Mine vehicle safe operation monitoring and early warning method and system based on wireless networking

Abstract

The application provides a mine vehicle safe operation monitoring and early warning method and system based on wireless networking, comprising: constructing a multi-dimensional perception layer to collect vehicle operation, track state and ground deformation data; establishing a multi-mode heterogeneous wireless network to realize reliable long-distance data transmission in the tunnel; performing edge real-time calculation to initially filter track abnormalities and driving risks; platform end fuses multi-source data to comprehensively analyze and judge global situation and track life. The application forms a complete safety protection closed loop through the close connection of five links of multi-dimensional perception, reliable transmission, edge calculation, platform judgment and hierarchical disposal; through the fusion of odometer and UWB precise positioning, the precise quantitative description of the starting position, the ending position and the maximum wear depth of the track damage is realized; combined with the ground subsidence detection, the damage causes are effectively distinguished, and combined with the dynamic load evaluation, the vehicle speed adaptive control is realized, which solves the problem that the traditional method cannot accurately position the damage position.

Description

Technical Field

[0001] This invention relates to the field of mine rail transport safety monitoring technology, and in particular to a method and system for monitoring and early warning of safe operation of mine vehicles based on wireless networking. Background Technology

[0002] Rail transport in mines is a crucial link in underground mine production. As the railcars travel from the mine entrance into the mine shaft, the rail structure is subjected to repeated impacts from heavy vehicles over a long period of time, which can easily lead to safety hazards such as rail wear, joint misalignment, and foundation settlement. If rail damage is not detected in time, it can easily cause major safety accidents such as vehicle derailment and overturning, resulting in casualties and property damage.

[0003] Currently, mine track safety inspections mainly rely on manual periodic inspections, which has the following prominent problems:

[0004] Manual inspections are time-consuming and inefficient, making it difficult to monitor track conditions in real time and detect sudden track damage in a timely manner. In particular, when tracked mining vehicles travel from the mine entrance to the mine shaft, the track undergoes continuous deformation under the dynamic load of the vehicle. Traditional methods cannot scan and detect the track synchronously during vehicle travel, resulting in a serious lag in the detection of track damage.

[0005] When track damage occurs, existing methods cannot accurately locate the damage. On the one hand, there is a lack of precise quantitative calculation capabilities for the depth and extent of track damage. On the other hand, there is a lack of effective detection methods for deep subsidence of the track installation ground. It is difficult to distinguish whether the damage is caused by wear of the track itself or by instability of the foundation, which makes maintenance decisions difficult.

[0006] The environment inside the mine is harsh, with problems such as long distances, many bends, and severe signal shielding. Traditional wired monitoring methods are difficult to lay and are easily damaged, while wireless communication methods face the problem of unstable signal transmission, making it difficult to reliably transmit monitoring data back to the ground monitoring center in real time.

[0007] Existing detection methods do not consider the coupled effect of load changes on track damage. When personnel or minerals are transported, the increased vehicle load generates additional vertical pressure and impact on the track. The safety risk level of the same track damage is completely different under different load conditions. Existing methods lack the ability to conduct a joint assessment of load, track damage, and train safety.

[0008] Therefore, it is necessary to provide a wireless networking-based method for monitoring and early warning of the safe operation of mining vehicles to solve the above-mentioned technical problems. Summary of the Invention

[0009] This invention provides a method for monitoring and early warning of safe operation of mining vehicles based on wireless networking, which solves the problems of difficulty in real-time monitoring of track status, inaccurate damage location, unreliable wireless transmission, and lack of load coupling assessment and graded early warning mechanism in mining rail transportation.

[0010] To solve the above-mentioned technical problems, the present invention provides a method for monitoring and early warning of safe operation of mining vehicles based on wireless networking, comprising the following steps:

[0011] S1. Construct a multi-dimensional perception layer to collect data on vehicle operation, track status, and foundation deformation: Deploy a lidar scanning array and an ultrasonic ranging module at the bottom of the track-mounted mining car to perform dynamic cross-sectional scanning of the track within a specified range in front of the vehicle during its operation, and obtain data on track top surface wear, side wear, and joint misalignment.

[0012] Dynamic weighing sensors are installed at the wheel axles of the vehicle to sense the instantaneous vertical pressure value on the track in real time due to changes in load.

[0013] A dual-axis inclinometer and ground-coupled radar are installed on the vehicle chassis to detect settlement and hollowing of the foundation beneath the track;

[0014] The system synchronously collects the vehicle's precise position, driving posture, track geometry parameters, dynamic load value, and abnormal ground reflection wave signals at a frequency of no less than 50Hz.

[0015] S2. Establish a multi-mode heterogeneous wireless network to achieve reliable long-distance data transmission inside the cave: Utilize leaky cables and UWB positioning base stations to construct a wireless transmission backbone with continuous coverage inside the cave. The vehicle terminal integrates a ZigBee self-organizing network module. When a vehicle enters a weak signal area of ​​the leaky cable, the vehicle-to-vehicle relay protocol is automatically activated, and adjacent vehicles transmit data in a ZigBee chain relay.

[0016] All track detection data and vehicle status data are transmitted back to the ground monitoring platform outside the tunnel in real time with an end-to-end latency of ≤150ms, and are also backed up locally on the vehicle.

[0017] S3. Perform real-time edge computing to perform initial filtering of track anomalies and driving risks: Run the track damage identification model in the vehicle edge computing terminal, process the cross-sectional point cloud data scanned by lidar in real time, and automatically extract three key indicators: track wear depth, joint misalignment amount and foundation settlement index.

[0018] At the same time, the actual safe operating speed threshold is dynamically calculated based on the current load value. If the measured vehicle speed exceeds the threshold, the vehicle's first-level audible and visual warning will be triggered immediately.

[0019] S4. The platform integrates multi-source data to conduct a comprehensive assessment of the overall situation and track life: After receiving track detection data, settlement data and vehicle operating conditions uploaded by all vehicles, the ground monitoring and early warning platform combines the mine's 3D GIS map and track BIM model, and uses spatiotemporal correlation analysis algorithms to conduct three core assessments: track damage location and quantification, heavy load cumulative impact assessment and foundation settlement trend prediction.

[0020] S5. Trigger the graded early warning mechanism and execute differentiated linkage control and precise maintenance instructions: Based on the comprehensive risk score and damage location results in S4, the platform automatically executes a three-level differentiated graded treatment strategy for mild risk, moderate risk and high risk.

[0021] Preferably, the track geometry parameters in S1 include track gauge, track height, and wear depth. The precise position is obtained by fusion positioning of the UWB positioning module and the encoder odometer. The ground reflection wave anomaly signal is used to invert whether there are cavities or subsidence within a specified depth range below the track.

[0022] Preferably, the leaky cable in S2 is laid along the mine wall and together with the UWB positioning base station forms a wireless transmission backbone with continuous coverage inside the mine. The ZigBee self-organizing network module automatically activates the vehicle relay forwarding protocol in weak signal areas and transmits data through chain relay between adjacent vehicles.

[0023] Preferably, the three key indicators extracted by the track damage identification model in S3 are: track wear depth is calculated by comparing with a standard track section template; if the wear exceeds the specified value, it is judged as a slight abnormality; if it exceeds the maximum specified value, it is judged as a serious abnormality.

[0024] The joint misalignment detection system measures the elevation difference and lateral misalignment at the track joint, and triggers an alarm when the misalignment exceeds the specified range.

[0025] The ground settlement index is based on the ground coupled radar echo signal to determine whether there are cavities or settlement within a specified depth below the track. An early warning is generated when the settlement rate exceeds the specified value for each day.

[0026] Preferably, the calculation method for the safe operating speed threshold in S3 is as follows: when the load increases by a certain amount, it is recommended that the maximum speed be reduced proportionally.

[0027] When the measured vehicle speed exceeds the threshold, the vehicle-mounted buzzer and LED flashing warning are immediately triggered, achieving a millisecond-level local response.

[0028] Preferably, the track damage localization and quantification in S4 is as follows: by fusing real-time vehicle odometer data with UWB positioning, the detected track wear and misalignment are accurately mapped onto a GIS map, and the damage start position, end position and maximum wear depth value are calculated and output to form a three-dimensional marker of the damage area.

[0029] Preferably, the heavy load cumulative impact assessment in S4 is as follows: count the historical load passing times of all vehicles in the same track section, predict the remaining track life through the fatigue cumulative model, and generate a track maintenance prompt when the cumulative tonnage exceeds the design load threshold of the section.

[0030] The foundation settlement trend prediction is as follows: longitudinal comparison is performed on the foundation echo data that passes through the same location multiple times to determine whether the settlement is accelerating. If the settlement increases in three consecutive detections, the section is automatically marked as a high-risk area of ​​foundation instability.

[0031] Preferably, the graded handling strategy in S5 is as follows: when the risk is mild, the platform marks the damaged area as a yellow warning on the GIS map, automatically generates a track section inspection work order containing the precise coordinates of the damage, wear data and on-site photos, and pushes it to the handheld terminal of the maintenance team to arrange planned maintenance.

[0032] In the case of moderate risk, the platform issues a speed limit instruction to subsequent vehicles that are about to enter the section, and links the traffic lights in the tunnel ahead of the section to switch to flashing yellow warning, while simultaneously sending a mandatory confirmation dialog box to the dispatch center.

[0033] In cases of high risk, the platform automatically triggers an emergency blockade order, broadcasting an emergency stop command to all vehicles within a specified range before and after the section via wireless network, prohibiting any vehicle from entering the danger zone. The GIS map marks the prohibited area in red, and the platform automatically sends emergency text messages with damage location maps and maintenance suggestions to the mine manager, safety director, and maintenance team simultaneously. Traffic can only resume after manual confirmation of repairs and lifting of the blockade.

[0034] A mine vehicle safety operation monitoring and early warning system based on wireless networking includes: a sensing layer, a transmission layer, an edge computing layer, a platform layer, and an application layer;

[0035] The sensing layer is set on the body of the rail-type mining car and includes a lidar scanning array and ultrasonic ranging module, dynamic weighing sensor, dual-axis inclinometer and ground-coupled radar, UWB positioning module and encoder odometer, used to collect vehicle operation and track status and foundation deformation data.

[0036] The transmission layer includes a leaky cable laid along the mine wall, a UWB positioning base station, a ZigBee self-organizing network module integrated into the vehicle terminal, and a vehicle local storage module, which are used to build a multi-mode heterogeneous wireless network to achieve reliable long-distance data transmission inside the mine.

[0037] The edge computing layer is set on the vehicle-mounted edge computing terminal and includes a track damage identification model, a safe speed threshold calculation module, and an audible and visual first-level early warning module, which are used to perform real-time edge calculation and initial risk filtering.

[0038] The platform layer is located in the ground monitoring and early warning center and includes a 3D GIS map of the mine and a BIM model of the track, a spatiotemporal correlation analysis algorithm, a fatigue accumulation model and a settlement trend prediction module, which are used to integrate multi-source data to conduct a comprehensive assessment of the global situation and track life.

[0039] The application layer includes a GIS map marking module, an inspection work order generation module, a speed limit instruction issuance module, a traffic light linkage control module, an emergency blockade instruction module, and an emergency SMS notification module, which are used to trigger a graded early warning mechanism and execute differentiated linkage control and precise maintenance instructions.

[0040] The data acquisition frequency of the sensing layer is no less than 50Hz. When the measured vehicle speed exceeds the safety threshold or a serious track anomaly is detected, the sound and light level one early warning module triggers the vehicle-mounted buzzer and LED flashing warning.

[0041] Compared with related technologies, the mine vehicle safety operation monitoring and early warning method based on wireless networking provided by this invention has the following beneficial effects:

[0042] This invention provides a method for monitoring and early warning of safe operation of mining vehicles based on wireless networking. Through a standardized process from S1 to S5, it achieves proactive early warning and intelligent control of mine rail transportation safety. This method deeply integrates track structure health monitoring with vehicle operation safety early warning. Through the close connection of five links—multi-dimensional perception, reliable transmission, edge computing, platform analysis, and graded handling—a complete safety assurance closed loop is formed. In particular, by integrating odometer and UWB precise positioning technology, it achieves accurate quantitative description of the starting and ending positions of track damage and the maximum wear depth. Combined with foundation settlement detection, it effectively distinguishes the causes of damage. Combined with dynamic load assessment, it achieves adaptive speed control. It fundamentally solves the problem of traditional methods that know there is a problem with the track but do not know the specific location, severity, cause, or safety under the current load. It provides a full-chain technical guarantee for mine rail transportation safety. Attached Figure Description

[0043] Figure 1 This is a schematic diagram of a preferred embodiment of the mine vehicle safety operation monitoring and early warning method based on wireless networking provided by the present invention. Detailed Implementation

[0044] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0045] Please refer to the following: Figure 1 ,in, Figure 1This is a schematic diagram of a preferred embodiment of the mine vehicle safety operation monitoring and early warning method based on wireless networking provided by the present invention. The mine vehicle safety operation monitoring and early warning method based on wireless networking includes the following steps:

[0046] S1. Construct a multi-dimensional perception layer to collect data on vehicle operation, track status, and foundation deformation: Deploy a lidar scanning array and an ultrasonic ranging module at the bottom of the track-mounted mining car to perform dynamic cross-sectional scanning of the track within a specified range in front of the vehicle during its operation, and obtain data on track top surface wear, side wear, and joint misalignment.

[0047] Dynamic weighing sensors are installed at the wheel axles of the vehicle to sense the instantaneous vertical pressure value on the track in real time due to changes in load.

[0048] A dual-axis inclinometer and ground-coupled radar are installed on the vehicle chassis to detect settlement and hollowing of the foundation beneath the track;

[0049] The system synchronously collects the vehicle's precise position, driving posture, track geometry parameters, dynamic load value, and abnormal ground reflection wave signals at a frequency of no less than 50Hz.

[0050] S2. Establish a multi-mode heterogeneous wireless network to achieve reliable long-distance data transmission inside the cave: Utilize leaky cables and UWB positioning base stations to construct a wireless transmission backbone with continuous coverage inside the cave. The vehicle terminal integrates a ZigBee self-organizing network module. When a vehicle enters a weak signal area of ​​the leaky cable, the vehicle-to-vehicle relay protocol is automatically activated, and adjacent vehicles transmit data in a ZigBee chain relay.

[0051] All track detection data and vehicle status data are transmitted back to the ground monitoring platform outside the tunnel in real time with an end-to-end latency of ≤150ms, and are also backed up locally on the vehicle.

[0052] S3. Perform real-time edge computing to perform initial filtering of track anomalies and driving risks: Run the track damage identification model in the vehicle edge computing terminal, process the cross-sectional point cloud data scanned by lidar in real time, and automatically extract three key indicators: track wear depth, joint misalignment amount and foundation settlement index.

[0053] At the same time, the actual safe operating speed threshold is dynamically calculated based on the current load value. If the measured vehicle speed exceeds the threshold, the vehicle's first-level audible and visual warning will be triggered immediately.

[0054] S4. The platform integrates multi-source data to conduct a comprehensive assessment of the overall situation and track life: After receiving track detection data, settlement data and vehicle operating conditions uploaded by all vehicles, the ground monitoring and early warning platform combines the mine's 3D GIS map and track BIM model, and uses spatiotemporal correlation analysis algorithms to conduct three core assessments: track damage location and quantification, heavy load cumulative impact assessment and foundation settlement trend prediction.

[0055] S5. Trigger the graded early warning mechanism and execute differentiated linkage control and precise maintenance instructions: Based on the comprehensive risk score and damage location results in S4, the platform automatically executes a three-level differentiated graded treatment strategy for mild risk, moderate risk and high risk.

[0056] The track geometry parameters in S1 include track gauge, track height, and wear depth. The precise location is obtained by fusion positioning of the UWB positioning module and the encoder odometer. The ground reflection wave anomaly signal is used to invert whether there are cavities or subsidence within a specified depth range below the track.

[0057] The leaky cable in S2 is laid along the mine wall and together with the UWB positioning base station, it forms a wireless transmission backbone with continuous coverage inside the mine. The ZigBee self-organizing network module automatically activates the vehicle relay forwarding protocol in the weak signal area and transmits data through the chain relay between adjacent vehicles.

[0058] The three key indicators extracted by the track damage identification model in S3 are as follows: the track wear depth is calculated by comparing it with the standard track section template. If it exceeds the specified value, it is judged as a slight abnormality, and if it exceeds the maximum specified value, it is a serious abnormality.

[0059] The joint misalignment detection system measures the elevation difference and lateral misalignment at the track joint, and triggers an alarm when the misalignment exceeds the specified range.

[0060] The ground settlement index is based on the ground coupled radar echo signal to determine whether there are cavities or settlement within a specified depth below the track. An early warning is generated when the settlement rate exceeds the specified value for each day.

[0061] The calculation method for the safe operating speed threshold in S3 is as follows: when the load increases by a certain amount, it is recommended that the maximum speed be reduced proportionally.

[0062] When the measured vehicle speed exceeds the threshold, the vehicle-mounted buzzer and LED flashing warning are immediately triggered, achieving a millisecond-level local response.

[0063] The track damage localization and quantification in S4 is as follows: by fusing real-time vehicle odometer data with UWB positioning, the detected track wear and misalignment are accurately mapped onto a GIS map, and the damage start position, end position and maximum wear depth value are calculated and output to form a three-dimensional marker of the damage area.

[0064] The S4 heavy load cumulative impact assessment is as follows: statistically analyze the historical load passing times of all vehicles in the same track section, predict the remaining track life through the fatigue cumulative model, and generate a track maintenance prompt when the cumulative tonnage exceeds the design load threshold of the section.

[0065] The foundation settlement trend prediction is as follows: longitudinal comparison is performed on the foundation echo data that passes through the same location multiple times to determine whether the settlement is accelerating. If the settlement increases in three consecutive detections, the section is automatically marked as a high-risk area of ​​foundation instability.

[0066] The specific graded handling strategy in S5 is as follows: When the risk is mild, the platform marks the damaged area as a yellow warning on the GIS map, automatically generates a track section inspection work order containing the precise coordinates of the damage, wear data and on-site photos, and pushes it to the handheld terminal of the maintenance team to arrange planned maintenance.

[0067] In the case of moderate risk, the platform issues a speed limit instruction to subsequent vehicles that are about to enter the section, and links the traffic lights in the tunnel ahead of the section to switch to flashing yellow warning, while simultaneously sending a mandatory confirmation dialog box to the dispatch center.

[0068] In cases of high risk, the platform automatically triggers an emergency blockade order, broadcasting an emergency stop command to all vehicles within a specified range before and after the section via wireless network, prohibiting any vehicle from entering the danger zone. The GIS map marks the prohibited area in red, and the platform automatically sends emergency text messages with damage location maps and maintenance suggestions to the mine manager, safety director, and maintenance team simultaneously. Traffic can only resume after manual confirmation of repairs and lifting of the blockade.

[0069] By integrating multi-source data on the platform for comprehensive analysis of the overall situation and track life, the system achieves precise location and quantification of track damage, scientific assessment of the cumulative impact of heavy loads, and accurate prediction of foundation settlement trends. In particular, through the fusion positioning technology of odometer and UWB, track damage is accurately mapped onto a 3D GIS map, outputting the damage start location, end location, and maximum wear depth value. This represents a qualitative leap from qualitative judgment to quantitative description. The system assesses the cumulative impact of heavy loads through fatigue accumulation models, identifies high-risk areas of foundation instability through settlement trend prediction, and implements differentiated and precise handling through a three-level graded early warning mechanism. This comprehensively improves the scientific and refined level of mine track transportation safety management.

[0070] Compared with related technologies, the mine vehicle safety operation monitoring and early warning method based on wireless networking provided by this invention has the following beneficial effects:

[0071] This invention provides a method for monitoring and early warning of safe operation of mining vehicles based on wireless networking. Through a standardized process from S1 to S5, it achieves proactive early warning and intelligent control of mine rail transportation safety. This method deeply integrates track structure health monitoring with vehicle operation safety early warning. Through the close connection of five links—multi-dimensional perception, reliable transmission, edge computing, platform analysis, and graded handling—a complete safety assurance closed loop is formed. In particular, by integrating odometer and UWB precise positioning technology, it achieves accurate quantitative description of the starting and ending positions of track damage and the maximum wear depth. Combined with foundation settlement detection, it effectively distinguishes the causes of damage. Combined with dynamic load assessment, it achieves adaptive speed control. It fundamentally solves the problem of traditional methods that know there is a problem with the track but do not know the specific location, severity, cause, or safety under the current load. It provides a full-chain technical guarantee for mine rail transportation safety.

[0072] A mine vehicle safety operation monitoring and early warning system based on wireless networking includes: a sensing layer, a transmission layer, an edge computing layer, a platform layer, and an application layer;

[0073] The sensing layer is set on the body of the rail-type mining car and includes a lidar scanning array and ultrasonic ranging module, dynamic weighing sensor, dual-axis inclinometer and ground-coupled radar, UWB positioning module and encoder odometer, used to collect vehicle operation and track status and foundation deformation data.

[0074] The transmission layer includes a leaky cable laid along the mine wall, a UWB positioning base station, a ZigBee self-organizing network module integrated into the vehicle terminal, and a vehicle local storage module, which are used to build a multi-mode heterogeneous wireless network to achieve reliable long-distance data transmission inside the mine.

[0075] The edge computing layer is set on the vehicle-mounted edge computing terminal and includes a track damage identification model, a safe speed threshold calculation module, and an audible and visual first-level early warning module, which are used to perform real-time edge calculation and initial risk filtering.

[0076] The platform layer is located in the ground monitoring and early warning center and includes a 3D GIS map of the mine and a BIM model of the track, a spatiotemporal correlation analysis algorithm, a fatigue accumulation model and a settlement trend prediction module, which are used to integrate multi-source data to conduct a comprehensive assessment of the global situation and track life.

[0077] The application layer includes a GIS map marking module, an inspection work order generation module, a speed limit instruction issuance module, a traffic light linkage control module, an emergency blockade instruction module, and an emergency SMS notification module, which are used to trigger a graded early warning mechanism and execute differentiated linkage control and precise maintenance instructions.

[0078] The data acquisition frequency of the sensing layer is no less than 50Hz. When the measured vehicle speed exceeds the safety threshold or a serious track anomaly is detected, the sound and light level one early warning module triggers the vehicle-mounted buzzer and LED flashing warning.

[0079] The working principle of the mine vehicle safety operation monitoring and early warning system based on wireless networking provided by this invention is as follows:

[0080] The sensing layer continuously collects three-dimensional data of the vehicle, track, and ground at a frequency of no less than 50Hz. The collected data is transmitted back in real time through the backbone network composed of the leaky cable of the transmission layer and the UWB positioning base station. When the vehicle enters a weak signal area, the ZigBee self-organizing network module automatically activates the inter-vehicle relay protocol to transmit data in a chain relay between adjacent vehicles to ensure that the data reliably reaches the ground monitoring platform. At the same time, the on-board local storage module backs up the data to prevent data loss.

[0081] The edge computing layer performs real-time calculations on the lidar point cloud data collected by the perception layer on the vehicle-mounted edge computing terminal. It extracts three key indicators—track wear depth, joint misalignment, and foundation settlement index—through the track damage identification model. The safe speed threshold calculation module dynamically calculates the safe speed threshold based on the current load. When a serious track anomaly or vehicle speed exceeding the limit is detected, the first-level audible and visual warning module immediately triggers the vehicle-mounted buzzer and LED flashing warning, achieving millisecond-level local response.

[0082] After receiving data uploaded by all vehicles, the ground monitoring and early warning platform uses a spatiotemporal correlation analysis algorithm to fuse odometer and UWB positioning data, accurately mapping track damage onto a 3D GIS map and outputting the damage start location, end location, and maximum wear depth value; the fatigue accumulation model assesses the cumulative impact of heavy loads and predicts the remaining track life; and the settlement trend prediction module determines whether the foundation settlement is accelerating.

[0083] The application layer executes tiered responses based on the comprehensive risk score from the platform layer: for minor risks, an inspection work order is generated to arrange planned maintenance; for moderate risks, a speed limit instruction is issued and traffic lights are activated for warning; for high risks, an emergency lockdown is triggered, a stop announcement is broadcast, and an emergency text message is sent, forming a complete closed loop from early warning to response.

[0084] Compared with related technologies, the mine vehicle safety operation monitoring and early warning system based on wireless networking provided by this invention has the following beneficial effects:

[0085] This invention provides a mine vehicle safety operation monitoring and early warning system based on wireless networking. Through the collaborative work of a five-layer architecture—sensing layer, transmission layer, edge computing layer, platform layer, and application layer—it achieves comprehensive perception, reliable transmission, intelligent computing, and precise handling of mine rail transportation safety. The system constructs a comprehensive perception system integrating vehicle, track, and ground by deploying multi-dimensional sensing devices such as lidar, dynamic weighing sensors, inclinometers, and ground-coupled radar on the rail-mounted mine vehicles. A multi-mode heterogeneous wireless networking scheme combining leaky cables and ZigBee self-organizing networks effectively solves the data transmission challenges in long-distance, multi-curved, and severely signal-blocked environments within mines. A two-layer computing architecture, with onboard edge computing terminals and a ground platform working collaboratively, achieves efficient collaboration between local millisecond-level early warning of track damage and global situational assessment at the platform end. By integrating odometer and UWB precise positioning technologies, it achieves accurate quantitative description of track damage locations, providing a reliable basis for precise maintenance. A three-level (light, medium, and high) early warning mechanism covers all scenarios from planned maintenance to emergency rescue.

[0086] The above description is merely an embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A method for monitoring and early warning of safe operation of mining vehicles based on wireless networking, characterized in that, Includes the following steps: S1. Construct a multi-dimensional perception layer to collect data on vehicle operation, track status, and foundation deformation: Deploy a lidar scanning array and an ultrasonic ranging module at the bottom of the track-mounted mining car to perform dynamic cross-sectional scanning of the track within a specified range in front of the vehicle during its operation, and obtain data on track top surface wear, side wear, and joint misalignment. Dynamic weighing sensors are installed at the wheel axles of the vehicle to sense the instantaneous vertical pressure value on the track in real time due to changes in load. A dual-axis inclinometer and ground-coupled radar are installed on the vehicle chassis to detect settlement and hollowing of the foundation beneath the track; The system synchronously collects the vehicle's precise position, driving posture, track geometry parameters, dynamic load value, and abnormal ground reflection wave signals at a frequency of no less than 50Hz. S2. Establish a multi-mode heterogeneous wireless network to achieve reliable long-distance data transmission inside the cave: Utilize leaky cables and UWB positioning base stations to construct a wireless transmission backbone with continuous coverage inside the cave. The vehicle terminal integrates a ZigBee self-organizing network module. When a vehicle enters a weak signal area of ​​the leaky cable, the vehicle-to-vehicle relay protocol is automatically activated, and adjacent vehicles transmit data in a ZigBee chain relay. All track detection data and vehicle status data are transmitted back to the ground monitoring platform outside the tunnel in real time with an end-to-end latency of ≤150ms, and are also backed up locally on the vehicle. S3. Perform real-time edge computing to perform initial filtering of track anomalies and driving risks: Run the track damage identification model in the vehicle edge computing terminal, process the cross-sectional point cloud data scanned by lidar in real time, and automatically extract three key indicators: track wear depth, joint misalignment amount and foundation settlement index. At the same time, the actual safe operating speed threshold is dynamically calculated based on the current load value. If the measured vehicle speed exceeds the threshold, the vehicle's first-level audible and visual warning will be triggered immediately. S4. The platform integrates multi-source data to conduct a comprehensive assessment of the overall situation and track life: After receiving track detection data, settlement data and vehicle operating conditions uploaded by all vehicles, the ground monitoring and early warning platform combines the mine's 3D GIS map and track BIM model, and uses spatiotemporal correlation analysis algorithms to conduct three core assessments: track damage location and quantification, heavy load cumulative impact assessment and foundation settlement trend prediction. S5. Trigger the graded early warning mechanism and execute differentiated linkage control and precise maintenance instructions: Based on the comprehensive risk score and damage location results in S4, the platform automatically executes a three-level differentiated graded treatment strategy for mild risk, moderate risk and high risk.

2. The method for monitoring and early warning of safe operation of mining vehicles based on wireless networking as described in claim 1, characterized in that, The track geometry parameters in S1 include track gauge, track height, and wear depth. The precise location is obtained by fusion positioning of the UWB positioning module and the encoder odometer. The ground reflection wave anomaly signal is used to invert whether there are cavities or subsidence within a specified depth range below the track.

3. The method for monitoring and early warning of safe operation of mining vehicles based on wireless networking according to claim 1, characterized in that, The leaky cable in S2 is laid along the mine wall and together with the UWB positioning base station, it forms a wireless transmission backbone with continuous coverage inside the mine. The ZigBee self-organizing network module automatically activates the vehicle relay forwarding protocol in the weak signal area and transmits data through the chain relay between adjacent vehicles.

4. The method for monitoring and early warning of safe operation of mining vehicles based on wireless networking according to claim 1, characterized in that, The three key indicators extracted by the track damage identification model in S3 are as follows: the track wear depth is calculated by comparing it with the standard track section template. If it exceeds the specified value, it is judged as a slight abnormality, and if it exceeds the maximum specified value, it is a serious abnormality. The joint misalignment detection system measures the elevation difference and lateral misalignment at the track joint, and triggers an alarm when the misalignment exceeds the specified range. The ground settlement index is based on the ground coupled radar echo signal to determine whether there are cavities or settlement within a specified depth below the track. An early warning is generated when the settlement rate exceeds the specified value for each day.

5. The method for monitoring and early warning of safe operation of mining vehicles based on wireless networking according to claim 1, characterized in that, The calculation method for the safe operating speed threshold in S3 is as follows: when the load increases by a certain amount, it is recommended that the maximum speed be reduced proportionally. When the measured vehicle speed exceeds the threshold, the vehicle-mounted buzzer and LED flashing warning are immediately triggered, achieving a millisecond-level local response.

6. The method for monitoring and early warning of safe operation of mining vehicles based on wireless networking according to claim 1, characterized in that, The track damage localization and quantification in S4 is as follows: by fusing real-time vehicle odometer data with UWB positioning, the detected track wear and misalignment are accurately mapped onto a GIS map, and the damage start position, end position and maximum wear depth value are calculated and output to form a three-dimensional marker of the damage area.

7. The method for monitoring and early warning of safe operation of mining vehicles based on wireless networking according to claim 1, characterized in that, The S4 heavy load cumulative impact assessment is as follows: statistically analyze the historical load passing times of all vehicles in the same track section, predict the remaining track life through the fatigue cumulative model, and generate a track maintenance prompt when the cumulative tonnage exceeds the design load threshold of the section. The foundation settlement trend prediction is as follows: longitudinal comparison is performed on the foundation echo data that passes through the same location multiple times to determine whether the settlement is accelerating. If the settlement increases in three consecutive detections, the section is automatically marked as a high-risk area of ​​foundation instability.

8. The method for monitoring and early warning of safe operation of mining vehicles based on wireless networking according to claim 1, characterized in that, The specific graded handling strategy in S5 is as follows: When the risk is mild, the platform marks the damaged area as a yellow warning on the GIS map, automatically generates a track section inspection work order containing the precise coordinates of the damage, wear data and on-site photos, and pushes it to the handheld terminal of the maintenance team to arrange planned maintenance. In the case of moderate risk, the platform issues a speed limit instruction to subsequent vehicles that are about to enter the section, and links the traffic lights in the tunnel ahead of the section to switch to flashing yellow warning, while simultaneously sending a mandatory confirmation dialog box to the dispatch center. In cases of high risk, the platform automatically triggers an emergency blockade order, broadcasting an emergency stop command to all vehicles within a specified range before and after the section via wireless network, prohibiting any vehicle from entering the danger zone. The GIS map marks the prohibited area in red, and the platform automatically sends emergency text messages with damage location maps and maintenance suggestions to the mine manager, safety director, and maintenance team simultaneously. Traffic can only resume after manual confirmation of repairs and lifting of the blockade.

9. A mine vehicle safety operation monitoring and early warning system based on wireless networking, comprising the mine vehicle safety operation monitoring and early warning method based on wireless networking as described in any one of claims 1-8, characterized in that, include: The layers are: perception layer, transport layer, edge computing layer, platform layer, and application layer. The sensing layer is set on the body of the rail-type mining car and includes a lidar scanning array and ultrasonic ranging module, dynamic weighing sensor, dual-axis inclinometer and ground-coupled radar, UWB positioning module and encoder odometer, used to collect vehicle operation and track status and foundation deformation data. The transmission layer includes a leaky cable laid along the mine wall, a UWB positioning base station, a ZigBee self-organizing network module integrated into the vehicle terminal, and a vehicle local storage module, which are used to build a multi-mode heterogeneous wireless network to achieve reliable long-distance data transmission inside the mine. The edge computing layer is set on the vehicle-mounted edge computing terminal and includes a track damage identification model, a safe speed threshold calculation module, and an audible and visual first-level early warning module, which are used to perform real-time edge calculation and initial risk filtering. The platform layer is located in the ground monitoring and early warning center and includes a 3D GIS map of the mine and a BIM model of the track, a spatiotemporal correlation analysis algorithm, a fatigue accumulation model and a settlement trend prediction module, which are used to integrate multi-source data to conduct a comprehensive assessment of the global situation and track life. The application layer includes a GIS map marking module, an inspection work order generation module, a speed limit instruction issuance module, a traffic light linkage control module, an emergency blockade instruction module, and an emergency SMS notification module, which are used to trigger a graded early warning mechanism and execute differentiated linkage control and precise maintenance instructions.

10. The mine vehicle safety operation monitoring and early warning system based on wireless networking according to claim 9, characterized in that, The data acquisition frequency of the sensing layer is no less than 50Hz. When the measured vehicle speed exceeds the safety threshold or a serious track anomaly is detected, the sound and light level one early warning module triggers the vehicle-mounted buzzer and LED flashing warning.

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