Low-power-consumption communication module for fire monitoring of Internet of Things

Abstract

The invention discloses a low-power-consumption communication module for fire monitoring of the Internet of Things, and belongs to the technical field of communication, and the module comprises a low-power-consumption wireless communication module which supports a plurality of network types and has a low-power-consumption working mode; the power management module comprises a low-power-consumption DC-DC converter and a power monitoring chip and is used for converting the output voltage of the battery into stable voltage required by each module and monitoring the electric quantity of the battery in real time; the wake-up control module is provided with two modes of timer wake-up and external interrupt wake-up and is used for controlling the equipment to be switched between a dormant state and a working state; the data processing module is used for receiving the sensor data, analyzing and processing the sensor data, and waking up the wireless communication module for data transmission when needed; according to a low-power-consumption sensor combination and data acquisition strategy, a low-power-consumption digital temperature sensor, an MEMS smoke sensor and a low-power-consumption infrared flame sensor are adopted, and a periodic data acquisition mode is matched, so that the power consumption of a data acquisition link is greatly reduced while the monitoring precision is ensured.

Description

Technical Field

[0001] The present invention belongs to the field of communication technology, and in particular relates to a low-power communication module for fire monitoring in the Internet of Things. Background Art

[0002] With the rapid development of IoT technology, its application is becoming increasingly widespread across various industries, particularly in the field of security monitoring. Confined spaces such as underground warehouses, cable tunnels, mine tunnels, and power distribution rooms are relatively closed, with limited access and poor air circulation. Once a fire breaks out, it can spread rapidly and be difficult to detect and extinguish, often resulting in serious casualties and property damage. Therefore, real-time fire monitoring and timely early warning are crucial in confined spaces. IoT technology, which collects data in real time through sensors and transmits it to monitoring centers via communication networks, provides a new solution for fire monitoring. However, in practical applications, IoT devices, particularly in confined spaces, are limited by power supply difficulties and maintenance challenges, placing extremely high demands on low power consumption.

[0003] Currently, traditional fire monitoring methods for confined spaces primarily rely on wired fire alarm systems, which present numerous limitations. High power consumption and short battery life: Existing devices, whether using wired power supply with traditional sensors and high-power communication modules, or some wireless devices using high-power sensors and communication components, all result in significant overall energy consumption. For example, wireless fire monitoring devices that use Wi-Fi communication consume significant power due to the continuous operation of the communication module, resulting in a battery life of only one to three months, making them unable to meet the needs of long-term unmanned monitoring in confined spaces.

[0004] High maintenance costs: High power consumption requires frequent battery replacements. Manual battery replacement in confined spaces not only poses a safety hazard but also significantly increases labor and time costs. Furthermore, once a wired system's wiring is damaged, troubleshooting and repairing it becomes difficult, further increasing maintenance costs.

[0005] Insufficient real-time and accuracy of monitoring: Although some low-power devices use timed wake-up to collect data, this mechanism is difficult to capture sudden changes in fire outbreaks, which may lead to delayed warnings; and simple low-power designs cannot optimize data processing and communication links, affecting the accuracy of monitoring data.

[0006] Poor flexibility and scalability: Traditional wired fire monitoring systems have fixed wiring, making them difficult to expand according to layout changes or monitoring needs in enclosed areas. Wireless devices are susceptible to signal interference in complex, enclosed environments, which also limits the system's flexible deployment and functional expansion.

[0007] Based on this, the present invention designs a low-power communication module for Internet of Things fire monitoring to solve the above problems. Summary of the Invention

[0008] The purpose of the present invention is to solve the problems in the above-mentioned background technology and propose a low-power communication module for Internet of Things fire monitoring. By optimizing sensor selection, power management strategy and communication protocol, the overall power consumption of the equipment is reduced, the battery life is extended, and an ultra-long battery life of 1-3 years is achieved, and the maintenance frequency is reduced. At the same time, real-time dynamic data acquisition and intelligent analysis algorithms are used to ensure the timeliness and accuracy of fire monitoring. In addition, a flexible wireless communication architecture is designed to enhance the deployment flexibility of the equipment in confined places and the system scalability to meet the fire monitoring needs of different confined scenarios.

[0009] In order to achieve the above object, the present invention adopts the following technical solutions: A low-power communication module for Internet of Things fire monitoring, comprising: Low-power wireless communication module, supports multiple network standards, and has low-power working mode; The power management module includes a low-power DC-DC converter and a power monitoring chip, which is used to convert the battery output voltage into the stable voltage required by each module and monitor the battery power in real time; The wake-up control module sets two modes: timer wake-up and external interrupt wake-up, which are used to control the device to switch between sleep and working states; The data processing module is used to receive sensor data, analyze and process it, and wake up the wireless communication module for data transmission when necessary.

[0010] As a further description of the above technical solution: the low-power wireless communication module includes the following steps: Initialize the network connection. After the device is powered on, the low-power wireless communication module searches for and connects to an available network format to establish a stable data transmission channel. Entering a low-power sleep state: When there is no data transmission demand, the module enters a low-power sleep state to reduce overall power consumption; It is awakened for data transmission. When data needs to be uploaded, it is awakened by the data processing module and the relevant data is sent to the remote monitoring center through the selected network format.

[0011] As a further description of the above technical solution: the power management module specifically includes the following steps: Voltage conversion: a low-power DC-DC converter converts the battery output voltage into the stable voltage required by each module; Power monitoring: The power monitoring chip monitors the battery power in real time and calculates the remaining usage time; Automatically adjust the working mode. When the power level is lower than the set threshold, the device working mode will be automatically adjusted to reduce power consumption and extend battery life.

[0012] As a further description of the above technical solution: the wake-up control module specifically includes the following steps: Start the timer. During the device initialization phase, the wake-up control module starts the timer and sets the wake-up cycle. Timer wake-up: according to the preset time period, the wake-up control module wakes up the device to perform data collection and upload operations; External interrupt wake-up: when an external interrupt signal is received, the device is immediately woken up and processed accordingly; Switch back to sleep mode. After the processing is completed, decide whether to continue sleeping based on the situation and switch back to sleep mode.

[0013] As a further description of the above technical solution: the interrupt signal generating step of the wake-up module: The sensor module continuously or periodically collects environmental data; Perform noise reduction on the collected raw data to improve data quality: The wavelet noise reduction algorithm is used to reduce the noise of the data collected by the sensor; Set abnormal thresholds for various sensors based on historical data and actual application requirements; Compare the preprocessed data with the set abnormality threshold to determine whether there is an abnormality; When an abnormality is determined, the wake-up control module generates an interrupt signal to wake up other modules for further processing.

[0014] As a further description of the above technical solution: the data processing module specifically includes the following steps: Data receiving, receiving temperature, smoke concentration, and flame signal data from the sensor module; Data analysis: a microprocessor is used for data processing. The microprocessor analyzes and processes the collected data through intelligent analysis algorithms to determine whether there is a fire hazard; Data storage, storing processed data as historical data; Wake up the wireless communication module to transmit data when data needs to be uploaded or a fire hazard is detected; Trigger the alarm processing flow. If an abnormality is detected, the local sound and light alarm device will be triggered, and the wireless communication module will be awakened to send detailed alarm information to the remote monitoring center; The communication module further includes a sensor module, which includes a low-power digital temperature sensor, a MEMS smoke sensor, and a low-power infrared flame sensor.

[0015] As a further description of the above technical solution: the intelligent analysis algorithm includes the following steps: First, the obtained data is normalized and the fire hazard score is calculated using the weighted average model: ; : Fire hazard score; : The weighted coefficients corresponding to the three monitoring indicators of temperature, smoke concentration and flame intensity satisfy ; : Represent the normalized values of temperature, smoke density and flame signal respectively, and are calculated as follows: ; T: current temperature sampling value; : Minimum and maximum temperature settings for normalization; : Current smoke density value; : The minimum and maximum values used for normalization of smoke concentration; : Current flame infrared detection voltage value; : The minimum and maximum values used for flame signal normalization.

[0016] As a further description of the above technical solution: the fire risk level is classified and judged according to the FDS value; When FDS<0.4, it is judged as low risk and no alarm is triggered; When 0.4≤FDS≤0.7, it is judged as medium risk and triggers the local sound and light alarm; When FDS ≥ 0.7, it is judged as high risk, triggering a local sound and light alarm, and sending a warning message to the remote terminal through the communication module.

[0017] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are: 1. In the present invention: Low-power sensor combination and data acquisition strategy: Using low-power digital temperature sensors, MEMS smoke sensors, and low-power infrared flame sensors, combined with a periodic data acquisition mode, significantly reduces power consumption in the data acquisition process while ensuring monitoring accuracy; Low-power communication solution based on Hezhou Air780E: Leveraging the multiple network formats and low-power operating modes supported by Air780E, combined with intelligent wake-up and data transmission strategies, this solution enables low-power remote transmission of monitoring data. Multi-mode wake-up control mechanism: Set timer wake-up and external interrupt wake-up to accurately control the device's switch between sleep and working states, effectively reducing the device's standby power consumption; Composite power management system: uses a lithium battery combined with a solar charging panel for power supply, equipped with DC-DC conversion and intelligent charge and discharge management circuits to extend device life and reduce dependence on external power sources.

[0018] 2. In the present invention: Significant low-power consumption: The Air780E supports multiple low-power operating modes and can enter a deep sleep state when no data is being transmitted. This reduces standby power consumption by over 90% compared to traditional high-power communication modules. Furthermore, its optimized communication protocol stack reduces ineffective energy consumption during data transmission. Combined with an intelligent wake-up strategy, the module is woken up only when necessary to transmit data, further reducing overall power consumption and effectively extending device battery life, meeting the needs of long-term monitoring in confined areas. Strong network adaptability: The Air780E supports multiple network standards, including 2G and NB-IoT, allowing flexible selection based on the network environment in confined locations. Compared to traditional wired communication methods, it eliminates wiring constraints and solves the problems of difficult wiring and poor scalability in confined locations. Compared to some devices that only support a single wireless standard, it ensures stable data transmission in complex network environments, improving device adaptability. High Integration and Cost-Effectiveness: This module is highly integrated, achieving low-power, high-efficiency communication while reducing the complexity and cost of external circuits. Compared to existing solutions that use multiple separate communication components, this simplifies device design, reduces hardware costs and development difficulty, while ensuring reliable remote data transmission and achieving a balance between low power consumption and high performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a schematic diagram of the components and connections of the present invention; Figure 2 This is a flowchart of the wake-up control module of the present invention; Figure 3 Schematic diagram of the interrupt signal generation process of the present invention. DETAILED DESCRIPTION

[0020] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0021] Please see the attached Figure 1-Attached Figure 3 The present invention provides a technical solution: a low-power communication module for Internet of Things fire monitoring, comprising: Low-power wireless communication module, supports multiple network standards, and has low-power working mode; The power management module includes a low-power DC-DC converter and a power monitoring chip, which is used to convert the battery output voltage into the stable voltage required by each module and monitor the battery power in real time; The wake-up control module sets two modes: timer wake-up and external interrupt wake-up, which are used to control the device to switch between sleep and working states; The data processing module is used to receive sensor data, analyze and process it, and wake up the wireless communication module for data transmission when necessary.

[0022] The Hezhou Air780E is used as the network communication module. The Air780E supports multiple low-power operating modes and can enter a deep sleep state when there is no data transmission. Compared with traditional high-power communication modules, the standby power consumption is reduced by more than 90%. Its optimized communication protocol stack reduces ineffective energy consumption during data transmission. Combined with an intelligent wake-up strategy, the module is woken up to send data only when necessary.

[0023] The communication module supports multiple network standards, including 2G and NB-IoT, allowing flexible selection based on the network environment in confined locations. Compared to traditional wired communication methods, it eliminates wiring constraints and addresses the challenges of difficult wiring and poor scalability in confined locations. The module's high level of integration enables low-power, high-efficiency communication while reducing the complexity and cost of external circuitry.

[0024] The low-power wireless communication module includes the following steps: Initialize the network connection. After the device is powered on, the low-power wireless communication module searches for and connects to an available network format to establish a stable data transmission channel. Entering a low-power sleep state: When there is no data transmission demand, the module enters a low-power sleep state to reduce overall power consumption; It is awakened to transmit data. When data needs to be uploaded, it is awakened by the data processing module and the relevant data is sent to the remote monitoring center through the selected network format. The low-power wireless communication module also includes a communication strategy selection unit, which specifically includes the following steps: Signal strength detection The low-power wireless communication module regularly detects the signal strength of the current network; Signal strength is divided into multiple levels, such as strong, medium, weak, and no signal, and each level corresponds to a signal strength threshold range; Network type identification Identify the currently connected network type, such as 2G, NB-IoT; Different network types have different transmission rates, power consumption, and coverage, which have a significant impact on the choice of data transmission strategies; Communication mode selection Communication mode selection based on signal strength Strong signal: Select a high-speed, high-power communication mode to quickly complete data transmission; Medium signal: Select a communication mode that balances power consumption and transmission rate; Weak signal or no signal: No real-time data transmission is performed, the local storage mechanism is enabled, and the data is temporarily stored in the module's internal memory; Optimization based on network type For modules that support multiple network types, further optimization is performed based on the characteristics of the network type (such as coverage, transmission rate, power consumption, etc.).

[0025] For example, within the coverage area of the NB-IoT network, NB-IoT is used for data transmission first to take advantage of its low power consumption and wide coverage.

[0026] Data transfer strategy Real-time data transmission When the signal is strong or medium, when data needs to be uploaded (such as when a fire hazard is detected, or when status is reported regularly), the low-power wireless communication module is immediately awakened for data transmission. Compress data before transmission to reduce the transmission volume; at the same time, use encryption technology to ensure data transmission security; Delayed transmission and data retransmission In the case of weak or no signal, real-time data transmission is not performed, but the data is temporarily stored in the local memory; Regularly check the network signal strength. Once the signal returns to a transmittable level (such as a medium or strong signal), immediately activate the data retransmission mechanism and upload the temporarily stored data to the remote monitoring center in sequence. During data retransmission, the transmission rate and retransmission strategy can be dynamically adjusted according to network conditions to ensure data reliability and integrity.

[0027] The power management module specifically includes the following steps: Voltage conversion: a low-power DC-DC converter converts the battery output voltage into the stable voltage required by each module; Power monitoring: The power monitoring chip monitors the battery power in real time and calculates the remaining usage time; Automatically adjust the working mode. When the power level is lower than the set threshold, the device working mode will be automatically adjusted to reduce power consumption and extend battery life.

[0028] The wake-up control module specifically includes the following steps: Start the timer. During the device initialization phase, the wake-up control module starts the timer and sets the wake-up cycle. Timer wake-up: according to the preset time period, the wake-up control module wakes up the device to perform data collection and upload operations; External interrupt wake-up: when an external interrupt signal is received, the device is immediately woken up and processed accordingly; Switch back to sleep mode. After the processing is completed, decide whether to continue sleeping based on the situation and switch back to sleep mode.

[0029] The periodic data collection mode significantly reduces the power consumption of the data collection process while ensuring monitoring accuracy; The interrupt signal generating step of the wake-up module: The sensor module continuously or periodically collects environmental data; Perform noise reduction on the collected raw data to improve data quality: The wavelet noise reduction algorithm is used to reduce the noise of the data collected by the sensor, which includes the following steps: First, the model with noise is expressed as: ; is a noisy signal, is the real signal, is the noise signal; The discrete wavelet function is expressed as: ; is the scale factor, is the translation factor; The corresponding discrete wavelet transform is expressed as: ; In the above formula, yes The complex conjugate of Discrete wavelet threshold denoising method: For noisy signals When calculating the wavelet transform of , perform wavelet multi-layer decomposition to obtain the wavelet decomposition coefficients of the corresponding level. ; Wavelet decomposition coefficients Perform threshold processing, ; in, is the threshold, is the wavelet decomposition coefficient; Using the denoised wavelet coefficients Perform signal reconstruction to obtain the reconstructed real signal ; Set abnormal thresholds for various sensors based on historical data and actual application requirements; Compare the preprocessed data with the set abnormality threshold to determine whether there is an abnormality; When an abnormality is determined, the wake-up control module generates an interrupt signal to wake up other modules for further processing.

[0030] The data processing module specifically includes the following steps: Data receiving, receiving temperature, smoke concentration, and flame signal data from the sensor module; Data analysis: a microprocessor is used for data processing. The microprocessor analyzes and processes the collected data through intelligent analysis algorithms to determine whether there is a fire hazard; Data storage, storing processed data as historical data; Wake up the wireless communication module to transmit data when data needs to be uploaded or a fire hazard is detected; Trigger the alarm processing flow. If an abnormality is detected, the local sound and light alarm device will be triggered, and the wireless communication module will be awakened to send detailed alarm information to the remote monitoring center; The communication module also includes a sensor module, which includes a low-power digital temperature sensor, a MEMS smoke sensor, and a low-power infrared flame sensor.

[0031] The sensor module also includes the KSD-01F temperature control switch, which uses a TO-220 package and a snap-action, single-pole / single-throw contact structure with 100% conduction. When the external temperature is too high, it is transmitted to the corrected bimetallic strip and quickly actuated to cut off or connect the circuit. When the temperature drops to the reset temperature, the bimetallic strip quickly resets and repeats the operation. Sensors are evenly distributed around the device housing and connected to corresponding interfaces on the circuit board through pins. Taking the temperature sensor as an example, its data line is directly connected to the GPIO pin of the microprocessor to realize data transmission; The microprocessor is configured to normalize the data collected by the sensor and calculate the fire hazard score FDS based on a weighted scoring model, specifically comprising the following steps: First, the obtained data is normalized and the fire hazard score is calculated using the weighted average model: ; : Fire hazard score; : The weighted coefficients corresponding to the three monitoring indicators of temperature, smoke concentration and flame intensity satisfy ; : Represent the normalized values of temperature, smoke density and flame signal respectively, and are calculated as follows: ; T: current temperature sampling value; : Minimum and maximum temperature settings for normalization; : Current smoke density value; : The minimum and maximum values used for normalization of smoke concentration; : Current flame infrared detection voltage value; : The minimum and maximum values used for flame signal normalization.

[0032] Classify and judge the fire risk level according to the FDS value; When FDS<0.4, it is judged as low risk and no alarm is triggered; When 0.4≤FDS≤0.7, it is judged as medium risk and triggers the local sound and light alarm; When FDS ≥ 0.7, it is judged as high risk, triggering a local sound and light alarm, and sending a warning message to the remote terminal through the communication module; The microprocessor also includes a dynamic weight adjustment module for automatically adjusting the weight factor according to the type of device installation environment to improve the accuracy of fire identification in different scenarios; The microprocessor is equipped with a trend mutation judgment module to identify signs of sudden fire. Its judgment conditions include: Temperature Sudden Change: When the temperature increment within a certain period satisfies: ; Smoke concentration mutation: When the smoke increment within a certain period meets the following conditions: ; : Temperature and smoke concentration of the current detection cycle; : Detection value of the previous cycle; : are the mutation alarm thresholds for temperature and smoke concentration respectively; When any sudden change condition occurs, an alarm will be issued immediately.

[0033] When using: 1. Hardware connection and inspection Connect sensor modules such as low-power digital temperature sensors, MEMS smoke sensors, and low-power infrared flame sensors to the GPIO pins or dedicated interfaces of the data processing module (MCU) through data cables or dedicated interfaces.

[0034] Connect the low-power wireless communication module (such as Hezhou Air780E) to the data processing module through the UART interface, SPI interface or other communication interface.

[0035] Connect the power management module (including a low-power DC-DC converter and a power monitoring chip) to the power input of each module to ensure stable power supply.

[0036] Check that all connections are secure and free of shorts or opens.

[0037] Software configuration and initialization Program the MCU and configure its GPIO pins, communication interfaces, timers and other peripherals.

[0038] Write or load software programs such as sensor drivers, communication protocol stacks, and intelligent analysis algorithms.

[0039] Start the low-power wireless communication module, search and connect to the available network standard, and establish a stable data transmission channel.

[0040] Initialize the power management module and set parameters such as battery power monitoring threshold and working mode switching strategy.

[0041] System self-test and calibration Perform a self-test on the sensor module to ensure that it is working properly and has no faults or errors.

[0042] Calibrate the intelligent analysis algorithm and adjust parameters such as weighting coefficients and anomaly thresholds based on historical data and actual application needs; 2. Perform noise reduction on the collected raw data to improve data quality: The wavelet noise reduction algorithm is used to reduce the noise of the data collected by the sensor, which includes the following steps: First, the model with noise is expressed as: ; is a noisy signal, is the real signal, is the noise signal; The discrete wavelet function is expressed as: ; is the scale factor, is the translation factor; The corresponding discrete wavelet transform is expressed as: ; In the above formula, yes The complex conjugate of Discrete wavelet threshold denoising method: For noisy signals When calculating the wavelet transform of , perform wavelet multi-layer decomposition to obtain the wavelet decomposition coefficients of the corresponding level. ; Wavelet decomposition coefficients Perform threshold processing, ; in, is the threshold, is the wavelet decomposition coefficient; Using the denoised wavelet coefficients Perform signal reconstruction to obtain the reconstructed real signal ; The fire danger score FDS is calculated based on the weighted scoring model, which specifically includes the following steps: First, the obtained data is normalized and the fire hazard score is calculated using the weighted average model: ; : Fire hazard score; : The weighted coefficients corresponding to the three monitoring indicators of temperature, smoke concentration and flame intensity satisfy ; : Represent the normalized values of temperature, smoke density and flame signal respectively, and are calculated as follows: ; T: current temperature sampling value; : Minimum and maximum temperature settings for normalization; : Current smoke density value; : The minimum and maximum values used for normalization of smoke concentration; : Current flame infrared detection voltage value; : The minimum and maximum values used for flame signal normalization.

[0043] Classify and judge the fire risk level according to the FDS value; When FDS<0.4, it is judged as low risk and no alarm is triggered; When 0.4≤FDS≤0.7, it is judged as medium risk and triggers the local sound and light alarm; When FDS ≥ 0.7, it is judged as high risk, triggering a local sound and light alarm, and sending a warning message to the remote terminal through the communication module; 3. Real-time power monitoring and working mode adjustment The power monitoring chip in the power management module monitors the battery power in real time and calculates the remaining usage time.

[0044] When the battery level is lower than the set threshold, the device operating mode is automatically adjusted, such as reducing the frequency of sensor data collection and the number of wake-up times of the communication module, to extend battery life.

[0045] Low-power communication strategy When there is no data transmission demand, the low-power wireless communication module enters a low-power sleep state to reduce overall power consumption.

[0046] When data needs to be uploaded, it is awakened by the data processing module and the relevant data is sent to the remote monitoring center through the selected network format.

[0047] Adopt optimized communication protocol stack to reduce invalid energy consumption during data transmission.

[0048] 4. Local alarm When a fire hazard is detected, the local sound and light alarm device is immediately triggered to alert on-site personnel.

[0049] Remote alarm and data upload At the same time, the low-power wireless communication module is awakened to send detailed alarm information (including the time, location, sensor data, etc. of the fire) to the remote monitoring center so that timely measures can be taken.

[0050] Data storage The processed data (including normal data and alarm data) is stored in local storage or remote server for subsequent query and analysis.

[0051] Specifically: Experimental equipment: multiple prototypes of the low-power communication module for IoT fire monitoring of the present invention, several mainstream IoT fire monitoring communication modules on the current market, a high-precision power consumption tester, a fire simulation device, and data transmission monitoring equipment.

[0052] Experimental environment: laboratory environment with temperature of 25℃±2℃ and relative humidity of 50%±10%, simulating different fire scenarios; Power consumption test: The communication module of the present invention and the existing communication module were installed with batteries of the same capacity. The power consumption of each module was recorded using a high-precision power consumption meter in the standby mode, data acquisition mode, and data transmission mode for 72 hours. Test results: The low-power communication module for IoT fire monitoring of the present invention consumes significantly less power than existing similar products in various operating states, effectively extending battery life and reducing equipment maintenance costs. Fire monitoring accuracy test: Test method: Use a fire simulation device to conduct fire simulation experiments in different scenarios (50 times of open flame fire and 50 times of smoldering fire), record the number of times the module of the present invention and the existing module correctly detect fire, and calculate the monitoring accuracy.

[0053] Test results The communication module of the present invention has a significantly higher fire monitoring accuracy than existing products, can detect fire hazards more timely and accurately, and provide more reliable protection for fire early warning; Data transmission stability test: Test method: In a simulated complex electromagnetic environment, the module of the present invention and the existing module are allowed to continuously collect and transmit data, and the number of data transmission interruptions and the probability of transmission errors are recorded using data transmission monitoring equipment. The test time is 48 hours.

[0054] Test results The communication module of the present invention has better data transmission stability, fewer data transmission interruptions, and low transmission error probability in a complex electromagnetic environment, thereby ensuring reliable transmission of fire monitoring data.

[0055] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solution and inventive concept of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. A low-power communication module for Internet of Things fire monitoring, characterized in that: include: Low-power wireless communication module, supports multiple network standards, and has low-power working mode; The power management module includes a low-power DC-DC converter and a power monitoring chip, which is used to convert the battery output voltage into the stable voltage required by each module and monitor the battery power in real time; The wake-up control module sets two modes: timer wake-up and external interrupt wake-up, which are used to control the device to switch between sleep and working states; The data processing module is used to receive sensor data, analyze and process it, and wake up the wireless communication module for data transmission when necessary; Sensor module,The sensor module includes a low-power digital temperature sensor, a MEMS smoke sensor, and a low-power infrared flame sensor.

2. The low-power communication module for Internet of Things fire monitoring according to claim 1, characterized in that: The low-power wireless communication module includes the following steps: Initialize the network connection. After the device is powered on, the low-power wireless communication module searches for and connects to an available network format to establish a stable data transmission channel. Entering a low-power sleep state: When there is no data transmission demand, the module enters a low-power sleep state to reduce overall power consumption; It is awakened to transmit data. When data needs to be uploaded, it is awakened by the data processing module and the relevant data is sent to the remote monitoring center through the selected network format. The low-power wireless communication module also includes a communication strategy selection unit, which specifically includes the following steps: The low-power wireless communication module regularly detects the signal strength of the current network; Signal strength is divided into multiple levels, such as strong, medium, weak, and no signal, and each level corresponds to a signal strength threshold range; Identify the type of network you are currently connected to; Communication mode selection based on signal strength: Strong signal: Select a high-speed, high-power communication mode to quickly complete data transmission; Medium signal: Select a communication mode that balances power consumption and transmission rate; Weak signal or no signal: No real-time data transmission is performed, the local storage mechanism is enabled, and the data is temporarily stored in the module's internal memory; When the signal is strong or medium, when data needs to be uploaded, the low-power wireless communication module is awakened for data transmission; Compress data before transmission to reduce the transmission volume; at the same time, use encryption technology to ensure data transmission security; In the case of weak or no signal, real-time data transmission is not performed and data is temporarily stored in the local memory; Regularly check the network signal strength. Once the signal recovers to a transmittable level, start the data retransmission mechanism and upload the temporarily stored data to the remote monitoring center in sequence. During data retransmission, the transmission rate and retransmission strategy can be dynamically adjusted according to network conditions to ensure data reliability and integrity.

3. The low-power communication module for Internet of Things fire monitoring according to claim 1, characterized in that: The power management module specifically includes the following steps: Voltage conversion: a low-power DC-DC converter converts the battery output voltage into the stable voltage required by each module; Power monitoring: The power monitoring chip monitors the battery power in real time and calculates the remaining usage time; Automatically adjust the working mode. When the power level is lower than the set threshold, the device working mode will be automatically adjusted to reduce power consumption and extend battery life.

4. The low-power communication module for Internet of Things fire monitoring according to claim 1, characterized in that: The wake-up control module specifically includes the following steps: Start the timer. During the device initialization phase, the wake-up control module starts the timer and sets the wake-up cycle. Timer wake-up: according to the preset timer period, the wake-up control module wakes up the device to perform data collection and upload operations; External interrupt wake-up: when an external interrupt signal is received, the device is immediately woken up and processed accordingly; Switch back to sleep mode. After the processing is completed, decide whether to continue sleeping based on the situation and switch back to sleep mode.

5. The low-power communication module for Internet of Things fire monitoring according to claim 4, characterized in that: The interrupt signal generating step of the wake-up module: The sensor module continuously or periodically collects environmental data; Perform noise reduction on the collected raw data to improve data quality; Set abnormal thresholds for various sensors based on historical data and actual application requirements; Compare the preprocessed data with the set abnormality threshold to determine whether there is an abnormality; When an abnormality is determined, the wake-up control module generates an interrupt signal to wake up other modules for further processing.

6. The low-power communication module for Internet of Things fire monitoring according to claim 1, characterized in that: The data processing module specifically includes the following steps: Data receiving, receiving temperature, smoke concentration, and flame signal data from the sensor module; Data analysis: a microprocessor is used for data processing. The microprocessor analyzes and processes the collected data through intelligent analysis algorithms to determine whether there is a fire hazard; Data storage, storing processed data as historical data; Wake up the wireless communication module to transmit data when data needs to be uploaded or a fire hazard is detected; Trigger the alarm processing flow. If an abnormality is detected, the local sound and light alarm device is triggered, and the wireless communication module is awakened to send detailed alarm information to the remote monitoring center.

7. The low-power communication module for Internet of Things fire monitoring according to claim 6, characterized in that: The microprocessor is used to normalize the data collected by the sensor and calculate the fire hazard score FDS based on a weighted scoring model; Classify and judge the fire risk level according to the FDS value; The microprocessor also includes a dynamic weight adjustment module for automatically adjusting the value of the weight factor according to the type of equipment installation environment to improve the accuracy of fire identification in different scenarios; The microprocessor is provided with a trend mutation judgment module for identifying signs of sudden fire.

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