Targeted nanorobot for coal mine goaf fire source positioning and situation evaluation

Abstract

The invention belongs to the technical field of coal mine safety, and particularly relates to an intelligent fire source positioning and fire situation evaluation system based on a nano-robot, which is particularly suitable for real-time monitoring, accurate positioning and development situation evaluation of a coal spontaneous combustion fire source in a coal mine goaf. According to the invention, a micro-nano electromechanical system, an environment sensing technology and a wireless communication technology are combined, active detection and dynamic analysis of a fire source in a complex closed space are realized, and technical support is provided for safety production of a coal mine.

Description

Technical Field

[0001] This invention belongs to the field of coal mine safety technology, specifically relating to an intelligent fire source location and fire situation assessment system based on nanorobots. It is particularly suitable for real-time monitoring, precise location, and development trend assessment of spontaneous combustion fire sources in coal mine goaf areas. This invention combines micro-nano electromechanical systems, environmental sensing technology, and wireless communication technology to achieve active detection and dynamic analysis of fire sources in complex, enclosed spaces, providing technical support for safe coal mine production. Background Technology

[0002] During coal mining, spontaneous combustion fires are easily triggered in goaf areas due to the heat accumulation caused by coal oxidation. These fires are characterized by their high degree of concealment, rapid development, and difficulty in extinguishing, seriously threatening the lives of miners and disrupting production. Currently, the mainstream fire source location methods in the industry mainly rely on the following technologies: Fixed sensor networks: Sensors such as temperature and carbon monoxide (CO) are pre-buried in the goaf, but they are limited by low deployment density and limited coverage, making it difficult to capture the dynamic migration process of fire sources; in addition, falling rocks in the goaf can easily damage the sensors, resulting in poor data reliability.

[0003] Manual inspection and thermal imaging: Miners carry portable equipment or infrared thermal imagers to enter the edge of the goaf for inspection. However, the goaf is a dangerous environment with high gas, low oxygen, and high dust. Manual intervention is extremely risky and cannot penetrate into complex areas.

[0004] Gas tracer method: tracer gas is injected and analyzed through sampling points, but the positioning accuracy is low and the response is lagging, making it impossible to determine the stage of fire development in real time.

[0005] The above technology has significant drawbacks: Inaccurate positioning: Fixed sensors can only provide area alarms and cannot accurately locate the source of the fire; Lack of situation assessment: Existing systems mostly focus on fire source detection and lack quantitative assessment of fire development stages (such as low-temperature oxidation stage, accelerated oxidation stage, and open flame stage). Poor environmental adaptability: The goaf has turbulent airflow and enclosed space, making it difficult for traditional equipment to actively enter or dynamically track fire sources.

[0006] Therefore, there is an urgent need for an innovative technological solution that can autonomously enter goaf areas, intelligently respond to environmental changes, and achieve high-precision positioning and situational analysis. Although nanorobot technology has applications in the biomedical field (such as targeted drug delivery), it has not yet been adapted to the special environment of coal mine goaf areas (high humidity, high dust, strong winds), and in particular, it lacks a synergistic mechanism between wind-driven and targeted movement. Summary of the Invention

[0007] The core of this invention lies in designing nanorobots as a dynamic monitoring system integrating "airflow drive, intelligent triggering, and targeted positioning." Specifically, it includes the following innovations: 1. Passive airflow diffusion mechanism: The nanorobots are tiny and can naturally enter the deep goaf area with the airflow at the air intake corner of the coal mining face without additional power, solving the problem of blind spots that traditional equipment cannot cover.

[0008] 2. Dual-parameter collaborative triggering mechanism: Integrating temperature and carbon monoxide (CO) sensors, subsequent actions are activated only when both exceed preset thresholds simultaneously, avoiding false alarms from a single parameter (such as CO possibly originating from blasting operations), and significantly improving detection reliability.

[0009] 3. Targeted active movement mechanism: After triggering, the micro battery-powered drive module enables the robot to break free from the airflow and move directionally towards the abnormal area along the concentration gradient, achieving a breakthrough from "passive monitoring" to "active tracking".

[0010] 4. Cloud-based situation analysis system: Upon arrival at the fire source, the system uploads coordinates and sensor data via the positioning module. The cloud server then uses a temperature-CO concentration model to quantify the fire's development stage and guide emergency decision-making. Attached Figure Description

[0011] Figure 1 A front view of a targeted nanorobot provided in an embodiment of this application; Figure 2 This is an axial cross-sectional view of a targeted nanorobot provided in an embodiment of this application; Figure 3 A flowchart of the nanorobot process provided as an application example of this application; Figure 4 A schematic diagram of an application scenario for a coal mine goaf area provided as an application example of this application; The Figure 1 Targeted nanorobots include the following modules: Sensing module: High-precision temperature sensor and electrochemical CO sensor, which collect environmental parameters in real time.

[0012] Control module: Low-power microprocessor, preset temperature threshold and CO concentration threshold, outputs a trigger signal when the two parameters are not synchronized.

[0013] Energy module: Solid-state micro lithium battery, normally in sleep mode, powered to drive the module for 5-10 minutes after triggering.

[0014] Drive module: A piezoelectric ceramic micro motor, in conjunction with a propeller structure, generates directional thrust after receiving control signals, enabling the robot to move against the wind or along the concentration gradient at a speed of 0.1–0.5 m / s.

[0015] Positioning and communication module: Positioning module: A miniature GPS chip is used in the surface area; the underground goaf area switches to an ultra-wideband inertial navigation system; Communication module: Bluetooth Low Energy transmitter, which encrypts and transmits location, temperature and CO concentration to the well base station, and then uploads it to the cloud server.

[0016] IV. Work Process 1. Deployment phase: Nanorobots are deployed in batches into the airflow from the air intake corner of the coal mining face using an aerosol injector.

[0017] 2. Diffusion monitoring stage: The robot enters the goaf area with the airflow, the sensing module works continuously, and the control module goes into sleep mode to save energy.

[0018] 3. Triggered Movement Phase: When a robot detects that the temperature is greater than the threshold and the CO concentration is greater than the threshold, the control module activates the micro battery, the drive module starts, and the robot breaks away from the airflow and moves in the direction of the highest concentration gradient.

[0019] 4. Location and Upload Phase: After arriving at the center of the abnormal area, the location module obtains the coordinates, and the communication module uploads the data to the cloud.

[0020] 5. Situation assessment phase: The cloud server executes the following based on the uploaded data: Fire source localization: Multi-robot coordinate clustering analysis to determine the location of the fire source; Situation assessment: Based on the temperature-CO concentration mapping model, output a report on the fire development stage. Detailed Implementation

[0021] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0022] The present invention will be further described in detail below with reference to embodiments. It should be emphasized that the embodiments are only used to explain the present invention and are not intended to limit the scope of protection; those skilled in the art can adjust the parameters according to actual needs, and all such adjustments will fall within the scope of the present invention.

[0023] I. Nanorobot Structural Design In this embodiment, the nanorobot is manufactured using silicon-based processes and is spherical in shape. The specific structure is as follows: Sensing module: Temperature sensor: Platinum resistance thin film sensor, integrated onto the robot surface; CO sensor: A miniature electrochemical sensor with a built-in filter membrane to resist dust interference, and outputs an analog signal that is converted into a digital value.

[0024] Control module: Ultra-low power microprocessor, preset temperature threshold of 60℃ and CO threshold of 50 ppm; triggers only when both parameters exceed the threshold for 5 consecutive seconds to avoid false alarms due to momentary interference.

[0025] Energy module: Solid-state lithium micro battery.

[0026] Drive module: Piezoelectric ceramic motor drives carbon nanotube propeller, enabling 0–360° omnidirectional steering.

[0027] II. Workflow Examples 1. Placement and Dissemination: At the air intake corner of a coal mining face, a high-pressure aerosol injector is used to inject nanorobots into the airflow according to the mine's needs.

[0028] The robot enters the depth of the goaf with the airflow and controls the diffusion time according to the depth of the goaf. During this period, the control module is in sleep mode, and only the sensing module samples at a frequency of 1 Hz.

[0029] 2. Anomaly Detection and Triggering: When the nanorobot enters a certain area of ​​the mined-out zone, it detects that the temperature and CO concentration have exceeded the threshold, and the control module activates the micro battery.

[0030] Drive module starts: piezoelectric motor vibrates at a frequency of 10 kHz, propeller propels robot to move against the wind at a speed of 0.3 m / s, approaching the high concentration area along the CO concentration gradient.

[0031] 3. Targeted movement and positioning: After moving for a period of time, the nanorobot arrived at the center of the fire source, and the positioning module obtained the coordinates.

[0032] The communication module encrypts the coordinates, temperature, and CO value before sending them to the well base station, which then forwards them to the cloud server.

[0033] 4. Situation Assessment and Application: The cloud server aggregates data from all active robots and uses cluster analysis to determine the location of the fire source; Based on the model of claim 9: according to the temperature and carbon monoxide concentration, it is determined to be in the stage of spontaneous combustion of coal, and an early warning is pushed to the dispatch center; The system recommends taking fire prevention and extinguishing measures to prevent the fire from escalating.

[0034] Please refer to Figure 1 , Figure 1 The front view of the targeted nanorobot shows that the robot is a solid sphere with a diameter of 1–3 mm. It uses a silicon-based encapsulation shell with an anti-static and dust-proof coating on the surface.

[0035] 101 Silicon-based casing Temperature sensors: 2, symmetrically embedded on the upper hemisphere of the spherical shell, with the sensor probes exposed, used to collect ambient temperature in real time; 102CO sensor: 1 unit, located on the lower hemisphere of the spherical shell, with a built-in dust filter membrane. The sensor's air inlet faces outward to avoid clogging by coal mine dust. 103 Bluetooth Low Energy Communication Antenna: Miniature patch type, embedded in the side of the housing, connected to the internal communication module for data transmission.

[0036] Please refer to Figure 2 , Figure 2 Axial cross-sectional view of the targeted nanorobot. 201 silicon-based inner shell, used to protect the core control module and power module. Control module (low-power microprocessor): Located at the center of the sphere, it is the core control unit and has a miniature cubic structure; 202 Energy Module (Solid-State Micro Lithium Battery): Surrounds the outside of the control module in a ring-shaped thin sheet, is electrically connected to the control module, and is normally in a dormant state; 203 Drive Module: Contains two core components, 203a and 203b, which are piezoelectric ceramic micro motors symmetrically mounted on the outside of the energy module. 204a and 204b carbon nanotube propellers are connected to the motor output shaft. The propellers are housed in the grooves reserved in the spherical shell. After being triggered, they can extend and achieve 0–360° omnidirectional steering. Positioning module: Includes a 204a micro GPS chip and a 204b inertial navigation unit, integrated in the interlayer between the drive module and the outer shell, automatically switching to inertial navigation mode in the underground goaf environment; 205 silicon-based package housing: The outermost structure, which protects internal components and isolates them from dust and humid environments.

Claims

1. A targeted nanorobot for locating fire sources and assessing the situation in goaf areas, characterized in that, include: The sensing module integrates a temperature sensor and a carbon monoxide sensor for real-time monitoring of environmental parameters. The control module, electrically connected to the sensing module, is configured to output a trigger signal when the following conditions are met simultaneously: (1) The temperature value exceeds the preset temperature threshold and the carbon monoxide concentration exceeds the preset concentration threshold; (2) Both the rate of temperature change and the rate of carbon monoxide concentration change are greater than the preset rate of change threshold; The energy module is electrically connected to the control module and activates power supply upon receiving a trigger signal; The drive module is electrically connected to the energy module and generates directional thrust when powered on, enabling the nanorobot to break free from the airflow and move actively along the carbon monoxide concentration gradient or the direction of higher temperature. The positioning module is configured to obtain the current location coordinates after arriving at the target area; The communication module is electrically connected to the positioning module and the sensing module, and is used to upload coordinate data, temperature value and carbon monoxide concentration value to an external server.

2. The targeted nanorobot according to claim 1, characterized in that, The triggering logic of the control module includes a dynamic compensation mechanism: The preset temperature threshold and preset concentration threshold are dynamically adjusted according to the ambient wind speed. When the ambient wind speed is low, the threshold is set to a lower range; when the ambient wind speed is high, the threshold is set to a higher range.

3. The targeted nanorobot according to claim 1, characterized in that, The targeted movement process of the drive module includes: The control module periodically collects carbon monoxide concentration and temperature values ​​and calculates the concentration gradient change; The drive module dynamically adjusts the movement direction according to the concentration gradient direction, enabling the robot to continuously approach the region of increasing concentration. When the concentration gradient change or temperature gradient change is lower than the preset threshold and the temperature value is higher than the preset high temperature threshold, it is determined that the fire source center area has been reached.

4. The targeted nanorobot according to claim 1, characterized in that, The linkage mechanism between the energy module and the drive module is as follows: The energy module supplies power in stages under the action of a trigger signal; If no effective concentration gradient change is detected, it enters a dormant state and attempts to reactivate. When the cumulative power supply time reaches the upper limit or the energy is exhausted, the drive module is forcibly terminated.

5. A method for ignition source localization and situation assessment, based on the targeted nanorobot described in any one of claims 1-4, characterized in that, The process includes the following phased operation: S1. Airflow diffusion stage: Nanorobots were deployed into the airflow from the air intake corner of the coal mining face. The robot spreads with the airflow to the depths of the goaf, the sensing module continuously monitors environmental parameters, and the control module is in a low-power state. S2. Two-parameter coordinated triggering phase: When the temperature and carbon monoxide concentration both exceed the dynamic threshold simultaneously, and the rate of change of both meet the preset conditions, the control module outputs a trigger signal. The trigger signal is delayed and filtered to eliminate transient interference; S3. Targeted Movement Phase: After the drive module is activated, the robot moves against the wind direction and tracks the area with the highest carbon monoxide concentration in real time. The direction of travel is dynamically adjusted during the movement until the core area of ​​the fire source with a stable concentration gradient is reached; S4. Location Upload Stage: The positioning module obtains the current location coordinates, and the communication module encrypts and uploads the coordinate data, temperature value, and carbon monoxide concentration value. S5. Situation Assessment Phase: Cloud servers upload data based on multiple robots: (1) Determine the precise location of the fire source through coordinate clustering; (2) Determine the fire development stage based on the characteristic range of the combination of temperature and carbon monoxide concentration.

6. The method according to claim 5, characterized in that, The targeted movement phase in step S3 includes a loss prevention mechanism: When the moving distance exceeds the preset safe distance, the robot automatically adjusts its direction and returns to the reverse airflow path; When the positioning signal strength is below the threshold, switch to backup drive mode to continue approaching the target area.

7. The method according to claim 5, characterized in that, The situation assessment phase in step S5 includes a dynamic early warning escalation mechanism: If continuous monitoring data reflects accelerated changes in fire parameters, the warning level will be raised. When the rate of change of parameters exceeds the critical threshold, the system is forcibly classified as an open flame stage and an emergency response is triggered.

8. A method for ignition source location and situation assessment, based on the targeted nanorobot described in any one of claims 1-7, characterized in that, Includes the following steps: Nanorobots are deployed from the air intake corner of the coal face into the airflow, allowing them to diffuse into the goaf with the airflow. The sensor module continuously monitors temperature and carbon monoxide concentration. When both parameters exceed the preset threshold simultaneously, the energy module is activated and the nanorobot is driven to move in a targeted manner toward the abnormal area. After moving to the center of the abnormal area, the location coordinates are obtained through the positioning module and uploaded to the cloud server through the communication module; The cloud server determines the location of the fire source based on the uploaded data and judges the stage of fire development based on the temperature-carbon monoxide concentration mapping model.

9. The method according to claim 8, characterized in that, The preset thresholds include a temperature threshold of 50–70℃ and a carbon monoxide concentration threshold of 30–80 ppm, and both parameters must be synchronously abnormal for the system to be triggered.

10. The method according to claim 7, characterized in that, The fire development stage judgment model includes: Incubation period: Temperature < 60℃ and carbon monoxide concentration < 50 ppm; Acceleration period: 60℃ ≤ temperature < 100℃ and 50 ppm ≤ carbon monoxide concentration < 200 ppm; Open flame period: temperature ≥100℃ and carbon monoxide concentration ≥200 ppm.

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