Full-automatic detection system and method for temperature sensor based on aircraft and internet of things

Abstract

This invention discloses a fully automated detection system and method for temperature-sensing detectors based on aircraft and the Internet of Things (IoT), belonging to the field of intelligent fire protection facility detection technology. The system includes a perception layer (the temperature-sensing detector being inspected is equipped with an IoT tag containing identification and auxiliary positioning units, and an outdoor positioning module supporting GNSS), a network layer (short-range and wide-area communication modules), a platform layer (detection task management and scheduling system), and an execution layer (a fully autonomous detection aircraft, including an autonomous navigation module, detection device, and communication control module). The method includes initial registration, task generation and scheduling, autonomous execution of detection (identity verification, hot air jetting, and dual-response detection), result feedback, and closed-loop management. This invention achieves fully unmanned detection of temperature-sensing detectors through IoT positioning and autonomous aircraft execution, offering advantages such as accurate positioning, high efficiency, and intelligent management, and is suitable for fire protection facility detection in complex building environments.

Description

Technical Field

[0001] This invention belongs to the field of intelligent detection technology for fire protection facilities, specifically involving a fully automatic detection system and method for temperature detectors that combines Internet of Things positioning technology with an aircraft platform, which can realize unmanned, fully autonomous detection and status management of all temperature detectors in a building. Background Technology

[0002] Heat detectors are core components of building fire protection systems and require regular functional testing using simulated fire smoke to ensure reliability. Traditional testing methods rely on manual operation or simple automated equipment, which have significant limitations: 1. Uncertain positioning: The detection location needs to be manually specified, and it cannot automatically identify the distribution and status of all detectors in the building; 2. Inefficient scheduling: When multiple detectors are used for detection, manual path planning is required, and there is a lack of dynamic task allocation mechanism; 3. Not fully automated: From task triggering, location, detection to result recording, all processes rely on manual intervention, making it impossible to achieve an "unmanned closed loop".

[0003] With the development of Internet of Things (IoT) and Global Navigation Satellite System (GNSS) technologies, by assigning devices unique identifiers and precise positioning capabilities, combined with intelligent scheduling platforms and autonomous mobile carriers, fully automated dispatching, execution, and feedback of detection tasks can be achieved. Current technologies have not yet effectively integrated IoT positioning, aircraft platforms, and intelligent scheduling systems, making it difficult to meet the high-efficiency and accurate detection requirements of temperature-sensing detectors in complex building environments. Summary of the Invention

[0004] I. Purpose of the Invention To address the shortcomings of existing technologies, this invention provides a fully automated detection system and method for temperature detectors based on aircraft and the Internet of Things. By equipping the temperature detector with positioning and identification modules, and combining it with an intelligent scheduling platform and a fully autonomous aircraft, the system achieves fully unmanned detection throughout the entire process of "automatic task generation - automatic aircraft scheduling - precise positioning and detection - automatic result feedback," thereby improving detection efficiency and the level of intelligent management. Technical solution

[0005] (I) System Composition This system consists of four parts: platform layer 1, network layer 2, execution layer 3, and perception layer 4. These layers work together to achieve fully automated detection. 1. Platform Layer 1: Detection Task Management and Scheduling System Deployed in the cloud or on-premises servers, core functionalities include: Database 11 of the inspected temperature detector 7: Stores the ID, location, status (normal / abnormal / pending inspection), and inspection cycle (e.g., once every six months) of all inspected temperature detectors 7, and supports BIM model import (automatically associates with the three-dimensional coordinates of the building).

[0006] Task generation module 12: Automatically generates a detection task queue based on the detection cycle, user instructions (such as mandatory detection before fire drills) or abnormal alarms (such as offline), and sorts them according to "area priority + location proximity" (such as detecting high-risk areas first, and then by floor order).

[0007] Aircraft scheduling module 13: Assigns tasks according to the task queue and aircraft status (battery, position, load), generates the optimal path (such as A* algorithm to plan obstacle avoidance path), and sends task instructions to the aircraft (including the ID, position coordinates, and detection parameters of the target temperature sensor 7).

[0008] Data visualization module 14: Real-time display of detection progress, result statistics (normal rate, abnormal list), and aircraft trajectory, supporting historical data tracing and report export.

[0009] 2. Network Layer 2: Data Transmission and Communication Module 21 Short-range communication 211: The tag of the detected temperature detector 7 transmits data with the aircraft / gateway via Bluetooth 5.0, ZigBee or UWB (ultra-wideband, accuracy ±10cm).

[0010] Wide Area Communication 212: Transmits mission instructions, detection results, and abnormal alarms between aircraft, dispatch platforms, and user terminals via 4G / 5G, LoRa, or Wi-Fi 6.

[0011] 3. Execution Layer 3: Fully Autonomous Detection Aircraft Platform Upgraded from the existing aircraft, it possesses fully autonomous mission execution capabilities, including: Aircraft body 31: Multi-rotor drone (such as DJI Matrice 30 series), supporting Global Navigation Satellite System (GNSS) and RTK differential positioning, six-way obstacle avoidance, IP55 protection, and a payload capacity of ≥3kg (equipped with detection devices and communication modules).

[0012] Autonomous navigation module 32: Outdoor Positioning 321: Employs Global Navigation Satellite System (GNSS) and RTK differential positioning technology to achieve centimeter-level positioning (accuracy ±2cm).

[0013] Indoor Positioning 322: Integrating visual SLAM (Simultaneous Localization and Mapping), UWB base stations (linked with the tags of the temperature detector 7 under inspection) and pre-stored BIM point cloud maps, it achieves accurate positioning (accuracy ≤ 5cm) in environments without GNSS.

[0014] Detection device 33: Cover 331: The inner wall is equipped with a miniature RFID reader / UWB receiver, which automatically reads the tag ID when it is close to the temperature detector under test 7 and compares it with the task command to confirm the target identity (preventing false detection); Cover 331 is a circular, upward-opening mask body (the inner diameter is slightly larger than the outer diameter of the temperature detector under test 7), with a hot air jet nozzle at the bottom inside (directly facing the temperature sensing head of the temperature detector under test 7), and a red light sensor (detecting the alarm red light of the temperature detector under test 7) ​​and a sound sensor (detecting the alarm sound - when a fire alarm sound function is provided) on the inner wall to make judgments on the response of the temperature detector under test 7, such as the red light lighting status after being sprayed with hot air.

[0015] Hot air generating module 332: includes electric heating wire / PTC heater, fan, air duct and temperature sensor (located in the air duct to monitor and control the hot air temperature).

[0016] Communication and Control Module 333: Integrates a 5G module and an edge computing unit, receives platform task instructions, uploads detection data (including video clips), and performs local obstacle avoidance and path fine-tuning.

[0017] 4. Sensing Layer 4: The positioning and identification module of the detected temperature detector 7 configures an IoT tag for each detected temperature detector 7 in the building, serving as an "identity ID + positioning anchor point," specifically including: Identification Unit 41: Uses RFID chip, Bluetooth beacon (iBeacon) or UWB tag to store basic information such as unique ID, installation location (floor, room number, coordinates), model, and last detection time.

[0018] Auxiliary positioning unit 42: Indoor temperature detectors 7 are affixed with a QR code / ArUco code (visual identifier) ​​containing ID and location information; outdoor or open area temperature detectors 7 integrate a satellite navigation module (such as GPS, BeiDou, or other global navigation satellite systems), with a civilian positioning accuracy of ±1m. The tag can be integrated inside the temperature detector 7 (pre-installed at the factory) or added to an external bracket (modifying existing temperature detectors 7), without affecting the original fire protection function.

[0019] (II) Fully Automated Detection Method and Procedures 1. System initialization and registration of the tested temperature detector 7: Install IoT tags on all the temperature detectors 7 being inspected, scan the QR code to enter the ID and location into the platform database; the aircraft scans the building using SLAM to generate a point cloud map with tagged coordinates, aligns it with the BIM model, and then stores it; the user sets the detection cycle, response timeout (determined based on actual measurement data, such as 5-60 seconds), and hot air temperature (such as 80℃).

[0020] 2. Automatic task generation and scheduling: The platform generates a task queue based on the detection cycle, user instructions, and abnormal alarms, and sorts them according to "regional priority + location proximity"; it queries the status of online aircraft (battery > 30%, no faults), selects the aircraft closest to the first task point, and sends a task package containing the ID, coordinates, detection parameters, and path planning of the detected temperature sensor 7.

[0021] 3. Autonomous detection by the aircraft: The functions are implemented by the communication and control module 333 (receiving instructions, uploading data, and local obstacle avoidance).

[0022] Takeoff and navigation: Takeoff from the base station, fly along a 3D path (obstacle avoidance), and switch to indoor positioning mode (UWB+visual SLAM) when approaching the target (the detected temperature sensor 7); outdoor navigation uses Global Navigation Satellite System (GNSS) and RTK differential positioning.

[0023] Identity verification: Read the tag ID through the reader inside the cover 331, compare it with the task command, and adjust the pan-tilt unit so that the cover 331 fits the temperature sensor (error ≤ 2cm).

[0024] Hot air jetting and response detection: The housing 331 houses a hot air generating module 332, which includes a heater, fan, air duct, and temperature sensor. A temperature sensor at the hot air nozzle monitors the temperature of the hot air emitted from the nozzle, ensuring the hot air temperature reaches the set value (e.g., 80°C). When the hot air jetting program is started, the temperature sensor 7 under test is subjected to directional hot air jetting. After a certain time (e.g., 5-60 seconds), the temperature sensor 7 under test responds, its red indicator light illuminates, and the red light sensor inside the housing 331 detects that the temperature sensor 7 under test has responded (red light illuminates), thus stopping the detection. A specific flashing light and buzzer sound signal can indicate whether the detection result is normal. If the temperature sensor 7 under test does not respond within the timeout period, it is considered to be faulty.

[0025] Results feedback: If the response lasts for ≥3 seconds, it is marked as "normal", the data is recorded and uploaded; if there is no response after the timeout, it is marked as "abnormal", and an alarm with on-site video is pushed to the maintenance personnel.

[0026] 4. Evacuation and Next Mission: After the inspection is completed, the aircraft's cover 331 separates from the inspected temperature sensor 7, and flies to the next inspected temperature sensor 7 according to the path until the mission queue is cleared, and then returns to the base station to recharge.

[0027] 5. Anomaly Handling and Closed-Loop Management: The platform adds the abnormal temperature detector 7 to the "priority re-inspection queue" and pushes an alarm; when the aircraft has low battery or malfunctions, the mission is suspended and rescheduled; all data is encrypted and stored, and a fire protection facility inspection compliance report is generated. Beneficial effects

[0028] 1. Fully automated process: From task generation to result feedback, no human intervention is required, improving detection efficiency by over 80%; 2. Accurate and reliable positioning: IoT tag + multi-source fusion positioning (Global Navigation Satellite System / GNSS + UWB + Visual SLAM + BIM), indoor positioning accuracy ≤5cm, false detection rate <0.1%; 3. Intelligent management: The platform monitors the status of the tested temperature detector 7 in real time and supports predictive maintenance (such as replacing the tested temperature detector 7 in advance if there are frequent abnormalities). 4. High scalability: Compatible with different brands / models of temperature detectors 7, and can be connected to the system via tag protocol adaptation (Modbus, MQTT); supports any global navigation satellite system (such as GPS, Beidou, etc.). Attached image description: Figure 1 System architecture diagram; Figure 2 : Schematic diagram of the installation of the IoT tag on the temperature sensor under inspection; Figure 3 Flowchart of autonomous vehicle testing; Figure 4 : A schematic diagram of the platform task scheduling interface; Figure 5 : Schematic diagram of each layer module; Figure 6 : Schematic diagram of the temperature-sensing detector 7 being tested by the aircraft. See system architecture diagram Figure 1 It includes platform layer 1, network layer 2, execution layer 3, and perception data layer 4.

[0029] See the diagram below for the installation of the IoT tag on the temperature sensor 7. Figure 2 The temperature detector under inspection 7 (integrated with RFID8 and QR code 6) can be affixed to one or more sides of the detector 7 for identification by the aircraft from different directions. RFID8 is built into the temperature detector 7. See [link / details]. Figure 6 .

[0030] See Figure 3 The aircraft autonomously executes the detection process 500: Takeoff and Navigation 501: Take off from the base station and fly along a 3D path (obstacle avoidance). When approaching the target, switch to indoor positioning mode (UWB + visual SLAM). Outdoor navigation uses Global Navigation Satellite System (GNSS) and RTK differential positioning.

[0031] Identity verification error 502: After reading the tag ID using the 331 internal reader and comparing it with the task command, adjust the pan-tilt unit to align the 331 cover with the temperature sensor (error ≤ 2cm). Figure 6 .

[0032] Hot air jetting and response detection 503: When the hot air generating module 332 is activated, the temperature sensor 7 under test is subjected to directional hot air jets. After a certain period of time (e.g., 5-60 seconds), the temperature sensor 7 responds and its red light illuminates. The red light sensor and sound sensor inside the housing 331 simultaneously detect the response (red light on + buzzer sound), at which point the detection can be stopped. If the temperature sensor 7 does not respond within the timeout period, it is considered to be faulty. The detection result can be indicated by specific flashing and buzzer sound signals to determine if it is normal. The hot air generating module 332 inside the housing 331 includes a heater, fan, air duct, and temperature sensor, all arranged in an adjustable manner. The air duct nozzle can be adjusted to spray hot air directly at the temperature sensor head of the temperature sensor 7.

[0033] Result feedback 504: If the response lasts for ≥3 seconds, it is marked as "normal", the data is recorded and uploaded; if there is no response after the timeout, it is marked as "abnormal", and an alarm with on-site video is pushed to the maintenance personnel.

[0034] Evacuation and Next Mission 505: After the test is completed, it flies to the next temperature sensor 7 to be tested, until the task queue is cleared, and then returns to the base station to charge.

[0035] Exception Handling and Closed-Loop Management 506: The platform adds the abnormal temperature detector 7 to the "priority re-inspection queue" and sends an alarm; when the aircraft has low battery or malfunctions, the mission is paused and rescheduled; all data is encrypted and stored, and a fire protection facility inspection compliance report is generated.

[0036] See Figure 4 The platform task scheduling interface diagram shows the content of automatic task generation and scheduling 100: The platform generates a task queue 101 according to the detection cycle / user instruction / abnormal alarm, sorts it by "regional priority + location proximity" 103, queries the status of online aircraft 104 (battery > 30%, no fault), selects the aircraft closest to the first task point 105, and sends a task package containing the ID, coordinates, detection parameters, and path planning of the detected temperature sensor 7 106. Detailed Implementation

[0037] Example 1: Fully Automated Inspection of a Commercial Complex Building Overview: 20 floors above ground, with 200 temperature detectors on each floor, each equipped with a UWB tag (ID format: F05-R12-C03, i.e., 12 rows and 3 columns on the 5th floor). The tag integrates a QR code (containing location information); the outdoor temperature detectors integrate a satellite navigation module (supporting GPS, Beidou and other systems).

[0038] System configuration: The platform is deployed on Alibaba Cloud ECS (MySQL database, A* path algorithm written in Python); 2 DJI M30T drones (equipped with 5G module, UWB receiver, detection device, supporting GNSS and RTK differential positioning), with the base station located in the lobby on the first floor; detection parameters (set range according to actual measurement data): such as hot air temperature 80℃, response timeout 60 seconds, and detection cycle once every six months.

[0039] Execution process: At 2:00 AM on June 1st and December 1st each year, the platform generates a task queue (by floor 1→20, from left to right on each floor), and schedules M30T-01 to execute it; after takeoff, the aircraft flies to the target temperature detector 7 according to the path (prerequisite: the building being inspected must have a dedicated open passage that allows the aircraft to reach the target—the temperature detector 7). Outdoor navigation uses GNSS+RTK differential positioning, and indoor navigation switches to UWB+visual SLAM; UWB reads the tag ID and performs 331 detection; if the response is normal, the data is recorded; if abnormal, it is marked and an alarm is pushed; after the mission is completed, it returns to the base station to charge.

Claims

1. An automatic detection system and method for temperature-sensing detectors based on aircraft and the Internet of Things, characterized by, include: Platform layer (1): The detection task management system deployed in the cloud includes the detected temperature detector (7), database (11) (storing ID, location, status, detection cycle), task generation module (12) (generating queues according to detection cycle, user instructions or abnormal alarms and sorting them according to "regional priority + location proximity"), aircraft scheduling module (13) (assigning tasks and planning paths), and data visualization module (14) (displaying progress, results, and trajectory). Network layer (2): Data transmission and communication module 21, including short-range communication module (211) (Bluetooth, ZigBee, UWB) and wide-area communication module (212) (4G, 5G, LoRa, Wi-Fi 6) for realizing data transmission between tags, aircraft and platforms; Execution layer (3): Fully autonomous detection aircraft platform, including aircraft body (31), autonomous navigation module (32) and detection device (33): The autonomous navigation module (32) includes outdoor positioning (321) (using GNSS and RTK differential positioning with an accuracy of ±2cm) and indoor positioning (322) (integrating visual SLAM, UWB base stations, and BIM point cloud maps with an accuracy of ≤5cm). The detection device (33) includes a cover (331), a hot air generating module (332), and a communication and control module (333): The cover (331) is equipped with a reader / writer, a hot air jet nozzle, a red light sensor, and a sound sensor; The hot air generating module (332) includes a heater, a fan, an air duct, and a temperature sensor; The communication and control module (333) (receives instructions, uploads data, and performs local obstacle avoidance) integrates a 5G module and an edge computing unit to receive platform task instructions, upload detection data (including video clips), and perform local obstacle avoidance and path fine-tuning. Perception layer (4): IoT tag configured for the detected temperature detector 7, including identification unit (41) (storing unique ID and location information) and auxiliary positioning unit (42) (RFID, Bluetooth beacon, UWB tag, QR code or satellite navigation module); the satellite navigation module supports Global Navigation Satellite System (GNSS) with outdoor positioning accuracy of ±1m.

2. The aircraft and IoT based automatic detection system and method for temperature sensing detector as claimed in claim 1, wherein, The autonomous navigation module (32) of the execution layer (3) integrates visual SLAM, UWB positioning and BIM point cloud map in the indoor environment, with a positioning accuracy of ≤5cm. 3.The full-automatic detection system and method for aircraft and Internet of Things based temperature detector according to claim 1, wherein The identification unit (41) of the IoT tag in the perception layer (4) adopts an RFID chip, Bluetooth beacon or UWB tag, and the auxiliary positioning unit (42) is an indoor QR code / ArUco code or an outdoor satellite navigation module.

4. The fully automated detection system and method for temperature-sensing detectors based on aircraft and the Internet of Things as described in claim 1, characterized in that, The cover (331) of the detection device (33) is a circular upward opening mask body with an inner diameter larger than the outer diameter of the temperature detector being tested. The inner wall is equipped with a miniature RFID reader / UWB receiver, a red light sensor and a sound sensor.

5. The fully automated detection system and method for temperature-sensing detectors based on aircraft and the Internet of Things according to any one of claims 1-4, characterized in that, Includes the following steps: Initial registration: Install IoT tags on the temperature detectors under inspection and enter them into the platform database. The aircraft scans the building to generate a point cloud map with the coordinates of the tags and aligns it with the BIM model. Task generation and scheduling: The platform generates a queue of detection tasks according to rules and schedules the aircraft to execute the tasks; Autonomous detection: The aircraft navigates to the target temperature detector (using GNSS+RTK differential positioning outdoors, and UWB+visual SLAM indoors), verifies its identity through the tag ID, puts on the detector for detection, sprays hot air, and responds to the signal through dual detection by red light sensor + sound sensor; Results feedback and closed loop: Feedback of test results to the platform to update the status of the tested temperature detectors. Anomaly handling includes marking anomalies, pushing alarms, prioritizing re-inspection, and aircraft relay scheduling.

6. The fully automated detection system and method for temperature-sensing detectors based on aircraft and the Internet of Things as described in claim 5, characterized in that, The task generation queue is sorted according to "regional priority + location proximity", with regional priority including priority for high-risk regions.

7. The fully automated detection system and method for temperature-sensing detectors based on aircraft and the Internet of Things as described in claim 5, characterized in that, The hot air temperature is 50-80℃, the response timeout is 5-60 seconds, and the response signal lasting ≥3 seconds is considered normal.

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