Inspection method, inspection system and inspection robot for mining belt conveyor

Abstract

The invention relates to the technical field of mine intelligent operation and maintenance and inspection robots, in particular to an inspection method, an inspection system and an inspection robot for a mining belt conveyor, which are used for acquiring temperature and vibration parameters along the line of the mining belt conveyor in real time; acquiring a target position point where an abnormal event occurs by using the temperature and vibration parameters of each position along the mining belt conveyor; obtaining a target inspection robot based on the distance between the target position point where the abnormal event occurs and all inspection robots along the mining belt conveyor and the residual electric quantity of each inspection robot; acquiring an optimal path from the target inspection robot to the target position point; the target inspection robot reinspects the target position after reaching the target position according to the optimal path, and the fault type of the target position point is determined; the speed of finding and responding to events such as conveyor belt deviation, bearing faults and temperature abnormity is remarkably increased, and the problems that a traditional system is limited in coverage and slow in response are solved.

Description

Technical Field

[0001] This application relates to the field of intelligent operation and maintenance and inspection robot technology in mines, specifically to an inspection method, inspection system and inspection robot for a mining belt conveyor. Background Technology

[0002] Mining belt conveyors mainly refer to belt conveyors used in coal mining, production, transportation, and processing. Mining belt conveyors are characterized by large transport capacity, complex working environment, strong load-bearing capacity, and long transport distance.

[0003] Currently, inspections are conducted using hanging inspection robots. For example, the invention patent "A Rail-mounted Inspection Robot System and Its Detection Method" disclosed in Chinese Patent Publication No. CN112526995A discloses a rail-mounted inspection robot system and its detection method. The system includes a robot and a track. The robot includes a buffer information acquisition unit, which is used to control the robot's inspection speed on the track and change the detection mode. The main problem it solves is the inability to maintain accuracy during the inspection process.

[0004] Chinese Patent Publication No. CN119820595A, entitled "An Inspection Robot System Integrating Autonomous Navigation and Real-Time Monitoring," discloses an inspection robot system integrating autonomous navigation and real-time monitoring. This system includes a mobile platform and a multi-sensor fusion module. The main function of this application is to utilize the integrated autonomous navigation and real-time monitoring inspection robot system, employing the multi-sensor fusion module to collect environmental data and generate maps, significantly improving the robot's positioning and navigation capabilities under various environmental conditions.

[0005] Chinese Patent Publication No. CN116360447A, entitled "An Inspection Robot System and Its Control Method," discloses an inspection robot system and its control method. The system includes an inspection robot, a control center, a control system, and a wireless communication system. The communication module is interactively connected to both the control center and the inspection robot. This invention addresses the problem of low efficiency in existing mobile robot inspections, which rely on methods such as magnetic strips, colored ribbons, and QR code recognition on the inspection ground for positioning due to the inability to perform autonomous positioning. However, these methods suffer from drawbacks such as potential redundant inspections and circuitous paths, limiting efficiency improvements. Furthermore, when large amounts of inspection data are transmitted back, the data processing and recognition module is slow, leading to data backlog and delays in outputting effective inspection results, thus hindering fault diagnosis and decision-making.

[0006] The existing belt conveyor inspection technology has the following main shortcomings: 1. Low inspection efficiency and limited coverage: Existing inspection robots mostly adopt fixed path or uniform speed inspection strategies, which cannot dynamically adjust the inspection focus and travel speed according to the actual condition of the equipment. This results in long monitoring cycles and low efficiency for long-distance belt conveyors. Abnormal event response relies on periodic inspections and lacks real-time, all-area perception means, making it difficult to identify faults such as conveyor belt misalignment, tearing, and bearing overheating in a timely manner.

[0007] 2. Blind spots and insufficient diagnostic depth exist in key areas: Traditional vision sensors cannot effectively cover areas such as the bottom of the belt conveyor drums, the back of the drive unit, and the gaps between idlers. These areas are greatly limited by obstructions, dust, and lighting conditions, easily creating monitoring blind spots. Existing robotic arms have complex structures, are heavy, and have multiple drive units, making them unsuitable for explosion-proof and space-constrained environments in mines, and unable to achieve rapid, flexible deployment and accurate close-range detection.

[0008] 3. Complex track environment and poor adaptability of robot walking mechanism: Underground tracks often have large slopes, small radius curves, uneven joints, etc. Existing robot walking systems generally lack anti-jamming, anti-slip and attitude self-adaptation capabilities, which can easily lead to derailment, jamming or data acquisition jitter, affecting continuous inspection and data validity.

[0009] 4. Lack of a dedicated fault detection and scheduling mechanism for belt conveyors: The existing system does not make full use of the advantages of long-distance and passive monitoring of fiber optic sensing, and has not built an event classification and dynamic task scheduling mechanism that matches the operating characteristics of the conveyor, so it cannot achieve rapid response to sudden faults and multi-objective collaborative optimization. Summary of the Invention

[0010] In order to overcome the problems existing in the prior art, the present invention provides a method, system and robot for inspecting mining belt conveyors.

[0011] To achieve the above objectives, the present invention adopts the following technical solution: a method for inspecting a mining belt conveyor, comprising: Real-time acquisition of temperature and vibration parameters at every location along the mining belt conveyor; The target location of the abnormal event can be obtained by using the temperature and vibration parameters at each position along the mining belt conveyor. The target inspection robot is determined by the distance between the target location of the abnormal event and all inspection robots along the mining belt conveyor, as well as the remaining power of each inspection robot. Obtain all paths for the target inspection robot to reach the target location point, and use the specific length of each path, the slope and curves in the path to obtain the optimal path for the target inspection robot to reach the target location point; After the target inspection robot reaches the target location according to the optimal path, it re-inspects the target location to determine the fault type of the target location point.

[0012] Preferably, the abnormal events include high-risk events, medium-risk events, and low-risk events; When the temperature at a certain location along the mining belt conveyor exceeds 70℃, or the vibration amplitude exceeds 10m / s², the abnormal event at that location is a high-risk event. When the temperature at a certain location along the mining belt conveyor is greater than 50℃ and less than or equal to 70℃, or the vibration amplitude is greater than 5m / s² and less than or equal to 10m / s², the abnormal event at that location is a medium-risk event. When the temperature at a certain location along the mining belt conveyor is less than or equal to 50℃ and the vibration amplitude is less than or equal to 5m / s², the abnormal event at that location is a low-risk event.

[0013] Preferably, the method for obtaining the target inspection robot is as follows: Obtain the shortest path distance from each inspection robot on the mining belt conveyor to the target location; A score is obtained for each inspection robot's performance at the target location by using the shortest path distance and remaining battery power. The formula for calculating the score is as follows: S i =ω dist *(1 / D i )+ω energy *E i In the formula, S i The score for the i-th inspection robot performing the inspection task at the target location; D i E represents the shortest path from the i-th inspection robot to the target location. i ω represents the remaining battery percentage of the i-th inspection robot; dist and ω energy Let be the weighting coefficient, and satisfy ω dist +ω energy =1; The target inspection robot is determined based on the scores obtained from all inspection robots performing the abnormal event detection task at the target location.

[0014] Preferably, the method for obtaining the optimal path for the target inspection robot to reach the target location includes: Obtain the estimated time for the target inspection robot to move along each path; The energy consumption cost of the target inspection robot to reach the target location point by using the basic operating power of the target inspection robot, the length of each path, and the estimated travel time on each path. The comprehensive cost of each path is obtained by using the basic path cost, estimated time, and energy consumption cost of the target inspection robot to reach the target location point through each path; The optimal path for the inspection robot to reach the target location is obtained by utilizing the comprehensive cost of each path.

[0015] Preferably, the method for obtaining the comprehensive cost of the target inspection robot reaching the target location via each path is as follows: F=G n +α·Time n +β·P n -γ·R; In the formula: G n Time represents the basic path cost for the inspection robot to reach the target location point via the nth path. n P is the estimated time for the inspection robot to reach the target location point via the nth path; n R represents the energy cost for the inspection robot to reach the target location point via the nth path; R is the priority level; α, β, and γ are weighting coefficients.

[0016] Preferably, the method for obtaining the estimated time of movement of the target inspection robot on each path includes: Get the length of all segments on each path; The estimated travel speed of the target inspection robot on each road segment is obtained by using the length, slope and curvature radius of each road segment on each path. The estimated travel time of the inspection robot on each road segment is obtained based on the estimated travel speed of the inspection robot on each road segment. The estimated travel time of the inspection robot on each path is obtained by using the estimated travel time of the inspection robot on each segment of each path.

[0017] Preferably, the method for obtaining the energy consumption cost of the target inspection robot reaching the target location point through each path includes: P n =Σ[(P base Time n )+(m·g·Δh n )+(k·m·a·L n )]; In the formula: P base The basic operating power of the target inspection robot; Time nL is the estimated time for the inspection robot to reach the target location point via the nth path; n The length of the nth path; m is the mass of the target inspection robot; g is the acceleration due to gravity; Δh n denoted as , where is the height change value along the nth path; 'a' is the average acceleration of the target inspection robot along the nth path; and 'k' is an empirical coefficient.

[0018] A mining belt conveyor inspection system includes: A distributed optical fiber sensing network is deployed along the entire length of the mining belt conveyor to acquire temperature and vibration parameters at every location along the conveyor. Inspection robots are used to conduct re-inspections on the entire belt conveyor line; The control system includes at least: The target location determination module uses temperature and vibration parameters collected at each position along the mining belt conveyor by a distributed optical fiber sensing network to obtain the target location of the abnormal event. The target inspection robot determination module determines the target inspection robot based on the distance between the target location where the abnormal event occurred and all inspection robots along the mine belt conveyor, as well as the remaining power of each inspection robot. Optimal path determination module: Obtain all paths for the target inspection robot to reach the target location point, and use the specific length of each path, the slope and curvature of the path to obtain the optimal path for the target inspection robot to reach the target location point; The control module controls the target inspection robot to move along the optimal path. After the target inspection robot reaches the target location, it controls the target inspection robot to re-inspect the target location and determine the fault type of the target location.

[0019] An inspection robot includes: A composite traveling mechanism is located on a track above the mining belt conveyor and travels along the track. The body is fixedly connected to the end of the composite walking mechanism; The telescopic robotic arm has one end fixedly connected to the machine body and the other end fixedly connected to a sensor used for re-inspection of the mining belt conveyor. The controller is used to send location information and its own power information to the control center, and to receive the task plan sent by the control center. It controls the composite walking mechanism to walk on the track according to the task plan. When it reaches the target location, the controller drives the telescopic robotic arm to drive the sensor to re-inspect the mining belt conveyor and sends the information collected by the sensor to the control center.

[0020] Preferably, the control center executes any of the mining belt conveyor inspection methods.

[0021] Compared with the prior art, the present invention has the following advantages: 1. By deploying a fiber optic sensor network along the entire conveyor tunnel, high-precision, long-distance, real-time monitoring of parameters such as vibration, sound, and temperature is achieved, establishing a comprehensive perception capability of equipment status. Based on the collected parameters, typical fault models and events of belt conveyors are classified, and dynamic task scheduling and path planning are constructed according to the classification. This enables closed-loop inspection by robots from "surface" to "point," significantly improving the detection and response speed to events such as conveyor belt misalignment, bearing failure, and abnormal temperature, overcoming the problems of limited coverage and slow response in traditional systems.

[0022] 2. To address the challenge of detecting defects in blind spots such as the bottom of rollers, drums, and the back of drive units in belt conveyors, a retractable, underactuated robotic arm detection module was invented. This robotic arm is compact, lightweight, and requires fewer drive units, allowing it to flexibly extend into narrow areas under explosion-proof constraints. Combined with high-resolution vision and contact sensors, it performs multi-angle, close-range, and precise re-inspections. By integrating anomaly location information provided by a fiber optic system, the robot can achieve rapid, close-range diagnosis, significantly improving the ability to identify and diagnose hidden defects.

[0023] 3. To address the issues of poor robot stability and susceptibility to jamming in the steep, narrow-radius curved tracks of underground coal mine roadways, a composite walking mechanism combining an anti-jamming drive device and a self-resetting structure is employed. This mechanism utilizes a dual-motor drive, a wheel-track combination, and a torsion spring clamping design, enabling it to perform small-radius turns (minimum 1m), steep climbs (maximum 45°), steep slope hovering, and emergency braking. The articulated guide wheel frame and tracked load-bearing wheels effectively enhance the robot's fit and adaptability to the track, preventing derailment, slippage, and collisions. This ensures stable and continuous robot operation under complex track conditions, guaranteeing high-quality data acquisition. Attached Figure Description

[0024] Figure 1 This is a flowchart of the inspection method for mining belt conveyors in this invention; Figure 2 This is a structural block diagram of the inspection system in an embodiment of the present invention; Figure 3 This is a schematic diagram of the inspection robot in an embodiment of the present invention; Figure 4 This is a schematic diagram of the telescopic robotic arm structure in an embodiment of the present invention; Figure 5 This is a schematic diagram of the composite walking mechanism in an embodiment of the present invention. Detailed Implementation

[0025] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0026] Example 1 The inspection system in this embodiment adopts a four-layer architecture similar to a nervous system: the perception layer acts as the "nerve endings," with a distributed fiber optic sensor network deployed along the entire conveyor as the main body, supplemented by high-definition visible light cameras, infrared thermal imagers, microphone arrays, and vibration sensors mounted on a track-mounted inspection robot, to achieve comprehensive and detailed perception of the equipment status; the execution layer plays the role of "hands and feet," with the track-mounted inspection robot at its core, integrating a composite walking mechanism, a retractable underactuated robotic arm, a sensor cabin, and a local controller, possessing autonomous movement and operation capabilities; the transmission layer constitutes a "neural network," using a hybrid network of industrial Ethernet ring network and 5G mining private network—fiber optic sensor data is directly connected to the Ethernet via a demodulator, while the robot and the control center rely on the 5G network to achieve high-bandwidth, low-latency real-time communication of video streams and control commands; the decision and control layer acts as the "brain" of the system, with a central scheduling unit (edge ​​brain) deployed in an underground explosion-proof computer responsible for real-time data processing, task scheduling, and path planning, while a cloud-based data analysis platform undertakes long-term data storage, fault model training, and macro-level operation and maintenance management, forming an edge-cloud collaborative intelligent decision-making system.

[0027] Specifically, in execution, such as Figure 1 The method for inspecting a mining belt conveyor shown includes: Real-time acquisition of temperature and vibration parameters at every location along the mining belt conveyor; Specifically, when collecting parameters such as temperature and vibration, a distributed optical fiber network is deployed above the mining belt conveyor. This network provides a spatial resolution of up to 1 meter, enabling continuous 24 / 7 temperature, vibration, and acoustic scanning along the conveyor line. For example, if the optical fiber detects an abnormally high temperature point exceeding the preset 65°C at kilometer marker K1250+50m (K1250+50m is a standard engineering kilometer marker used to accurately locate specific positions on long-distance linear engineering projects (such as highways, railways, tunnels, pipelines, and conveyors), or if abnormal vibration with a frequency of 800Hz (potentially matching the characteristic frequency of a fault in the inner ring of the idler roller bearing) and an effective vibration acceleration exceeding 4m / s² is detected at kilometer marker K1300+10m, an "abnormal event" will be immediately generated, including the location, parameters, and severity.

[0028] The target location of the abnormal event can be obtained by using the temperature and vibration parameters at each position along the mining belt conveyor. The acquired temperature and vibration parameters are sent to the control center, which compares and analyzes the received parameters with the expert system to classify abnormal events occurring at different locations.

[0029] In this embodiment, abnormal events are classified as high-risk, medium-risk, and low-risk events. When an event is determined to be high-risk or medium-risk, the mining belt conveyor at the corresponding location and target location needs to be re-inspected by an inspection robot to determine whether it needs maintenance or shutdown. For low-risk events, normal inspection is sufficient.

[0030] In this embodiment, the method used for classifying abnormal events includes: When the temperature at a certain location along the mining belt conveyor exceeds 70℃ or the vibration amplitude exceeds 10m / s², the abnormal event at that location is a high-risk event; or when the temperature at a certain location exceeds 70℃ and the vibration amplitude exceeds 10m / s², the abnormal event at that location is a high-risk event. When the temperature at a certain location along the mining belt conveyor is greater than 50℃ but less than or equal to 70℃, or the vibration amplitude is greater than 5m / s² but less than or equal to 10m / s², the abnormal event at that location is a medium-risk event; or when the temperature at a certain location is greater than 50℃ but less than or equal to 70℃, and the vibration amplitude is greater than 5m / s² but less than or equal to 10m / s², the abnormal event at that location is a medium-risk event.

[0031] When the temperature at a certain location along the mining belt conveyor is less than or equal to 50℃ and the vibration amplitude is less than or equal to 5m / s², the abnormal event at that location is a low-risk event.

[0032] For example, if the temperature at K1250+50m is detected to be 75℃, while the vibration frequency is 800Hz and the amplitude is only 6m / s², and the detected temperature of 75℃ is greater than 70℃, and the vibration amplitude is less than 10m / s², then the abnormal event at this location is judged to be a high-risk event.

[0033] This application utilizes a distributed optical fiber sensing network for full-line coverage detection, enabling high-precision, long-distance, and real-time monitoring of parameters such as vibration, sound, and temperature, and establishing a comprehensive perception capability for equipment status.

[0034] After determining the abnormal event, it is necessary to identify which inspection robot should re-inspect the location where the abnormal event occurred. In this embodiment, when determining which target inspection robot should perform the re-inspection task, the optimal target inspection robot should be selected based on the position of the inspection robots deployed on the track (distance from the target location point) and the remaining battery power of each inspection robot. Specifically, in this embodiment, the method used is to obtain the target inspection robot based on the distance between the target location point of the abnormal event and all inspection robots along the mine belt conveyor, as well as the remaining battery power of each inspection robot. Specifically, it involves obtaining the shortest path distance from each inspection robot on the mining belt conveyor to the target location. A score for each inspection robot's performance in detecting abnormal events at the target location is obtained using the shortest path distance from each robot to the target location and the robot's remaining battery power. The formula for calculating the score is as follows: S i =ω dist *(1 / D i )+ω energy *E i In the formula, S i The score for the i-th inspection robot performing the target location detection task; D i E represents the shortest path from the i-th inspection robot to the target location. i ω represents the remaining battery percentage of the i-th inspection robot; dist and ω energy Let be the weighting coefficient, and satisfy ω dist +ω energy =1; The target inspection robot is determined based on the scores obtained from all inspection robots performing the abnormal event detection task at the target location.

[0035] In one embodiment, assume that robot A is located at K1200 with 85% battery power, and robot B is located at K1230 with 40% battery power. The event occurs at K1250+50m. Using a preliminary calculation based on the global map, the estimated distance D from robot A to the event point is... A =5050 meters, the estimated distance D for robot B B =2050 meters. For high-risk events, the system is set to ω. dist =0.7, ω energy =0.3, emphasizing rapid response; calculate their respective scheduling priority scores: Robot A's score: S A =0.7*(1 / 5050)+0.3*0.85≈0.25; Robot B's score: S B =0.7*(1 / 2050)+0.3*0.40≈0.12; Calculations show that Robot A has a significantly higher scheduling priority score than Robot B. Therefore, despite the greater distance, Robot A's substantial battery capacity makes it the better choice. The system will dispatch Robot A to perform this emergency task and simultaneously mark it in the task queue. Once the task is completed, Robot A must immediately proceed to the nearest charging point for recharging.

[0036] After selecting the inspection robot to be executed, that is, after obtaining the target inspection robot, it is necessary to determine what path the inspection robot will take (since there are multiple paths for the robot to reach the target location from the starting point during the execution of the task, the optimal path needs to be identified to execute the task).

[0037] In this embodiment, the method for selecting the optimal path is as follows: obtain all paths for the target inspection robot to reach the target location point, and use the specific length of each path, the slope and curvature of the path to obtain the optimal path for the target inspection robot to reach the target location point. This includes: obtaining the estimated time for the target inspection robot to move along each path; Since each day's path includes multiple road segments, and each segment has different characteristics such as slope, length, and curvature, obtaining the time requires first acquiring the time the inspection robot spends on each road segment. Then, the overall estimated time is derived from the time spent on each road segment. Specific methods include: Get the length of all segments on each path; The estimated travel speed of the target inspection robot on each road segment is obtained by using the length, slope and curvature radius of each road segment on each path. The method for obtaining this estimated driving speed is as follows: V segment t=V max ×C slope (θ)×C curve (ρ); In the formula: C slope (θ) and C curve (ρ) is the deceleration coefficient between 0 and 1. For example, for every 10° increase in slope θ, C slope The value decreases linearly from 1.0 to 0.6; when the radius of curvature ρ of the curve is less than 2 meters, C curve Decreased to 0.5; V max The maximum speed that can be traveled on this road segment is determined; the estimated travel speed for each road segment is obtained through this method.

[0038] The estimated travel time of the inspection robot on each road segment is obtained based on the estimated travel speed of the inspection robot on each road segment. The estimated travel time of the inspection robot on each path is obtained by using the estimated travel time of the inspection robot on each segment of each path. The specific method for obtaining the estimated time is as follows: Time n =Σ(L segment / V segment ); In the formula, L segment V represents the length of each road segment. segment This is the estimated travel speed of the robot on this section of road.

[0039] After obtaining the estimated travel speed, the energy consumption cost of the target inspection robot to reach the target location point through each path is obtained using the basic operating power of the target inspection robot, the length of each path, and the estimated travel time on each path; the specific equation for this energy consumption cost is as follows: P n =Σ[(P base Time n )+(m·g·Δh n )+(k·m·a·L n )]; In the formula: P base The basic operating power of the target inspection robot; Time n L is the estimated time for the inspection robot to reach the target location point via the nth path; n The length of the nth path; m is the mass of the target inspection robot; g is the acceleration due to gravity; Δh n denoted as , where is the height change value along the nth path; 'a' is the average acceleration of the target inspection robot along the nth path; and 'k' is an empirical coefficient.

[0040] After obtaining the energy consumption cost, the comprehensive cost of each path is obtained by using the basic path cost, estimated time and energy consumption cost of the target inspection robot to reach the target location point through each path. The expression for the overall cost is: F=G n +α·Time n +β·P n -γ·R; In the formula: G n Time represents the basic path cost for the inspection robot to reach the target location point via the nth path. n P is the estimated time for the inspection robot to reach the target location point via the nth path;n R represents the energy cost for the inspection robot to reach the target location point via the nth path; R is the priority level; α, β, and γ are weighting coefficients.

[0041] R represents the priority reward, a negative cost term whose value is directly mapped to the event's risk level: high-risk events correspond to R = 100, medium-risk events to R = 50, and low-risk / routine inspection tasks to R = 10. The higher the task priority, the larger the R value, and the more is subtracted from the total cost F, thus significantly reducing the overall cost of that path and making it a preferred choice for the algorithm.

[0042] The weighting coefficients α, β, and γ are dynamically configured according to the task scenario to achieve different optimization objectives (the data here is for illustrative purposes only): High-risk response mode: α=0.6 (emphasis on speed), β=0.1 (energy consumption is secondary), γ=0.3 (high priority reward); Standard inspection mode (for low-risk events): α=0.2, β=0.6 (focusing on energy saving), γ=0.2; Equilibrium model (for medium-risk events): α=0.4, β=0.4, γ=0.2; The optimal path for the inspection robot to reach the target location is obtained by utilizing the comprehensive cost of each path.

[0043] In one embodiment, for example, at 14:05:00 on a certain day, a distributed fiber optic sensor network detected that at a roadway mileage of K1250+50m (i.e., 1250.05 kilometers from the starting point), the temperature rose continuously from 55°C to 78°C within 30 seconds, accompanied by abnormal vibrations with a frequency of 120Hz and an effective acceleration of 12m / s².

[0044] First, a preliminary judgment is made: the system makes a judgment based on the built-in rule base. Rule 1: Temperature > 70℃ → Triggers high-risk conditions.

[0045] Rule 2: Vibration frequency within the bearing failure characteristic range and amplitude >10m / s² → triggering high-risk conditions.

[0046] Conclusion: This event meets two high-risk conditions simultaneously and is therefore marked as a high-risk event. The system immediately generates an alarm and activates the central dispatch unit.

[0047] 2. Dynamic robot scheduling (1) Available robot status: Robot A: Located at K1245+00m, with 85% battery, currently performing a routine inspection task. Robot B: Located at K1255+00m, with 60% battery, currently in idle standby mode.

[0048] (2) Scheduling calculation: Target location: K1250+50m.

[0049] b. Estimated distance: Based on the global map, the scheduling system quickly calculates the distance. The shortest path distance D from robot A to the target point A =550 meters; the shortest path distance D from robot B to the target point B =450 meters c-weight setting: Due to the high-risk nature of this event, ω is set... dist =0.7,ω energy =0.3 d. Calculate the scheduling priority score (S): S A =0.7*(1 / 550)+0.3*0.85=0.25 S B =0.7*(1 / 450)+0.3*0.60=0.18 Scheduling Decision: Robot A's scheduling score (0.25) is higher than Robot B's (0.18). Although Robot B is slightly closer, Robot A's significant power advantage makes it the more reliable choice. The system decides to dispatch Robot A to perform the emergency task and orders it to immediately interrupt its current routine inspection.

[0050] 3. Multi-objective path planning Planning objective: To plan an optimal path for robot A from K1245+00m to K1250+50m.

[0051] Algorithm A: An improved multi-objective optimization algorithm A is adopted.

[0052] Scenario and parameters: Robot mass m = 80kg, base power P base =50W, maximum speed V max =1.5m / s.

[0053] The event is high-risk, so the priority reward is R=100, and the weights are set to α=0.6, β=0.1, γ=0.3.

[0054] The global map shows that the path includes an uphill section with L1=200 meters and a slope of θ=15°, and a sharp bend with a radius of curvature ρ=1.5 meters.

[0055] Example of critical path point cost estimation (taking a sharp bend as an example): Speed ​​adjustment: C slope (15°)≈0.7 (based on the linear decrease of the function) C curve(1.5m) = 0.5 (because ρ < 2m) V segment =1.5m / s * 0.7 * 0.5 = 0.525m / s Time cost: If the length of this segment is L n =10m, then t segment =10 / 0.525≈19.0s. This time will be included in the Time... n .

[0056] Energy consumption cost: Basic energy consumption: P base *Time n =50W * 19.0s = 950J Energy consumption for climbing (assuming an elevation gain of Δh = 0.5m): mgΔh n =80kg * 9.8m / s² * 0.5m = 392J Acceleration energy consumption (assuming acceleration from 0 to 0.525 m / s, with an empirical coefficient k of 1.1): k*m*a*L n= 1.1 * 80 kg * 0.0138 m / s² * 10 m ≈ 12.1 J (negligible) The energy cost P of this path segment =950+392=1342J, which will be included in P. n .

[0057] Planning results: The algorithm comprehensively calculates G for all nodes along the entire path. n Time n P n And substitute it into function F n =G n +0.6Time n +0.1P n The evaluation was conducted at -0.3100. A comprehensive optimal path was ultimately planned, with a total travel distance of 550 meters, an estimated travel time of 12 minutes, and relatively low energy consumption. This path guided the robot smoothly through steep slopes and sharp bends.

[0058] After determining the optimal path, robot A arrived at K1250+50m at 14:17 along the planned path. It then deployed its underactuated robotic arm and extended the infrared thermal imager to the vicinity of the drive roller bearing housing.

[0059] Data feedback and confirmation: High-resolution thermal images showed that the bearing outer ring temperature reached 81℃, and the vibration spectrum confirmed an abnormal component at 120Hz. The system confirmed the fault as "overheating caused by severe bearing wear," maintained a high-risk alarm, and immediately pushed the diagnostic results and maintenance recommendations to the ground dispatch center.

[0060] Closed loop: The dispatch center arranges a maintenance team. The central dispatch unit updates the status and directs robot A to return to the interruption point after completing its task and continue routine inspections.

[0061] In this embodiment, task queueing and multi-machine collaborative logic are used to ensure the system's efficient response and stable operation in dynamic environments. When a high-risk task suddenly occurs, the scheduling system will immediately interrupt the robot's current routine inspection task and accurately record the breakpoint location (e.g., "Routine inspection completed up to K1215m"). After the high-priority task is completed, the robot can return to the breakpoint to continue the unfinished inspection according to the instruction, or the system can reassign new tasks according to the global state, ensuring the continuity of operations and optimal resource utilization. Regarding multi-machine collaboration, the system monitors the remaining battery power of all robots in real time and makes intelligent decisions on task allocation accordingly: such as... Figure 4 As shown in the rightmost branch, if the robot performing an emergency task has insufficient power (for example, below the 15% redundancy threshold required to complete the task), the system will not risk dispatching it. Instead, it will automatically activate the coordination mechanism—schedule the nearest robot with sufficient power to take over the task, while instructing the original robot to go to the nearest wireless charging point to recharge. During this process, the central scheduling unit will plan conflict-free travel paths for the two robots and ensure that they do not meet or block each other on the track through a spatiotemporal avoidance strategy, thereby achieving safe, efficient and reliable multi-robot collaborative operation.

[0062] To achieve blind-spot-free monitoring, the sensing optical fiber is deployed using a "one-line, multi-zone" topology: the main sensing optical cable is laid along the longitudinal beam of the belt conveyor, parallel to the conveyor belt, such as... Figure 3 The device is used to continuously monitor the temperature and acoustic vibration fields of the entire transmission line. At the same time, in key component areas such as drive stations, rollers, and tensioning devices, optical fibers are reinforced by spiral winding or dense looping to significantly improve the sensing spatial resolution and monitoring sensitivity of these key parts, thereby achieving high-precision perception of the status of key equipment.

[0063] Two advanced fiber optic sensing technologies are employed to achieve multi-dimensional state perception: In temperature and strain monitoring, based on BOTDR (Brillouin Optical Time Domain Reflectometry) technology, temperature and strain information distributed along the fiber is demodulated by detecting changes in the Brillouin frequency shift. The relationship is described by the formula: VB(ε,T)=VB(0,0)+Cε·ε+CT·T, where VB is the Brillouin frequency shift, ε is the strain, T is the temperature, and Cε and CT are the corresponding strain and temperature coefficients, respectively. In vibration and acoustic wave recognition, DAS (Distributed Acoustic Sensing) technology is used to transform the entire fiber into a continuous microphone array. By analyzing the phase change of the backscattered Rayleigh light, vibration signals along the line are sensed. Combined with the built-in convolutional neural network (CNN) model, the acoustic characteristics of specific faults are accurately identified from complex background noise. For example, a conveyor belt tear manifests as a brief high-energy "bang" or continuous friction noise, a stuck idler roller presents a periodic "creak" accompanied by a lack of vibration, and a damaged bearing manifests as a high-frequency, periodic impact sound. This enables intelligent identification and early warning of faults in critical equipment.

[0064] Example 2 In another embodiment, such as Figure 2 The present invention provides a mining belt conveyor inspection system, comprising: A distributed optical fiber sensing network is deployed along the entire length of the mining belt conveyor to acquire temperature and vibration parameters along the conveyor. This network is used to continuously monitor the temperature and acoustic / vibration fields of the entire conveyor line. Simultaneously, in key component areas such as the drive station, rollers, and tensioning devices, optical fibers are reinforced through spiral winding or dense looping to significantly improve the sensing spatial resolution and monitoring sensitivity of these key areas, thereby achieving high-precision sensing of the status of critical equipment.

[0065] The inspection robot is used to re-inspect the entire belt conveyor line. The inspection robot is set on the track directly above the mining belt conveyor and can run along the track. When it reaches the target position, the inspection robot will re-inspect the location where the abnormal event occurred.

[0066] The control system includes at least: The target location determination module uses the temperature and vibration parameters of each position along the mining belt conveyor 5 collected by the distributed optical fiber sensing network to obtain the target location of the abnormal event. The target inspection robot determination module determines the target inspection robot based on the distance between the target location of the abnormal event and all inspection robots along the mine belt conveyor, as well as the remaining power of each inspection robot. Optimal path determination module: Obtain all paths for the target inspection robot to reach the target location point, and use the specific length of each path, the slope and curvature of the path to obtain the optimal path for the target inspection robot to reach the target location point; The control module controls the target inspection robot to move along the optimal path. After the target inspection robot reaches the target location, it controls the target inspection robot to re-inspect the target location and determine the fault type of the target location.

[0067] Example 3 This embodiment provides a method such as Figure 3 , Figure 4 and Figure 5 The inspection robot shown is designed with the mechanical structure and drive control of its main body in mind, focusing on three main objectives: stability, flexibility, and self-adaptability. Its core is a composite walking mechanism integrating anti-jamming drive and self-resetting functions. For example... Figure 4 As shown, the telescopic robotic arm 3 in this embodiment mainly includes a telescopic rod 301, a connecting block 302, a connecting rod 303, a rotating rod 304, and a sensor 305 integrated at the end. The fixed end of the telescopic rod 301 is rigidly connected to the body 2, and the telescopic end is fixedly connected to the connecting rod 303 through the connecting block 302. The connecting rod 303 is arranged perpendicularly to the telescopic rod 301, and its end is hinged to the rotating rod 304 through a rotating shaft and driven by a drive motor to achieve rotation. Various re-inspection sensors (such as infrared thermal imagers, vision cameras, etc.) are installed at the end of the rotating rod 304. The telescopic rod 301 serves as a Z-axis lifting mechanism and uses an electric push rod to achieve linear extension and retraction. The connecting rod 303 and the rotating rod 304 form a planar double-link mechanism, which is driven collaboratively by two servo motors 306 (M1, M2) to achieve end-effector positioning in the XY plane. The entire robotic arm uses only three drive units (one electric push rod + two servo motors), resulting in a compact structure and significantly reduced weight.

[0068] This robotic arm employs an underactuated architecture. With fewer drive units than mechanical degrees of freedom, it achieves precise positioning of the end effector in three-dimensional space through specific link size ratios and motion coupling relationships. Specifically: when the planar double-link mechanism M1 and M2 rotate according to a predetermined cooperative law, the end effector can smoothly extend from a retracted state to the target position along a preset trajectory; the telescopic rod provides Z-axis displacement, decoupled from the planar motion, thereby achieving X, Y, and Z-degree-of-freedom positioning. This design reduces the number of motors used, not only lowering the overall weight and power consumption, but also facilitating deployment and portability in explosion-proof and confined mine environments. Furthermore, through closed-loop control based on inverse kinematics, it still ensures detection position accuracy and attitude stability.

[0069] The robot employs a dual-motor independent differential drive system, with each side equipped with a drive unit consisting of an explosion-proof servo motor, a planetary reducer, and drive wheels. By adjusting the speed difference between the left and right wheels, it can achieve flexible steering with a minimum turning radius of only 1 meter. The motor controller, based on a PID algorithm, can still output constant torque even under a maximum 45° incline, effectively preventing slippage. To ensure reliable contact on complex tracks, the robot is equipped with a clamping and guiding system: guide wheels and pressure roller assemblies grip the robot body from both the top and bottom of the I-beam track. The guide wheel frame uses a hinged structure to adapt to track curvature; the pressure roller assembly has an embedded torsion spring, providing a constant downward pressure of approximately 150N, ensuring sufficient friction while also buffering impacts from track joints or uneven areas. In addition, the load-bearing components adopt a wheel-track composite design—rubber tracks wrap around multiple load-bearing wheels, significantly improving anti-slip performance and ground contact area; the self-resetting structure consists of adjusting pads, adjusting bolts, and buffer springs. When abnormalities such as track deformation or misalignment occur, this structure allows the wheel assembly to generate adaptive displacement within a certain range through the elastic deformation of the spring. After overcoming the obstacle, it automatically resets under the action of the spring's restoring force, effectively suppressing vehicle body bumps and thus ensuring the stability and continuity of data acquisition from the sensors above.

[0070] Specifically, such as Figure 5As shown, the composite walking mechanism 1 includes a walking body 101, drive wheels 102, a steering assembly 103, a load-bearing assembly 104, guide wheels 105, and a pressure wheel assembly 106. The walking body 101 serves as both the mounting base and the load-bearing frame; its main body is a rigid structure and is fixedly connected to the upper body 2 by bolts or welding. The walking body 101 has symmetrical mounting seats in its middle section to support the drive system. The drive system consists of two drive wheels 102 and their drive units. Each drive wheel 102 is movably connected to the mounting seat in the middle of the walking body 101 via an axle and bearing, and can rotate freely around its axis. The drive wheels 102 are directly driven by explosion-proof servo motors via planetary reducers. The motor and reducer are integrally encapsulated and fixed to the inside of the walking body 101 via flanges. The output shaft is keyed to the axle of the drive wheel 102, thereby transmitting power to the drive wheel 102. The left and right drive wheels 102 are driven independently, and steering is achieved through differential control. The steering assembly 103 is hinged to the front or rear of the vehicle body 101 and connected to the steering knuckle or independent steering wheel of the drive wheel 102. Specifically, the steering assembly 103 includes a steering servo, a linkage mechanism, and a steering vertical shaft. The steering vertical shaft is vertically mounted on the vehicle body 101 via upper and lower bearing seats, with its lower end connected to the guide wheel frame or a dedicated steering wheel, and its upper end connected to the servo output arm via a linkage. The servo receives control signals and drives the vertical shaft to rotate via the linkage, thereby causing the guide wheel 105 to deflect and achieve active guidance. The load-bearing assembly 104 adopts a wheel-track composite design, with each load-bearing assembly including a load-bearing wheel and a rubber track covering it. The load-bearing wheel is movably connected to the front and rear ends of the vehicle body 101 via a short axle and a suspension arm. One end of the suspension arm is hinged to the vehicle body 101, and the other end is equipped with a load-bearing wheel via a bearing. A buffer spring is provided at the hinge point, forming an independent suspension system, allowing each load-bearing wheel to independently adapt to the undulations of the track 4. The rubber track tightly wraps around the outer circumference of the load-bearing wheel and maintains appropriate tension through the track tensioning mechanism. Guide wheels 105 and pressure wheel assemblies 106 are respectively installed at both ends of the traveling body 101, embracing the I-beam track 4 from the top and bottom. The guide wheel 105 is installed at one end of the traveling body 101 via a wheel frame and pin. Its wheel frame adopts a vertical hinge design, allowing it to adapt to the left and right curvature of the track within a certain angle around the hinge point. The pressure wheel assembly 106 is installed at the other end of the traveling body 101 and includes a pressure wheel, a swing arm, and a torsion spring. The middle of the swing arm is hinged to the traveling body 101, with a pressure wheel installed at one end and the other end subjected to the torsion spring, causing the pressure wheel to continuously press against the lower surface of the upper flange of the track, providing stable positive pressure, preventing derailment, and buffering impact. A self-resetting structure 107 is integrated at the hinge point of the load-bearing assembly 104 or the steering assembly 103, mainly composed of adjusting pads, adjusting bolts, and buffer springs. When the track is partially deformed or misaligned, the buffer spring is compressed or stretched, allowing the wheelset to produce limited passive displacement; after overcoming the obstacle, the spring releases its stored energy, causing the wheelset to automatically return to the center position, thereby avoiding jamming and maintaining smooth driving.

[0071] A telescopic robotic arm 3 is provided on the machine body 2. The machine body 2 is fixedly connected to the composite walking mechanism 1, and the telescopic robotic arm 3 is driven by the composite walking mechanism 1 to move on the track 4. Figure 4 The device includes at least a telescopic rod 301. The fixed end of the telescopic rod 301 is fixedly connected to the machine body 2. A connecting block 302 is fixedly connected to the telescopic end, and a connecting rod 303 is fixedly connected to the top of the telescopic rod 301. The end of the connecting rod 303 is rotatably connected to a rotating rod 304. The rotating rod 304 is connected to the connecting rod 303 through a rotating shaft and is driven by a drive motor. The end of the rotating rod 304 is fixedly connected to a sensor that extends into the roller 6 on the mining belt conveyor 5 for re-inspection, to complete the re-inspection of the target position point and the roller where abnormal events occur.

[0072] The telescopic robotic arm 3 used in this embodiment, through its telescopic underactuated design, achieves precise positioning of the end effector in three-dimensional space using only two servo motors (M1, M2) and one electric actuator, balancing lightweight design and flexibility. Its core is a planar double-link mechanism. Utilizing specific link dimensional relationships, when M1 and M2 rotate in a coordinated manner, the end effector can complete the movement from retraction to full extension in the XY plane. This double-link assembly is mounted on a telescopic rod driven by the electric actuator, thereby obtaining lifting capability in the Z direction. Ultimately, three drive units efficiently achieve control of the X, Y, and Z degrees of freedom. In terms of control and sensing, each joint is equipped with a high-precision encoder to provide real-time feedback on joint angles. The system is based on an inverse kinematics model and can automatically calculate the target angle or stroke of the three drive units for the user-specified end target position (such as "extend forward 500mm, down 100mm, and reach the bottom of roller 6"). At the same time, the laser rangefinder integrated into the end effector provides closed-loop distance feedback, dynamically adjusts the robot arm's posture, and ensures that it maintains a constant distance from the detection target, thereby ensuring the clarity of visible light / infrared images and the accuracy of temperature measurement.

[0073] This embodiment also includes a control module, which is mainly used to send location information and its own power information to the control center, receive task plans sent by the control center, and control the walking mechanism to move on the track according to the task plan. When the target location is reached, the controller drives the telescopic robotic arm to drive the sensors to re-inspect the mining belt conveyor, and sends the information collected by the sensors to the control center. The control center executes a mining belt conveyor inspection method.

[0074] This embodiment employs a dual-motor drive, wheel-track combination, and torsion spring clamping design, enabling it to perform small-radius turns (minimum 1m), steep climbs (maximum 45°), steep slope hovering, and emergency braking. The articulated guide wheel frame and tracked load-bearing wheel design effectively enhance the robot's fit and adaptability to the track, preventing derailment, slippage, and collisions. This ensures the robot operates smoothly and continuously under complex track conditions, guaranteeing high-quality data acquisition.

[0075] The above embodiments are merely illustrative examples of the present invention and do not constitute a limitation on the scope of protection of the present invention. Any designs that are the same as or similar to the present invention are within the scope of protection of the present invention.

Claims

1. A method of mine belt conveyor inspection, characterized in that, The application relates to a method for acquiring a target inspection robot of a mine belt conveyor. The method comprises the following steps: acquiring temperature and vibration parameters of each position along the mine belt conveyor; acquiring a target position point of an abnormal event by using the temperature and vibration parameters of each position along the mine belt conveyor; acquiring a target inspection robot based on the distance between the target position point of the abnormal event and all inspection robots along the mine belt conveyor and the residual power of each inspection robot; acquiring all paths of the target inspection robot to the target position point and acquiring an optimal path of the target inspection robot to the target position point by using the specific length of each path, the slope and the curve in the path; 2. The method of claim 1, wherein, the target inspection robot re-inspects the target position according to the optimal path to determine the fault type of the target position point. The abnormal event comprises a high-risk event, a medium-risk event and a low-risk event. When the temperature of a certain position along the mine belt conveyor is greater than 70 DEG C or the vibration amplitude is greater than 10 m / s2, the abnormal event of the position is a high-risk event. When the temperature of a certain position along the mine belt conveyor is greater than 50 DEG C and less than or equal to 70 DEG C or the vibration amplitude is greater than 5 m / s2 and less than or equal to 10 m / s2, the abnormal event of the position is a medium-risk event.

3. The method of claim 1, wherein, When the temperature of a certain position along the mine belt conveyor is less than or equal to 50 DEG C and the vibration amplitude is less than or equal to 5 m / s2, the abnormal event of the position is a low-risk event. The method for acquiring the target inspection robot comprises the following steps: acquiring the shortest path distance of each inspection robot of the mine belt conveyor to the target position point; S i =ω dist *(1 / D i )+ω energy *E i In the formula, S i is the score of the i-th inspection robot performing the detection task at the target location point; D i is the shortest path of the i-th inspection robot to the target location point; E i is the remaining percentage of the i-th inspection robot; ω dist and ω energy are weight coefficients, and ω dist + ω energy = 1. acquiring the score of each inspection robot to execute a detection task to the target position point by using the shortest path distance of each inspection robot to the target position point and the residual power, wherein the score is calculated according to the formula:

4. The method of claim 1, wherein, determining the target inspection robot by using the scores of all inspection robots to execute the detection task to the target position point. The method for acquiring the optimal path of the target inspection robot to the target position point comprises the following steps: acquiring the estimated time of the target inspection robot on each path; acquiring the energy consumption cost of the target inspection robot on each path to the target position point by using the basic running power of the target inspection robot, the length of each path and the estimated time on each path; acquiring the comprehensive cost of each path by using the basic path cost, the estimated time and the energy consumption cost of the target inspection robot on each path to the target position point; 5. The method of claim 4, wherein, acquiring the optimal path of the target inspection robot to the target position point by using the comprehensive cost of each path. F = G n + a · Time n + β · P n - γ · R; In the formula, G n is the basic path cost of the target inspection robot passing through the nth path to reach the target position point, Time n is the estimated time of the target inspection robot passing through the nth path to reach the target position point; P n is the energy consumption cost of the target inspection robot passing through the nth path to reach the target position point; R is the priority level; and a, b, and g are weight coefficients.

6. The method of claim 4, wherein, The method for acquiring the comprehensive cost of the target inspection robot on each path to the target position point comprises the following steps: acquiring the estimated time of the target inspection robot on each path; the method for acquiring the estimated time of the target inspection robot on each path comprises the following steps: acquiring the length of all road sections on each path; acquiring the estimated driving speed of the target inspection robot on each road section by using the length, the slope and the curve radius of each road section on each path; acquiring the estimated time of the target inspection robot on each road section by using the estimated driving speed of the target inspection robot on each road section; The estimated time of the inspection robot running on each path is obtained by the estimated time of the inspection robot running on each path section.

7. The method of claim 4, wherein, The method for obtaining the energy consumption cost of the target inspection robot passing through each path to reach the target position point comprises: P n =∑[(P base ·Time n )+(m·g·Δh n )+(k·m·a·L n )]; In the formula: P base is the basic running power of the target inspection robot; Time n is the estimated time for the target inspection robot to reach the target position point via the nth path; L n is the length of the nth path; m is the mass of the target inspection robot, g is the acceleration of gravity, Δh n is the height change value of the nth path; a is the average acceleration of the target inspection robot on the nth path; and k is an empirical coefficient.

8. A mine belt conveyor inspection system characterized by, The method comprises: A distributed optical fiber sensing network is arranged along the entire mine belt conveyor to obtain the temperature and vibration parameters of each position along the belt conveyor. An inspection robot is used to recheck the entire belt conveyor. The control system comprises at least: A target position point determination module obtains the target position point of the abnormal event by using the temperature and vibration parameters of each position along the mine belt conveyor collected by the distributed optical fiber sensing network. A target inspection robot determination module obtains the target inspection robot based on the distance between the target position point of the abnormal event and all inspection robots along the mine belt conveyor and the remaining power of each inspection robot. An optimal path determination module obtains all paths of the target inspection robot to the target position point and obtains the optimal path of the target inspection robot to the target position point by using the specific length of each path, the slope and the curvature in the path. A control module controls the target inspection robot to move according to the optimal path, and controls the target inspection robot to recheck the target position point after the target inspection robot reaches the target position point to determine the fault type of the target position point.

9. A patrol robot, wherein is characterized by, The composite walking mechanism is located on a track above the mine belt conveyor and walks along the track. A body is fixedly connected to the end of the composite walking mechanism. A telescopic mechanical arm is fixedly connected to the body at one end and fixedly connected to a sensor for rechecking the mine belt conveyor at the other end. A controller is used to send position information and its own power information to the control center, accept the task plan sent by the control center, and control the composite walking mechanism to walk on the track according to the task plan. The control center implements any one of the mine belt conveyor inspection methods according to claims 1-7.

10. The inspection robot of claim 9, wherein, The control center implements any one of the mine belt conveyor inspection methods according to claims 1-7.

Images