Low-power-consumption self-repairing and energy self-supplying mine dust monitoring sensor network system

Abstract

The invention provides a low-power-consumption self-repairing and energy self-supplying mine dust monitoring sensor network system, which comprises a plurality of sensor nodes and a central monitoring platform, and is characterized in that data of all the sensor nodes are transmitted to the central monitoring platform in real time; the sensor network synchronizes environmental parameters, and performs data analysis, processing and visual display on the central monitoring platform; a plurality of sensors are integrated in each sensor node, and the sensors are used for monitoring different environmental parameters in a mine environment; the types of the sensors comprise a dust concentration sensor and a temperature and humidity sensor, and the environmental parameters comprise dust concentration, temperature and humidity; the problems of long-time operation, insufficient energy supply, node failure, unstable data transmission and the like of a dust monitoring system in a complex mine environment are solved. The system integrates a multi-source environment energy collection module, and the technologies of mechanical vibration, airflow kinetic energy, thermoelectric power generation and the like are adopted, so that self-power supply of sensor nodes is realized, and the requirement of frequently replacing batteries or wiring is avoided.

Description

Technical Field

[0001] The present invention relates to mine dust monitoring technology, specifically to a mine dust monitoring sensor network system with low power consumption, self-repairing and energy self-supply, which is suitable for long-term monitoring of dust concentration and related environmental parameters in mines to ensure mine operation safety and workers' health. Background Art

[0002] The mine environment is complex and harsh. Excessive dust concentration not only poses a threat to equipment operation, but long-term exposure to high-concentration dust will have a serious impact on the health of miners. Therefore, mine dust monitoring has become an important means to ensure safe production in mines. However, existing dust monitoring systems generally have problems such as high energy consumption, power supply difficulties, easy damage to equipment, and monitoring interruption caused by node failure. At the same time, the replacement or maintenance cost of sensor equipment in mines is high. Therefore, the existing technology urgently needs a dust monitoring network with low power consumption, energy self-supply and self-repair capabilities to improve the working stability and reliability of the system in harsh environments. Summary of the invention

[0003] The present invention aims to at least solve the technical problems existing in the prior art, and in particular innovatively proposes a mine dust monitoring sensor network system with low power consumption, self-repairing and energy self-supply.

[0004] In order to achieve the above-mentioned purpose of the present invention, the present invention provides a low-power self-repairing and energy self-supplied mine dust monitoring sensor network system, including multiple sensor nodes and a central monitoring platform, and the data of all sensor nodes are transmitted to the central monitoring platform in real time; the sensor network synchronously monitors environmental parameters such as dust concentration, particle size distribution, air flow velocity, temperature and humidity through multi-parameter sensors, and performs data analysis, processing and visualization on the central monitoring platform; each sensor node integrates multiple sensors, and the sensors are used to monitor different environmental parameters in the mine environment; the types of sensors include dust concentration sensors and temperature and humidity sensors, and the environmental parameters include dust concentration, temperature and humidity; the sensor nodes serve as carriers and integrated platforms for these sensors, realizing synchronous monitoring of multiple environmental parameters.

[0005] The sensor node includes a multi-source energy collection module and a low power consumption management module;

[0006] Among them, the multi-source energy collection module integrates a piezoelectric energy collection device, a micro airflow turbine and a temperature difference power generation unit, which is used to collect energy from the mechanical vibration, airflow kinetic energy and temperature difference in the mine environment and convert it into electrical energy to achieve self-powering of the sensor; in addition, the sensor node has an environmental adaptive function, which can automatically adjust the working state of the energy collection module according to the wind speed, vibration or temperature changes in the mine to optimize the energy collection efficiency.

[0007] The low power management module is used to dynamically adjust the working mode of the sensor according to the sensor power and environmental conditions to minimize power consumption; the working mode includes standby and monitoring modes. When the environmental parameters remain within the preset fluctuation value range during the set time period and the sensor node has no major environmental changes, it enters the low power standby mode; when the environmental parameters exceed the set threshold during the set time period, it automatically activates to enter the high-precision monitoring mode.

[0008] Preferably, the data of all sensor nodes are transmitted to the central monitoring platform in real time, which is achieved through an integrated module, and the sensor nodes transmit the data to the WiMAX base station through the integrated module; the integrated module includes: a wireless communication module (i.e., a WiMAX module), a multi-band switching module and a controller (STM32 single-chip microcomputer); all sensor nodes are interconnected through the wireless communication module to form a self-organizing network. The wireless communication module is connected to the controller through SPI communication, and the multi-band switching module is connected to the controller through one of SPI, I2C, UART, GPIO or any combination of communication methods;

[0009] The multi-band switching module is used to switch the frequency range of electromagnetic waves according to the location of the sensor, realizing the switching between low frequency band (such as 2.5GHz) and high frequency band (such as 5.8GHz); when the terminal device is far away from the base station, the system will automatically switch to the low frequency band to ensure the signal strength. When the device is close to the base station, the high frequency band is preferred to obtain a higher data transmission rate.

[0010] When the multi-band switching module communicates with the STM32 microcontroller, the functions of each serial port are defined as follows: UART (Universal Asynchronous Receiver and Transmitter): used for point-to-point communication, transmission of control instructions and feedback data. SPI (Serial Peripheral Interface): used for high-speed data transmission, such as frequency band setting and configuration of frequency synthesizer parameters. 2 C (two-wire interface): used for frequency band status data collection and multi-module collaboration. Interface connection: The relevant serial port of STM32 (such as USART1, SPI1) is directly connected to the control end of the frequency band switching module, and the necessary GPIO is configured for interrupt and status indication.

[0011] Data transmission protocol: Instruction format: A custom instruction protocol is used, and the data packet format includes the following parts: Start bit (1 byte): identifies the start of the data packet, and commonly used fixed values ​​are such as 0xAA. Instruction type (1 byte): defines the specific operation type, such as switching frequency bands and querying status. Parameter data (2-4 bytes): specific parameters that need to be carried according to the instruction, such as the target frequency band value. Check bit (1 byte): ensure data integrity through checksum / CRC check. End bit (1 byte): identifies the end of the data packet, and commonly used fixed values ​​are such as 0xFF. Communication rate: The UART rate is usually set to 9600bps or 115200bps. The switching process is: When STM32 detects that the current frequency band is seriously interfered (through parameters such as signal strength and bit error rate), the switching process is triggered: STM32 selects a new frequency band according to the algorithm and sends a switching command through the serial port. The frequency band switching module adjusts the frequency synthesizer and switches to the target frequency band. After the switch is completed, the module feedbacks the status to STM32 to ensure that the frequency band switch is successful.

[0012] Preferably, the integrated module further comprises an automatic routing module, and the automatic routing module is connected to the controller via one or any combination of communication modes including SPI, I2C, UART, GPIO;

[0013] The automatic routing module is used to automatically adjust the data transmission path according to the changes in the sensor's position and the changes in the network topology. The module determines the optimal communication path by detecting the real-time location information and signal strength of each device, especially in the case of signal obstruction and frequent device movement, and ensures the network connectivity and transmission efficiency through automatic routing.

[0014] The sensor network is a self-repairing network, and the sensor nodes have distributed self-organizing network functions. Once a sensor node fails or communication is interrupted, the adjacent sensor nodes automatically adjust the data transmission path through a dynamic routing algorithm and automatically rebuild the communication path, thereby ensuring network stability and data transmission continuity.

[0015] Preferably, the type of sensor further includes a gas detection sensor, and the environmental parameter further includes air flow velocity.

[0016] Preferably, the sensor node has a temperature and humidity compensation function, and can dynamically adjust the dust concentration monitoring result according to the temperature and humidity changes in the environment to ensure the monitoring accuracy under different climatic conditions:

[0017] S1, the system collects ambient temperature and humidity data in real time through the temperature and humidity sensors in the sensor nodes, while the dust concentration sensor measures the particle concentration (usually in mg / m 3The collected ambient temperature and humidity data are processed by a controller (STM32 single-chip microcomputer) to filter out noise or abnormal data to ensure the accuracy of the input data; the original signal of the dust concentration sensor is also filtered to remove spike interference to improve the authenticity of the measured value.

[0018] S2, after the collected data is initially calibrated, it enters the compensation calculation stage; the controller dynamically adjusts the dust concentration measurement value based on the pre-established compensation model, taking into account the different effects of temperature and humidity on dust characteristics. For example, under high humidity conditions, the hygroscopicity of particulate matter will cause the measurement value to be high, and the concentration value needs to be lowered for correction; in a high temperature environment, changes in air density may affect the light scattering characteristics of particulate matter, and the dust concentration value needs to be compensated by the following formula:

[0019] Ccorrected=Cmeasured×(1-K RH RH)×(1+K T ·(TT ref ))×(1+K dust ·P dust )

[0020] Among them, Ccorrected represents the dust concentration value after compensation;

[0021] Cmeasured represents the original dust concentration measurement value, that is, the dust concentration value measured by the dust concentration sensor;

[0022] K RH and K T Represents humidity and temperature compensation coefficients respectively. For non-hygroscopic particles such as coal dust, K RH ≈0, while for hygroscopic rock dust, K RH =0.1-0.2;

[0023] RH is the real-time humidity in the coal mine;

[0024] T is the real-time temperature in the coal mine;

[0025] T ref is the reference temperature, which is usually the room temperature when the instrument is calibrated;

[0026] K T is the temperature compensation coefficient, which reflects the effect of temperature on the measurement system or particle scattering, usually 0.01-0.05;

[0027] P dust is the proportion of coal dust in mine dust; for the proportion of coal dust P dust The proportion of coal dust in all dust can be determined by sampling coal mine dust and performing chemical composition analysis.

[0028] K dust It is the dust type compensation coefficient, which reflects the different types of dust in the mine.

[0029] Preferably, the humidity compensation coefficient K RH , Temperature compensation coefficient K T The calibration includes the following steps:

[0030] (1) Determine the benchmark conditions

[0031] Reference dust concentration: Use a reference concentration measurement device to measure the dust concentration under reference humidity / temperature conditions and record it as C true ;

[0032] (2) Changing humidity / temperature conditions

[0033] Adjust the humidity / temperature gradually to multiple values ​​within the target range (such as 10%, 30%, 50%, 70%, 90%, etc.), keep each humidity / temperature point stable, and record the actual humidity / temperature value as RH i ;

[0034] (3) Measuring concentration changes

[0035] At each humidity / temperature condition, the dust concentration was measured using a dust monitoring device and recorded as C measured ,i; Repeat the measurement multiple times to ensure the accuracy of the data.

[0036] (4) Calculate humidity / temperature correction factors;

[0037] For each humidity / temperature value, calculate the concentration error ratio:

[0038] ΔC i =(C measured ,iC true ) / C true

[0039] Where, ΔC i is the relative value of the measurement error;

[0040] (5) Fitting humidity / temperature correction model;

[0041] Establish a linear or nonlinear relationship between humidity / temperature and concentration error ratio:

[0042] ΔC i =K RH RH i

[0043] ΔC i =K T RH i

[0044] Linear regression was performed on the experimental data to fit the humidity compensation coefficient K RH And the temperature compensation coefficient K T .

[0045] During real-time monitoring, the controller combines a variety of temperature and humidity sensors to dynamically obtain the current temperature and humidity through the built-in algorithm and update the compensation parameters in a timely manner. The compensated dust concentration value is transmitted from the controller to the display device or monitoring center. The data output also includes the current ambient temperature and humidity information to provide a more comprehensive reference for the environmental status.

[0046] Preferably, the multi-source energy collection module provides energy for the sensor nodes and the MESH sensor network, and stores the excess energy in a redundant energy storage unit; the redundant energy storage unit includes a super capacitor and a high-efficiency lithium battery connected in parallel;

[0047] The multi-source energy collection module provides energy for the sensor node, including:

[0048] The energy provided by the piezoelectric energy harvesting device is used for the sampling task of the current sensor and the wireless communication module;

[0049] The stable output energy of the micro airflow turbine is used for the wireless communication module;

[0050] The energy of the thermoelectric battery is used for redundant energy storage units;

[0051] S100, determining whether the energy provided by the piezoelectric energy harvesting device meets the energy required for the current sampling task, if so, jumping to S200; if not, calling the energy of the redundant energy storage unit for the current sampling task;

[0052] S200, determining whether the stable output energy of the micro airflow turbine meets the energy required by the current wireless communication module. If so, no other operations are required, and the excess energy is stored in a redundant energy storage unit; if not, executing the next step;

[0053] S300, determine whether the excess energy of the piezoelectric energy collection device meets the excess energy required by the wireless communication module. If so, the energy provided by the piezoelectric energy collection device is used for the wireless communication module, and the excess energy is stored in a redundant energy storage unit; if not, the energy of the redundant energy storage unit is called to be used for the wireless communication module; the excess energy of the piezoelectric energy collection device is the difference between the energy provided by the piezoelectric energy collection device and the energy required for the current sampling task, and the excess energy required for the wireless communication module is the difference between the stable output energy of the micro airflow turbine and the energy required by the current wireless communication module.

[0054] Preferably, steps S100 to S300 further include:

[0055] (1) According to the energy requirements of the sampling task and communication, the immediate energy is used first:

[0056] Ep-usage(t)=min(Ep(t),Ec(t)+Ecom(t))

[0057] Et-usage(t)=min(Et(t),Ecom(t)-Et-usage(t-1))

[0058] Among them, Ep-usage(t) represents the energy of piezoelectric energy allocated to the current sampling task and communication module at time t;

[0059] Ep(t) represents the instantaneous output energy from the piezoelectric material at time t;

[0060] Ec(t) represents the energy requirement of the chip's current sampling task at time t;

[0061] Ecom(t) represents the energy requirement of the communication module at time t;

[0062] Et-usage(t) represents the energy of turbine energy allocated to the communication module at time t;

[0063] Et(t) represents the steady output energy from the turbine at time t;

[0064] Et-usage(t-1) represents the energy that has been allocated to the turbine at the previous moment, which prevents repeated calculation of turbine energy;

[0065] (2) Calculate the remaining excess energy: When the piezoelectric and turbine cannot meet all the needs, calculate the remaining energy for use by the redundant energy storage unit:

[0066] Eexcess(t)=Ep(t)+Et(t)+Ed(t)-(Ec(t)+Ecom(t))

[0067] Where, Eexcess(t) represents the remaining energy;

[0068] Ed(t) represents the output energy from the thermoelectric cell at time t;

[0069] (3) Energy allocation strategy when charging redundant energy storage units

[0070] When Eexcess(t)>0, first charge the supercapacitor:

[0071] Ecap-charging(t)=min(ηcap ·Eexcess(t),Ecap-max-Ecap(t))

[0072] Where, Ecap-charging(t) represents the energy of supercapacitor charging;

[0073] η cap Indicates the charging efficiency of the supercapacitor;

[0074] Ecap-max is the maximum energy storage capacity of the supercapacitor;

[0075] Ecap(t) represents the current energy storage of the supercapacitor;

[0076] The remaining energy is then used to charge the lithium-ion battery:

[0077] Eli-charging(t)=max(0,η li (Eexcess(t)-Ecap-charging(t))

[0078] Among them, Eli-charging (t) represents the energy of lithium battery charging;

[0079] η li Indicates the charging efficiency of lithium-ion batteries;

[0080] (4) Redundant energy storage unit charging and updating, updating the energy status of supercapacitors and lithium batteries:

[0081] Ecap(t+1)=Ecap(t)+Ecap-charging(t)

[0082] Eli(t+1)=Eli(t)+Eli-charging(t)

[0083] Where Eli(t) represents the current energy storage capacity of the lithium-ion battery.

[0084] Preferably, the supercapacitor of the redundant energy storage unit is charged first, and when the supercapacitor is full, the lithium-ion battery is charged. This strategy can improve the energy response speed and reduce the dependence on traditional energy storage systems.

[0085] Preferably, the goal of steps S100 to S300 is to maximize the efficiency of instantaneous energy use while minimizing the reliance on redundant energy storage units, and the objective function is:

[0086] Efficiency(t)=(Ep-usage(t)+Et-usage(t)) / (Ec(t)+Ecom(t))

[0087] Among them, Ep-usage(t) represents the energy of piezoelectric energy allocated to the current task and communication module at time t;

[0088] Et-usage(t) represents the energy of turbine energy allocated to the communication module at time t;

[0089] Ec(t) represents the energy requirement of the chip's current sampling task at time t;

[0090] Ecom(t) represents the energy requirement of the communication module at time t.

[0091] This efficiency function is used to evaluate the efficiency of instantaneous energy use. When the value is close to 1, it means that the instantaneous energy is maximized and the dependence on redundant energy storage units is reduced.

[0092] In summary, due to the adoption of the above technical solutions, the present invention provides self-power supply capability for sensor nodes through multi-source environmental energy collection modules, and ensures the continuous and stable operation of the equipment for a long time through intelligent power consumption management mechanism and self-repairing network structure. The system can monitor the dust concentration, particle size, air flow velocity, temperature and humidity and other environmental parameters in the mine in real time, and transmit the data to the central monitoring platform through the wireless communication module for processing and analysis.

[0093] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0094] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0095] Figure 1 It is a schematic diagram of the system structure of the present invention.

[0096] Figure 2 This is a communication connection mode between the automatic routing module and the multi-band switching module of the present invention and the STM32 single-chip microcomputer.

[0097] Figure 3 This is a schematic diagram of the Mesh network of the present invention. DETAILED DESCRIPTION

[0098] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be understood as limiting the present invention.

[0099] The present invention relates to a mine dust monitoring sensor network system with low power consumption, self-repair and energy self-supply, such as Figure 1 As shown in the figure, it aims to solve the problems of energy supply, node failure, unstable data transmission, etc. of long-term operation of dust monitoring equipment in mine environment. The system provides self-power for sensors through multi-source energy collection technology, and combines with self-healing network structure to ensure that the equipment can work continuously and stably in harsh environments, while realizing real-time monitoring and transmission of dust concentration and related environmental parameters. Specifically include:

[0100] (1) Sensor node structure. ① Multi-source energy collection module: including piezoelectric energy collection, airflow energy collection and temperature difference power generation unit, which is used to convert vibration, airflow kinetic energy and temperature difference in the mine environment into electrical energy to provide the energy required by the sensor. ② Low-power management system: The node dynamically adjusts the working mode through an intelligent management mechanism to ensure that power consumption is minimized. For example, when there is no major environmental change, the sensor is in a low-power standby state; when the dust concentration or other parameters exceed the preset threshold, the sensor automatically enters a high-precision working mode. ③ Self-healing network structure: The sensor node has a distributed self-organizing network function. Once a node failure is detected, the adjacent nodes will automatically rebuild the communication path to ensure uninterrupted data transmission. ④ Wireless communication module: The node transmits the monitoring data to the central monitoring platform through low-power wireless communication protocols such as WiMAX to ensure long-distance coverage and low-power operation of data transmission.

[0101] (2) Energy management system. ① Piezoelectric energy collection unit: converts the vibration generated by the operation of mechanical equipment in the mine into electrical energy through piezoelectric materials. ② Airflow energy collection unit: uses micro-turbine power generation technology to convert the kinetic energy of airflow in the mine ventilation system into electrical energy. ③ Temperature difference power generation unit: utilizes the area with large temperature difference in the mine and uses the Seebeck effect to collect thermal energy. ④ Energy storage and regulation module: stores the electrical energy generated by the energy collection unit in ultra-low power consumption capacitors or high-efficiency micro batteries to ensure that the sensor can still operate normally when the energy supply is insufficient in the short term.

[0102] (3) Self-repairing network. The present invention designs a distributed sensor network with self-repairing function. Each sensor node keeps the network topology dynamically updated through a dynamic routing algorithm. When a node fails or communication is interrupted, the network can automatically rebuild the route to ensure the overall stability of the system and the continuity of data transmission. The network also has a load balancing function to avoid overloading of a single node, thereby extending the life of the node.

[0103] (4) The data collected by the central monitoring platform from all sensor nodes are transmitted to the central monitoring platform in real time. The platform can analyze, process and visualize the data. ① Data analysis and visualization: Real-time monitoring of dust concentration trends, establishment of dust diffusion models based on historical data, and prediction of high-risk areas. ② Intelligent early warning system: Based on artificial intelligence algorithms and combined with data monitoring results, a correlation model between dust concentration and occupational health risks is established, and risk assessment and intelligent early warning are provided. ③ Remote control and maintenance: The platform can automatically adjust the mine ventilation system or dust control equipment based on sensor data, and maintain and manage the sensor network through remote diagnosis functions.

[0104] The WiMAX forms a Mesh network and supports multi-band operation. It can flexibly switch between low-band (2.5GHz), mid-band (3.5GHz) and high-band (5.8GHz). The low-band provides a farther coverage range, while the high-band is used to provide a higher data rate.

[0105] The automatic routing module can automatically adjust the data transmission path according to the change of device location and network topology. The module determines the optimal communication path by detecting the real-time location information and signal strength of each device, especially in the case of signal obstruction and frequent device movement, and ensures the connectivity and transmission efficiency of the network through automatic routing.

[0106] The present invention provides a mine dust monitoring sensor network system with low power consumption, self-repairing and self-energy supply. The system is based on WiMAX technology, combined with a multi-band communication module and an automatic routing mechanism, and self-repairs through a distributed intelligent algorithm. Once a node fails or fails, its adjacent nodes can automatically select a new routing path to ensure uninterrupted data transmission. Through multi-source energy collection technology, the sensor can be self-powered, reducing external power dependence and maintenance costs. The system can adapt to different environmental conditions and application scenarios by actively detecting signal quality and adjusting frequency band usage in real time, ensuring stable data transmission and efficient communication. The system supports applications such as remote monitoring, equipment tracking, and personnel positioning, providing a reliable wireless communication solution for underground operations.

[0107] The sensor node of the present invention has multi-source energy collection capability, and can obtain mechanical vibration, airflow kinetic energy and temperature difference energy from the mine environment, and convert them into electrical energy for use by the sensor. The sensor node is built with piezoelectric materials, micro turbines and thermoelectric batteries, which are responsible for collecting different types of environmental energy respectively. The use of piezoelectric materials, micro turbines and thermoelectric battery technologies in mines to achieve energy self-supply of sensors, combined with energy storage, requires design from multiple aspects such as energy collection, conversion, storage and management. The rich energy types in the mine environment provide a good foundation for these technologies. Piezoelectric materials can collect mechanical energy generated by mechanical vibration, mine car movement or equipment operation, and convert vibration energy into electrical energy by installing piezoelectric sheets on mine structures or sensor fixing points. This method is suitable for scenarios with low vibration frequency but continuous energy, and can improve energy collection efficiency by optimizing the vibration amplification structure. The micro turbine generates electricity through the air flow in the mine ventilation system or the kinetic energy of water in the pipeline, and is suitable for use in mine environments with good ventilation or drainage requirements. The design of the turbine needs to adapt to the common low wind speed or small water flow conditions in mines, and be integrated with the sensor node through miniaturized design. Thermoelectric batteries use the temperature difference in the mine, such as the temperature difference between the equipment heat dissipation area and the surrounding environment or the temperature gradient between different depths in the mine, to convert thermal energy into electrical energy. The output power of these technologies is usually in the microwatt to milliwatt level, which is enough to meet the needs of low-power sensors. Through these energy harvesting methods, sensors can be self-powered and get rid of the dependence on external power supplies or frequent battery replacement, which is particularly suitable for harsh environments such as mines with limited maintenance conditions.

[0108] The sensor nodes optimize their energy consumption mode through intelligent power management mechanism. The system can dynamically adjust the working mode according to the energy supply situation, perform high-frequency monitoring when the energy is sufficient, and enter low-power standby mode when the energy is insufficient to extend the working time. The sensor nodes can not only monitor the dust concentration in real time, but also detect multiple environmental parameters such as particle size, air flow velocity, temperature and humidity, thereby providing comprehensive environmental data support for the safe production of mines.

[0109] In order to ensure the long-term stable operation of the system, the present invention designs a self-repairing network structure. All sensor nodes are interconnected through wireless communication modules (such as WiMAX) to form a self-organizing network. When a node fails or communication is interrupted, the system can automatically adjust the network topology through a dynamic routing algorithm to ensure continuous data transmission and system stability. The self-repairing mechanism enables the system to have strong anti-interference and fault recovery capabilities, and can continue to operate normally when some nodes fail. In addition, the network also has a load balancing function, which allocates the data transmission tasks of each node through an intelligent algorithm to avoid failure of some nodes due to overload, thereby further improving the reliability of the network.

[0110] In terms of data collection and transmission, the sensor nodes transmit the monitored dust concentration and other environmental data to the central monitoring platform through the wireless communication module. In order to reduce the bandwidth requirements for data transmission, the system compresses and fuses the collected data to ensure stable and long-distance data transmission in the complex environment of the mine. The central monitoring platform is responsible for receiving the data transmitted by all sensor nodes and processing and analyzing them in real time. The platform can generate dust concentration distribution maps and trend prediction maps through big data technology to help mine managers understand the dust diffusion situation and predict high-risk areas. In order to generate dust concentration distribution maps and trend prediction maps, the platform needs to use big data technology to comprehensively process the data collected by mine sensors. First, the sensor nodes collect dust concentration, temperature and humidity and other related environmental data in the mine in real time, and upload them to the central data processing system of the platform through wireless networks (such as LoRa, Wi-Fi or Zigbee). After receiving this information, the data platform will perform preprocessing, including data cleaning, calibration and time synchronization. Data cleaning is used to remove abnormal data, such as erroneous readings caused by sensor failure or communication interruption, and to interpolate and repair missing values; calibration is to correct dust concentration values ​​based on known environmental compensation models to reduce interference from environmental factors such as temperature, humidity, and pressure; time synchronization ensures that data from multiple sensors are aligned within the same time frame for subsequent analysis. After data preprocessing is completed, the platform generates a dust concentration distribution map through spatial modeling. First, a spatial distribution model of dust concentration is established based on the three-dimensional coordinate positions of sensor nodes and the collected concentration values. Through spatial interpolation algorithms (such as Kriging interpolation or inverse distance weighted IDW), the concentration of blank areas between sensors is estimated to fill the missing points of spatial data. Subsequently, the platform uses visualization tools to map the concentration data into a two-dimensional or three-dimensional distribution map, and the color gradient represents the dust concentration level in different areas. For example, high-concentration areas are displayed in red, and low-concentration areas are displayed in blue or green, which makes it easier for mine managers to quickly identify key areas of dust distribution. In order to achieve real-time performance, the platform uses a stream processing framework to continuously update sensor data so that the distribution map can dynamically reflect the dust diffusion situation. In terms of dust concentration trend prediction, the platform predicts data through time series analysis and machine learning models. The platform extracts historical dust concentration data from the database, extracts characteristic variables in combination with other environmental factors (such as ventilation status, equipment operation, temperature and humidity), and uses these variables to train time series models, such as ARIMA models, long short-term memory networks (LSTM) or Prophet frameworks, to predict the dust concentration trend in various regions in the future. The prediction results are presented as trend curves or concentration distribution maps at future times. The platform further combines the predicted data with safety thresholds to conduct risk assessments on areas that may reach high concentrations in the future, and highlights them in the distribution map.For areas predicted to exceed the standard, the system can generate alarm information to prompt managers to take preventive measures.

[0111] In terms of energy management, the sensor node not only relies on the collection of environmental energy, but also has a redundant energy storage unit, which includes a super capacitor and a high-efficiency lithium battery, where the connection method of the super capacitor and the lithium battery is parallel. Through super capacitors and high-efficiency lithium batteries, the system can store excess electrical energy when the energy supply is sufficient to cope with emergency needs when energy collection is insufficient. The energy management chip built into the system can intelligently allocate energy according to task requirements and energy supply conditions. The chip optimizes energy scheduling through a multi-source energy integration mechanism, giving priority to the immediate output energy from piezoelectric, turbine, and thermoelectric battery, reducing dependence on redundant energy storage units. For example, when piezoelectric materials provide instantaneous high energy, it can be directly used for the current sampling task, while the stable output energy of the turbine is used to support the communication module. At the same time, when the energy supply is surplus, the chip dynamically adjusts the charging strategy, giving priority to charging the super capacitor to quickly absorb energy, and then providing long-term energy reserves for the lithium-ion battery.

[0112] 1. Immediate energy needs are met

[0113] Based on the energy requirements of sampling tasks and communication, immediate energy is used first:

[0114] Ep-usage(t)=min(Ep(t),Ec(t)+Ecom(t))

[0115] Et-usage(t)=min(Et(t),Ecom(t)-Et-usage(t-1))

[0116] Among them, Ep-usage(t) represents the energy of piezoelectric energy allocated to the current sampling task and communication module at time t;

[0117] Ep(t) represents the instantaneous output energy from the piezoelectric material at time t;

[0118] Ec(t) represents the energy requirement of the chip's current sampling task at time t;

[0119] Ecom(t) represents the energy requirement of the communication module at time t;

[0120] Et-usage(t) represents the energy of turbine energy allocated to the communication module at time t;

[0121] Et(t) represents the steady output energy from the turbine at time t;

[0122] Et-usage(t-1) represents the energy that has been allocated to the turbine at the previous moment, which prevents repeated calculation of turbine energy;

[0123] 2. Calculate the remaining excess energy

[0124] When the piezoelectric and turbine cannot meet all the demands, the remaining energy is calculated for use by the redundant energy storage unit:

[0125] Eexcess(t)=Ep(t)+Et(t)+Ed(t)-(Ec(t)+Ecom(t))

[0126] Where, Eexcess(t) represents the remaining energy;

[0127] Ed(t) represents the output energy from the thermoelectric cell at time t;

[0128] 3. Energy allocation strategy (dynamic adjustment)

[0129] (a) Supercapacitor charging priority:

[0130] When Eexcess(t)>0, first charge the supercapacitor:

[0131] Ecap-charging(t)=min(η cap ·Eexcess(t),Ecap-max-Ecap(t))

[0132] Where, Ecap-charging(t) represents the energy of supercapacitor charging;

[0133] η cap Indicates the charging efficiency of the supercapacitor;

[0134] Ecap-max is the maximum energy storage capacity of the supercapacitor;

[0135] Ecap(t) represents the current energy storage of the supercapacitor;

[0136] (b) Lithium battery charging:

[0137] The remaining energy is used to charge the lithium-ion battery:

[0138] Eli-charging(t)=max(0,η li (Eexcess(t)-Ecap-charging(t))

[0139] Among them, Eli-charging (t) represents the energy of lithium battery charging;

[0140] η li Indicates the charging efficiency of lithium-ion batteries;

[0141] 4. Dynamically adjust energy scheduling

[0142] If there is excess energy in the system (i.e., Eexcess(t)>0), the following dynamic strategy can be adopted:

[0143] High-priority tasks (such as sampling tasks) will first consume immediate energy (Ep(t)) to reduce dependence on redundant energy storage units.

[0144] The communication module uses the steady energy output of the turbine (Et(t)).

[0145] When mission and communication demands are low, the surplus energy is immediately used to charge redundant energy storage units, especially supercapacitors (which have the characteristic of fast charging).

[0146] When Eexcess(t) is large, the supercapacitor is charged first, and if the supercapacitor is full, the lithium battery is charged. This strategy can improve the energy response speed and reduce the dependence on traditional energy storage systems.

[0147] 5. Redundant energy storage unit charging update

[0148] Update the energy status of supercapacitor and lithium battery:

[0149] Ecap(t+1)=Ecap(t)+Ecap-charging(t)

[0150] Eli(t+1)=Eli(t)+Eli-charging(t)

[0151] Where Eli(t) represents the current energy storage of the lithium-ion battery;

[0152] 6. Optimize the objective function

[0153] The goal is to maximize the efficiency of instantaneous energy use while minimizing reliance on redundant energy storage units. A comprehensive objective function can be set to measure system performance:

[0154] Efficiency(t)=(Ep-usage(t)+Et-usage(t)) / (Ec(t)+Ecom(t))

[0155] Among them, Ep-usage(t) represents the energy of piezoelectric energy allocated to the current task and communication module at time t;

[0156] Et-usage(t) represents the energy of turbine energy allocated to the communication module at time t;

[0157] Ec(t) represents the energy requirement of the chip's current sampling task at time t;

[0158] Ecom(t) represents the energy requirement of the communication module at time t;

[0159] This efficiency function is used to evaluate the efficiency of instantaneous energy use. When the value is close to 1, it means that the instantaneous energy is maximized and the dependence on redundant energy storage units is reduced.

[0160] In addition, the chip uses a predictive adjustment algorithm to predict future energy supply and demand based on historical data and task plans. For example, when the sampling peak of the dust concentration sensor is detected, the chip reserves the energy required for key tasks in advance; if it is predicted that the energy supply may be insufficient in the future, the chip will actively reduce the operating intensity of non-essential tasks to extend the operating time of the equipment, ensuring that key tasks such as dust monitoring and data transmission are given priority in energy support.

[0161] In addition, the sensor node also has a self-diagnosis function, which can detect its own working status, power level and communication status in real time. When an abnormality occurs, the node will automatically send a fault report to the central monitoring platform and try to recover itself. If the problem cannot be solved, the system will remind the maintenance personnel to intervene manually. At the same time, the central monitoring platform has a remote control function, which can arrange maintenance plans according to the status of the sensor node, and support remote upgrades of the sensor firmware to ensure that the equipment always maintains the best working condition.

[0162] Furthermore, if Figure 2 As shown in the figure, the multi-band switching module and the automatic routing module are integrated with the STM32 microcontroller. For the multi-band switching module, SPI is used to efficiently control and read the data and status of the multi-band switching module; 2 C can send control commands through the low-speed interface, such as switching frequency bands or adjusting communication parameters; UART is used to send debugging information and switching status, suitable for simple configuration communication; GPIO is used to send switching signals or other hardware control signals, such as triggering switching to a specified frequency band. For the automatic routing module, SPI (Serial Peripheral Interface) is used for high-speed data communication, suitable for data transmission control between the automatic routing module and STM32; I 2 C (Integrated Circuit Interface) is a low-speed communication protocol suitable for small-scale data transmission and is the control interface for connecting routing modules; UART (Universal Asynchronous Receiver / Transmitter) is often used for debugging and simple serial communication, and can be used for configuration and status reporting of routing modules; GPIO (General Purpose Input Output) is used to trigger simple control signals, such as routing switching or mode switching.

[0163] A step further is that Figure 3The Mesh network structure diagram shown in the figure is composed of many mesh routers to form a basic Mesh network. Finally, all information is gathered at the gateway, and the gateway transmits it to the computer. The Mesh network structure increases the information transmission link and can independently select relatively uncongested transmission links to ensure efficient transmission of information.

[0164] Although the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the claims and their equivalents.

Claims

1. A low-power self-repair and energy self-supply mine dust monitoring sensor network system, including multiple sensor nodes and a central monitoring platform, the data of all sensor nodes are transmitted to the central monitoring platform in real time; the sensor network synchronizes a number of environmental parameters, and performs data analysis, processing and visualization on the central monitoring platform; it is characterized in that: Each sensor node has multiple sensors integrated inside, and the sensors are used to monitor different environmental parameters in the mine environment; the types of sensors include dust concentration sensors and temperature and humidity sensors, and the environmental parameters include dust concentration, temperature and humidity; The sensor node includes a multi-source energy collection module and a low power consumption management module; The multi-source energy harvesting module integrates a piezoelectric energy harvesting device, a micro airflow turbine and a temperature difference power generation unit, which is used to collect energy from the mechanical vibration, airflow kinetic energy and temperature difference in the mine environment and convert it into electrical energy to achieve self-powering of the sensor. The low power management module is used to dynamically adjust the working mode of the sensor according to the sensor power and environmental conditions to minimize power consumption; the working mode includes standby and monitoring modes. When the environmental parameters remain within the preset fluctuation value range during the set time period, the low power standby mode is entered; when the environmental parameters exceed the set threshold during the set time period, the monitoring mode is automatically activated.

2. The low-power self-repairing and energy self-supply mine dust monitoring sensor network system according to claim 1 is characterized in that: The data of all sensor nodes are transmitted to the central monitoring platform in real time, which is realized by an integrated module, and the sensor nodes transmit the data to the WiMAX base station through the integrated module; the integrated module includes: a wireless communication module, a multi-band switching module and a controller; wherein the wireless communication module is connected to the controller through SPI communication, and the multi-band switching module is connected to the controller through one of SPI, I2C, UART, GPIO or any combination of communication methods; The multi-band switching module is used to switch the frequency range of the electromagnetic wave according to the position of the sensor, so as to realize the switching between the low frequency band and the high frequency band.

3. The low-power self-repairing and energy self-supply mine dust monitoring sensor network system according to claim 2 is characterized in that: The integrated module also includes an automatic routing module, which is connected to the controller via one or any combination of communication modes including SPI, I2C, UART, and GPIO; The automatic routing module is used to automatically adjust the data transmission path according to the position change of the sensor and the change of the network topology structure; The sensor network is a self-repairing network, and the sensor nodes have a distributed self-organizing network function. Once a sensor node fails or communication is interrupted, the adjacent sensor nodes automatically adjust the data transmission path through a dynamic routing algorithm and automatically rebuild the communication path.

4. The low-power self-repairing and energy self-supply mine dust monitoring sensor network system according to claim 1 is characterized in that: The types of sensors also include gas detection sensors, and the environmental parameters also include air flow velocity.

5. The low-power self-repairing and energy self-supply mine dust monitoring sensor network system according to claim 1 is characterized in that: The sensor node has a temperature and humidity compensation function, which can dynamically adjust the dust concentration monitoring results according to the temperature and humidity changes in the environment to ensure the monitoring accuracy under different climatic conditions: S1, the system collects ambient temperature and humidity data in real time through the temperature and humidity sensors in the sensor nodes, and the dust concentration sensor measures the particle concentration; the collected ambient temperature and humidity data are processed by the controller to filter out noise or abnormal data; S2, after the collected data is initially calibrated, it enters the compensation calculation stage; the dust concentration value needs to be compensated by the following formula: Ccorrected=Cmeasured×(1-K RH ·RH)×(1+K T ·(T-T ref ))×(1+K dust ·P dust ) Among them, Ccorrected represents the dust concentration value after compensation; Cmeasured represents the original dust concentration measurement value, that is, the dust concentration value measured by the dust concentration sensor; K RH and K T Respectively represent humidity and temperature compensation coefficients; RH is the real-time humidity in the coal mine; T is the real-time temperature in the coal mine; T ref is the reference temperature, which is usually the room temperature when the instrument is calibrated; K T is the temperature compensation coefficient, which reflects the effect of temperature on the measurement system or particle scattering, usually 0.01-0.05; P dust is the proportion of coal dust in mine dust; K dust It is the dust type compensation coefficient, which reflects the different types of dust in the mine.

6. The low-power self-repairing and energy self-supply mine dust monitoring sensor network system according to claim 5 is characterized in that: Humidity compensation coefficient K RH , Temperature compensation coefficient K T The calibration includes the following steps: (1) Determine the benchmark conditions Reference dust concentration: Use a reference concentration measuring device to measure the dust concentration under reference humidity / temperature conditions and record it as C true ; (2) Changing humidity / temperature conditions Adjust the humidity / temperature step by step to multiple values ​​within the target range, keep each humidity / temperature point stable, and record the actual humidity / temperature value as RH i ; (3) Measuring concentration changes At each humidity / temperature condition, the dust concentration was measured using a dust monitoring device and recorded as C measured ,i; (4) Calculate humidity / temperature correction factors; For each humidity / temperature value, calculate the concentration error ratio: ΔC i =(C measured ,i-C true ) / C true Where, ΔC i is the relative value of the measurement error; (5) Fitting humidity / temperature correction model; Establish a linear or nonlinear relationship between humidity / temperature and concentration error ratio: ΔC i =K RH ·RH i ΔC i =K T ·RH i Linear regression was performed on the experimental data to fit the humidity compensation coefficient K RH And the temperature compensation coefficient K T .

7. The low-power self-repairing and energy self-supply mine dust monitoring sensor network system according to claim 1 is characterized in that: The multi-source energy harvesting module provides energy for the sensor nodes and stores the excess energy in the redundant energy storage unit; The redundant energy storage unit includes a super capacitor and a high-efficiency lithium battery connected in parallel; The multi-source energy collection module provides energy for the sensor node, including: The energy provided by the piezoelectric energy harvesting device is used for the sampling task of the current sensor and the wireless communication module; The stable output energy of the micro airflow turbine is used for the wireless communication module; The energy of the thermoelectric battery is used for redundant energy storage units; S100, determining whether the energy provided by the piezoelectric energy harvesting device meets the energy required for the current sampling task, if so, jumping to S200; if not, calling the energy of the redundant energy storage unit for the current sampling task; S200, determining whether the stable output energy of the micro airflow turbine meets the energy required by the current wireless communication module. If so, no other operations are required, and the excess energy is stored in a redundant energy storage unit; if not, executing the next step; S300, determine whether the excess energy of the piezoelectric energy collection device meets the excess energy required by the wireless communication module. If so, the energy provided by the piezoelectric energy collection device is used for the wireless communication module, and the excess energy is stored in a redundant energy storage unit; if not, the energy of the redundant energy storage unit is called to be used for the wireless communication module; the excess energy of the piezoelectric energy collection device is the difference between the energy provided by the piezoelectric energy collection device and the energy required for the current sampling task, and the excess energy required for the wireless communication module is the difference between the stable output energy of the micro airflow turbine and the energy required by the current wireless communication module.

8. The low-power self-repairing and energy self-supply mine dust monitoring sensor network system according to claim 7 is characterized in that: Steps S100 to S300 also include: (1) According to the energy requirements of the sampling task and communication, the immediate energy is used first: Ep-usage(t)=min(Ep(t),Ec(t)+Ecom(t)) Et-usage(t)=min(Et(t),Ecom(t)-Et-usage(t-1)) Among them, Ep-usage(t) represents the energy of piezoelectric energy allocated to the current sampling task and communication module at time t; Ep(t) represents the instantaneous output energy from the piezoelectric material at time t; Ec(t) represents the energy requirement of the chip’s current sampling task at time t; Ecom(t) represents the energy requirement of the communication module at time t; Et-usage(t) represents the energy of turbine energy allocated to the communication module at time t; Et(t) represents the steady output energy from the turbine at time t; Et-usage(t-1) represents the energy that has been allocated to the turbine at the previous moment, which prevents repeated calculation of turbine energy; (2) Calculate the remaining excess energy: When the piezoelectric and turbine cannot meet all the needs, calculate the remaining energy for use by the redundant energy storage unit: Eexcess(t)=Ep(t)+Et(t)+Ed(t)-(Ec(t)+Ecom(t)) Where, Eexcess(t) represents the remaining energy; Ed(t) represents the output energy from the thermoelectric cell at time t; (3) Energy allocation strategy when charging redundant energy storage units When Eexcess(t)>0, first charge the supercapacitor: Ecap-charging(t)=min(η cap ·Eexcess(t),Ecap-max-Ecap(t)) Where, Ecap-charging(t) represents the energy of supercapacitor charging; η cap Indicates the charging efficiency of the supercapacitor; Ecap-max is the maximum energy storage capacity of the supercapacitor; Ecap(t) represents the current energy storage of the supercapacitor; The remaining energy is then used to charge the lithium-ion battery: Eli-charging(t)=max(0,η li (Eexcess(t)-Ecap-charging(t)) Among them, Eli-charging (t) represents the energy of lithium battery charging; η li Indicates the charging efficiency of lithium-ion batteries; (4) Redundant energy storage unit charging and updating, updating the energy status of supercapacitors and lithium batteries: Ecap(t+1)=Ecap(t)+Ecap-charging(t) Eli(t+1)=Eli(t)+Eli-charging(t) Where Eli(t) represents the current energy storage capacity of the lithium-ion battery.

9. The low-power self-repairing and energy self-supply mine dust monitoring sensor network system according to claim 7 or 8, characterized in that: First, the supercapacitor of the redundant energy storage unit is charged, and when the supercapacitor is full, the lithium-ion battery is charged.

10. The low-power self-repairing and energy self-supply mine dust monitoring sensor network system according to claim 7, characterized in that: The goal of steps S100 to S300 is to maximize the efficiency of instantaneous energy use while minimizing the reliance on redundant energy storage units. The objective function is: Efficiency(t)=(Ep-usage(t)+Et-usage(t)) / (Ec(t)+Ecom(t)) Among them, Ep-usage(t) represents the energy of piezoelectric energy allocated to the current task and communication module at time t; Et-usage(t) represents the energy of turbine energy allocated to the communication module at time t; Ec(t) represents the energy requirement of the chip’s current sampling task at time t; Ecom(t) represents the energy requirement of the communication module at time t.

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