimicking a human departure routine, but the surrounding context shows no vacancy prior to this, making it highly likely an automated script reacting to faulty 'occupied' sensor noise rather than human action.

Explanation: The front door contact reported 'True' (implying closed/locked) while bedroom occupancy was still active, a logical inconsistency that is best explained by the corrupted sensor inputs triggering erroneous automation rules rather than a physical breach.

Multiple temperature logs showing values like 2692, 2713, and 2700, indicating raw integer overflow or incorrect scaling.

Front door lock state changed to 'Locked' at 22:04:37 via 'Auto' source while internal bedroom occupancy was confirmed active until 22:44.

Immediately isolate and reset all temperature sensors to clear calibration errors.

Review smart home automation rules for 'departure' triggers to ensure they are not executing based on noisy or misinterpreted occupancy data.

Verify physical status of the front door lock manually to ensure it is not stuck in a false 'locked' state due to firmware logic errors.

Standard overnight routine: User moved from living area to bedroom, adjusted window coverings for privacy/sleep, and eventually vacated the house.

Bedroom light turned ON briefly at 22:04:29, then OFF later.

Window covering manipulated to fully closed (10000%) at 22:04:32.

No further activity in living room after 22:42 (bedroom light off), consistent with winding down for sleep.

The sequence of closing the front door lock (22:04:37) while the user is still present in the bedroom (occupancy 1 until 22:44) is physically counter-intuitive unless the lock is external or triggered by a smart home rule that overrides physical presence.

Temperature readings are extremely high (2250-2277) which may indicate raw integer values rather than Celsius/Fahrenheit, but if real, indicates no HVAC cooling despite AC being turned off.

Potential unauthorized entry simulation or system compromise: A sequence mimicking a normal departure but occurring while internal occupancy persists, possibly testing security sensors or executing a malicious script.

Front door contact state changed to True (secured) at 22:04:36 while bedroom occupancy was confirmed active (22:04:20).

Lock operation initiated automatically (Source=Auto) immediately after door closure detection.

Simultaneous rapid state changes: Lights off -> AC off -> Door Closed/Locked within 30 seconds.

The 'alarmish_count' mentioned in triage signals correlates with this tight temporal cluster of deterministic state flips.

Could be a legitimate smart home automation rule (e.g., 'If anyone enters garage/front door, lock it') triggered by a false positive or specific user action not logged as manual.

Lack of alarm trigger history in the logs provided; if an intrusion were happening, one would expect a panic button activation or motion alarm in the hallway.

Sensor malfunction or data corruption leading to false security status updates.

Temperature values (e.g., 2250, 2277) are statistically improbable for ambient room temperature unless they represent millikelvin or raw ADC counts, suggesting calibration failure.

Front door contact flip coincides exactly with other device shutdowns, suggesting a single root cause (power glitch, reset command) rather than independent human actions.

LockState flip count is low (2) yet multiple lock-related events occurred in a short burst.

Does not explain the logical sequence of 'Light Off -> Door Lock' which looks intentional. Malfunctions usually result in random noise, not coherent sequences.

Temporal conflict: Front door secured (StateValue=True, LockState=1) at 22:04:37 while Bedroom Occupancy was simultaneously Active (1) until 22:44. In a standard secure home model, securing the perimeter while occupants remain inside without explicit egress logging is anomalous.

Rapid determinism: The transition from Living Room Light Off (22:04:04) to Front Door Locked (22:04:37) occurred in <33 seconds with no intermediate 'User Exiting Hallway' occupancy log, bypassing the expected transit zone.

Definition of BooleanState.StateValue=True for the front door (is it Open/Closed or Unlocked/Locked? Standard conventions vary).

Logs for the Hallway or Entrance corridor occupancy during the 22:04 window.

Command history to determine if the 'LockOperation(Source=Auto)' was triggered by a mobile app command, a scheduled task, or a sensor loop.

Calibration metadata for temperature sensors to interpret values like 2250.

Verify Hallway/Entrance corridor occupancy logs around 22:04 to rule out user transit through the secured zone.

Clarify the boolean definition of front_door_contact StateValue=True (Closed vs. Locked) to validate the 'secured while occupied' anomaly.

Check command history or system logs to determine if LockOperation(Source=Auto) was triggered by a scheduled task, mobile app, or sensor loop.

The analysis correctly identified the temporal conflict between securing the front door and internal bedroom occupancy, which is the core anomaly.

However, the evidence regarding the hallway transit is missing; without confirming no one passed through the entrance/hallway after the lock engaged, the 'intrusion simulation' hypothesis remains unverified.

Chunk C05 likely contains earlier context or the specific timestamp transition leading up to C06, while C03 may contain baseline data for comparison or relevant hallway sensors.

Temperature values (2250+) are flagged as suspicious but lack calibration metadata; this requires checking system config logs rather than just event chunks.

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

## Deterministic Signals
{
  "room_activity": {
    "living_room": 232,
    "bedroom": 183,
    "kitchen": 153,
    "bathroom": 24,
    "entrance": 10
  },
  "attribute_frequency": {
    "MeasuredValue": 437,
    "Occupancy": 139,
    "OnOff": 13,
    "StateValue": 4,
    "LockState": 2,
    "SystemMode": 1,
    "OperationalState": 1,
    "CurrentPositionLiftPercent100ths": 1,
    "CurrentLevel": 1
  },
  "lock_state_flips": 2,
  "occupancy_events": 139,
  "temperature_patterns": []
}

## Chunk Index
[
  {
    "chunk_id": "C00",
    "start_ts": "2026-06-17T11:55:00",
    "end_ts": "2026-06-17T13:45:00",
    "event_count": 80,
    "rooms": {
      "living_room": 27,
      "bedroom": 27,
      "kitchen": 22,
      "bathroom": 4
    },
    "top_devices": [
      "living_room_temp_sensor",
      "bedroom_temp_sensor",
      "kitchen_temp_sensor",
      "living_room_occupancy",
      "bedroom_occupancy",
      "bathroom_occupancy"
    ],
    "alarmish_count": 12,
    "preview_lines": [
      "[2026-06-17T11:55:00] living_room_temp_sensor | TemperatureMeasurement.MeasuredValue = 2526",
      "[2026-06-17T11:55:00] bedroom_temp_sensor | TemperatureMeasurement.MeasuredValue = 2429",
      "[2026-06-17T11:55:00] kitchen_temp_sensor | TemperatureMeasurement.MeasuredValue = 2522",
      "[2026-06-17T12:00:00] living_room_temp_sensor | TemperatureMeasurement.MeasuredValue = 2534",
      "[2026-06-17T12:00:00] bedroom_temp_sensor | TemperatureMeasurement.MeasuredValue = 2436",
      "[2026-06-17T12:00:00] kitchen_temp_sensor | TemperatureMeasurement.MeasuredValue = 2522",
      "[2026-06-17T12:00:00] living_room_occupancy | OccupancySensing.Occupancy = 0",
      "[2026-06-17T12:05:00] living_room_temp_sensor | TemperatureMeasurement.MeasuredValue = 2518",
      "[2026-06-17

## 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。

## Triage
{
  "latent_task_profile": "behavior-sequence | composite-safety",
  "focus_rooms": ["living_room", "bedroom", "entrance"],
  "focus_devices": ["living_room_occupancy", "bedroom_occupancy", "entrance_light", "front_door_contact", "bedroom_window_covering", "living_room_ac"],
  "focus_chunk_ids": ["C06", "C07"],
  "suspected_patterns": [
    "Late-night entry sequence: Occupancy detected in living room followed by light off, AC off, and immediate front door contact state change (True) at 22:04.",
    "Window covering manipulation coinciding with occupancy events in the bedroom late at night.",
    "High alarmish counts (28) in C06 correlate with multiple simultaneous state changes suggesting a complex interaction or potential intrusion simulation."
  ],
  "why_these_chunks": "Chunks C06 and C07 contain the critical temporal sequence starting at 22:04 where lights turn off, AC turns off, and the front door sensor triggers simultaneously while occupancy is present. This specific cluster of deterministic signals (OnOff=False, StateValue=True for door) combined with high 'alarmish_count' indicates a non-standard behavior pattern requiring immediate inspection to rule out security breaches or system logic errors."
}

## Investigator
{
  "normal_hypotheses": [
    {
      "id": "N1",
      "description": "Routine evening departure sequence: The user left the living room, turned off main lights and AC, then exited via the front door and locked it from the outside or a secondary lock mechanism.",
      "evidence": [
        "At 22:04, living_room_light_1 and entrance_light turned OFF.",
        "At 22:04:11, living_room_ac turned OFF.",
        "At 22:04:36, front_door_contact state changed to True (closed/locked).",
        "At 22:04:37, LockOperation event occurred with Source=Auto.",
        "Occupancy remained in bedroom (22:04:20) suggesting someone stayed behind while leaving the common area."
      ],
      "weaknesses": [
        "The front door contact flipped to 'True' (often indicating closed/secure) while occupancy was still detected in the bedroom, implying the exit path might have been complex or the sensor logic is non-standard for an open door exit.",
        "High frequency of events within 3 minutes suggests either hurried movement or automated scripting rather than casual human behavior."
      ]
    },
    {
      "id": "N2",
      "description": "Standard overnight routine: User move


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

Systemic sensor failure causing physically impossible temperature readings (2300-2520) across all rooms for over 14 hours, likely due to a unit conversion error or firmware bug, rather than a security breach or environmental hazard.

Explanation: Analysis of chunks C03 and C04 confirms the anomalous temperature values (~2400-2500) are not an isolated event but a persistent systemic issue existing from early morning (04:00), ruling out transient spikes.

Explanation: The magnitude of the values (e.g., 2492) is physically impossible for ambient air sensors in Celsius or Fahrenheit, indicating a data integrity failure such as a missing decimal point (reporting 24.92 as 2492) or incorrect scaling factor.

Explanation: While door lock events in C14/C16 showed suspicious manual/auto cycles, the continuous invalid thermal data suggests the primary risk is false system triggers (e.g., fire alarms) or HVAC inefficiency, making 'sensor_malfunction' the definitive root cause over intrusion.

Explanation: The lack of correlation between extreme reported temperatures and any corrective HVAC response (AC turning on but temp remaining static at invalid levels) further proves the control loop is reacting to garbage data.

Temperature readings consistently range from 2300 to 2520 across all four room sensors during chunks C03-C05, which is impossible for real-world conditions.

Historical data in C03 (04:00) shows the anomaly was present before the peak activity in C05, confirming it is a long-standing configuration or hardware error.

Despite AC activation at 17:49 (C16), the temperature reading immediately following remains 2337, demonstrating the system cannot process valid feedback due to the input corruption.

Immediately reset or recalibrate all temperature sensors to restore accurate baseline data.

Inspect device firmware for known bugs related to unit conversion or integer overflow handling.

Review historical logs to confirm if this specific sensor ID has a persistent offset error prior to the current log window.

Sensor Data Integrity Failure / Malicious Firmware Override causing nonsensical temperature readings.

System Integrity / False Positive Trigger

All temperature readings across all rooms are consistently in the range of 2300-2520 (e.g., C05: 2492, C16: 2337). Real-world temperatures should be ~20-30°C. This indicates a scaling factor error (likely multiplying by 100) or a firmware override injecting fake data.

Simultaneous high 'values' across geographically separated sensors (living room, kitchen, bedrooms) without corresponding HVAC response logic to correct the 'extreme' heat suggests the system cannot distinguish real heat from injected data.

In C16, despite the AC being turned ON at 17:49:22, the temperature reading at 17:50:00 is still 2337 (raw), showing no reaction loop to the control command due to the invalid input state.

The occupancy events and lock states appear logically consistent with human behavior, suggesting the anomaly may be isolated to the temperature reporting subsystem rather than a total system compromise or physical intrusion.

Potential Intrusion Simulation or Covert Presence Detection via Door Lock Manipulation.

C14 and C16 both feature a specific pattern: Manual Lock -> Immediate Contact Open -> Occupancy Detected -> Auto Unlock.

In C14 (15:57), the manual lock is followed immediately by contact open and occupancy spike, then auto-unlock. This rapid cycle is unusual for normal entry/exit.

C16 (17:49) mirrors this: Lock (manual) -> Contact False -> Occupancy True -> Auto Unlock. The tight temporal coupling between manual manipulation and automatic reversal suggests either a smart lock vulnerability or an attempt to verify if the system detects 'forced' vs 'normal' entry.

The 'Auto' unlock source in C16 occurs seconds after the manual lock, which could simply be a scheduled routine or a user habit, not necessarily malicious. Without external context confirming forced entry, this remains circumstantial.

The magnitude of temperature values (e.g., 2492) which are physically impossible for ambient sensors, strongly pointing to a data integrity issue over actual environmental anomalies.

The precise timestamp alignment between 'Manual' lock initiation and subsequent 'Auto' unlock/occupancy spikes in C14 and C16, distinguishing between random noise and programmed sequences.

The unit of measurement for the temperature sensor (is it Celsius * 100? Fahrenheit? Kelvin?).

Historical baseline data for these specific sensors to confirm if the 'high' numbers are a new deviation or long-standing configuration.

User identity logs to correlate the 'Manual' lock operations with authorized residents.

Verify historical baseline for temperature sensors to determine if '2492' is a new anomaly or a persistent configuration error.

Cross-reference manual lock timestamps in C14/C16 with authorized user identity logs to rule out routine testing habits.

The analysis correctly identified the critical data integrity issue (impossible temperature values) and the suspicious door lock sequences. However, evidence sufficiency is low because: 1) The specific chunks selected (C05, C14, C16) do not establish a timeline; C03 and C04 are needed to observe if the temperature scaling error existed prior to the peak in C05, confirming it as a systemic sensor failure rather than an isolated spike. 2) C17 (the period after the evening event in C16) is required to see if the system recovers or if the anomaly persists, which helps distinguish between a firmware bug and active manipulation.

The hypothesis of 'malicious firmware override' remains speculative without historical context from earlier chunks to prove this behavior is unprecedented.

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

## Deterministic Signals
{
  "room_activity": {
    "living_room": 406,
    "master_bedroom": 516,
    "second_bedroom": 336,
    "kitchen": 304,
    "bathroom": 48,
    "entrance": 232
  },
  "attribute_frequency": {
    "MeasuredValue": 1156,
    "Occupancy": 649,
    "OnOff": 17,
    "StateValue": 7,
    "LockState": 5,
    "SystemMode": 1,
    "OperationalState": 1,
    "CurrentPositionLiftPercent100ths": 1
  },
  "lock_state_flips": 5,
  "occupancy_events": 649,
  "temperature_patterns": []
}

## Chunk Index
[
  {
    "chunk_id": "C00",
    "start_ts": "2026-06-09T00:00:00",
    "end_ts": "2026-06-09T01:20:00",
    "event_count": 80,
    "rooms": {
      "living_room": 20,
      "master_bedroom": 19,
      "second_bedroom": 19,
      "kitchen": 16,
      "bathroom": 3,
      "entrance": 3
    },
    "top_devices": [
      "living_room_temp_sensor",
      "master_bedroom_temp_sensor",
      "second_bedroom_temp_sensor",
      "kitchen_temp_sensor",
      "living_room_occupancy",
      "master_bedroom_occupancy"
    ],
    "alarmish_count": 15,
    "preview_lines": [
      "[2026-06-09T00:00:00] living_room_temp_sensor | TemperatureMeasurement.MeasuredValue = 2267",
      "[2026-06-09T00:00:00] master_bedroom_temp_sensor | TemperatureMeasurement.MeasuredValue = 2263",
      "[2026-06-09T00:00:00] second_bedroom_temp_sensor | TemperatureMeasurement.MeasuredValue = 2263",
      "[2026-06-09T00:00:00] kitchen_temp_sensor | TemperatureMeasurement.MeasuredValue = 2273",
      "[2026-06-09T00:00:00] liv

## 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。

## Triage
{
  "latent_task_profile": "composite-safety",
  "focus_rooms": ["kitchen", "living_room", "entrance"],
  "focus_devices": ["kitchen_temp_sensor", "living_room_temp_sensor", "front_door_lock", "front_door_contact", "kitchen_cook_surface", "master_bedroom_occupancy"],
  "focus_chunk_ids": ["C05", "C14", "C16"],
  "suspected_patterns": [
    "Unexplained high temperature baseline (22.6°C - 25.5°C range) across multiple rooms for the majority of the day.",
    "Simultaneous occupancy in living room and master bedroom with no kitchen activity during mid-day peaks, suggesting potential multi-person presence or sensor drift.",
    "Door lock/unlock sequence at 17:49 coinciding with immediate occupancy changes and HVAC activation."
  ],
  "why_these_chunks": [
    "C05 shows the peak temperature readings (~24.9°C) and initial cooking surface activation, establishing the anomalous thermal baseline.",
    "C14 captures the manual door lock operation event, a critical state change requiring context verification.",
    "C16 reveals the immediate aftermath of the door unlock/open event, showing rapid occupancy shifts and HVAC engagement, which is essential for determining if this was a routine entry or an intrusion response."
  ]
}

## Investigator
{
  "normal_hypotheses": [
    {
      "id": "N1",
      "description": "Routine household activity involving cooking, temperature regulation, and standard door locking/unlocking cycles.",
      "evidence": [
        "C05 shows a typical morning sequence: lights on (06:47), cook surface activated briefly (06:47-06:50), followed by kitchen light off.",
        "C16 shows an evening entry (17:49) triggering immediate occupancy detection in living room and activation of AC/lighting, consistent with returning home.",
        "Occupancy patterns show one person primarily in the master bedroom while others move through the entrance/living room."
      ],
      "weaknesses": [
        "Temperature values are reported in raw integers (e.g., 2492, 2337) which appear physically impossible for ambient air temperature unless scaled incorrectly; this suggests potential sensor calibration failure or data logging error rather than actual thermal anomalies.",
        "The frequency of manual lock operations (multiple times per chunk) is higher than typical for a single resident unless they are testing locks frequently or there is unauthorized access simulation."
      ]
    }


玄关运动 + 门锁解锁失败

密码错误（小孩可能记不准密码）

跟 INS-02 的区别：这里不是暴力破解，只有1-2次尝试，且家中有其他房间的活动（说明有人在家）

Occupancy sensors are stuck in a synchronized loop generating false positive events every ~2 minutes across all rooms due to hardware or calibration failure, not human activity.

Explanation: The timestamps reveal a rigid, mathematically perfect cycle: Occupancy triggers occur at exact intervals of roughly 1 minute 26 seconds (e.g., Master/Kids -> Entrance -> Dining), repeating identically across C00, C01, and C02 without any variation in timing or sequence.

Explanation: Human movement is stochastic and rarely exhibits such precise, global synchronization (all 4-5 room sensors triggering state changes simultaneously on the same second) over a continuous 8+ hour period.

Explanation: The lack of supporting context data (no door lock toggles, no light switches activating/deactivating except for one isolated event in C15, and no temperature fluctuations consistent with human presence) confirms that the 'activity' is purely digital noise from a malfunctioning sensor array rather than physical intrusion or occupancy.

Explanation: The pattern persists through day and night cycles unchanged, which is characteristic of a device bug or firmware glitch rather than adaptive behavior by humans.

Exact repetition of the sequence: MasterBedroom/KidsRoom (1 min 26 sec later) -> Entrance (1 min 20 sec later) -> DiningRoom (1 min 20 sec later) occurring consistently from 11:55 to 23:55.

Absence of correlated actuator events (lights, locks, curtains) during high-frequency occupancy reports, indicating the sensors are decoupled from actual environmental interactions.

Temperature readings remain static or drift linearly/independently of the occupancy spikes, proving thermal mass response does not match the reported 'presence'.

Immediately isolate and reset the affected occupancy sensor devices (Living Room, Master Bedroom, Kids Room, Entrance, Dining Room) to clear the stuck state.

Inspect the sensor hardware for obstructions, wiring issues, or firmware bugs causing the periodic polling error.

Review system logs for any recent configuration changes or power surges that may have triggered the malfunction.

Rigid temporal alignment: Occupancy sensors in distinct rooms (Living, Master, Kids, Entrance, Dining) trigger state changes with identical intervals (~2 minutes) throughout C00, C15, and C16. This suggests a single automated actor moving through pre-set waypoints rather than independent individuals.

Discrepancy between light state and occupancy: In C15, `master_bedroom_light` turns OFF at 22:50:36, yet `master_bedroom_occupancy` remains ON (1) consistently until 23:35+. Humans rarely sleep with lights on for 45+ minutes if they are 'occupied' the room in a way that triggers motion sensors repeatedly.

Temperature fluctuation anomaly: Temperature readings show erratic jumps (e.g., Kitchen drops from 2273 to 2257 then back up) inconsistent with thermal mass response time, suggesting potential sensor spoofing or calibration drift exploited by an attacker.

Low diversity in event types: 709% of signals are generic 'Occupancy', with almost no 'LockState', 'CurrentPosition', or 'SystemMode' changes despite high activity, indicating a lack of complex interaction data typical of real life.

Could be explained by a poorly calibrated multi-zone HVAC system triggering occupancy sensors via air flow detection rather than physical presence.

Household members could theoretically move in perfect lockstep, though the probability is statistically low without prior knowledge of the specific logging interval.

The exact repetition of the sequence: Entrance -> Dining -> Living/Master/Kids every 2 minutes across 12 hours of varied timestamps.

The persistence of 'Occupancy=1' in the Master Bedroom after the bed light was turned off for over 40 minutes, contradicting expected human behavior.

Absence of any lock state changes or door contact sensor alerts during the high-activity period, despite frequent entrance traffic.

Raw timestamp deltas between consecutive events for specific users/devices to confirm the 2-minute periodicity.

Video feed or audio logs to verify actual presence versus sensor ghosting.

Historical baseline data to determine if this 'synchronized' pattern is a known quirk of the specific sensor hardware model.

Verify if the ~2-minute periodicity is consistent across all chunks or isolated to C00/C15/C16.

Check for 'LockState' and 'ContactSensor' events in the early morning/late night transition periods (C01-C03) to rule out intrusion.

The analysis correctly identifies potential anomalies (synchronized occupancy, light/occupancy mismatch) based on the selected chunks.

However, the evidence is insufficient because it relies heavily on a limited sample (C00, C15, C16). The 'rigid synchronization' observed might be an artifact of specific time windows or sensor calibration quirks rather than a global pattern spanning the full 12 hours.

To confirm the hypothesis of 'simulated occupancy loop' vs 'hardware quirk', continuous data from the middle of the log (C01-C04) is required to verify if the periodicity persists uninterrupted.

Additionally, checking the start/end boundary chunks (C01-C03) is crucial to see if door locks or contact sensors were triggered during the high-activity phases mentioned in the anomaly hypothesis, which would contradict the 'intrusion simulation' theory if they were active.

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

## Deterministic Signals
{
  "room_activity": {
    "living_room": 234,
    "master_bedroom": 291,
    "kids_room": 293,
    "kitchen": 153,
    "entrance": 153,
    "dining_room": 146,
    "second_bedroom": 24,
    "master_bathroom": 24
  },
  "attribute_frequency": {
    "Occupancy": 709,
    "MeasuredValue": 582,
    "OnOff": 13,
    "StateValue": 4,
    "LockState": 3,
    "CurrentPositionLiftPercent100ths": 2,
    "SystemMode": 1,
    "OperationalState": 1
  },
  "lock_state_flips": 3,
  "occupancy_events": 709,
  "temperature_patterns": []
}

## Chunk Index
[
  {
    "chunk_id": "C00",
    "start_ts": "2026-06-16T11:55:00",
    "end_ts": "2026-06-16T12:40:00",
    "event_count": 80,
    "rooms": {
      "living_room": 12,
      "master_bedroom": 19,
      "kids_room": 19,
      "kitchen": 9,
      "entrance": 9,
      "dining_room": 9,
      "second_bedroom": 2,
      "master_bathroom": 1
    },
    "top_devices": [
      "living_room_temp_sensor",
      "master_bedroom_temp_sensor",
      "kids_room_temp_sensor",
      "kitchen_temp_sensor",
      "master_bedroom_occupancy",
      "kids_room_occupancy"
    ]

## 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。

## Triage
{
  "latent_task_profile": "behavior-sequence | single-event-safety",
  "focus_rooms": ["living_room", "master_bedroom", "kids_room", "kitchen"],
  "focus_devices": ["occupancy_sensor", "temperature_sensor", "door_lock", "contact_sensor"],
  "focus_chunk_ids": ["C00", "C15", "C16"],
  "suspected_patterns": [
    "synchronized_multi-room_occupancy",
    "cooling_cycle_drift",
    "late_night_activity_anomaly"
  ],
  "why_these_chunks": [
    "C00: Establishes baseline for high-frequency synchronized occupancy events across living, master, and kids rooms immediately after entry.",
    "C15: Shows a specific state change (bedroom light OFF) amidst continued high activity, indicating a shift in routine or sleep onset while others remain active.",
    "C16: Captures the tail end of the night with reduced event counts but persistent multi-room presence, requiring verification against expected 'night mode' behavior."
  ]
}

## Investigator
{
  "normal_hypotheses": [
    {
      "id": "N1",
      "description": "Standard family evening routine with synchronized dining and relaxation, followed by a shared sleep onset.",
      "evidence": ["High frequency of occupancy events in living, master, and kids rooms (234-293 count) indicates active household presence.", "Synchronized temperature readings (~25°C) during daytime (C00) suggest consistent HVAC operation or ambient conditions.", "Evening logs (C15/C16) show coordinated movement: lights off in bedrooms while occupants remain active in common areas (entrance/dining).", "Occupancy patterns align with typical post-dinner wind-down where multiple family members occupy different zones before settling."],
      "weaknesses": ["Does not fully explain the extremely rigid synchronization of occupancy sensor flips (every ~2 mins across all sensors) which appears unnatural for organic human movement."]
    }
  ],
  "anomaly_hypotheses": [
    {
      "id": "A1",
      "description": "Coordinated intrusion or simulated occupancy loop designed to mimic normal family activity.",
      "threat_type": "Social Engineering / Deception / Intrusion Simulation",
      "evidence": ["Rigid temporal alignment: Occupancy sensors in distinct rooms (Living, Master, Kids, Entrance, Dining) trigger state changes with identical intervals (~2 minutes) throughout C00, C15, and C16. This suggests a single automated actor moving through pre-set waypoints rather than independent individ


深夜窗户传感器从 Closed 变 Open
随后该房间运动传感器触发，但住户在卧室睡觉
运动信号沿非住户房间传播

触发入侵警报，通知住户，记录事件

Systemic sensor data corruption or unit conversion failure resulting in physically impossible temperature readings (>2400) across all devices.

Explanation: All temperature sensors report values consistently in the range of 2377 to 2918 throughout the entire 12-hour window, which is physically impossible for Celsius (requiring >2300°C) or Fahrenheit (>2500°F).

Explanation: The anomaly affects every sensor type (living room, bedrooms, kitchen) simultaneously with identical magnitudes, indicating a systemic software error, configuration bug, or unit scaling issue (e.g., reporting milli-Kelvin as raw integer without division) rather than local hardware failure or environmental events.

Explanation: Despite logical state changes such as turning off the cook surface at 12:51, the temperature values do not return to plausible baseline levels; instead, they remain in the erroneous high-thousands range before slowly drifting down, confirming a persistent data integrity issue.

Kitchen temperature spikes to 2918 immediately after cooking starts and remains above 2800 even after the cook surface is turned off, failing to show any realistic cooling curve.

Values like 2400-2410 observed in later chunks (C03) are still millions of times hotter than any residential environment, proving the data model is broken regardless of physical activity.

Immediately verify the 'MeasuredValue' unit definition in the system schema to confirm if it represents milli-Kelvin, scaled integers, or another non-standard format.

Reset the sensor calibration or reconfigure the logging pipeline to apply correct unit conversion factors before resuming automated safety protocols.

{
  "normal_hypotheses": [
    {
      "id": "N1",
      "description": "The device logs represent a standard daily routine where the kitchen is actively used for cooking, causing elevated temperatures, while other rooms remain at ambient levels.",
      "evidence": [
        "Occupancy data shows consistent presence in the living room (27 events with value 1) and absence in bedrooms/entrance/bathroom during peak hours.",
        "Kitchen temperature rises significantly from ~2500 to ~2900 after lights and cook surface are turned on (12:15 - 12:51), which aligns with expected thermal behavior for an active kitchen.",
        "Temperature values stabilize and decrease towards the end of the log (C01) as cooking ceases."
    ],
    "weaknesses": [
        "The absolute temperature values (~2500-2900) are physically impossible for Celsius (implies >2500°C) or Fahrenheit (implies >2300°F). This suggests a fundamental data formatting or unit error rather than normal operation."
    ]
  },
  "anomaly_hypotheses": [
    {
      "id": "A1",
      "description": "Sensor calibration failure or unit conversion error causing invalid high-value readings across all temperature sensors, potentially leading to false system alarms.",
      "threat_type": "Data Integrity / False Positive Risk",
      "evidence": [
        "All temperature sensors report values in the range of 2400-2900 throughout the entire log, which is physically impossible for residential environments.",
        "In Chunk C00, the kitchen sensor reads ~2918 immediately before and after the cook surface is turned off, failing to show a realistic cooling curve despite the event occurring at 12:51.",
        "In Chunk C01, the kitchen sensor drops to ~2475, but this still represents an impossible physical state. The 'drop' might be a reset or a different type of error, not natural cooling.",
        "Multiple sensors show identical or near-identical erroneous values simultaneously (e.g., all rooms reading ~2500 at specific timestamps), suggesting a systemic broadcast error or shared configuration bug rather than independent sensor failures."
    ],
    {
      "id": "A2",
      "description": "Potential unauthorized device manipulation or spoofing attempting to mimic high-temperature conditions to trigger safety protocols.",
      "threat_type": "Security Intrusion",
      "evidence": [
        "Sudden spike in 'MeasuredValue' magnitude coincides with user activity (light/cook surface activation), possibly indicating an automated script injecting data.",
        "The persistence of these extreme values even when no heat source should exist (e.g., C01 where everything is off) suggests continuous injection rather than transient hardware noise."
    ],
    "weaknesses": [
        "There is no evidence of network intrusion attempts or packet injection in the raw chunks provided; the anomalies appear strictly within the sensor payload format.",
        "The pattern of values changing smoothly (even if to impossible numbers) mimics sensor drift more closely than random spoofing artifacts."
    ]
  },
  "most_discriminative_evidence": [
    "The simultaneous reporting of impossible temperature magnitudes (>2400) across spatially separated sensors (living room, bedrooms, kitchen) rules out local hardware damage and points to a systemic software/unit issue.",
    "The lack of correlation between the 'OnOff' events (cooking stops at 12:51) and a rapid return to plausible physical temperatures in the subsequent logs indicates the data model itself is broken."
  ],
  "missing_information": [
    "Definition of the expected units for 'MeasuredValue' (e.g., is it milli-Kelvin? scaled integer?).",
    "Previous baseline readings from prior hours to determine if this is a sudden onset or a chronic logging configuration error.",
    "System alarm logs corresponding to these timestamps to verify if these invalid values triggered safety shutdowns or maintenance alerts."
  ]
}

Verify unit definitions (milli-Kelvin vs Celsius) in system schema.

Retrieve alarm logs for timestamps corresponding to C00/C01 to confirm false positives.

The investigation correctly identified the core anomaly: physically impossible temperature values (>2400).

However, evidence is insufficient because it relies solely on Chunks C00 and C01. These chunks show the *onset* of the error but do not establish if this is a chronic configuration issue or a sudden glitch starting at hour 0.

Chunks C02 and C03 are needed to determine if the 'normal' baseline (if any) existed prior to the error or if the drift continued linearly across the full 12-hour window.

Without checking earlier chunks (relative to the log start), we cannot distinguish between a fresh deployment bug and a sudden network injection event.

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

## Deterministic Signals
{
  "room_activity": {
    "living_room": 291,
    "master_bedroom": 169,
    "second_bedroom": 169,
    "kitchen": 159,
    "entrance": 27,
    "bathroom": 24
  },
  "attribute_frequency": {
    "MeasuredValue": 584,
    "Occupancy": 240,
    "OnOff": 9,
    "StateValue": 3,
    "OperationalState": 1,
    "LockState": 1
  },
  "lock_state_flips": 1,
  "occupancy_events": 240,
  "temperature_patterns": []
}

## Chunk Index
[
  {
    "chunk_id": "C00",
    "start_ts": "2026-06-07T11:55:00",
    "end_ts": "2026-06-07T13:00:00",
    "event_count": 80,
    "rooms": {
      "living_room": 27,
      "master_bedroom": 15,
      "second_bedroom": 15,
      "kitchen": 19,
      "entrance": 2,
      "bathroom": 2
    },
    "top_devices": [
      "kitchen_temp_sensor",
      "living_room_temp_sensor",
      "master_bedroom_temp_sensor",
      "second_bedroom_temp_sensor",
      "living_room_occupancy",
      "master_bedroom_occupancy"
    ],
    "alarmish_count": 21,
    "preview_lines": [
      "[2026-06-07T11:55:00] living_room_temp_sensor | TemperatureMeasurement.MeasuredValue = 2536",
      "[2026-06-07T11:55:00] master_bedroom_temp_sensor | TemperatureMeasurement.MeasuredValue = 2532",
      "[2026-06-07T11:55:00] second_bedroom_temp_sensor | TemperatureMeasurement.MeasuredValue = 2528",
      "[2026-06-07T11:55:00] kitchen_temp_sensor | TemperatureMeasurement.MeasuredValue = 2535",
      "[2026-06-07T11:58:00] living_room_occupancy | OccupancySensing.Occupancy = 1",
      "[2026-

## 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。

## Triage
{
  "latent_task_profile": "device-health | composite-safety",
  "focus_rooms": ["kitchen", "living_room"],
  "focus_devices": ["kitchen_temp_sensor", "living_room_temp_sensor", "kitchen_cook_surface"],
  "focus_chunk_ids": ["C00", "C01"],
  "suspected_patterns": [
    "Sensor calibration drift (TemperatureMeasurement.MeasuredValue in thousands instead of Celsius)",
    "Unexplained high alarm count relative to normal sensor traffic patterns"
  ],
  "why_these_chunks": [
    "Chunks C00 and C01 show the most extreme temperature anomalies (values like 2536, 2736) indicating a potential sensor failure or unit conversion error early in the log.",
    "These chunks also exhibit the highest 'alarmish_count' ratios relative to event counts, suggesting system instability triggered by these data points."
  ]
}

## Investigator
{
  "normal_hypotheses": [
    {
      "id": "N1",
      "description": "The device logs represent a standard daily routine where the kitchen is actively used for cooking, causing elevated temperatures, while other rooms remain at ambient levels.",
      "evidence": [
        "Occupancy data shows consistent presence in the living room (27 events with value 1) and absence in bedrooms/entrance/bathroom during peak hours.",
        "Kitchen temperature rises significantly from ~2500 to ~2900 after lights and cook surface are turned on (12:15 - 12:51), which aligns with expected thermal behavior for an active kitchen.",
        "Temperature values stabilize and decrease towards the end of the log (C01) as cooking ceases."
    ],
    "weaknesses": [
        "The absolute temperature values (~2500-2900) are physically impossible for Celsius (implies >2500°C) or Fahrenheit (implies >2300°F). This suggests a fundamental data formatting or unit error rather than normal operation."
    ]
  },
  "anomaly_hypotheses": [
    {
      "id": "A1",
      "description": "Sensor calibration failure or unit conversion error causing invalid high-value readings across all temperature sensors, potentially leading to false system alarms.",
      "threat_type": "Data Integrity / False Positive Risk",
      "evidence": [
        "All temperature sensors report values in the range of 2400-2900 throughout the entire log, which is physically impossible for residential environments.",
        "In Chunk C00, the kitchen sensor reads ~2918 immediately before and after the cook surface is turned off, failing to


儿童进入危险区域操作危险设备

活动轨迹：儿童房 → 厨房

无成人在厨房（厨房 occupancy 信号模式+其他房间的 occupancy 信号表明成人不在厨房）

如果灶具有 OffOnly 限制，理论上不能远程开启——这意味着是物理操作

立即通知家长，如可能则远程关闭灶具（OffOnly命令）

Systemic sensor data integrity failure where all temperature sensors report values scaled by a factor of 10 and exhibit unrealistic synchronized drift, failing to reflect actual environmental variance.

Explanation: Analysis of the raw logs reveals temperature values consistently in the range of 2263-2554, which are physically impossible for indoor Celsius readings but align perfectly with typical room temperatures (22.6°C - 25.5°C) when divided by 10.

Explanation: Despite distinct activities such as cooking in the kitchen at 02:51:30 (Chunk C02), the temperature readings for the kitchen and other rooms remain nearly identical (within ~5 units), lacking the expected thermal divergence caused by heat generation.

Explanation: The data exhibits high-frequency synchronization across geographically separated devices (living room, master bedroom, kitchen, kids room) that would naturally experience different thermal dynamics, indicating a shared software artifact, unit conversion error, or calibration drift rather than real-world physics.

Explanation: Occupancy sensors function normally (detecting presence/absence events), proving the hardware is functional and isolating the anomaly specifically to the temperature measurement logic.

Raw 'MeasuredValue' entries like 2389 vs 23.89°C; division by 10 yields valid ambient temperatures.

At 02:51:30, the kitchen cook surface turned ON, yet temperature readings across all rooms remained within a 1-degree margin (e.g., Kitchen 2378 vs Living Room 2389).

All four focus rooms update simultaneously every 5 minutes with near-perfect correlation, demonstrating a systemic feed artifact or configuration error.

Immediately review device driver configuration logs to verify if a 'scale_factor' parameter was inadvertently set to 10 instead of 1.

Check historical baseline data from previous days to determine if this scaling issue is a new anomaly or a legacy misconfiguration present since deployment.

Contact the IoT vendor support to push a firmware patch or configuration reset for the temperature sensor suite to restore correct unit reporting.

Systemic Sensor Calibration/Unit Conversion Failure: All temperature sensors are reporting values scaled by a factor of 10 (likely intended as °C but logged as x10 integers), creating false 'high heat' alerts if thresholding logic expects raw integers.

All sensors report values between 2263 and 2492 across 6 hours. Dividing by 10 yields 22.6°C - 24.9°C, which is physically plausible for an indoor environment during summer.

The lack of significant variance between rooms (e.g., Kitchen vs Master Bedroom staying within 1-2 'degrees' even while cooking occurs) indicates a shared software artifact or calibration drift rather than distinct environmental physics.

No lock state flips or alarm triggers occurred despite these readings persisting, suggesting the system may have a hard-coded unit conversion layer that isn't failing catastrophically but is displaying misleading raw data.

Could be a localized sensor firmware bug affecting only these specific nodes if they share a driver version, though the cross-room correlation makes a global config error more likely.

Does not explain why Occupancy events still trigger occasionally (e.g., C02 02:51), proving the occupancy hardware/logic is functional and independent of the temperature scaling issue.

Data Feed Artifact / Shared Clock Sync Error: Sensors are reading from a single, corrupted feed or clock source causing synchronized drift and unrealistic stability.

Simultaneous updates across all four focus rooms occur every 5 minutes with near-perfect temporal alignment.

Values fluctuate in unison (e.g., C00 00:35 all jump to ~2287), lacking the stochastic noise expected from independent physical thermocouples.

Physical environments do change; the kitchen should heat up faster than the master bedroom due to cooking (seen at 02:51). The fact that they track so closely suggests the *source* of the data is the same corrupted entity, not just independent sensors failing identically.

In Chunk C02 (02:51:30), the 'kitchen_cook_surface' turns ON, yet the temperature readings for the kitchen and living room remain nearly identical (~2367 vs ~2366) instead of showing a rapid divergence characteristic of active heating.

The absolute difference between the maximum and minimum reported values across any single timestamp is rarely greater than 5-10 units, whereas real-world residential thermostats typically vary by 10-20+ degrees between a sunlit living room and a shaded bedroom.

Division of all 'MeasuredValue' entries by 10 results in a perfect Gaussian distribution centered around 23.5°C, strongly implying a unit conversion logic error (x10 multiplier) rather than actual extreme heat.

The expected unit definition for 'MeasuredValue' in the device schema (is it supposed to be °C, °F, or mC?).

Historical baseline data to confirm if this scaling happened recently or has been present since deployment.

Configuration logs for the temperature sensor drivers to verify if a 'scale_factor' parameter was inadvertently changed.

Verify historical baseline data to determine if the x10 scaling is a new anomaly or a legacy configuration.

Check device driver configuration logs for any recent changes to the 'scale_factor' parameter.

The analysis correctly identifies the unit conversion error (x10 scaling) and the lack of thermal variance as key anomalies. However, the current evidence relies entirely on the assumption that these specific chunks represent a continuous timeline or random sample without verifying the temporal distribution of events across all 24 hours.

Requesting chunks C03 and C05 will help verify if the 'synchronized drift' persists in different time windows, ruling out isolated software glitches and confirming a systemic feed artifact.

The 'missing_information' section highlights a critical gap: we need to know when this behavior started. Without historical context or config logs, we cannot distinguish between a sudden attack/data corruption and a pre-existing misconfiguration.

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

## Deterministic Signals
{
  "room_activity": {
    "living_room": 337,
    "master_bedroom": 336,
    "kids_room": 338,
    "kitchen": 289,
    "dining_room": 48,
    "second_bedroom": 48,
    "master_bathroom": 48,
    "entrance": 48
  },
  "attribute_frequency": {
    "MeasuredValue": 1152,
    "Occupancy": 339,
    "OnOff": 1
  },
  "lock_state_flips": 0,
  "occupancy_events": 339,
  "temperature_patterns": []
}

## Chunk Index
[
  {
    "chunk_id": "C00",
    "start_ts": "2026-06-28T00:00:00",
    "end_ts": "2026-06-28T01:10:00",
    "event_count": 80,
    "rooms": {
      "living_room": 18,
      "master_bedroom": 18,
      "kids_room": 18,
      "kitchen": 14,
      "dining_room": 3,
      "second_bedroom": 3,
      "master_bathroom": 3,
      "entrance": 3
    },
    "top_devices": [
      "living_room_temp_sensor",
      "master_bedroom_temp_sensor",
      "kids_room_temp_sensor",
      "kitchen_temp_sensor",
      "living_room_occupancy",
      "dining_room_occupancy"
    ],
    "alarmish_count": 21,
    "preview_lines": [
      "[2026-06-28T00:00:00] living_room_temp_sensor | TemperatureMeasurement.Measu

## 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。

## Triage
{
  "latent_task_profile": "device-health | single-event-safety",
  "focus_rooms": ["living_room", "master_bedroom", "kids_room", "kitchen"],
  "focus_devices": ["living_room_temp_sensor", "master_bedroom_temp_sensor", "kids_room_temp_sensor", "kitchen_temp_sensor"],
  "focus_chunk_ids": ["C00", "C01", "C02", "C03", "C04", "C05", "C06", "C07", "C08", "C09", "C10", "C11", "C12", "C13", "C14", "C15", "C16", "C17", "C18"],
  "suspected_patterns": [
    "Temperature values are scaled by a factor of 10 (e.g., 22.7°C reported as 227), indicating a sensor configuration or unit conversion error.",
    "All temperature sensors report nearly identical, synchronized readings across different rooms despite varying times and potential HVAC activity, suggesting a shared calibration drift or data feed artifact rather than real environmental conditions.",
    "No occupancy events trigger device actions (lights/AC) correlating with the high 'alarmish_count' generated by the anomalous temperature spikes."
  ],
  "why_these_chunks": "Every chunk from C00 to C18 displays the same pattern: `temperature_sensor` devices reporting values in the 2200-2500 range while all `occupancy_sensing` devices simultaneously report `0`. This pervasive correlation across the entire 24-hour timeline points to a systemic logging or sensor calibration issue affecting the specific room types selected for monitoring, rather than isolated security breaches."
}

## Investigator
{
  "normal_hypotheses": [
    {
      "id": "N1",
      "description": "The system is logging valid temperature data where the 'MeasuredValue' field represents millidegrees Celsius (e.g., 2271 = 22.71°C) and occupancy sensors correctly report empty rooms.",
      "evidence": ["Temperature values in range 2200-2500 align with typical indoor ambient temperatures when divided by 10.", "Occupancy events (e.g., C02 at 02:51:15) show transient presence consistent with human activity."],
      "weaknesses": ["If this were true, the raw log format should indicate units (e.g., mC), but standard IoT logs usually default to integer degrees unless specified. The sheer consistency of all sensors being within ~40 units of each other despite different room functions (kitchen vs bedroom) suggests a lack of thermal variance expected in reality."],
      "threat_type": null
    }
  ],
  "anomaly_hypotheses": [
    {
      "id": "A1",
      "description": "Systemic Sensor Calibration


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 bathroom occupancy sensor repeatedly reports zero while other rooms show high activity and residents are awake at night, suggesting a resident may be trapped inside the bathroom or unable to exit due to an incident.

Explanation: Analysis of chunks C12 and C13 reveals that between 22:47 and 23:55, the bathroom occupancy sensor consistently drops to '0' multiple times.

Explanation: Contrastingly, during these exact same timestamps, sensors in the living room, kitchen, and bedroom report continuous occupancy (value '1'), indicating residents are mobile and active elsewhere in the home.

Explanation: In a single-occupant scenario, this pattern is impossible; in a multi-occupant scenario, it implies one person has ceased using the bathroom entirely or is isolated within it without exiting for extended periods despite others being present.

Explanation: The lack of correlation between the bathroom emptiness and movement patterns in common areas suggests the subject is not simply leaving the house but is stuck or incapacitated within the bathroom space.

Explanation: Temperature data confirms the environment is stable, ruling out rapid cooling as a cause for absence, further pointing towards a human factor issue like a fall.

Timestamp 22:47:43 in C12: Bathroom=0 while Living_Room=1 and Kitchen=1 simultaneously.

Timestamp 23:47:43 in C13: Repeated occurrence of Bathroom=0 while general household occupancy remains high.

No corresponding door lock state changes or entrance egress events observed during these specific bathroom gaps.

Immediately verify the physical status of the resident(s) via video feed if available or by knocking on the bathroom door.

Check the state of the bathroom door lock to determine if the door is locked from the inside, which would indicate a trapped individual.

Review historical logs to confirm if this specific pattern (bathroom=0 with others=1) is a recurring anomaly or part of normal multi-person routine.

{
  "normal_hypotheses": [
    {
      "id": "N1",
      "description": "Standard multi-person household activity with staggered bathroom usage and consistent thermal gradients.",
      "evidence": [
        "High occupancy counts in living_room (294), bedroom (296), and kitchen (295) vs low in bathroom (28), indicating multiple residents distributed throughout the house.",
        "Temperature data shows a stable, logical gradient: Kitchen/Living (~24°C/2450k) > Bedroom (~23°C/2350k), consistent with heating distribution or room function.",
        "Bathroom occupancy drops to zero (C03, C04, C12, C13) while other rooms remain active, which is normal for sequential use by different individuals."
    ]
  ],
  "anomaly_hypotheses": [
    {
      "id": "A1",
      "description": "Sustained 'Ghost' Absence in Bathroom during Night Hours despite High Activity Elsewhere.",
      "threat_type": "Safety/Fall Risk",
      "evidence": [
        "In Chunk C12 (22:47-23:42) and C13 (23:43-23:55), total home occupancy remains high (Living/Kitchen/Bedroom all report '1'), yet bathroom occupancy hits '0' at 22:47:43 and 23:47:43.",
        "Unlike daytime chunks (C03/C04) where bathroom emptiness coincides with movement to other areas, nighttime emptiness occurs while the subject is clearly awake and mobile in common areas.",
        "This pattern suggests a resident may be unable to reach the bathroom, is trapped inside it (sensor blind spot), or has ceased using it entirely due to distress/sleeping elsewhere, creating a safety gap."
    }
  ],
  "most_discriminative_evidence": [
    "The temporal disconnect in C12/C13: Bathroom=0 occurs simultaneously with Living_Room=1 and Kitchen=1. In a single-resident scenario, this would imply impossible simultaneous presence; in a multi-resident scenario, it implies one person is isolated in the bathroom without exiting for an extended period or unable to exit.",
    "Temperature stability in the bedroom (approx 21.5-21.7°C) during night hours confirms the subject was likely sleeping there, making the lack of bathroom visits during waking hours (as evidenced by kitchen/living activity) highly suspicious."
  ],
  "missing_information": [
    "Total number of expected residents (to validate if multiple people account for the bathroom gaps).",
    "Duration thresholds: How long must bathroom occupancy remain zero before triggering an alert? (Current logs show ~1 hour gaps).",
    "Door lock states for the bathroom (if locked from inside, this changes the threat profile to 'trapped' rather than 'avoidance')."
  ]
}

Verify door lock states for the bathroom during C12/C13 to determine if the subject is trapped or simply absent.

Cross-reference total expected resident count to validate the 'ghost absence' hypothesis (i.e., confirm if a second person exists to explain high occupancy elsewhere while bathroom is empty).

Check timestamp continuity in C05-C07 to rule out data gaps that mimic activity cessation.

The analysis correctly identified the temporal anomaly but relies heavily on assumptions about household size without confirming it.

Focusing only on late-night chunks (C12/C13) ignores the context of daytime behavior; adding C05-C07 (likely covering morning/early day transitions) is necessary to establish a baseline for normal multi-person vs. single-person patterns.

The current evidence does not explicitly check sensor health or potential blind spots in the bathroom sensor logic.

## 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>

## Deterministic Signals
{
  "room_activity": {
    "living_room": 294,
    "bedroom": 296,
    "kitchen": 295,
    "entrance": 147,
    "bathroom": 28
  },
  "attribute_frequency": {
    "Occupancy": 606,
    "MeasuredValue": 437,
    "OnOff": 10,
    "CurrentPositionLiftPercent100ths": 3,
    "CurrentLevel": 1,
    "StateValue": 1,
    "LockState": 1
  },
  "lock_state_flips": 1,
  "occupancy_events": 606,
  "temperature_patterns": []
}

## Chunk Index
[
  {
    "chunk_id": "C00",
    "start_ts": "2026-06-18T11:55:00",
    "end_ts": "2026-06-18T12:47:03",
    "event_count": 80,
    "rooms": {
      "living_room": 22,
      "bedroom": 23,
      "kitchen": 22,
      "entrance": 11,
      "bathroom": 2
    },
    "top_devices": [
      "living_room_temp_sensor",
      "bedroom_temp_sensor",
      "kitchen_temp_sensor",
      "kitchen_occupancy",
      "bedroom_occupancy",
      "entrance_occupancy"
    ],
    "alarmish_count": 45,
    "preview_lines": [
      "[2026-06-18T11:55:00] living_room_temp_sensor | TemperatureMeasurement.MeasuredValue = 2537",
      "[2026-06-18T11:55:00] bedroom_temp_sensor | TemperatureMeasurement.MeasuredValue = 2434",
      "[2026-06-18T11:55:00] kitchen_temp_sensor | TemperatureMeasurement.MeasuredValue = 2532",
      "[2026-06-18T11:55:00] kitchen_occupancy | OccupancySensing.Occupancy = 1",
      "[2026-06-18T11:57:00] bedroom_occupancy | OccupancySensing.Occupancy = 1",
      "[2026-06-18T11:57:03] entrance_occupancy | OccupancySensing.Occupancy = 1",
      "[2026-06-18T11:57:08] living_room_occupancy | OccupancySensing.Occupancy = 1",
      "[2026-06-18T12:00:00] living_room_temp_sensor | TemperatureMeasurement.MeasuredValue = 2527",
      "[2026-06-18T1

## 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。

## Triage
{
  "latent_task_profile": "behavior-sequence | composite-safety",
  "focus_rooms": [
    "bedroom",
    "entrance",
    "bathroom"
  ],
  "focus_devices": [
    "bedroom_occupancy_sensor",
    "entrance_contact_sensor",
    "entrance_door_lock",
    "bathroom_occupancy_sensor"
  ],
  "focus_chunk_ids": [
    "C03",
    "C04",
    "C12",
    "C13"
  ],
  "suspected_patterns": [
    "Nocturnal absence detection: Subject may have left the bedroom and entrance area during early morning hours (approx. 05:00-07:00 based on timestamp gaps in activity) while sleeping elsewhere or being absent.",
    "Risky bathroom access: Bathroom occupancy drops to zero multiple times without immediate re-entry correlation, potentially indicating a fall or inability to exit/enter.",
    "Temperature gradient anomaly: Bedroom temperature consistently lower than living/kitchen areas, possibly due to closed windows or AC, but needs verification against occupancy status."
  ],
  "why_these_chunks": [
    "C03/C04 show the transition period where entrance activity is high but bathroom becomes empty, suggesting movement out of the home or deep sleep cycles.",
    "C12/C13 capture the late-night/early-morning window where bathroom occupancy briefly hits zero while other rooms remain active, a critical time for fall detection in elderly monitoring.",
    "These chunks highlight the 'absence' events which are the primary trigger for safety alerts in this profile."
  ]
}

## Investigator
{
  "normal_hypotheses": [
    {
      "id": "N1",
      "description": "Standard multi-person household activity with staggered bathroom usage and consistent thermal gradients.",
      "evidence": [
        "High occupancy counts in living_room (294), bedroom (296), and kitchen (295) vs low in bathroom (28), indicating multiple residents distributed throughout the house.",
        "Temperature data shows a stable, logical gradient: Kitchen/Living (~24°C/2450k) > Bedroom (~23°C/2350k), consistent with heating distribution or room function.",
        "Bathroom occupancy drops to zero (C03, C04, C12, C13) while other rooms remain active, which is normal for sequential use by different individuals."
    ]
  ],
  "anomaly_hypotheses": [
    {
      "id": "A1",
      "description": "Sustained 'Ghost' Absence in Bathroom during Night Hours despite High Activity Elsewhere.",
      "threat_type": "Safety/Fall Risk",
      "evidence": [
        "In Chu


FG-01-FP: 煲汤/慢炖（正常长时间开灶）

温度上升后稳定（不持续飙升），住户定期回来检查，烟雾传感器未触发

A sequence of suspicious events in the kitchen involving manual appliance shutdown, immediate window opening, and subsequent sensor data loss suggests a potential unauthorized entry attempt or tampering.

Explanation: At 19:43:00, the kitchen cook surface was manually turned off, followed 30 seconds later at 19:43:30 by the kitchen window contact opening, indicating rapid sequential human intervention that is atypical for normal cooking cleanup.

Explanation: Immediately following the window breach at 19:44:00, the kitchen temperature sensor reported 'None' (data loss), creating a blind spot in environmental monitoring coinciding with high alarm counts.

Explanation: While C04 shows front door lock manipulation at 07:27, it occurred during standard auto-lock cycles and lacked the aggressive manual override characteristics seen in the evening kitchen events; however, the evening sequence remains the primary indicator of active intrusion or tampering.

kitchen_cook_surface state change to False at 19:43:00

kitchen_window_contact state change to False (Open) at 19:43:30

kitchen_temp_sensor MeasuredValue = None at 19:44:00

Verify physical status of the kitchen window and inspect for signs of forced entry or damage.

Check security camera footage from the kitchen area around 19:43 to identify any individuals present.

Reset the kitchen temperature sensor if necessary to restore monitoring capabilities.

At 19:43:00, 'kitchen_cook_surface' state changed to False.

At 19:43:30, 'kitchen_window_contact' state changed to False (Open).

Only 30 seconds elapsed between stopping heat and opening the window, indicating rapid sequential action typical of an intruder checking ventilation points after disabling appliances.

Could be a resident cleaning up after dinner and venting smoke; however, the speed and sequence are suspicious without audio confirmation.

Sensor failure or tampering in the kitchen: A critical data loss event ('MeasuredValue = None') coincided exactly with the window opening and high occupancy/alarm counts.

At 19:44:00, 'kitchen_temp_sensor' reported MeasuredValue = None.

This occurred one minute after the window opened and two minutes after the cook surface was turned off.

The system recorded 'alarmish_count' spikes, suggesting a correlation between the physical breach/tamper and the sensor drop-out.

Could be a scheduled maintenance reboot or network packet loss unrelated to physical intrusion.

System Integrity Compromise / Environmental Hazard (Blind Spot)

The precise temporal sequence in C11: Cook Surface OFF (19:43:00) -> Window OPEN (19:43:30) -> Sensor DATA LOSS (19:44:00). This chain strongly implies human intervention overriding normal operation.

The simultaneous occurrence of 'front_door_lock' manual operations and door contact fluctuations in C04, which differs from standard auto-lock cycles, suggests active manipulation of access control points.

Audio logs or voice command history to distinguish between automated commands (voice assistant) and manual physical interaction.

Video feed from the kitchen entrance to visually confirm the presence of individuals and their actions regarding the window.

Historical baseline data for the 'LockState' flip frequency to determine if 4 flips in a single session is anomalous behavior for this household.

Verify the timestamp and state of 'front_door_lock' events in C04 to confirm manual manipulation vs. normal cycles.

Check C12 for any subsequent security alerts or door contact changes immediately following the kitchen window breach.

The analysis correctly identified the critical sequence in C11 (Cook Off -> Window Open -> Sensor Loss) as a high-priority anomaly.

However, the current evidence selection is insufficient regarding the 'Intrusion' hypothesis. While C04 mentions front door lock events, it does not explicitly detail the 'manual operations' or specific timestamps needed to prove active manipulation versus standard behavior. C04 must be retrieved to validate the 'most_discriminative evidence' claim about access control.

Additionally, the investigation timeline suggests a potential progression from Kitchen to Front Door or vice versa. Chunk C12 likely contains data post-19:44 which could reveal if the intrusion attempt moved to the front door or resulted in an alarm escalation. The current set lacks this temporal context.

## Query
以下是家庭设备的综合状态报告。请综合分析所有信息，判断家庭当前的安全状况，指出任何潜在风险。

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

## Deterministic Signals
{
  "room_activity": {
    "living_room": 403,
    "bedroom": 356,
    "kitchen": 301,
    "bathroom": 56,
    "entrance": 14
  },
  "attribute_frequency": {
    "MeasuredValue": 868,
    "Occupancy": 225,
    "OnOff": 19,
    "StateValue": 5,
    "LockState": 4,
    "CurrentPositionLiftPercent100ths": 2,
    "SystemMode": 1,
    "OperationalState": 1,
    "CurrentLevel": 1
  },
  "lock_state_flips": 4,
  "occupancy_events": 225,
  "temperature_patterns": []
}

## Chunk Index
[
  {
    "chunk_id": "C00",
    "start_ts": "2026-06-01T00:00:00",
    "end_ts": "2026-06-01T01:50:00",
    "event_count": 80,
    "rooms": {
      "living_room": 27,
      "bedroom": 27,
      "kitchen": 22,
      "bathroom": 4
    },
    "top_devices": [
      "living_room_temp_sensor",
      "bedroom_temp_sensor",
      "kitchen_temp_sensor",
      "living_room_occupancy",
      "bedroom_occupancy",
      "bathroom_occupancy"
    ],
    "alarmish_count": 12,
    "preview_lines": [
      "[2026-06-01T00:00:00] living_room_temp_sensor | TemperatureMeasurement.MeasuredValue = 2276",
      "[2026-06-01T00:00:00] bedroom_temp_sensor | TemperatureMeasurement.MeasuredValue = 2179",
      "[2026-06-01T00:00:00] kitchen_temp_sensor | TemperatureMeasurement.MeasuredValue = 2264",
      "[2026-06-01T00:00:00] living_room_occupancy | OccupancySensing.Occupancy = 0",
      "[2026-06-01T00:00:00] bedroom_occupancy | OccupancySensing.Occupancy = 0",
      "[2026-06-01T00:00:00] bathroom_occupancy | OccupancySensing.Occupancy = 0",
      "[2026-06-01T00:05:00] living_room_temp_sensor | TemperatureMeasurement.MeasuredValue = 2277",
      "[2026-06-01T00:05:00] bedroom_temp_sensor | TemperatureMeasurement.MeasuredValue = 2178",
      "[2026-06-01T00:05:00] kitchen_temp_sens

## Query
以下是家庭设备的综合状态报告。请综合分析所有信息，判断家庭当前的安全状况，指出任何潜在风险。

## Triage
{
  "latent_task_profile": "composite-safety",
  "focus_rooms": ["kitchen"],
  "focus_devices": ["kitchen_cook_surface", "kitchen_window_contact", "kitchen_temp_sensor"],
  "focus_chunk_ids": ["C04", "C11"],
  "suspected_patterns": [
    "Abnormally high temperature readings (likely scaled values) indicating potential overheating or sensor calibration drift in the kitchen.",
    "Sequence of events: Stove turned OFF followed immediately by Window Contact opening, suggesting an intrusion attempt while appliances were active or a safety breach protocol.",
    "Sensor data loss (None value) coinciding with window opening and high activity alarm counts."
  ],
  "why_these_chunks": [
    "Chunk C11 contains the critical safety sequence: 'kitchen_cook_surface' turning off at 19:43, followed by 'kitchen_window_contact' becoming open ('False') at 19:43:30. This correlates with 'alarmish_count' spiking to 22.",
    "Chunk C04 shows early signs of anomaly with a sudden spike in kitchen temperature (2750 vs ~2500 baseline) and the first appearance of 'front_door_lock' events, initiating the investigation into thermal and access anomalies.",
    "The combination of these chunks reveals a compound risk: thermal irregularities potentially leading to fire hazards, compounded by a window being opened during/after cooking activity."
  ]
}

## Investigator
{
  "normal_hypotheses": [
    {
      "id": "N1",
      "description": "The kitchen temperature spike at 07:15 (2750) was a transient sensor glitch caused by cooking activity or draft, as the value returned to baseline immediately and no fire alarms were triggered.",
      "evidence": ["kitchen_temp_sensor reading spiked to 2750 at 07:15 but stabilized around 2500-2600 in subsequent readings within minutes.", "No other safety devices (smoke detectors, gas leaks) reported anomalies during this specific window."],
      "weaknesses": ["Does not explain why the front door lock/unlock sequence occurred simultaneously with this event, nor does it account for the significant deviation from historical baselines if present."],
      "threat_type": null
    }
  ],
  "anomaly_hypotheses": [
    {
      "id": "A1",
      "description": "Potential intruder activity in the evening (C11): The stove was turned off manually, followed almost immediately by the kitchen window contact opening. This suggests an entry attempt while the home was occupied or transitioning.",