Distributed underground space environment air monitoring method

Abstract

The invention belongs to the technical field of air monitoring, and particularly relates to a distributed underground space environment air monitoring method, which comprises the following specific steps: underground space environment characteristic investigation: dividing monitoring areas, selecting 3-5 representative measuring points in each area, such as a subway tunnel, a pipe gallery and an underground space environment; according to the underground space environment monitoring system, through the low-power-consumption sensor, the self-power supply, the anti-vibration and anti-corrosion shell, intelligent early warning and centralized maintenance, the battery replacement period is prolonged to 2 years from 6 months, the single-time maintenance cost is reduced to 800 yuan, and the shell fracture rate is smaller than or equal to 5%; through LoRa + WiFi 6, a local optical fiber hybrid architecture, dual-gateway hot standby and link optimization, the wireless signal attenuation is reduced by 60%, the packet loss rate is less than or equal to 1%, the optical fiber wiring cost is reduced by 40%, and the maintenance interruption is less than or equal to 10 minutes.

Description

Technical Field

[0001] This invention relates to the field of air monitoring technology, specifically a method for monitoring ambient air in distributed underground spaces. Background Technology

[0002] With the acceleration of urbanization and the deepening of the development and utilization of underground space, underground space has become the core carrier for urban function expansion, resource storage and special scenario applications. Its ambient air quality is directly related to personnel safety, equipment stability and ecological sustainability.

[0003] Underground spaces are diverse in type and their functional importance is becoming increasingly prominent. They mainly cover urban public underground spaces (subway tunnels, underground shopping malls, underground utility tunnels, underground parking garages), resource development underground spaces (underground mines, underground gas storage facilities), and special protection underground spaces (underground civil defense projects, underground laboratories). The sources and risk characteristics of air pollutants in different scenarios vary significantly. Therefore, it is necessary to use underground space ambient air monitoring methods to monitor the air quality of underground spaces in real time.

[0004] However, existing methods for monitoring ambient air quality in underground spaces still have the following technical problems when in use:

[0005] The dual constraints of energy supply and equipment reliability: Distributed sensors mostly rely on battery power, but replacing batteries in underground spaces requires manual entry into restricted areas, with a single maintenance cost exceeding 2,000 yuan; mechanical vibration and chemical corrosion in the underground environment can cause the sensor housing to crack.

[0006] Bottlenecks in communication transmission stability and real-time performance: The reinforced concrete structure in underground spaces can cause wireless signal attenuation, and multipath effects can easily lead to data packet loss; although fiber optic transmission has high stability, the cost of laying fiber optics in narrow tunnels is extremely high, and maintenance requires interruption of monitoring.

[0007] Therefore, a distributed method for monitoring ambient air in underground spaces is proposed to address the aforementioned problems. Summary of the Invention

[0008] The purpose of this invention is to provide a distributed method for monitoring ambient air in underground spaces, so as to solve the problems mentioned in the background art.

[0009] To achieve the above objectives, the present invention provides the following technical solution: a distributed underground space ambient air monitoring method, the specific steps of which are as follows:

[0010] Step 1: Underground Space Environmental Characteristics Survey: Divide the monitoring area and select 3-5 representative monitoring points in each area, such as subway tunnels and utility tunnels. Pre-install air environment sensors at the selected locations. Monitor the ambient air in the underground space in real time through the air environment sensors, collect key air environment parameters, and compare these parameters with the standard values ​​of the environmental parameters to be set, so as to determine the air conditions at the monitored locations.

[0011] Step 2, Core Equipment Selection: Select a low-power, interference-resistant air environment sensor and set its monitoring thresholds for carbon monoxide and dust concentrations for easy comparison and judgment; select a self-powered module adapted for automatic charging and discharging management in underground scenarios; select a hybrid architecture and interference-resistant communication module; select a data network management system with edge computing capabilities. The self-powered module needs to be adapted for threshold-triggered power supply. When the air environment sensor detects data approaching the warning threshold, it automatically switches to high-power power supply mode to ensure that the sensor and communication module operate at full load and avoid data transmission delays when the threshold is exceeded due to insufficient power supply.

[0012] Step 3: Sensor Node and Self-Powered Module Deployment: Install sensors, avoiding high-risk areas, and prioritize installation on wall supports in well-ventilated locations with vibration acceleration ≤0.1g. Reinforce the sensor housing with protective measures. Deploy the self-powered modules: a vibration-generating module is fixed to the side wall of the subway tunnel and connected in series with the sensor via wires; excess energy is stored in a supercapacitor. A thermoelectric generator is attached to the surface of the hot and cold pipes in the utility tunnel, with a stable output voltage of 3.3V, directly powering the sensors. An emergency lithium battery serves as a backup power source, automatically switching when the self-powered supply is insufficient; the battery life is designed to be 2 years.

[0013] Step 4: Hybrid Communication Network Construction: Deploy the wireless communication layer, deploying LoRa gateways at 500m / node intervals, and encrypting them to 300m / node intervals at bends. Reduce signal attenuation by adjusting the gateway's transmission power. Overlay WiFi 6 modules in key areas to achieve high-bandwidth real-time transmission and support automatic frequency hopping. Deploy lightweight optical fibers, laying them only in long sections of narrow tunnels or high-interference areas, using a sidewall slot laying method. Use waterproof and corrosion-resistant junction boxes at fiber optic connectors, and reserve two spare connectors for redundant gateway deployment. Set up one set of dual hot standby gateways every 2km. The gateways are interconnected via wired links. When the main gateway fails, the backup gateway takes over data transmission within 0.5s to avoid area monitoring interruption.

[0014] Step 5: Automatic Sensor Calibration and Communication Link Testing: Deploy mobile calibration nodes to automatically patrol underground spaces during non-operational periods, sending calibration commands to each sensor via wireless signals and generating a report upon completion of calibration; set a regular calibration cycle, automatically triggering an early warning when the calibration deviation exceeds ±5%; simulate complex underground scenarios, continuously sending test data packets for 24 hours to monitor link packet loss rate and latency; for areas with excessive packet loss rates, adjust the LoRa gateway location or add relay nodes;

[0015] Step Six: Real-time Operation Monitoring and Intelligent Early Warning: Multi-dimensional status monitoring is performed. The sensor end collects real-time data on battery level, casing sealing, and vibration acceleration, which is transmitted synchronously with environmental monitoring data. The communication end uses a gateway to monitor signal strength, link connection count, and packet loss rate in real time, automatically switching channels or activating a backup gateway when an anomaly occurs. The intelligent early warning mechanism automatically sends an alert to the operation and maintenance platform and marks the fault location when the battery level is ≤20%, the humidity inside the casing is ≥60%, or the packet loss rate is ≥3%.

[0016] After the air environment sensor collects data, it first compares it with the preset threshold locally. Based on the comparison results, it outputs normal, warning, and alarm information. Then, it transmits the raw data and status label to the data network management system. The data network management system performs a second threshold verification. After receiving the data, the data network management system corrects the threshold based on the regional environmental characteristics and makes a specific judgment based on the specific monitoring location. It performs a second comparison to avoid threshold misjudgment caused by scene changes.

[0017] Step 7, Centralized Intelligent Maintenance: Based on early warning information, the system integrates multiple faults in the same area into a single maintenance task, avoiding repeated entry into restricted areas, planning the optimal maintenance path, and shortening maintenance time; it adopts a modular design, with sensors, self-powered modules, and communication modules all being plug-and-play, allowing direct module replacement during maintenance, with a replacement time of ≤10 minutes / node; for sensors with damaged casings, they are taken back to the ground for repair, recalibrated, and then redeployed;

[0018] Step 8: Data Review and Equipment Iteration: Monthly statistics on sensor failure rate, communication packet loss rate, and maintenance cost indicators; analysis of environmental characteristics in high-failure areas; optimization of self-powered strategies; equipment iteration and upgrade: introduction of more advanced low-power technologies and more corrosion-resistant materials; gradual replacement of old equipment.

[0019] Preferably, the air environment sensor needs to have a threshold pre-judgment function, support the local preset threshold, and automatically increase the sampling frequency when the collected data approaches the warning threshold.

[0020] Preferably, in step two, the selected air environment sensor has a static current ≤10μA, an IP68-rated housing, and is made of 316L stainless steel; the selected self-powered module has vibration power generation and thermoelectric power generation functions.

[0021] Preferably, in step three, the supercapacitor has a capacity of ≥5F, and the thermoelectric power generation module can generate electricity with a temperature difference of ≥8℃.

[0022] Preferably, in step four, the gateway's transmission power is ≤27dBm; and the WiFi 6 module's transmission rate is ≥150Mbps.

[0023] Preferably, in step five, the mobile calibration node is equipped with a standard gas temperature and humidity source; the test data packet rate is 100kbps.

[0024] Compared with the prior art, the beneficial effects of the present invention are:

[0025] 1) This application extends the battery replacement cycle from 6 months to 2 years through low-power sensors, self-powered systems, vibration-resistant and corrosion-resistant housings, intelligent early warning systems, and centralized maintenance, reducing the cost of a single maintenance to 800 yuan and the housing breakage rate to ≤5%.

[0026] 2) This application achieves a 60% reduction in wireless signal attenuation, a packet loss rate of ≤1%, a 40% reduction in fiber optic cabling costs, and a maintenance interruption of ≤10 minutes through LoRa+WiFi 6, a local fiber optic hybrid architecture, dual gateway hot standby, and link optimization. Attached Figure Description

[0027] Figure 1 This is a flowchart of the method steps. Detailed Implementation

[0028] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0029] Example:

[0030] Please see Figure 1 The present invention provides a technical solution:

[0031] A distributed method for monitoring ambient air quality in underground spaces, the specific steps of which are as follows:

[0032] Step 1: Underground Space Environmental Characteristics Survey: Divide the monitoring area and select 3-5 representative monitoring points in each area, such as subway tunnels and utility tunnels. Pre-install air environment sensors at the selected locations. Use the air environment sensors to monitor the ambient air in the underground space in real time, collect key air environment parameters, and compare these parameters with the standard values ​​of the environmental parameters to be set, so as to determine the air conditions at the monitored locations.

[0033] Step 2, Core Equipment Selection: Select a low-power, interference-resistant air environment sensor and set its monitoring thresholds for carbon monoxide and dust concentrations for easy comparison and judgment later. The air sensor can monitor not only carbon monoxide and dust concentrations, but also carbon dioxide, volatile organic compounds, PM2.5, etc.; a self-powered module adapted for automatic charging and discharging management in underground scenarios; a hybrid architecture and interference-resistant communication module; and a data network management system with edge computing capabilities. The self-powered module needs to be adapted for threshold-triggered power supply. When the air environment sensor detects data approaching the warning threshold, it automatically switches to high-power power supply mode to ensure the sensor and communication module operate at full load, avoiding data transmission delays due to insufficient power supply when thresholds are exceeded.

[0034] Step 3: Sensor Node and Self-Powered Module Deployment: Install sensors, avoiding high-risk areas, prioritizing installation on well-ventilated wall supports with vibration acceleration ≤0.1g, and reinforce their housings for protection; deploy the self-powered modules: vibration power generation modules are fixed to the side wall of the subway tunnel and connected in series with the sensors via wires, with excess energy stored in a supercapacitor; thermoelectric power generation modules are attached to the surface of hot and cold pipes in the utility tunnel, with a stable output voltage of 3.3V, directly powering the sensors; emergency lithium batteries serve as backup power, automatically switching when self-powered power is insufficient, with a designed battery life of 2 years; through vibration / thermal power generation and low-power sensors, over 90% of nodes can operate without manual battery replacement; shock-absorbing rubber and IP68 housings reduce the sensor housing breakage rate to ≤5%.

[0035] When the sensor is installed in a different location, its installation method can be changed. The installation location and method can be varied depending on the specific installation location and environment.

[0036] Step 4: Hybrid Communication Network Construction: Deploy the wireless communication layer, deploying LoRa gateways at 500m / node intervals, with densification at bends to 300m / node intervals. Reduce signal attenuation by adjusting gateway transmission power. Overlay WiFi 6 modules in key areas to achieve high-bandwidth real-time transmission and support automatic frequency hopping. Deploy lightweight optical fibers, laying them only in long sections of narrow tunnels or high-interference areas, using sidewall slots. Use waterproof and corrosion-resistant junction boxes at fiber optic connectors, and reserve two spare connectors for redundant gateway deployment. Set up one set of dual hot standby gateways every 2km, interconnected by wired links. When the primary gateway fails, the backup gateway takes over data transmission within 0.5s, avoiding area monitoring interruptions. LoRa+WiFi 6 solves wireless signal attenuation (transmission loss reduced to ≤10dB / 500m), and localized fiber optics avoid the high cost of full-length cabling. Dual gateways and backup fiber optics ensure a communication packet loss rate of ≤1% and maintenance downtime of ≤10 minutes.

[0037] Step 5: Automatic Sensor Calibration and Communication Link Testing: Deploy mobile calibration nodes to automatically patrol underground spaces during non-operational periods, sending calibration commands to each sensor via wireless signals and generating a report upon completion of calibration; set a regular calibration cycle, automatically triggering an alert when the calibration deviation exceeds ±5%; simulate complex underground scenarios, continuously sending test data packets for 24 hours to monitor link packet loss rate (≤1%) and latency (≤500ms); for areas with excessive packet loss rates, adjust the LoRa gateway location or add relay nodes;

[0038] The specific steps for sending calibration commands to each sensor via wireless signal are as follows:

[0039] The sensor needs to be paired with interference-resistant communication modules such as LoRa and WiFi 6 modules. These modules support bidirectional data transmission, enabling them to not only upload monitoring data but also receive external commands. Meanwhile, the mobile calibration node is equipped with a similar wireless communication unit, allowing it to cover the sensor's area via wireless signals during patrols.

[0040] Command transmission and calibration execution process:

[0041] Command transmission: The mobile calibration node has a built-in calibration command generation module, which can generate standardized commands based on preset calibration standards. These commands include information such as calibration reference values, error compensation parameters, and calibration cycles. The commands are then transmitted to the target sensor via wireless signals (such as LoRa spread spectrum communication or WiFi 6 orthogonal frequency division multiplexing technology).

[0042] Command reception and parsing: The sensor node integrates a microcontroller. After receiving wireless commands, it parses the command content through the built-in firmware and calls the preset calibration algorithm.

[0043] Calibration execution and feedback: The sensor adjusts its internal parameters according to the instructions, such as correcting zero-point offset caused by temperature drift and compensating for sensor sensitivity attenuation. After completing the calibration, a calibration result report is generated and transmitted back to the mobile calibration node via wireless communication, forming a closed loop.

[0044] Methods for anti-interference and precise positioning:

[0045] To address signal attenuation in underground spaces (such as tunnel wall obstruction and electromagnetic interference), the calibration command transmission is optimized as follows: utilizing the hybrid communication network in step four, the long-distance transmission characteristics of LoRa and the high bandwidth of WiFi 6 ensure reliable delivery of commands in complex environments; the mobile calibration node is equipped with a positioning module, which, combined with the location information of the sensor node, accurately sends commands to the sensors in the target area, avoiding misoperation.

[0046] Sensors with this function must be intelligent air environment sensors that integrate wireless communication modules, support remote parameter configuration, and have built-in calibration logic. Specifically, they include: hardware that integrates a microcontroller (MCU), a wireless communication unit (LoRa / WiFi6 module), and configurable sensing elements (such as optical PM2.5 sensors, infrared CO2 sensors, electrochemical gas sensors, etc.); and software that has built-in calibration firmware, supports calling calibration programs via commands, and has a parameter storage unit to save calibration reference values ​​and historical records.

[0047] Typical sensor examples:

[0048] Intelligent PM2.5 sensor: Adjusts the reference threshold of laser scattering via wireless commands to compensate for measurement deviations caused by dust adhesion;

[0049] Wireless temperature and humidity sensor: After receiving the command, it performs zero-point calibration of temperature, such as using the temperature of an ice-water mixture as a reference, to correct drift after long-term use;

[0050] Gas sensors (such as CO and VOCs): update the gas concentration calibration curve via instructions to compensate for sensor sensitivity decay, such as electrochemical electrode aging.

[0051] Step Six: Real-time Operation Monitoring and Intelligent Early Warning: Multi-dimensional status monitoring is performed. At the sensor end, real-time data collection is conducted on battery power (or self-powered voltage), casing sealing (built-in humidity sensor to detect water ingress), and vibration acceleration parameters, which are transmitted synchronously with environmental monitoring data. At the communication end, the gateway monitors signal strength, link connection count, and packet loss rate in real time, automatically switching channels or activating a backup gateway when an anomaly occurs. The intelligent early warning mechanism automatically sends an alert to the maintenance platform and marks the fault location when the system detects battery power ≤20%, internal humidity ≥60%, or packet loss rate ≥3%.

[0052] After the air environment sensor collects data, it first compares it with the preset threshold locally. Based on the comparison results, it outputs normal, warning, and alarm information. Then, it transmits the raw data and status label to the data network management system. The data network management system performs a second threshold verification. After receiving the data, the data network management system corrects the threshold based on the regional environmental characteristics and makes a specific judgment based on the specific monitoring location. It performs a second comparison to avoid threshold misjudgment caused by scene changes.

[0053] Real-time battery power acquisition: The power sources for the sensor nodes include self-powered modules and emergency lithium batteries. Power acquisition relies on a combination of hardware and algorithms for voltage monitoring and power conversion.

[0054] Hardware foundation: The sensor integrates a voltage detection circuit (such as a voltage divider resistor network and an ADC analog-to-digital converter), which is connected in series with the power supply circuit. This circuit can monitor the output voltage of the battery or supercapacitor in real time;

[0055] Principle and logic: There is a corresponding relationship between battery power and output voltage. For example, when the lithium battery voltage drops to 3.2V, the power is about 20%. The microcontroller (MCU) built into the sensor converts the analog voltage signal into a digital signal through the ADC, and then calls the preset voltage-power calibration curve. It is pre-made for different power types, such as the discharge characteristic curve of lithium battery and supercapacitor, to calculate the real-time power percentage.

[0056] Real-time performance guarantee: A timed sampling and dynamic triggering mechanism is adopted. By default, voltage data is collected every 30 seconds. When the power is below 30%, the sampling time is shortened to 10 seconds per time to ensure timely detection of the power decline trend and provide data support for the power ≤20% warning in step six.

[0057] Real-time monitoring of enclosure airtightness: Monitoring enclosure airtightness essentially involves detecting changes in humidity inside the enclosure to determine if there is air leakage / water seepage. This relies heavily on built-in environmental sensors.

[0058] Hardware configuration: A high-precision temperature and humidity sensor is integrated inside the sensor housing (in the area that does not detect external air) to specifically monitor the humidity of the microenvironment inside the housing;

[0059] Principle and Logic: Under normal sealed conditions, the humidity inside the casing is stable, typically consistent with the ambient humidity during packaging, approximately 30%-50% RH. If gaps appear in the casing due to vibration, corrosion, etc., humid external air (humidity in underground spaces often exceeds 80%) will seep in, causing the internal humidity to rise rapidly. The sensor collects the internal humidity value in real time and compares it with the baseline value of the initial sealed state. When the humidity is ≥60% (threshold in step six), it is determined that the seal has failed.

[0060] Anti-interference design: The temperature and humidity sensor is tightly fitted to the inner wall of the housing and kept away from the external air detection channel to avoid direct interference from the external ambient humidity and ensure that the data collected is the true sealed state inside the housing.

[0061] Real-time acquisition of vibration acceleration: Monitoring vibration acceleration is used to determine whether the sensor installation is stable (step three requires vibration acceleration ≤ 0.1g), which is achieved using a MEMS accelerometer.

[0062] Hardware selection: The sensor node integrates a triaxial MEMS accelerometer, which is directly fixed on a rigid bracket inside the sensor housing, and can detect vibration acceleration in the X, Y and Z directions in real time.

[0063] Principle and Logic: The accelerometer converts mechanical vibration into an electrical signal through changes in internal capacitance. After amplification and AD conversion, it outputs the real-time acceleration value. The sensor's built-in algorithm calculates the resultant acceleration (√(X)) along the three axes. 2 +Y 2 +Z 2 The vibration is compared with the threshold (0.1g) set in step three. If the vibration continuously exceeds the threshold, it is determined to be abnormal, such as loose installation or proximity to a strong vibration source.

[0064] Data processing: To avoid interference from instantaneous vibrations (such as short-term vibrations from a passing subway), a sliding window filtering algorithm is used to continuously collect 10 sets of data and take the average value as the current vibration acceleration value to ensure the stability of the monitoring results.

[0065] Coordination and transmission of multi-parameter acquisition

[0066] The acquisition of the above three types of parameters is not carried out independently, but is coordinated by the main controller (MCU) of the sensor node: the main controller synchronously triggers the battery voltage detection circuit, internal temperature and humidity sensor and acceleration sensor to work at a fixed period (e.g., 1 second / time), and temporarily stores the acquired data in the local cache; the acquired status parameters are packaged with the core environmental monitoring data of the sensor and uploaded to the cloud or edge gateway in real time through the communication module (LoRa / WiFi6) selected in step two, so as to realize the synchronous transmission of status monitoring and environmental monitoring data.

[0067] Step 7, Centralized Intelligent Maintenance: Based on early warning information, the system integrates multiple faults in the same area into a single maintenance task, avoiding repeated entry into restricted areas, planning the optimal maintenance path, and shortening maintenance time; it adopts a modular design, with sensors, self-powered modules, and communication modules all being plug-and-play, allowing direct module replacement during maintenance, with a replacement time of ≤10 minutes / node; for sensors with damaged casings, they are taken back to the ground for repair, recalibrated, and then redeployed;

[0068] Step 8: Data Review and Equipment Iteration: Monthly statistics on sensor failure rate, communication packet loss rate, and maintenance cost indicators; analysis of environmental characteristics in high-failure areas; optimization of self-powered strategies; equipment iteration and upgrade: introduction of more advanced low-power technologies and more corrosion-resistant materials; gradual replacement of old equipment.

[0069] The air environment sensor needs to have a threshold pre-judgment function, support the local preset threshold, and automatically increase the sampling frequency when the collected data approaches the warning threshold.

[0070] In step two, the selected air environment sensor has a static current ≤10μA, IP68 protection and 316L stainless steel housing; the selected self-powered module has vibration power generation and thermoelectric power generation functions.

[0071] The selected air environment sensor features a static current of ≤10μA, IP68 protection, and 316L stainless steel housing, enabling its low-power design. In underground spaces where power supply is limited, the low static current significantly reduces the sensor's energy consumption, decreases reliance on self-powered modules, extends the backup time of emergency lithium batteries, prevents monitoring interruptions due to insufficient power, and improves system endurance. Furthermore, its high-strength protection is enhanced; the IP68 protection rating is currently the highest waterproof and dustproof standard, ensuring the sensor remains waterproof and dust-free in humid underground environments, protecting internal circuitry from damage. The 316L stainless steel offers excellent corrosion resistance, resisting corrosive gases or liquids that may be present in underground spaces, extending the sensor housing's lifespan, and reducing malfunctions caused by housing damage.

[0072] The self-powered module adapts to the energy characteristics of underground spaces to achieve sustainable power supply. The vibration power generation module is designed for scenarios with continuous vibration, such as subway tunnels, and converts mechanical energy into electrical energy, providing stable energy acquisition without the need for external wiring. The thermoelectric power generation module is designed for scenarios with significant temperature differences, such as pipe corridors, and converts thermal energy into electrical energy through the thermoelectric effect, making full use of the environmental energy of the underground space itself. The combination of the two power generation methods can cover the energy characteristics of different underground areas, improve the stability and continuity of self-powered supply, and reduce dependence on traditional power sources.

[0073] Reduce maintenance costs and enhance system independence: Underground spaces are often restricted areas, making wiring difficult and costly. Self-powered modules can eliminate dependence on wired power sources, reducing the complexity of initial wiring projects. At the same time, by using renewable energy sources such as vibration and temperature differences for power supply, the frequency of long-term battery replacements is reduced, indirectly reducing maintenance costs.

[0074] In step three, the supercapacitor has a capacity of ≥5F, and the thermoelectric power generation module can generate electricity with a temperature difference of ≥8℃.

[0075] In step four, the gateway's transmission power is ≤27dBm; the WiFi 6 module's transmission rate is ≥150Mbps.

[0076] In step five, the mobile calibration node is equipped with a standard gas temperature and humidity source; the test data packet rate is 100kbps.

[0077] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or basic characteristics. Therefore, the embodiments should be considered exemplary and non-limiting in all respects. The scope of the invention is defined by the appended claims rather than the foregoing description. Therefore, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention, and no reference numerals in the claims should be construed as limiting the scope of the claims.

[0078] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method of distributed underground space environmental air monitoring, characterized by, The specific step flow of the distributed underground space environment air monitoring method is as follows: Step one, underground space environment feature survey: divide the monitoring area, select 3-5 representative measuring points in each area, such as subway tunnels and pipe galleries, and pre-install air environment sensors at the selected positions. Real-time monitoring of underground space environment air is carried out through air environment sensors to collect key air environment parameters. These parameters are compared with the set environmental parameter standard value to determine the air quality of the monitored position. Step two, core equipment selection: select low-power, anti-interference air environment sensors and set the monitoring threshold of carbon monoxide concentration and dust concentration for the air environment sensors to facilitate subsequent comparison and judgment; adapt the self-powered module for automatic charging and discharging management in underground scenes; select a mixed architecture and an anti-interference communication module; select a data network management with edge computing. The self-powered module needs to adapt to the threshold trigger power supply. When the air environment sensor detects that the data is close to the warning threshold, it automatically switches to high-power supply mode to ensure that the sensor and communication module run at full capacity, avoiding data transmission delay due to insufficient power supply when the threshold is exceeded. Step three, sensor node and self-powered module deployment: install the sensor, avoid high-risk areas, and preferentially install it on a well-ventilated wall bracket with a vibration acceleration of ≤0.1g. The shell is protected and strengthened. Deploy the self-powered module. The vibration power module is fixed to the side wall of the subway tunnel. It is connected in series with the sensor through a wire, and the excess power is stored in a super capacitor. The thermoelectric module is attached to the surface of the pipe gallery cold and hot pipe. The output voltage is stable at 3.3V, directly powering the sensor. The emergency lithium battery serves as a backup power supply. When the self-powered supply is insufficient, it automatically switches. The battery life is designed for 2 years. Step four, mixed communication network construction: deploy the wireless communication layer. Place LoRa gateways at intervals of 500m per node. The encryption distance is increased to 300m per node in curved sections. Adjust the gateway transmission power to reduce signal attenuation. In key areas, superimpose WiFi6 modules to achieve high-bandwidth real-time transmission and support automatic channel frequency hopping. Deploy lightweight optical fibers only in long-distance sections of narrow tunnels or high-interference areas. Use the side wall slot laying method. Use waterproof and corrosion-resistant junction boxes at optical fiber junctions. Two spare joints are reserved for redundant gateway deployment. Set up a double hot standby gateway every 2km. The gateways are interconnected through wired links. When the main gateway fails, the standby gateway takes over data transmission within 0.5s to avoid regional monitoring interruption. Step five, sensor automatic calibration and communication link test: deploy mobile calibration nodes. During non-operation periods of underground space, automatically cruise and send calibration instructions to each sensor through wireless signals. Generate a report after calibration. Set a regular calibration period. Automatically trigger a warning when the calibration deviation exceeds ±5%. Simulate complex underground scenarios and continuously send test data packets for 24 hours to monitor link packet loss rate and delay. Adjust the LoRa gateway position or add relay nodes for areas with excessive packet loss rate. Step six, real-time operation monitoring and intelligent early warning: multi-dimensional state monitoring, sensor end is real-time collection of battery power, shell sealing, vibration acceleration state parameters, and environmental monitoring data synchronous transmission; Communication end is gateway real-time monitoring signal strength, link connection number, packet loss rate, automatic switching channel or enable standby gateway when abnormal; Intelligent early warning mechanism is when the battery power ≤20%, the humidity in the shell ≥60%, the packet loss rate ≥3%, the system automatically sends an early warning to the operation and maintenance platform, and marks the fault location; After the air environment sensor collects data, it compares with the preset threshold value first, and outputs normal, early warning, and alarm information according to the comparison result. Then the original data and state label are transmitted to the data network management for secondary threshold value verification. After the data network management receives the data, it corrects the threshold value according to the regional environmental characteristics and makes specific judgments according to the specific monitoring place, and makes a secondary comparison to avoid threshold value misjudgment caused by scene changes. Step seven, centralized intelligent maintenance: the system integrates multiple faults in the same area into a single maintenance task to avoid repeated entry into restricted areas, plans the optimal maintenance path, and shortens the maintenance time; Modular design is adopted, and the sensor, self-powered module, and communication module are plug-and-play, and the replacement time is ≤10 minutes / node; For damaged shell sensors, bring them back to the ground for repair, and after repair, recalibrate and redeploy them; Step eight, data review and equipment iteration: monthly statistics of sensor failure rate, communication packet loss rate, and maintenance cost indicators, and analysis of environmental characteristics of high-fault areas; Optimize the self-powered strategy, update the equipment, introduce more advanced low-power consumption technology and more corrosion-resistant materials, and gradually replace old equipment.

2. The method of claim 1, wherein: The air environment sensor needs to have a threshold pre-determination function, which supports local preset threshold value. When the collected data approaches the early warning threshold value, the sampling frequency is automatically increased.

3. The method of claim 1, wherein: In step two, the selected air environment sensor has a static current ≤10μA, an IP68 protective shell, and a 316L stainless steel; The selected self-powered module has vibration power generation and temperature difference power generation functions.

4. The method of claim 1, wherein: In step three, the capacity of the super capacitor is ≥5F, and the temperature difference of the temperature difference power generation module is ≥8℃ to generate electricity.

5. The method of claim 1, wherein: In step four, the gateway transmission power is ≤27dBm; The transmission rate of the WiFi6 module is ≥150Mbps.

6. The method of claim 1, wherein: In step five, the mobile calibration node is equipped with a standard gas temperature and humidity source; The test data packet rate is 100kbps.

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