Lane Safety Monitoring Method and System Based on Multi-Sensor Fusion

Abstract

The present application provides a tunnel safety monitoring method and system based on multi-sensor fusion, which relates to the field of mine safety monitoring technology. The method and system obtain an integrated monitoring module by integrating multiple sensors on a circuit board, wherein the multiple sensors include an ultrasonic sensor, a gyroscope sensor, and a buzzer module; the integrated monitoring module receives the tunnel distance monitoring signal transmitted by the ultrasonic sensor, and the tunnel attitude monitoring signal transmitted by the gyroscope sensor; the tunnel distance monitoring signal and the tunnel attitude monitoring signal are input into a tunnel safety risk identification model to perform signal anomaly analysis and output a tunnel safety index; the tunnel safety index is input into the buzzer module to match the buzzer warning signal, and a tunnel safety warning is performed based on the buzzer warning signal. The present application solves the technical problem that a single sensor monitoring method has insufficient monitoring accuracy due to a single monitoring index and susceptibility to environmental influences, thereby achieving the technical effect of improving the accuracy and reliability of tunnel safety monitoring.

Description

Technical Field

[0001] The present application relates to the technical field of mine safety monitoring, and in particular to a tunnel safety monitoring method and system based on multi-sensor fusion. Background Art

[0002] In underground engineering projects such as mines and tunnels, roadway safety monitoring is crucial for ensuring the safety of workers and the stable operation of the project. Traditional roadway safety monitoring methods rely on manual inspections and fixed-point monitoring. For example, these methods use laser rangefinders, theodolites, and other equipment for regular measurements to assess the roadway's structural stability and potential risks. However, these methods have significant limitations. Manual inspections are inefficient and difficult to achieve continuous monitoring. Fixed-point monitoring has limited coverage, cannot fully reflect the dynamic changes in the roadway, and is significantly affected by environmental factors.

[0003] In recent years, with the rapid development of sensor technology and the Internet of Things (IoT), sensor-based tunnel safety monitoring methods have become a hot topic in research and application. Examples include mine tunnel deformation monitoring systems based on ultrasonic ranging and gyroscope sensors that acquire tunnel tilt and deformation information. These methods utilize sensor networks integrated within tunnels to enable real-time collection and remote monitoring of tunnel environmental parameters, continuously monitoring tunnel structural changes. However, single sensor monitoring methods, due to their limited monitoring indicators, are unable to fully reflect the tunnel's deformation status and are susceptible to the complex tunnel environment, resulting in insufficient monitoring accuracy. Summary of the Invention

[0004] This application provides a tunnel safety monitoring method and system based on multi-sensor fusion, which solves the technical problem that a single sensor monitoring method has insufficient monitoring accuracy due to a single monitoring indicator and is easily affected by the complex tunnel environment, and achieves the technical effect of improving the accuracy and reliability of tunnel safety monitoring.

[0005] In view of the above problems, on the one hand, the present application provides a tunnel safety monitoring method based on multi-sensor fusion, which includes: obtaining an integrated monitoring module, which is obtained by integrating multiple sensors on a circuit board, wherein the multiple sensors include an ultrasonic sensor, a gyroscope sensor and a buzzer module; the integrated monitoring module receives the tunnel distance monitoring signal transmitted by the ultrasonic sensor, and the tunnel attitude monitoring signal transmitted by the gyroscope sensor; the tunnel distance monitoring signal and the tunnel attitude monitoring signal are input into a tunnel safety risk identification model to perform signal anomaly analysis and output a tunnel safety index; the tunnel safety index is input into the buzzer module, the buzzer module matches the buzzer warning signal according to the tunnel safety index, and performs a tunnel safety warning based on the buzzer warning signal.

[0006] On the other hand, the present application also provides a tunnel safety monitoring system based on multi-sensor fusion, the system including: an integrated monitoring unit, the integrated monitoring unit is used to obtain an integrated monitoring module, the integrated monitoring module is obtained by integrating multiple sensors on a circuit board, wherein the multiple sensors include an ultrasonic sensor, a gyroscope sensor and a buzzer module; a signal receiving unit, the signal receiving unit is used to receive the tunnel distance monitoring signal transmitted by the ultrasonic sensor and the tunnel attitude monitoring signal transmitted by the gyroscope sensor through the integrated monitoring module; an abnormality analysis unit, the abnormality analysis unit is used to input the tunnel distance monitoring signal and the tunnel attitude monitoring signal into a tunnel safety risk identification model to perform signal abnormality analysis and output a tunnel safety index; a safety warning unit, the safety warning unit is used to input the tunnel safety index into the buzzer module, the buzzer module matches the buzzer warning signal according to the tunnel safety index, and performs a tunnel safety warning based on the buzzer warning signal.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0008] An integrated monitoring module is obtained, wherein the integrated monitoring module is obtained by integrating multiple sensors on a circuit board, wherein the multiple sensors include an ultrasonic sensor, a gyroscope sensor, and a buzzer module; the integrated monitoring module receives a tunnel distance monitoring signal transmitted by the ultrasonic sensor, and a tunnel attitude monitoring signal transmitted by the gyroscope sensor; the tunnel distance monitoring signal and the tunnel attitude monitoring signal are input into a tunnel safety risk identification model to perform signal anomaly analysis, and a tunnel safety index is output; the tunnel safety index is input into the buzzer module, the buzzer module matches a buzzer warning signal according to the tunnel safety index, and performs a tunnel safety warning based on the buzzer warning signal.

[0009] In summary, the integrated monitoring module of this application integrates multiple sensors such as ultrasonic sensors, gyroscope sensors, and buzzer warning modules on the same platform, realizing the comprehensive capture of multi-dimensional data such as tunnel distance and posture. This integrated design not only improves the efficiency of data acquisition, but also enhances the accuracy and reliability of monitoring results through data fusion, effectively avoiding the monitoring blind spots and errors that may be caused by local failures or environmental factors of a single sensor. In addition, this application also introduces a complex tunnel safety risk identification model to conduct in-depth analysis of the signals transmitted by the sensors and identify abnormal patterns, thereby accurately assessing the safety status of the tunnel, and matching the warning signals according to the safety indicators through the buzzer module, realizing instant feedback on the safety status of the tunnel, greatly improving the efficiency and practicality of tunnel safety monitoring, and providing strong guarantees for mine safety production.

[0010] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 A schematic diagram of a process flow of a tunnel safety monitoring method based on multi-sensor fusion provided in an embodiment of the present application;

[0012] Figure 2 A schematic diagram of a flow chart for performing signal anomaly analysis in a tunnel safety monitoring method based on multi-sensor fusion provided in an embodiment of the present application;

[0013] Figure 3 A schematic diagram of the process of matching buzzer warning signals in the tunnel safety monitoring method based on multi-sensor fusion provided in an embodiment of the present application;

[0014] Figure 4 A schematic diagram of the structure of a tunnel safety monitoring system based on multi-sensor fusion provided in an embodiment of the present application.

[0015] Explanation of the reference numerals: integrated monitoring unit 10 , signal receiving unit 20 , abnormality analysis unit 30 , safety warning unit 40 . DETAILED DESCRIPTION

[0016] The embodiments of the present application provide a tunnel safety monitoring method and system based on multi-sensor fusion, which solves the technical problem that a single sensor monitoring method has insufficient monitoring accuracy due to a single monitoring indicator and susceptibility to the complex tunnel environment, thereby achieving the technical effect of improving the accuracy and reliability of tunnel safety monitoring.

[0017] Example 1, as Figure 1 As shown, the embodiment of the present application provides a roadway safety monitoring method based on multi-sensor fusion, the method comprising:

[0018] Step S1: obtaining an integrated monitoring module, wherein the integrated monitoring module is obtained by integrating multiple sensors on a circuit board, wherein the multiple sensors include an ultrasonic sensor, a gyroscope sensor, and a buzzer module.

[0019] Specifically, the integrated monitoring module is a hardware device that integrates multiple sensors and processing units, and is used to collect, process and feedback tunnel environment monitoring data. Among them, the multiple sensors in the integrated monitoring module include ultrasonic sensors, gyroscope sensors and buzzer modules. The ultrasonic sensor calculates the distance by emitting and receiving ultrasonic pulses and measuring the time it takes for the echo to return. In tunnel monitoring, it can detect the proximity to the tunnel wall and help assess the stability of the tunnel. The gyroscope sensor is used to measure and maintain direction and can sense changes in rotation and tilt angles, thereby monitoring slight displacements or tilts in the tunnel. The buzzer module is a component that can generate sound signals. It is used to issue an alarm when an abnormal situation is detected, reminding on-site personnel to pay attention to safety.

[0020] First, the overall architecture of the integrated monitoring module is determined, including the type and number of sensors, the configuration of the signal processing unit, and the power management solution. Next, based on the monitoring environment and accuracy requirements, the appropriate sensor model is selected. Multiple sensors and the processing unit are assembled on a circuit board to create the integrated monitoring module. After assembly, the entire integrated monitoring module is calibrated and tested. Calibration ensures the accuracy of the sensor output, while testing verifies the module's performance in a real-world environment.

[0021] For example, based on the tunnel environment and actual monitoring accuracy requirements, an integrated monitoring module was designed. This module consists of four ultrasonic sensors, one for each direction, a high-precision gyroscope sensor, a buzzer module, and a processing unit. The HC-SR04 ultrasonic sensor was selected for its high measurement accuracy and stability. The MPU-6050 module, a three-axis acceleration + three-axis gyroscope, was selected as the gyroscope sensor. It integrates a gyroscope and accelerometer to provide attitude information, and the buzzer module uses an active buzzer as an alarm device. According to the designed circuit board layout, the various components were assembled onto the STM32 microcontroller core board to form an integrated monitoring module. The entire integrated monitoring module was then calibrated and tested, including testing the ultrasonic sensor's measurement accuracy and the gyroscope's attitude detection capabilities in a simulated tunnel environment. The result is an integrated monitoring module capable of real-time monitoring of tunnel environmental changes, providing a reliable data acquisition technology for identifying tunnel safety risks.

[0022] Step S2: The integrated monitoring module receives the lane distance monitoring signal transmitted by the ultrasonic sensor and the lane attitude monitoring signal transmitted by the gyroscope sensor.

[0023] Specifically, the tunnel distance monitoring signal is an electrical signal emitted by the ultrasonic sensor, reflecting the tunnel distance information. The tunnel attitude monitoring signal is an electrical signal emitted by the gyroscope sensor, reflecting the tunnel tilt or rotation state.

[0024] The signal processor in the integrated monitoring module receives distance monitoring signals from the ultrasonic sensor and attitude monitoring signals from the gyroscope. These signals are typically transmitted in analog or digital form and read through corresponding interfaces. The ultrasonic sensor measures the distance in the roadway by transmitting and receiving ultrasonic pulses, converting this information into electrical signals and transmitting them to the integrated monitoring module. Similarly, information about the roadway's tilt and rotation detected by the gyroscope is converted into electrical signals and transmitted to the integrated monitoring module. The integrated monitoring module processes the received signals, including filtering and data conversion, to provide accurate data support for subsequent safety assessments and early warnings.

[0025] For example, when the HC-SR04 ultrasonic sensor emits an ultrasonic pulse and receives an echo, it sends a signal through the TRIG and ECHO pins. The integrated monitoring module reads the duration of the high level on the ECHO pin to calculate the ultrasonic round-trip time and, therefore, the distance. The MPU-6050 communicates with the integrated monitoring module's microcontroller via the I2C interface, transmitting raw data from the gyroscope and accelerometer. The integrated monitoring module filters and transforms this data to obtain precise rotation angle and acceleration information.

[0026] Step S3: Inputting the lane distance monitoring signal and the lane posture monitoring signal into a lane safety risk identification model to perform signal anomaly analysis and output a lane safety index.

[0027] Specifically, the tunnel safety risk identification model is an algorithm or software model used to analyze sensor data and identify potential safety risks. The tunnel safety index is a quantitative indicator that reflects the safety status of the tunnel.

[0028] The tunnel distance monitoring signals and tunnel posture monitoring signals are input into the tunnel safety risk identification model, which uses statistical analysis, time series analysis, or machine learning algorithms to detect anomalies in the signals. This process involves comparing the current signal with the signal under normal conditions to identify any abnormal patterns or trends. For example, if the distance detected by the ultrasonic sensor suddenly decreases, it may indicate that the tunnel has collapsed. By analyzing this data, the model will output a tunnel safety indicator, which can be a stability score or risk level of the tunnel. Through the above process, the tunnel safety risk identification model can accurately assess the safety status of the tunnel based on the signal data of the integrated monitoring module through signal anomaly analysis.

[0029] Step S4: inputting the lane safety index into the buzzer module, the buzzer module matches the buzzer warning signal according to the lane safety index, and performs a lane safety warning based on the buzzer warning signal.

[0030] Specifically, the buzzer warning signal refers to a specific sound signal emitted by the buzzer module, which is used to indicate different warning levels. First, safety indicator thresholds must be set based on the characteristics of the tunnel and historical data. When the monitored indicators exceed or approach these thresholds, the buzzer module is activated and issues a warning signal. The tunnel safety indicators output by the tunnel safety risk identification model are transmitted to the buzzer module. Based on the values of the tunnel safety indicators, the buzzer module uses an internal signal matching algorithm to select the corresponding signal command. Upon receiving the matching signal command, the buzzer module outputs the corresponding buzzer warning signal through its built-in speaker or buzzer. The frequency and intensity of the signal are typically correlated with the severity of the safety indicator, ensuring the alert is targeted and effective. Operators can quickly identify the warning level based on sound characteristics such as frequency, intensity, and rhythm, and take appropriate risk avoidance measures. For example, if the safety indicators indicate a high risk in the tunnel, the buzzer module may emit a series of rapid beeps to signal an emergency evacuation.

[0031] This early warning mechanism enables the tunnel safety monitoring system to immediately notify on-site personnel when potential danger is detected, so that necessary preventive measures or emergency evacuation can be taken to ensure the safety of personnel.

[0032] Further, such as Figure 2 As shown, step S3 of the embodiment of the present application also includes:

[0033] Step S31: extracting abnormal features from the lane distance monitoring signal and the lane posture monitoring signal to obtain abnormal distance features and abnormal posture features.

[0034] Step S32: The lane safety risk identification model includes a distance safety threshold and a posture safety threshold.

[0035] Step S33: the tunnel safety risk identification model compares the distance abnormality feature and the posture abnormality feature with the distance safety threshold and the posture safety threshold respectively, and outputs a distance safety index and a posture safety index.

[0036] Step S34: Calculate the distance safety index and the posture safety index to output a lane safety index.

[0037] Specifically, distance anomaly features are abnormal patterns identified in the tunnel distance monitoring signal, such as sudden distance changes or measurement values that exceed the range of historical data. Posture anomaly features are abnormal patterns detected in the tunnel posture monitoring signal, such as abnormal tilt angles or rotation rates. The distance safety threshold is set based on the structure and operating conditions of the tunnel and is a reference value used to determine whether the distance monitoring signal is normal. The posture safety threshold is also set based on the characteristics of the tunnel and is a reference value used to evaluate whether the posture monitoring signal is within a safe range. The distance safety index and posture safety index are quantitative indicators obtained by comparing the distance anomaly features and posture anomaly features with the safety thresholds, respectively, reflecting the safety status of the tunnel in the two dimensions of distance and posture.

[0038] First, the model analyzes the roadway distance and posture monitoring signals, identifying abnormal features using statistical methods such as mean and standard deviation calculations, time series analysis, or machine learning techniques such as anomaly detection algorithms. For example, if the monitored distance suddenly increases or decreases beyond a set threshold, or if the tilt angle in the posture signal exceeds the normal range, these may be flagged as abnormal features. Based on the roadway's structural characteristics, historical data, and engineering experience, distance and posture safety thresholds are set. These thresholds are used to determine the normal range of the monitoring signals; exceeding the thresholds may indicate a safety risk.

[0039] The model then compares the extracted distance and posture anomaly features with the set safety threshold. If the anomaly exceeds the safety threshold, the model outputs a corresponding distance safety index, indicating a safety risk in that dimension. For example, if the distance anomaly exceeds the distance safety threshold, the output distance safety index may be high-risk; similarly, if the posture anomaly exceeds the posture safety threshold, the output posture safety index will also be high-risk. Based on the distance safety index and posture safety index, the model performs further calculations and analysis, integrating the safety information from these two dimensions through methods such as weighted calculations, and outputs the final roadway safety index, which comprehensively reflects the current safety status of the roadway.

[0040] Through the above steps, the tunnel safety risk identification model can carefully analyze the monitoring signals, identify potential abnormal characteristics, and output specific safety indicators by comparing them with safety thresholds, providing a scientific basis for tunnel safety management.

[0041] Furthermore, in step S2 of the embodiment of the present application, outputting the lane distance monitoring signal includes:

[0042] The integrated monitoring module receives the transmitted ultrasonic signal and the reflected ultrasonic signal of the ultrasonic sensor through an encrypted transmission channel; calculates the time interval based on the transmitted ultrasonic signal and the reflected ultrasonic signal, calculates the tunnel distance based on the measured time interval, and outputs the tunnel distance monitoring signal.

[0043] Specifically, an encrypted transmission channel is a secure communication method that encrypts data using an encryption algorithm, ensuring the security and integrity of data during transmission. The ultrasonic sensor first transmits an ultrasonic signal, which is reflected back to the sensor after hitting the tunnel wall or obstacle. The integrated monitoring module receives this signal via the encrypted transmission channel, ensuring the security of the signal during transmission. After receiving the transmitted and reflected ultrasonic signals, the integrated monitoring module calculates the time difference between the two, known as the time interval. This is typically performed by an internal microcontroller or signal processor, ensuring accurate and real-time calculations. Based on the calculated time interval, the integrated monitoring module uses the speed of ultrasonic waves in air to calculate the distance between the tunnel wall and the sensor. Finally, the integrated monitoring module outputs the calculated tunnel distance as a monitoring signal, which is used to monitor the tunnel's structural stability and potential safety risks in real time. For example, the ultrasonic sensor automatically transmits eight 40 kHz square waves and monitors for any return signals. If a return signal is detected, a high level is output via the I / O pin. The duration of the high level is the time from the ultrasonic wave's transmission to its return. The measured distance = (high level duration × speed of sound (340 m / s)) / 2.

[0044] Furthermore, the integrated monitoring module described in the embodiment of the present application includes an OLED module and a storage module. The OLED module is used to display the signal measured by the integrated monitoring module on the OLED screen, and the storage module is used to back up and store the signal measured by the integrated monitoring module.

[0045] Specifically, the OLED module (organic light-emitting diode) is used to display information visually. The OLED screen, integrated into the OLED module, is used to display information. It can display text, numbers, graphics, and other information, providing real-time feedback on monitoring data. The storage module is the data storage unit within the integrated monitoring module, storing monitored signal data for subsequent analysis, backtracking, or long-term storage. The storage module can be a variety of storage media, including flash memory, SD cards, and hard drives.

[0046] In the integrated monitoring module described in the embodiment of the present application, an OLED module and a storage module can also be integrated, and these two components each assume important functions. The function of the OLED module is to display the signals measured by the integrated monitoring module, such as the lane distance monitoring signal and the lane attitude monitoring signal, in the form of graphics or text on the OLED screen. The OLED screen can also display alarm information and other content. In this way, on-site personnel can read real-time monitoring data directly through the screen without going through other equipment or software, providing real-time monitoring feedback for on-site operators, so that they can respond quickly based on the displayed data. For example, when the integrated monitoring module monitors the safety of the lane, the OLED screen displays distance data in real time, such as "Distance: 10.2 meters", and attitude data "Tilt angle: 0.5°". If an abnormality is detected, such as a sudden change in distance, the screen will display a warning message, such as "Warning: Distance abnormality, please check lane stability".

[0047] The storage module is responsible for backing up and storing the signals measured by the integrated monitoring module. This means that all collected data, including raw data and processed data, as well as possible alarm logs and event records, will be saved for subsequent analysis or troubleshooting. Data storage can be real-time or periodic, depending on system settings and requirements. Storage modules are usually non-volatile and can keep data from being lost even in the event of a power outage. Storage modules can be of various types, such as SD cards, NAND flash memories, etc., which provide sufficient capacity for long-term data storage and ensure data integrity and traceability. For example, the integrated monitoring module can be equipped with a 32GB MicroSD card for long-term storage of monitoring data.

[0048] By integrating the OLED module and the storage module, the integrated monitoring module can not only display monitoring data in real time and provide direct feedback for on-site operations, but also store data safely and reliably, ensuring the persistence and traceability of the data, providing a basis for subsequent analysis and troubleshooting, thereby improving the overall efficiency and safety of the tunnel safety monitoring system.

[0049] Further, such as Figure 3 As shown, step S4 of the embodiment of the present application also includes:

[0050] Step S41: constructing a tunnel safety index sample, dividing the tunnel safety index sample into intervals, and obtaining a stepped tunnel safety index.

[0051] Step S42: Generate a step buzzer warning signal according to the step tunnel safety index.

[0052] Step S43: Encoding and mapping the step tunnel safety index and the step buzzer warning signal is performed through a coding module, and a warning signal matching model is output.

[0053] Step S44: the warning signal matching model matches the buzzer warning signal according to the lane safety index.

[0054] Specifically, the tunnel safety index sample is a set of tunnel safety index data used to train the early warning signal matching model, including indicators of normal status and different risk levels. The stepped tunnel safety index refers to the conversion of continuous tunnel safety indicators into segmented, stepped safety level representations after interval division, which facilitates the matching of early warning signals of different levels. The stepped buzzer early warning signal is a series of early warning signals corresponding to the stepped tunnel safety index. Each signal has a different frequency, intensity or pattern, and is used to represent different degrees of safety risks. The encoding module is used to convert tunnel safety indicators and buzzer early warning signals into processable digital codes to achieve mapping between indicators and signals. The early warning signal matching model is a model trained using data processed by the encoding module for automatically matching safety indicators and early warning signals. The model can be obtained based on rules, machine learning or deep learning algorithms.

[0055] First, a large amount of tunnel safety indicator data is collected, including indicators under normal operation, slight risk, medium risk, and high risk conditions. These indicators are divided into intervals according to risk levels, and corresponding stepped buzzer warning signals are designed and generated for each safety indicator interval. Next, the encoding module converts the tunnel safety indicators and the corresponding buzzer warning signals into digital codes to form a training data set. These data sets are then used to train the warning signal matching model. The model can be based on rules, such as lookup tables, or machine learning algorithms, such as decision trees, support vector machines, and neural networks. The goal of training is to teach the model how to automatically output the corresponding buzzer warning signal based on the input tunnel safety indicators. This model is essentially a mapping table or decision logic that ensures that each safety indicator accurately triggers the corresponding warning signal.

[0056] For example, the tunnel safety index is categorized as 0%-10% for safe status, 11%-30% for minor risk, 31%-60% for medium risk, and 61%-100% for high risk. A different buzzer sound is designed for each risk level: no buzzer for safe status; a 2000Hz buzzer for minor risk; a 3000Hz buzzer for medium risk; and a 4000Hz buzzer for high risk, with increasing intensity and duration.

[0057] The encoding module converts each safety indicator range and corresponding buzzer warning signal into a digital code, such as 0%-10% corresponds to 1, 11%-30% corresponds to 2, 31%-60% corresponds to 3, and 61%-100% corresponds to 4. This encoded data is then used to train a warning signal matching model. This model can be a simple lookup table or a decision tree-based classifier. It can quickly find and output the corresponding buzzer warning signal code based on the input safety indicator code.

[0058] After the early warning signal matching model is trained, when the integrated monitoring module outputs the tunnel safety index, the early warning signal matching model will automatically identify the safety interval in which the index is located, and then output the buzzer warning signal corresponding to the interval, realizing the automation and intelligence of safety warning.

[0059] Through the above steps, the warning signal matching model can realize the intelligent matching of tunnel safety indicators and buzzer warning signals, thereby improving the accuracy and efficiency of safety warnings.

[0060] Furthermore, before step S3 of the embodiment of the present application inputs the lane distance monitoring signal and the lane posture monitoring signal into the lane safety risk identification model for signal anomaly analysis, the method further includes:

[0061] A signal compensator is provided, and the signal compensator is connected to the input end of the tunnel safety risk identification model; signal environment error analysis, signal timing error analysis and signal noise error analysis are performed on the tunnel distance monitoring signal and the tunnel posture monitoring signal, and a distance error signal and a posture error signal are output; the signal compensator compensates the tunnel distance monitoring signal and the tunnel posture monitoring signal according to the distance error signal and the posture error signal respectively.

[0062] Specifically, before inputting the roadway distance and posture monitoring signals into the roadway safety risk identification model, a signal compensator is used to preprocess the signals to improve signal quality and model accuracy. A signal compensator is a signal processing component, either a hardware device or a software algorithm, used to correct and optimize the monitoring signals to eliminate or reduce errors caused by environmental, timing, and noise. First, a signal compensator is set up and connected to the input of the roadway safety risk identification model to correct or compensate for errors or deviations in the signals.

[0063] Signal environmental error analysis analyzes errors caused by environmental factors such as temperature, humidity, and electromagnetic interference during signal acquisition. Signal timing error analysis analyzes errors caused by time synchronization or signal processing timing issues during signal acquisition. Signal noise error analysis analyzes errors caused by noise, such as random noise and interference signals. The distance error signal and attitude error signal are calculated by the signal compensator to describe the magnitude and characteristics of the errors in the roadway distance monitoring signal and roadway attitude monitoring signal, respectively.

[0064] The signal compensator first analyzes environmental influences on the monitoring signal. For example, temperature changes may affect the sound velocity measurement of an ultrasonic sensor, or vibration may cause fluctuations in the attitude sensor's readings. By establishing a model that models the relationship between environmental factors and signal errors, it can calculate distance error and attitude error signals to quantify the extent of environmental influences. Next, the signal's timing deviation is examined. For example, the signal compensator may use timestamp information to correct for signal delay or implement synchronization algorithms such as the Network Time Protocol (NTP) to ensure accurate signal synchronization. This helps eliminate monitoring inaccuracies caused by timing errors. The signal compensator then uses filtering algorithms such as low-pass filtering and median filtering, or noise suppression techniques, to identify and reduce noise components in the signal. Noise analysis can be based on statistical methods, such as calculating the signal's standard deviation or mean, to determine signal stability. This series of monitoring signal analysis processes outputs distance error and attitude error signals.

[0065] Finally, the signal compensator adjusts the original monitoring signal based on the distance error signal and the attitude error signal. For example, if the ultrasonic sensor is affected by temperature fluctuations during measurement, the signal compensator will adjust the signal based on the results of the environmental error analysis to compensate for the impact of temperature changes on the ultrasonic propagation speed. Similarly, if the attitude monitoring signal is affected by sensor timing synchronization issues, the signal compensator will adjust the signal based on the results of the timing error analysis to ensure data accuracy. The adjustment process involves applying mathematical operations or using signal processing algorithms such as Kalman filtering to correct the signal to reduce errors caused by the environment, timing, and noise. This ensures that the signal input to the safety risk identification model is as accurate as possible and reduces misjudgments due to errors and deviations.

[0066] Furthermore, the method described in the embodiment of the present application also includes:

[0067] Initialize the gyro sensor and collect angular velocity data and acceleration data of the gyro sensor in each axis; calculate the rotation angle and tilt angle based on the angular velocity data and acceleration data; adjust the rotation angle and tilt angle to meet the horizontal rotation angle and horizontal tilt angle, and record the placement position of the gyro sensor.

[0068] Specifically, before use, the gyroscope sensor is initialized. This includes setting parameters such as the sampling frequency and range, and performing a zero-point calibration to ensure that the sensor output is zero when stationary. After initialization, the gyroscope sensor begins collecting angular velocity data, while the integrated accelerometer begins measuring acceleration data. This data is typically output as a digital signal and can be read via a serial interface such as SPI or I2C. By integrating the angular velocity data, the object's rotation angle around each axis is calculated. This integration can use simple numerical integration methods such as the trapezoidal rule or Simpson's rule, or more complex algorithms such as the Kalman filter to reduce cumulative errors during the integration process. Using the acceleration data, the object's tilt angle along the three axes is determined by calculating the gravity component. Generally, acceleration data should point toward the center of the Earth when stationary. By analyzing the acceleration components along the three axes, the object's tilt angle relative to the horizontal plane can be calculated. Based on the calculated rotation and tilt angles, the sensor position is adjusted to ensure that the horizontal rotation and tilt angles are met, ensuring that the sensor is horizontal or in a preset reference position. Then, record the gyroscope sensor placement at this time as a reference point for subsequent monitoring.

[0069] Furthermore, after recording the placement position of the gyroscope sensor, the embodiment of the present application further includes:

[0070] Acquire multiple recording positions, perform external force tests on the multiple recording positions, obtain a test data set, and obtain multiple placement stabilities corresponding to the multiple recording positions based on the test data set; determine a first position based on the multiple placement stabilities, and place the gyroscope sensor at the first position.

[0071] Specifically, the test dataset consists of a series of angular velocity and acceleration data output by the gyroscope sensor when subjected to external force testing, used to evaluate sensor performance under different conditions. Placement stability describes the gyroscope sensor's resistance to external forces and the stability of its output data when placed in a specific location. Higher placement stability indicates more reliable and accurate measurement data provided by the sensor in that location. The first position is the optimal placement position, determined by analyzing the placement stability of multiple recorded locations. This position provides the best sensor performance.

[0072] During preliminary placement attempts or based on pre-designed positions, record the placement of multiple gyroscope sensors. These locations may be distributed across the device to assess the impact of different placements on sensor performance. Perform force testing on the gyroscope sensors at each recorded location. This testing simulates various external forces likely encountered in actual use, such as vibration, shock, and tilt. During the test, the sensor continuously outputs angular velocity and acceleration data. At each test location, the sensor's angular velocity and acceleration data are collected to form a test dataset. Based on the test dataset, data analysis and signal processing methods, such as time and frequency domain analysis and statistical analysis, are used to evaluate the stability of the gyroscope sensors at different placement locations. The stability assessment may include metrics such as data volatility, noise level, and data consistency. Based on the stability evaluation of the multiple recorded locations, the location with the best performance is selected as the primary location. The gyroscope sensor at this location provides the most stable and accurate measurement data when subjected to external forces, ensuring the reliability and effectiveness of the monitoring system. Through these steps, the optimal placement of the gyroscope sensor can be scientifically determined, optimizing the performance of the monitoring system and improving the accuracy and stability of the monitoring data.

[0073] In summary, the tunnel safety monitoring method based on multi-sensor fusion provided by the embodiments of the present application has the following technical effects:

[0074] An integrated monitoring module is obtained by integrating multiple sensors on a circuit board, wherein the multiple sensors include an ultrasonic sensor, a gyroscope sensor, and a buzzer module. The stability of multiple placement positions of the gyroscope sensor is obtained by testing the external force, and the optimal first position is found to place the gyroscope sensor. The integrated monitoring module also includes an OLED module and a storage module. The OLED module is used to display the signal measured by the integrated monitoring module on the OLED screen, and the storage module is used to back up and store the signal measured by the integrated monitoring module. The integrated monitoring module receives the tunnel distance monitoring signal transmitted by the ultrasonic sensor and the tunnel attitude monitoring signal transmitted by the gyroscope sensor; inputs the tunnel distance monitoring signal and the tunnel attitude monitoring signal into a tunnel safety risk identification model to perform signal anomaly analysis and output a tunnel safety index; inputs the tunnel safety index into the buzzer module, and the buzzer module matches the buzzer warning signal according to the tunnel safety index and issues a tunnel safety warning based on the buzzer warning signal.

[0075] Overall, the integrated monitoring module of the embodiment of the present application integrates multiple sensors such as ultrasonic sensors, gyroscope sensors, and buzzer warning modules on the same platform, realizing the comprehensive capture of multi-dimensional data such as tunnel distance and posture. In addition, OLED modules and storage modules are also integrated to realize data visualization and secure storage. This integrated design not only improves the efficiency of data acquisition, but also enhances the accuracy and reliability of monitoring results through data fusion, effectively avoiding the monitoring blind spots and errors that may be caused by local failures or environmental factors of a single sensor. In addition, the present application also introduces a complex tunnel safety risk identification model, conducts in-depth analysis of the signals transmitted by the sensors, identifies abnormal patterns, and thus accurately evaluates the safety status of the tunnel, and matches the warning signals according to the safety indicators through the buzzer module, thereby realizing instant feedback on the safety status of the tunnel, greatly improving the efficiency and practicality of tunnel safety monitoring, and providing strong guarantees for mine safety production.

[0076] Example 2, as Figure 4 As shown, an embodiment of the present application provides a laneway safety monitoring system based on multi-sensor fusion, the system comprising:

[0077] The integrated monitoring unit 10 is used to obtain an integrated monitoring module, and the integrated monitoring module is obtained by integrating multiple sensors on a circuit board, wherein the multiple sensors include an ultrasonic sensor, a gyroscope sensor and a buzzer module.

[0078] The signal receiving unit 20 is used to receive the lane distance monitoring signal transmitted by the ultrasonic sensor and the lane attitude monitoring signal transmitted by the gyroscope sensor through the integrated monitoring module.

[0079] The abnormality analysis unit 30 is used to input the tunnel distance monitoring signal and the tunnel posture monitoring signal into a tunnel safety risk identification model to perform signal abnormality analysis and output a tunnel safety index.

[0080] The safety warning unit 40 is used to input the lane safety index into the buzzer module, and the buzzer module matches the buzzer warning signal according to the lane safety index and performs a lane safety warning based on the buzzer warning signal.

[0081] Furthermore, the abnormality analysis unit 30 in the embodiment of the present application is further configured to perform the following steps:

[0082] Abnormal features of the tunnel distance monitoring signal and the tunnel posture monitoring signal are extracted to obtain distance abnormal features and posture abnormal features; the tunnel safety risk identification model includes a distance safety threshold and a posture safety threshold; the tunnel safety risk identification model compares the distance abnormal features and the posture abnormal features with the distance safety threshold and the posture safety threshold respectively, and outputs a distance safety index and a posture safety index; calculation is performed using the distance safety index and the posture safety index to output a tunnel safety index.

[0083] Furthermore, the integrated monitoring unit 10 of the embodiment of the present application is further configured to perform the following steps:

[0084] The integrated monitoring module receives the transmitted ultrasonic signal and the reflected ultrasonic signal of the ultrasonic sensor through an encrypted transmission channel; calculates the time interval based on the transmitted ultrasonic signal and the reflected ultrasonic signal, calculates the tunnel distance based on the measured time interval, and outputs the tunnel distance monitoring signal.

[0085] Furthermore, the integrated monitoring module described in the embodiment of the present application includes an OLED module and a storage module. The OLED module is used to display the signal measured by the integrated monitoring module on the OLED screen, and the storage module is used to back up and store the signal measured by the integrated monitoring module.

[0086] Furthermore, the security warning unit 40 of the embodiment of the present application is further configured to perform the following steps:

[0087] Construct a tunnel safety index sample, divide the tunnel safety index sample into intervals, and obtain a step tunnel safety index; generate a step buzzer warning signal based on the step tunnel safety index; encode and map the step tunnel safety index and the step buzzer warning signal through a coding module, and output a warning signal matching model; the warning signal matching model matches the buzzer warning signal according to the tunnel safety index.

[0088] Furthermore, the abnormality analysis unit 30 in the embodiment of the present application is further configured to perform the following steps:

[0089] A signal compensator is provided, and the signal compensator is connected to the input end of the tunnel safety risk identification model; signal environment error analysis, signal timing error analysis and signal noise error analysis are performed on the tunnel distance monitoring signal and the tunnel posture monitoring signal, and a distance error signal and a posture error signal are output; the signal compensator compensates the tunnel distance monitoring signal and the tunnel posture monitoring signal according to the distance error signal and the posture error signal respectively.

[0090] Furthermore, the system described in the embodiment of the present application is also used to perform the following steps:

[0091] Initialize the gyro sensor and collect angular velocity data and acceleration data of the gyro sensor in each axis; calculate the rotation angle and tilt angle based on the angular velocity data and acceleration data; adjust the rotation angle and tilt angle to meet the horizontal rotation angle and horizontal tilt angle, and record the placement position of the gyro sensor.

[0092] Furthermore, after recording the placement position of the gyroscope sensor, the system of the embodiment of the present application is further configured to perform the following steps:

[0093] Acquire multiple recording positions, perform external force tests on the multiple recording positions, obtain a test data set, and obtain multiple placement stabilities corresponding to the multiple recording positions based on the test data set; determine a first position based on the multiple placement stabilities, and place the gyroscope sensor at the first position.

[0094] Through the detailed description of the tunnel safety monitoring method based on multi-sensor fusion in the foregoing specification, those skilled in the art can clearly understand the tunnel safety monitoring system based on multi-sensor fusion in this embodiment. For the system disclosed in Example 2, since it corresponds to the method disclosed in Example 1 and has corresponding functional units and beneficial effects, the relevant details can be referred to the method section.

[0095] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present application. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application is not limited to the embodiments shown herein, but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A tunnel safety monitoring method based on multi-sensor fusion, characterized in that: The method comprises: Acquire an integrated monitoring module, wherein the integrated monitoring module is obtained by integrating multiple sensors on a circuit board, wherein the multiple sensors include an ultrasonic sensor, a gyroscope sensor, and a buzzer module; The integrated monitoring module receives the lane distance monitoring signal transmitted by the ultrasonic sensor and the lane attitude monitoring signal transmitted by the gyroscope sensor; Inputting the lane distance monitoring signal and the lane posture monitoring signal into a lane safety risk identification model to perform signal anomaly analysis and output a lane safety index; Inputting the lane safety index into the buzzer module, the buzzer module matching a buzzer warning signal according to the lane safety index, and performing a lane safety warning based on the buzzer warning signal; The lane distance monitoring signal and the lane posture monitoring signal are input into a lane safety risk identification model to perform signal anomaly analysis, the method comprising: Extracting abnormal features from the lane distance monitoring signal and the lane posture monitoring signal to obtain abnormal distance features and abnormal posture features; The laneway safety risk identification model includes a distance safety threshold and a posture safety threshold; The lane safety risk identification model compares the distance abnormality feature and the posture abnormality feature with the distance safety threshold and the posture safety threshold respectively, and outputs a distance safety index and a posture safety index; Calculating the distance safety index and the posture safety index to output a laneway safety index; The buzzer module matches the buzzer warning signal according to the lane safety index, and the method includes: Constructing a tunnel safety index sample, dividing the tunnel safety index sample into intervals, and obtaining a stepped tunnel safety index; Generating a step buzzer warning signal according to the step lane safety index; The step tunnel safety index and the step buzzer warning signal are coded and mapped by a coding module, and a warning signal matching model is output; The warning signal matching model matches the buzzer warning signal according to the lane safety index; Before inputting the lane distance monitoring signal and the lane posture monitoring signal into a lane safety risk identification model for signal anomaly analysis, the method further includes: A signal compensator is provided, wherein the signal compensator is connected to an input end of the lane safety risk identification model; Performing signal environment error analysis, signal timing error analysis, and signal noise error analysis on the lane distance monitoring signal and the lane attitude monitoring signal, and outputting a distance error signal and an attitude error signal; The signal compensator compensates the lane distance monitoring signal and the lane posture monitoring signal according to the distance error signal and the posture error signal respectively.

2. The method according to claim 1, wherein Outputting the lane distance monitoring signal, the method includes: The integrated monitoring module receives the transmitted ultrasonic signal and the reflected ultrasonic signal of the ultrasonic sensor through an encrypted transmission channel; The time interval is calculated according to the transmitted ultrasonic signal and the reflected ultrasonic signal, the tunnel distance is calculated according to the measured time interval, and the tunnel distance monitoring signal is output.

3. The method according to claim 1, wherein The integrated monitoring module includes an OLED module and a storage module. The OLED module is used to display the signal measured by the integrated monitoring module on an OLED screen, and the storage module is used to back up and store the signal measured by the integrated monitoring module.

4. The method according to claim 1, wherein The method further comprises: Initializing the gyro sensor and collecting angular velocity data and acceleration data of the gyro sensor in each axis; Calculating a rotation angle and a tilt angle according to the angular velocity data and the acceleration data; The rotation angle and the tilt angle are adjusted to meet the horizontal rotation angle and the horizontal tilt angle, and the placement position of the gyro sensor is recorded.

5. The method according to claim 4, wherein After recording the placement position of the gyro sensor, the method further includes: Acquire multiple recording positions, perform external force tests on the multiple recording positions to obtain a test data set, and acquire multiple placement stabilities corresponding to the multiple recording positions based on the test data set; A first position is determined based on the multiple placement stabilities, and the gyro sensor is placed at the first position.

6. The tunnel safety monitoring system based on multi-sensor fusion is characterized by: The system is used to perform the method according to any one of claims 1 to 5, and the system includes: An integrated monitoring unit, the integrated monitoring unit being used to obtain an integrated monitoring module, the integrated monitoring module being obtained by integrating multiple sensors on a circuit board, wherein the multiple sensors include an ultrasonic sensor, a gyroscope sensor, and a buzzer module; a signal receiving unit, the signal receiving unit being configured to receive, through the integrated monitoring module, a lane distance monitoring signal transmitted by the ultrasonic sensor and a lane attitude monitoring signal transmitted by the gyroscope sensor; An abnormality analysis unit, configured to input the lane distance monitoring signal and the lane posture monitoring signal into a lane safety risk identification model to perform signal abnormality analysis and output a lane safety index; A safety warning unit, the safety warning unit is used to input the lane safety index into the buzzer module, the buzzer module matches a buzzer warning signal according to the lane safety index, and performs a lane safety warning based on the buzzer warning signal; The exception analysis unit is further configured to perform the following steps: Abnormal features of the tunnel distance monitoring signal and the tunnel posture monitoring signal are extracted to obtain distance abnormal features and posture abnormal features; the tunnel safety risk identification model includes a distance safety threshold and a posture safety threshold; the tunnel safety risk identification model compares the distance abnormal features and the posture abnormal features with the distance safety threshold and the posture safety threshold respectively, and outputs a distance safety index and a posture safety index; calculation is performed using the distance safety index and the posture safety index to output a tunnel safety index.

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