Navigation method for tunneling equipment based on multi-source information fusion

Abstract

The application relates to the technical field of mine intellectualization and automatic tunneling, and discloses a tunneling equipment navigation method based on multi-source information fusion. The method obtains multi-dimensional data through a multi-source sensing and data acquisition module, optimizes visual information through an image enhancement module, analyzes data characteristics through a multi-dimensional data analysis module, intelligently selects an optimal sensor combination through a sensor dynamic switching module, eliminates the complexity interference of dust and shielding through a complex environment interference correction module, generates accurate positioning results, controls equipment operation according to the latest positioning results through a navigation instruction execution module, calibrates model parameters in real time through a data feedback module, visually presents the working situation through a navigation state display module, and quickly responds to risk events through an abnormal emergency processing module. The above modules are cooperatively operated to form a closed-loop control, so that the application has the technical advantages of high-precision positioning, strong environmental adaptability and real-time dynamic adjustment.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of mine intelligence and automatic tunneling, in particular to a tunneling equipment navigation method based on multi-source information fusion. BACKGROUND

[0002] In complex underground operation scenes such as coal mine underground and tunneling, traditional single-mode navigation technology faces severe environmental adaptability challenges. On the one hand, high concentration of dust and low light conditions seriously restrict the effectiveness of visual pose detection technology. Optical measurement devices such as total station laser and laser radar are easily affected by dust scattering and device shielding, and the blockage of light path directly causes positioning failure. On the other hand, pose detection technology based on laser ranging has response lag and data jitter problems in dynamic working conditions, which is difficult to meet the real-time requirements. Although the inertial navigation system can provide short-term high-precision output, the error accumulates with time, which leads to the continuous expansion of long-term positioning deviation. The environmental adaptability of single sensor solution is particularly prominent. For example, the wheeled odometer only relies on wheel rotation parameters to calculate displacement, and after a long time of running, it produces significant cumulative error due to factors such as slipping and idling, causing the device to "get lost". More complex is that in special geological conditions such as fracture zone and aquifer, radar echoes are disturbed by uneven rock mass, false alarms and missed detections occur frequently, further weakening the reliability of the positioning system. These technical bottlenecks show that a single sensor cannot cope with the dynamics, complexity and extremity of the underground environment, and it is urgent to integrate the advantages of different sensors through multi-source information fusion technology to build a robust and stable comprehensive navigation system. SUMMARY

[0003] (I) Technical problems solved

[0004] In view of the deficiencies of the prior art, the present application provides a tunneling equipment navigation method based on multi-source information fusion, which has the advantages of high-precision positioning, strong environmental adaptability and real-time dynamic adjustment, and solves the problems of low positioning accuracy, poor real-time performance and insufficient reliability of traditional single-mode navigation technology in complex underground environments.

[0005] (II) Technical solutions

[0006] To achieve the above purpose, the present application provides the following technical solutions: a tunneling equipment navigation method based on multi-source information fusion, comprising the following steps:

[0007] Step 1, establish a system, the system includes multi-source sensing and data acquisition module, image enhancement module, multi-dimensional data analysis module, sensor dynamic switching module, complex environment interference correction module, navigation instruction execution module, data feedback module, navigation state display module and abnormal emergency processing module;

[0008] Step two, the multi-source sensing and data acquisition module is responsible for laying out various sensors and collecting raw data;

[0009] Step three, the image enhancement module receives the visual image in the raw sensor data and the environmental monitoring data, and pre-processes it, and outputs the enhanced image pixel value ;

[0010] Step four, the multi-dimensional data analysis module outputs the sensor adaptation degree , error correction value and radar data correction value ;

[0011] Step five, the sensor dynamic switching module sets the adaptation degree threshold according to the sensor adaptation degree When the sensor adaptation degree , the system automatically switches to the sensor with higher adaptation degree;

[0012] Step five, the complex environment interference correction module combines the calculation formula of the error correction value and the radar data correction value to optimize the positioning data twice;

[0013] Step seven, the navigation instruction execution module receives the corrected accurate positioning data, combines the preset trajectory of the tunneling operation, generates the navigation instruction of the device steering and speed adjustment, and controls the tunneling device to work according to the planned path;

[0014] Step eight, the data feedback module real-time collects the actual pose data of the device after executing the navigation instruction, compares the deviation with the preset trajectory, and calculates the deviation value ;

[0015] Step nine, the navigation state display module connects the underground visual terminal through the 4G / 5G wireless backup network, and displays the current position of the tunneling device, the navigation accuracy, the working state of each sensor and the environmental parameters in real time through the underground visual terminal.

[0016] Step ten, the abnormal emergency processing module is linked with the navigation state display module, when an extreme abnormal situation occurs, automatically execute emergency stop, position locking, fault reporting and recovery guidance.

[0017] Preferably, the multi-source sensing and data acquisition module includes a sensor array unit and an environmental real-time monitoring unit.

[0018] ​Preferably, the sensor array unit collects raw sensor data including visual image data, laser radar point cloud data, inertial navigation system angular velocity and acceleration data, wheeled odometer displacement data, ultrasonic / millimeter wave radar distance data, total station absolute position data, device attitude data and sensor state data through sensors.

[0019] Preferably, the environmental real-time monitoring unit collects environmental monitoring data including dust concentration data, light intensity data, environmental temperature data, environmental humidity data, noise level data, vibration data, harmful gas concentration data and electromagnetic interference data through integration of laser dust concentration sensors and high dynamic range illuminance sensors.

[0020] Preferably, the image enhancement module adopts an image defogging algorithm based on an atmospheric scattering model for enhancement, and the calculation formula is as follows:

[0021]

[0022] In the formula, represents the pixel value of the enhanced image, i.e. the pixel intensity of the image at position after defogging processing, represents the pixel intensity of the original image at position , represents the environmental light intensity, represents the transmittance map, represents the minimum threshold value of transmittance.

[0023] Preferably, the multi-dimensional data analysis module includes a sensor environmental adaptability analysis unit, a complex environment positioning error analysis unit and a special geological radar interference analysis unit.

[0024] Preferably, the sensor environmental adaptability analysis unit calculates the sensor adaptability for judging the reliability of each sensor in the current environment, and the calculation formula is as follows:

[0025]

[0026] In the formula, represents the adaptability of the i-th sensor, is the current effective data rate of the sensor, represents the standard effective data rate of the sensor, represents the interference intensity of the sensor, represents the interference intensity threshold, represents the effective data rate weight, represents the anti-interference intensity weight.

[0027] Preferably, the complex environment positioning error analysis unit calculates error correction values for positioning deviations caused by dust and blockage The calculation formula is as follows:

[0028]

[0029] In the formula, represents the error correction value, , respectively represent the error coefficients of dust concentration and blockage probability, represents the real-time measured dust concentration, represents the maximum dust concentration, represents the laser path blockage probability, represents the original positioning value of the laser sensor.

[0030] Preferably, the special geological radar interference analysis unit calculates radar data correction values for radar echo interference of fracture zones and aquifers The calculation formula is as follows:

[0031]

[0032] In the formula, represents the radar data correction value, represents the geological interference coefficient, represents the rock mass heterogeneity parameter, represents the rock mass heterogeneity threshold value, represents the original positioning value of the radar.

[0033] Preferably, the complex environment interference correction module combines the calculation formulas of the error correction value and the radar data correction value to perform secondary optimization on the positioning data, and the final positioning value , wherein is the initial positioning value of the sensor;

[0034] The data feedback module compares the actual pose data after the equipment executes the navigation instruction with the preset trajectory to calculate the deviation value , wherein, is the preset trajectory positioning value;

[0035] When the navigation state display module detects the following situations, the system automatically triggers sound and light alarms:

[0036] (1) Trajectory deviation exceeds threshold value: when the deviation value > 0.3m, it indicates that the current trajectory has serious deviation, which will cause overbreak or underbreak of the roadway;

[0037] (2) Sensor failure risk: certain main sensors <0.4, indicating that the current data reliability is extremely low and cannot support navigation;

[0038] (3) Environmental parameter exceeds standard: real-time measured dust concentration >10mg / m³, indicating that the current dust concentration has harmed the health of personnel and affected the accuracy of the sensor;

[0039] (4) Extreme abnormal situation: when the overall navigation system fails, the deviation value >0.5m or all main sensors fail, indicating that the current navigation system cannot guarantee the safety of the equipment.

[0040] Compared with the prior art, the tunneling equipment navigation method based on multi-source information fusion provided by the present application has the following beneficial effects:

[0041] 1. The present application quantitatively evaluates the reliability of each sensor in the complex underground environment through the calculation of sensor adaptation degree , distinguishes the environmental adaptation ability of different sensors, provides a clear quantitative basis for sensor dynamic switching, thereby achieving the beneficial effects of avoiding the selection of sensors based on subjective judgment, ensuring switching to a sensor combination with higher adaptation degree, and ensuring the continuous and stable output of navigation data, ultimately solving the problem of relying on experience to select traditional sensors and the inability to adapt to dynamic changes in underground environments.

[0042] 2. The present application precisely quantifies the degree of interference of dust and shielding on laser positioning through the calculation of error correction value , and specifically offsets the positioning deviation caused by the two factors, controls the laser positioning error within an acceptable range (±0.1m), thereby achieving the beneficial effects of improving the positioning accuracy of laser sensors in complex environments, avoiding navigation trajectory deviation caused by dust and shielding, and providing reliable error data for the subsequent complex environment interference correction module, ultimately solving the problem of large error of traditional laser positioning in underground high-dust and multi-shielding environments.

[0043] 3. The present application precisely captures the interference of broken zones and aquifers on radar echoes through the calculation of radar data correction value , quantifies the positioning deviation caused by the unevenness of rock mass and specifically corrects the error of radar original data, thereby achieving the beneficial effects of improving the positioning reliability of radar sensors under special geological conditions, avoiding the decline of navigation accuracy caused by geological interference, and providing geological interference correction basis for final positioning value optimization, so that the present application solves the problems of traditional radar positioning in special geological environments, such as false alarm and missed detection, and large positioning deviation.

[0044] 4、The present application sets alarm trigger conditions by scene, realizes accurate identification of abnormal conditions, provides targeted emergency treatment measures, realizes automatic response, facilitates manual monitoring, finally solves the problems of difficult discovery of abnormal conditions, lack of basis for emergency treatment, low fault recovery efficiency and non-intuitive manual monitoring in underground tunneling navigation, so as to ensure safe and efficient tunneling operation, and reduce the incidence of construction accidents and operation interruption time. BRIEF DESCRIPTION OF DRAWINGS

[0045] Figure 1 The method steps of the present application are shown in the figure. DETAILED DESCRIPTION

[0046] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0047] Please refer to Figure 1 , the tunneling equipment navigation method based on multi-source information fusion, comprising the following steps:

[0048] Step one, establish a system, the system contains multi-source sensing and data acquisition module, image enhancement module, multi-dimensional data analysis module, sensor dynamic switching module, complex environment interference correction module, navigation instruction execution module, data feedback module, navigation state display module and abnormal emergency treatment module;

[0049] Step two, the multi-source sensing and data acquisition module is responsible for laying out various sensors and collecting raw data, and packaging all raw sensor data and environment monitoring data and transmitting them to the next module;

[0050] Step three, the image enhancement module receives visual images in the raw sensor data and environment monitoring data, and pre-processes them. In view of the problems of image blur and low contrast caused by dust and low light, an image defogging algorithm based on atmospheric scattering model is used for enhancement, and the enhanced image pixel value is output, which improves the clarity and feature distinguishability of the image, and provides high-quality data support for subsequent image analysis and processing;

[0051] Step four, the multi-dimensional data analysis module outputs sensor adaptation degree , error correction value and radar data correction value based on collected data;

[0052] Step five, the sensor dynamic switching module switches the sensor according to the sensor adaptation degree Set the adaptation threshold, when the sensor adaptation is the minimum threshold), the system automatically switches to a higher adaptation sensor (such as sensor adaptation <0.6, switch to laser + inertial fusion navigation), to ensure the continuous and reliable navigation data;

[0053] Step five, the complex environment interference correction module combines the error correction value and the calculation formula of radar data correction value , the final positioning value of the positioning data is optimized twice, which is , wherein is the initial positioning value of the sensor, and the positioning accuracy problem caused by dust shielding and geological interference is solved through the module;

[0054] Step seven, the navigation instruction execution module receives the corrected accurate positioning data, combines the preset trajectory of the tunneling operation, generates navigation instructions such as equipment steering and speed adjustment, and controls the tunneling equipment to work according to the planned path;

[0055] Step eight, the data feedback module real-time collects the actual pose data of the equipment after executing the navigation instruction, compares the deviation with the preset trajectory, calculates the deviation value , is the preset trajectory positioning value), and feeds back the deviation data to the multi-dimensional data analysis module for optimizing the subsequent fusion algorithm parameters;

[0056] Step nine, the navigation state display module connects the underground visual terminal through 4G / 5G wireless backup network, and displays the current position of the tunneling equipment, navigation accuracy, working state of each sensor and environmental parameters in real time through the underground visual terminal, which is convenient for operators to monitor in real time. When abnormal conditions (such as deviation exceeding threshold) are displayed, the abnormal emergency treatment module is linked to perform automatic alarm;

[0057] Step ten, the abnormal emergency treatment module is linked with the navigation state display module, when extreme abnormal conditions (such as overall failure of the navigation system, >0.5m, all the main sensors fail), automatically execute emergency stop, position locking, fault reporting and recovery guidance.

[0058] Multi-source sensing and data acquisition module includes sensor array unit and environment real-time monitoring unit.

[0059] ​​The sensor array unit respectively installs the core sensors of explosion-proof high-definition infrared camera (for low-light vision), micro-pulse laser radar (strong anti-dust interference ability), optical fiber inertial navigation system (high-precision angular velocity / acceleration), anti-slip encoder (wheeled odometer), ultrasonic / millimeter wave radar (for short-range obstacle detection) and total station cooperative target (provides absolute position reference) at the front, both sides and top of the tunneling equipment, and collects raw sensor data, including visual image data, laser radar point cloud data, inertial navigation system angular velocity and acceleration data, wheeled odometer displacement data, ultrasonic / millimeter wave radar distance data, total station absolute position data, equipment attitude data and sensor state data.

[0060] The environmental real-time monitoring unit integrates a laser dust concentration sensor (real-time monitoring of dust concentration) and a high dynamic range illuminance sensor (real-time monitoring of environmental light data), and collects environmental monitoring data, including dust concentration data, light intensity data, environmental temperature data, environmental humidity data, noise level data, vibration data, harmful gas concentration data and electromagnetic interference data. The above environmental data provides key inputs for subsequent image preprocessing and sensor weight allocation.

[0061] The advantage is that by comprehensively capturing the tunneling equipment state and underground environment information, the navigation deviation caused by single data loss is avoided, and reliable data support is provided for subsequent image enhancement and sensor adaptation analysis, which solves the problem of incomplete navigation data collection and difficult environmental interference perception in underground environment from the data source.

[0062] The image enhancement module uses an image defogging algorithm based on an atmospheric scattering model for enhancement, and the calculation formula is:

[0063]

[0064] In the formula, represents the enhanced image pixel value, i.e. the pixel intensity of the image at position after defogging processing, represents the original image pixel value, represents the original image pixel intensity at position , which is collected by the explosion-proof high-definition infrared camera installed at the front, both sides and top of the tunneling equipment, and is used to provide visual information of the environment around the tunneling equipment, represents the environmental light intensity, which can be estimated by illuminance sensor data, and reflects the influence of environmental light on the image, which is monitored in real time by the high dynamic range illuminance sensor and is used to estimate the influence of environmental light on the image and help adjust the intensity of image enhancement, represents the transmittance map, i.e. the attenuation degree of light propagation in the medium, and its estimated value is negatively correlated with the dust concentration sensor data, i.e. the higher the dust concentration, The smaller the value is, the transmittance map reflects the degree of image blurring caused by scattering and absorption of light by dust and other media, which is estimated by dust concentration sensor data. The dust concentration sensor monitors the dust concentration in the tunneling environment in real time, and the transmittance map reflects the degree of influence of dust on light propagation. The higher the dust concentration is, the lower the transmittance is, and the more blurred the image is, represents the minimum threshold of transmittance, which is used to avoid numerical instability caused by dividing by too small transmittance values, ensuring the stability of the calculation, and is a preset constant value used to ensure that no calculation errors occur during the calculation process caused by dividing by zero or very small values, ensuring the stability and reliability of the image enhancement process;

[0065] The advantage is that the enhanced image pixel value is calculated, and combined with the collected data, the image enhancement unit can effectively restore the image details and improve the success rate of feature point extraction and matching, thereby providing clearer and more accurate visual information for subsequent navigation and positioning.

[0066] The multi-dimensional data analysis module includes a sensor environment adaptability analysis unit, a complex environment positioning error analysis unit, and a special geological radar interference analysis unit.

[0067] The sensor environment adaptability analysis unit calculates the sensor adaptability , which is used to judge the reliability of each sensor in the current environment, and its calculation formula is:

[0068]

[0069] In the formula, represents the adaptability of the i-th sensor (such as vision, laser, and inertia), is the current effective data rate of the sensor, represents the standard effective data rate of the sensor, represents the interference intensity of the sensor (such as dust interference for vision and obstruction interference for laser), represents the interference intensity threshold, represents the effective data rate weight (reflecting the priority of data continuity), represents the anti-interference intensity weight (reflecting the priority of environmental adaptability).

[0070] The advantage is that the sensor adaptability is calculated to quantitatively evaluate the reliability of each sensor in the complex underground environment, and to distinguish the environmental adaptability of different sensors, providing a clear quantitative basis for sensor dynamic switching, thereby avoiding the need to rely on subjective judgment to select sensors, ensuring that the sensor combination with higher adaptability is switched to, and ensuring the continuous and stable output of navigation data, ultimately solving the problem of relying on experience to select traditional sensors and the inability to adapt to dynamic changes in underground environments.

[0071] The complex environment positioning error analysis unit calculates error correction values for positioning deviations caused by dust and obstructions The calculation formula is:

[0072]

[0073] In the formula, represents the error correction value, , and represent error coefficients of dust concentration and obstruction probability, represents the real-time measured dust concentration, represents the maximum dust concentration, represents the laser path obstruction probability (0-1, calculated by device obstruction monitoring data), represents the original positioning value of the laser sensor.

[0074] The advantage is that through the calculation of the above error correction value , the interference degree of dust and obstruction on laser positioning is accurately quantified, and the positioning deviation caused by the two factors is counteracted, the laser positioning error is controlled within an acceptable range (±0.1m), thereby achieving the beneficial effects of improving the positioning accuracy of the laser sensor in complex environments, avoiding navigation trajectory deviation caused by dust and obstruction, providing reliable error data for the subsequent complex environment interference correction module, and ultimately solving the problem of large error of traditional laser positioning in high-dust and multi-obstruction underground environments.

[0075] The special geology radar interference analysis unit calculates radar data correction values for radar echo interference of fracture zones and aquifers The calculation formula is:

[0076]

[0077] In the formula, represents the radar data correction value, represents the geological interference coefficient, represents the rock mass heterogeneity parameter (calculated by radar echo fluctuation frequency), represents the rock mass heterogeneity threshold, represents the original positioning value of the radar.

[0078] The advantage is that through the above radar data correction value The calculation is used for accurately capturing the interference of the broken zone and the aquifer on the radar echo, quantifying the positioning deviation caused by the unevenness of the rock mass and correcting the error of the radar original data, so that the positioning reliability of the radar sensor in the special geological conditions is improved, the navigation precision caused by the geological interference is avoided, and the geological interference correction basis for the final positioning value optimization is provided.

[0079] When the navigation state display module monitors the following conditions, the system automatically triggers an audible and light alarm (the terminal built-in buzzer emits a "tick-tock" alarm, and the screen flashes a red frame):

[0080] (1) Track deviation threshold: when the deviation value >0.3 m, it indicates that the current track has a serious deviation, which will cause overbreak or underbreak of the roadway, and the advancing speed of the tunneling equipment needs to be immediately reduced to below 0.3 m / min, and the steering adjustment needs to be increased, and the deviation value ≤0.1 m; if the deviation is still not reduced after 30 seconds of adjustment, the operator needs to manually pause the navigation through the visual terminal to check the cause of the positioning data anomaly (such as whether the laser radar is blocked or whether the inertial navigation has accumulated error);

[0081] (2) Sensor failure risk: when the adaptation degree of a certain type of main sensor <0.4, it indicates that the current data reliability is extremely low and cannot support navigation, and the system needs to automatically trigger the sensor emergency switching process to preferentially use the backup sensor combination with an adaptation degree ≥0.7 (such as the "fiber inertial navigation + total station cooperative target" mode); the operator needs to check the status of the failed sensor (such as cleaning the dust on the visual camera lens or adjusting the installation angle of the laser radar) at the same time, and after ≥0.6, the original sensor combination can be manually switched back;

[0082] (3) Environmental parameter exceeds the standard: when the real-time measured dust concentration >10 mg / m³, it indicates that the current dust concentration has already harmed the health of personnel and affected the accuracy of the sensor, and the underground dust spray system needs to be immediately started (such as opening the high-pressure spray device at the front of the equipment and the top of the roadway) to reduce the dust concentration to below 6 mg / m³; if the dust concentration is still >10 mg / m³ after 10 minutes of dust spray, the tunneling operation needs to be paused, the operator needs to wear a dust mask to enter the working face to check whether the dust reduction system is malfunctioning, and the sensors that are seriously affected by dust (such as the visual sensor) need to be turned off and switched to the laser + inertial navigation fusion mode that is resistant to dust;

[0083] (4) Extreme abnormal situation: when the navigation system as a whole fails, the deviation value When the deviation is greater than 0.5 m or all main sensors fail, it indicates that the current navigation system can no longer guarantee the safety of the equipment, which may cause serious accidents such as equipment collision with the roadway wall (for example, when the deviation is 0.5 m, the distance between the machine body and the side wall is only 0.5 m, and the collision risk is 90% for a machine body with a width of 3 m and a roadway with a width of 5 m), personnel casualties (for example, when the equipment deviates from the track and collides with the operating personnel), and the like. Therefore, the module needs to automatically execute emergency stop, position locking, fault reporting and recovery guidance. After the fault is eliminated (for example, replacing the failed sensor and restarting the navigation system), the module automatically retrieves the stop position coordinates, connects to the nearest path point of the preset track, and starts navigation after the operator confirms, without the need to re-plan the entire track.

[0084] The advantages are that the above-mentioned scene setting alarm trigger conditions are used to realize accurate identification of abnormal situations, and targeted emergency treatment measures are provided to realize automatic response and facilitate manual monitoring. Finally, the problems of difficult discovery of abnormal situations, lack of emergency treatment basis, low fault recovery efficiency, and non-intuitive manual monitoring in underground tunneling navigation are solved, thereby ensuring safe and efficient tunneling operation, and reducing the occurrence rate of construction accidents and the length of operation interruption.

[0085] Although the embodiments of the present application have been shown and described, it can be understood by those skilled in the art that various changes, modifications, replacements and variations can be made to the embodiments without departing from the principles and spirits of the present application, and the scope of the present application is defined by the appended claims and their equivalents.

Claims

1. A navigation method for tunneling equipment based on multi-source information fusion, characterized in that, Includes the following steps: Step 1: Establish the system, which includes a multi-source sensing and data acquisition module, an image enhancement module, a multi-dimensional data analysis module, a sensor dynamic switching module, a complex environment interference correction module, a navigation command execution module, a data feedback module, a navigation status display module, and an anomaly emergency handling module. Step 2: The multi-source sensing and data acquisition module is responsible for deploying various sensors and collecting raw data; Step 3: The image enhancement module receives visual images from the raw sensor data and environmental monitoring data, preprocesses them, and outputs the enhanced image pixel values. ; Step 4: The multi-dimensional data analysis module outputs sensor adaptability based on the collected data. Error correction value Radar data correction value ; Step 5: The sensor dynamic switching module switches based on sensor compatibility. Set an adaptation threshold; when the sensor adaptation... < When the system is in use, it automatically switches to a sensor with a higher compatibility. Step 5: Complex environment interference correction module combined with error correction value Radar data correction value The calculation formula is used to perform secondary optimization on the positioning data; Step 7: The navigation command execution module receives the corrected precise positioning data, combines it with the preset trajectory of the tunneling operation, generates navigation commands for equipment steering and speed adjustment, and controls the tunneling equipment to operate according to the planned path; Step 8: The data feedback module collects the actual pose data of the device after executing navigation commands in real time, compares it with the preset trajectory, and calculates the deviation value. ; Step 9: The navigation status display module connects to the underground visualization terminal via a 4G / 5G wireless backup network, and displays the current position of the tunneling equipment, navigation accuracy, working status of each sensor, and environmental parameters in real time through the underground visualization terminal. Step 10: The emergency handling module and the navigation status display module work together to automatically perform emergency shutdown, location locking, fault reporting, and recovery guidance in the event of extreme abnormal situations. The multi-dimensional data analysis module includes a sensor environment adaptability analysis unit, a complex environment positioning error analysis unit, and a special ground-penetrating radar interference analysis unit. The sensor environmental adaptability analysis unit calculates the sensor adaptability. This is used to determine the reliability of each sensor under the current environment, and its calculation formula is: In the formula, This represents the fit of the i-th type of sensor. The current effective data rate of the sensor. Indicates the standard effective data rate of the sensor. Indicates the intensity of interference to the sensor. Indicates the threshold of interference intensity. Indicates the effective data rate weight. Indicates the weight of anti-interference strength; The complex environment positioning error analysis unit calculates error correction values ​​to address positioning deviations caused by dust and obstructions. The calculation formula is as follows: In the formula, Indicates the error correction value. , These represent the error coefficients for dust concentration and obstruction probability, respectively. This indicates the dust concentration measured in real time. Indicates the maximum dust concentration. This indicates the probability of laser beam path obstruction. This represents the original positioning value of the laser sensor.

2. The tunneling equipment navigation method based on multi-source information fusion according to claim 1, characterized in that: The multi-source sensing and data acquisition module includes a sensor array unit and a real-time environmental monitoring unit.

3. The tunneling equipment navigation method based on multi-source information fusion according to claim 2, characterized in that: The sensor array unit collects raw sensor data through sensors, including visual image data, lidar point cloud data, inertial navigation system angular velocity and acceleration data, wheel odometer displacement data, ultrasonic / millimeter-wave radar distance data, total station absolute position data, equipment attitude data, and sensor status data.

4. The tunneling equipment navigation method based on multi-source information fusion according to claim 2, characterized in that: The real-time environmental monitoring unit integrates a laser dust concentration sensor and a high dynamic range illuminance sensor to collect environmental monitoring data, including dust concentration data, light intensity data, ambient temperature data, ambient humidity data, noise level data, vibration data, harmful gas concentration data, and electromagnetic interference data.

5. The tunneling equipment navigation method based on multi-source information fusion according to claim 1, characterized in that: The image enhancement module employs an image dehazing algorithm based on an atmospheric scattering model for enhancement. The calculation formula is as follows: In the formula, This represents the enhanced image pixel value, that is, the image at the position after dehazing. Pixel intensity at that location Indicates the location The original image pixel intensity at that location, Indicates ambient light intensity. This represents a transmittance diagram. This represents the minimum threshold for transmittance.

6. The tunneling equipment navigation method based on multi-source information fusion according to claim 1, characterized in that: The specialized ground-penetrating radar interference analysis unit calculates radar data correction values ​​to address radar echo interference from fractured zones and aquifers. The calculation formula is as follows: In the formula, This indicates the radar data correction value. Indicates the geological disturbance coefficient. Indicates parameters of rock mass heterogeneity. Indicates the threshold of rock mass heterogeneity. This represents the original radar positioning value.

7. The tunneling equipment navigation method based on multi-source information fusion according to claim 1, characterized in that: The complex environment interference correction module combines error correction values Radar data correction value The calculation formula is used to perform secondary optimization on the positioning data, resulting in the final positioning value. ,in This is the initial positioning value for the sensor; The data feedback module collects the actual pose data of the device after executing navigation commands in real time, compares it with the preset trajectory, and calculates the deviation value. ,in, Preset trajectory positioning values; The system will automatically trigger an audible and visual alarm when the navigation status display module detects the following situations: (1) Trajectory deviation exceeds threshold: When the deviation value A deviation of >0.3m indicates a serious deviation in the current trajectory, which could lead to over- or under-excavation of the tunnel. (2) Sensor failure risk: a certain type of main sensor A value less than 0.4 indicates that the current data reliability is extremely low and cannot support navigation. (3) Environmental parameters exceed the standard: real-time measured dust concentration When the concentration is >10mg / m³, it indicates that the current dust concentration has already endangered human health and also affects the accuracy of the sensor. (4) Extreme abnormal situations: When the entire navigation system fails or the deviation value is abnormal. If the distance is greater than 0.5m or all main sensors fail, it indicates that the current navigation system can no longer guarantee the safe operation of the equipment.

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