Control method and control system of coal bunker inspection robot

Abstract

The invention discloses a coal bunker inspection robot control method and control system, and the method comprises the steps: 1, a centralized control platform sends an inspection instruction, carries out the self-inspection of an inspection robot and a coal bunker, and guarantees the normal work of the inspection robot and the normal work of other auxiliary equipment used for the work of the inspection robot; 2, the inspection robot enters the coal bunker, panoramic scanning is conducted through a laser radar, a thermal imaging picture is obtained through a thermal imaging camera, and the concentration of gas in the coal bunker is detected through a gas sensor; 3, the centralized control platform obtains a 3D point cloud model of the coal bunker according to the panoramic scanning, the 3D point cloud model comprises the contour of the coal bunker and the current coal level, the thermal image is analyzed to obtain the internal temperature of the coal bunker, and the concentration value of methane in the gas is obtained; and step 4, the centralized control platform carries out multi-level alarm according to the difference between the current coal level and the designed height, the internal temperature and whether the concentration value of methane exceeds the limit or not. The pre-judgment and disposal efficiency of the safety risk of the coal bunker is effectively improved.

Description

Technical Field

[0001] This invention relates to the field of coal bunker safety monitoring and intelligent control technology, and in particular to a control method and control system for a coal bunker inspection robot. Background Technology

[0002] Coal bunkers are core facilities for storing raw coal in the coal mining production chain. Their internal space is enclosed and the environment is harsh. They are subjected to the impact and friction of raw coal over a long period of time, which can easily lead to safety hazards such as excessive coal level causing blockage, abnormal local temperature, and excessive levels of harmful gases. Since manual inspection requires entering the enclosed space, which poses significant safety risks such as falls from heights, poisoning, and explosions, coal bunker inspection robots have gradually replaced manual labor and become the mainstream inspection method.

[0003] In the existing technology, a typical solution for a coal bunker inspection system is as follows: the inspection robot is equipped with sensors and a built-in battery pack, and uses a servo motor-driven lifting device to lift and lower the top of the coal bunker; the remote control center and data processing center receive sensor data transmitted by the robot through a fiber optic network and send control commands based on the data; the auxiliary decision-making system uses millimeter-wave radar, signal transceivers, etc. to realize detection inside the coal bunker and communication with the robot, aiming to replace manual labor with automated equipment to ensure the safety of the coal bunker.

[0004] However, existing technologies have the following key shortcomings in practical applications: Current inspection systems rely solely on simple status checks by the robot itself, lacking a collaborative self-inspection process involving the centralized control platform, inspection robots, and auxiliary equipment. They also fail to uniformly monitor the operational status of auxiliary equipment such as lifting drive modules, sensor communication links, and door linkage devices. This means that if auxiliary equipment malfunctions before inspection, they cannot be detected in advance, easily leading to inspection interruptions, data acquisition failures, or even robot jamming and other safety accidents. Furthermore, existing inspection robots do not integrate and process source data, resulting in large errors in coal level calculations and an inability to comprehensively assess the collaborative safety risks associated with coal level, temperature, and harmful gases within the coal bunker. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of existing technologies that rely solely on the simple status detection of the inspection robot itself without establishing a centralized control platform, a collaborative self-inspection process between the inspection robot and auxiliary equipment, and the lack of integrated processing of multi-source data, resulting in large errors in coal level calculation and an inability to comprehensively assess the combined safety risks of coal level, temperature, and harmful gases in the coal bunker. This invention provides a control method and control system for a coal bunker inspection robot. The system sends inspection commands through a centralized control platform and completes self-inspections of the inspection robot and auxiliary equipment. After the inspection robot enters the coal bunker, it collects data using lidar, thermal imaging cameras, and gas sensors. The centralized control platform then constructs a 3D point cloud model containing the coal bunker outline and the current coal level, analyzes the internal temperature and methane concentration, and performs multi-level alarms based on the difference between the coal level and the designed height, and whether the internal temperature and methane concentration exceed limits. This coal bunker inspection robot control method improves the reliability of coal bunker inspection and the accuracy of status monitoring, achieves effective early warning of coal bunker safety risks, and ensures the safe operation of the coal bunker.

[0006] The objective of this invention is achieved through the following technical solution: A control method for a coal bunker inspection robot includes the following steps: Step 1: The centralized control platform sends an inspection command to perform self-inspection on the inspection robot and coal bunker to ensure that the inspection robot and its auxiliary equipment are working properly. Step 2: The inspection robot enters the coal bunker, performs a panoramic scan using lidar, acquires thermal images using a thermal imaging camera, and detects the concentration of gas inside the coal bunker using a gas sensor. Step 3: The centralized control platform obtains a 3D point cloud model of the coal bunker based on the panoramic scan. The 3D point cloud model includes the outline of the coal bunker and the current coal level. The thermal imaging image is analyzed to obtain the internal temperature of the coal bunker and the concentration value of methane in the gas. Step 4: The centralized control platform will issue multi-level alarms based on the difference between the current coal level and the design height, the internal temperature, and whether the methane concentration exceeds the limit.

[0007] As a preferred embodiment, the control method for the coal bunker inspection robot also includes step 5: after the inspection robot enters the coal bunker and descends to a set height, the lidar scans the coal bunker wall to obtain 3D point cloud data of the coal bunker, the thermal imaging camera obtains thermal imaging images, and the visible light camera obtains visible light images; the AI ​​identifies and marks the coal-adhering areas and cracks on the bunker wall based on the 3D point cloud data, thermal imaging images, and visible light images, and issues an alarm message indicating that there is coal adhering or cracks on the bunker wall.

[0008] Preferably, in step 2, the process of the inspection robot entering the coal bunker is as follows: The infrared sensor detects whether the inspection robot is at the docking position outside the coal bunker. If it is, the infrared sensor sends a signal to the central control platform, which then sends a command to the door drive motor to open the door at the top of the coal bunker, allowing the inspection robot to enter the coal bunker. The door then closes. The process of the inspection robot exiting the coal bunker is as follows: The infrared sensor detects whether the inspection robot is in the docking position inside the coal bunker. If it is, the infrared sensor sends a signal to the central control platform, which then sends a command to the door drive motor to open the door at the top of the coal bunker. The inspection robot then exits the coal bunker, and the door closes.

[0009] As a preferred method, after obtaining the 3D point cloud model of the coal bunker, noise caused by dust is removed using a Kalman filter model, specifically: Using the spatial coordinates and reflectivity of the 3D point cloud of the coal bunker as the state vector, a state transition equation is constructed based on the geometric constraints preset in the coal bunker design structure diagram. The filter gain coefficient is set to balance the weights of the Kalman filter model prediction and the actual observation. The original point cloud data is iteratively processed using the Kalman filter algorithm: the theoretical spatial distribution of the current frame point cloud is predicted based on the effective geometric features of the previous frame point cloud, and discrete points that exceed the design geometric boundary of the coal bunker are excluded; the original point cloud of the current frame is compared with the predicted value, the residual is calculated, and discrete points with reflectivity below the threshold and residual exceeding the set range are identified as dust noise points and filtered out, retaining continuous point cloud data that conforms to the actual geometric features of the coal bunker.

[0010] As a preferred option, the effectiveness of using the Kalman filter model to remove noise caused by dust was also verified, specifically: After Kalman filtering, a denoised 3D point cloud model is output. The geometric accuracy of the 3D point cloud model is verified by comparing it with the theoretical model of the coal bunker design structure diagram. At the same time, visual verification is performed by combining the acquired visible light images. If the bunker wall outline and coal position boundary in the point cloud model are consistent with the actual shape in the visible light image, the noise removal is deemed qualified and can be used for subsequent coal position calculation and bunker wall anomaly identification. If there is a deviation, the noise covariance matrix of the Kalman filter is readjusted, and the above iterative process is repeated until the accuracy requirements are met.

[0011] Preferably, in step 4, the multi-level alarm specifically refers to: When the coal level exceeds 80% of the design height, the temperature is greater than 60℃, or the methane concentration is 0.5-1%LEL, a level one alarm will be activated. When the coal level exceeds 90% of the design height, the temperature is greater than 80℃, or the methane concentration is 1-3%LEL, a level two alarm will be activated. When the coal level exceeds the design height, the temperature is greater than 100℃, or the methane concentration is 3%LEL, a level three alarm will be activated.

[0012] As a preferred option, when a Level 3 alarm occurs, the inspection robot immediately stops its inspection, immediately leaves the coal bunker, and at the same time, the central control platform sends an emergency alarm signal to the remote end.

[0013] A control system for a coal bunker inspection robot, applicable to a control method for a coal bunker inspection robot, includes an inspection robot, a lifting drive module, and a centralized control platform. The centralized control platform is equipped with an instruction sending unit, a self-testing unit, a data receiving and processing unit, and a multi-level alarm unit. The instruction sending unit is used to send inspection instructions. The self-testing unit is used to detect the working status of the inspection robot, the operating status of the lifting drive module, and the readiness status of the linkage protection components after receiving the inspection instructions, to ensure that the inspection robot can operate normally and the lifting drive module can be driven normally. The inspection robot is equipped with a lidar, a thermal imaging camera, and a multi-parameter gas sensor. The lidar is used to perform a panoramic scan after the inspection robot enters the coal bunker to collect spatial point cloud data of the coal bunker. The thermal imaging camera is used to acquire a thermal image of the inside of the coal bunker after the inspection robot enters the coal bunker. The multi-parameter gas sensor is used to detect the concentration of gas in the coal bunker after the inspection robot enters the coal bunker. The lifting drive module is connected to the inspection robot and is used to drive the inspection robot into or out of the coal bunker. The data receiving and processing unit is used to receive spatial point cloud data collected by lidar, thermal imaging images acquired by thermal imaging cameras, and gas concentration data detected by multi-parameter gas sensors. It constructs a 3D point cloud model of the coal bunker based on the spatial point cloud data, analyzes the thermal imaging images to obtain the internal temperature of the coal bunker, and parses the gas concentration data to obtain the methane concentration value inside the coal bunker. The multi-level alarm unit is used to determine whether the alarm conditions are met based on the difference between the current coal level and the designed height of the coal bunker, the internal temperature of the coal bunker, and the methane concentration value obtained by the data receiving and processing unit. If the conditions are met, the corresponding level of alarm is triggered.

[0014] Preferably, the control system of the coal bunker inspection robot also includes a linkage protection component, which includes an infrared positioning sensor and a bunker door drive motor. Infrared positioning sensors are used to detect the position of the inspection robot: when the inspection robot is detected to be at the docking position outside the warehouse, it sends a position signal to the intelligent control platform; when the inspection robot is detected to be at the docking position inside the warehouse, it also sends a position signal to the intelligent control platform. The intelligent centralized control platform also includes a warehouse door control unit. Upon receiving a signal from an infrared positioning sensor indicating that the inspection robot is at the docking position outside the warehouse opening, the warehouse door control unit sends an opening command to the warehouse door drive motor to open the warehouse door at the top of the coal warehouse, allowing the inspection robot to enter. After the inspection robot enters the coal warehouse, it sends a closing command to the warehouse door drive motor to close the warehouse door. Furthermore, upon receiving a signal from an infrared positioning sensor indicating that the inspection robot is at the docking position inside the warehouse opening, it sends an opening command to the warehouse door drive motor to open the warehouse door, allowing the inspection robot to exit the coal warehouse. After the inspection robot exits the coal warehouse, it sends a closing command to the warehouse door drive motor to close the warehouse door.

[0015] Preferably, the inspection robot is also equipped with a visible light camera, and the visible light camera and the thermal imaging camera are integrated on an infrared dual-light gimbal.

[0016] The beneficial effects of this invention are as follows: This invention achieves collaborative self-inspection of the inspection robot and auxiliary equipment through a closed-loop control logic of unified self-inspection via a centralized control platform, collaborative acquisition by multiple sensors, integrated processing of multiple parameters, and multi-level alarms. This avoids the problems of existing technologies that rely solely on simple robot detection, leading to undetected auxiliary equipment malfunctions, inspection interruptions, or robot jamming, thus ensuring reliability during the inspection startup phase. Simultaneously, by acquiring multi-source data from lidar, thermal imaging cameras, and gas sensors, combined with the centralized control platform to construct a 3D point cloud model of the coal bunker outline and current coal level, and analyzing internal temperature and methane concentration, this invention solves the problems of fragmented data and large errors in coal level calculation in existing technologies, achieving comprehensive and accurate perception of key coal bunker conditions. Finally, multi-level alarms based on the linkage of three parameters—coal level, temperature, and methane concentration—replace the single-parameter alarm mode of existing technologies. Differential warnings can be triggered according to risk levels, avoiding over-warning or delayed warnings, effectively improving the efficiency of predicting and handling coal bunker safety risks, and reducing accidents such as fires and explosions caused by misjudgments of conditions.

[0017] After the inspection robot descends to a set height, LiDAR scans the silo wall to obtain 3D point cloud data, while thermal imaging and visible light cameras collect images of the silo wall. Then, AI algorithms fuse multi-source data to identify coal-adhering areas and cracks and mark their coordinates. This solves the shortcomings of existing technologies that cannot detect the condition of the silo wall: on the one hand, the identification of coal-adhering areas on the silo wall can provide early warning of the risk of silo blockage, avoiding the accumulation of raw coal that could prevent the coal silo from being unloaded normally and affect the coal mine production chain; on the other hand, real-time detection and alarm of silo wall cracks can promptly identify structural hazards and prevent silo wall collapse accidents caused by crack expansion. At the same time, AI recognition replaces manual judgment, reducing the probability of visual misjudgment in dusty environments, improving the accuracy and efficiency of silo wall anomaly detection, and ensuring the structural safety of the coal silo and the continuity of production.

[0018] Infrared positioning sensors accurately detect the robot's docking position, enabling intelligent linkage between the warehouse door and the robot's entry and exit. When the robot enters, the docking position outside the warehouse triggers the door to open; when it exits, the docking position inside the warehouse triggers the door to open. The door automatically closes after the robot enters or exits, solving the problems of existing technologies that fail to achieve linkage between the robot and the warehouse door and have long warehouse door exposure times. Firstly, this shortens the warehouse door opening time, reducing dust spillage and leakage of harmful gases such as methane and carbon monoxide from the coal warehouse, thus lowering pollution to the surrounding working environment and the risk of personnel poisoning. Secondly, it avoids delays and misoperations caused by manual operation of the warehouse door, ensuring the accuracy of the robot's entry and exit timing. Simultaneously, the wire rope detector in the lifting drive module monitors the rope's broken strands and wear status in real time, preventing the robot from falling due to wire rope failure, further improving system operational safety and forming multiple safeguards for warehouse entry and exit safety, equipment operation safety, and environmental sealing safety. Attached Figure Description

[0019] Figure 1 This is a flowchart of the present invention. Detailed Implementation

[0020] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided to make this application more comprehensive and complete, and to fully convey the concept of the exemplary embodiments to those skilled in the art.

[0021] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a thorough understanding of embodiments of this application. However, those skilled in the art will recognize that the technical solutions of this application can be practiced without one or more of the specific details, or other methods, components, apparatuses, steps, etc., can be employed. In other instances, well-known methods, apparatuses, implementations, or operations are not shown or described in detail to avoid obscuring various aspects of this application.

[0022] The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all content and operations / steps, nor do they necessarily have to be performed in the described order. For example, some operations / steps can be broken down, while others can be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.

[0023] Example: A control method for a coal bunker inspection robot, such as Figure 1 As shown, it includes the following steps: Step 1: The centralized control platform sends an inspection command to perform self-inspection on the inspection robot and coal bunker to ensure that the inspection robot and its auxiliary equipment are working properly. Step 2: The inspection robot enters the coal bunker, performs a panoramic scan using lidar, acquires thermal images using a thermal imaging camera, and detects the concentration of gas inside the coal bunker using a gas sensor. Step 3: The centralized control platform obtains a 3D point cloud model of the coal bunker based on the panoramic scan. The 3D point cloud model includes the outline of the coal bunker and the current coal level. The thermal imaging image is analyzed to obtain the internal temperature of the coal bunker and the concentration value of methane in the gas. Step 4: The centralized control platform will issue multi-level alarms based on the difference between the current coal level and the design height, the internal temperature, and whether the methane concentration exceeds the limit. Step 5: After the inspection robot enters the coal bunker and descends to the set height, the lidar scans the coal bunker wall to obtain 3D point cloud data of the coal bunker, the thermal imaging camera obtains thermal imaging images, and the visible light camera obtains visible light images. Based on the 3D point cloud data, thermal imaging images, and visible light images, the AI ​​identifies and marks the coal-adhering areas and cracks on the bunker wall, and issues an alarm message indicating that there is coal adhering or cracks on the bunker wall.

[0024] Specifically, when the inspection robot descends 0-10 meters, the lidar performs a 360° full scan, and the infrared camera takes a set of thermal images every 5 meters. When it descends 10-30 meters, it focuses on scanning the coal bunker wall (±15° vertical field of view) to identify coal adhesion and cracks.

[0025] In step 2, the process of the inspection robot entering the coal bunker is as follows: The infrared sensor detects whether the inspection robot is at the docking position outside the coal bunker. If it is, the infrared sensor sends a signal to the central control platform, which then sends a command to the door drive motor to open the door at the top of the coal bunker, allowing the inspection robot to enter the coal bunker. The door then closes. The process of the inspection robot exiting the coal bunker is as follows: The infrared sensor detects whether the inspection robot is in the docking position inside the coal bunker. If it is, the infrared sensor sends a signal to the central control platform, which then sends a command to the door drive motor to open the door at the top of the coal bunker. The inspection robot then exits the coal bunker, and the door closes.

[0026] After obtaining the 3D point cloud model of the coal bunker, noise caused by dust was removed using a Kalman filter model, specifically: Using the spatial coordinates and reflectivity of the 3D point cloud of the coal bunker as the state vector, a state transition equation is constructed based on the geometric constraints preset in the coal bunker design structure diagram. The filter gain coefficient is set to balance the weights of the Kalman filter model prediction and the actual observation. The original point cloud data is iteratively processed using the Kalman filter algorithm: the theoretical spatial distribution of the current frame point cloud is predicted based on the effective geometric features of the previous frame point cloud, and discrete points that exceed the design geometric boundary of the coal bunker are excluded; the original point cloud of the current frame is compared with the predicted value, the residual is calculated, and discrete points with reflectivity below the threshold and residual exceeding the set range are identified as dust noise points and filtered out, retaining continuous point cloud data that conforms to the actual geometric features of the coal bunker.

[0027] The effectiveness of using the Kalman filter model to remove noise caused by dust was also verified, specifically: After Kalman filtering, a denoised 3D point cloud model is output. The geometric accuracy of the 3D point cloud model is verified by comparing it with the theoretical model of the coal bunker design structure diagram. At the same time, visual verification is performed by combining the acquired visible light images. If the bunker wall outline and coal position boundary in the point cloud model are consistent with the actual shape in the visible light image, the noise removal is deemed qualified and can be used for subsequent coal position calculation and bunker wall anomaly identification. If there is a deviation, the noise covariance matrix of the Kalman filter is readjusted, and the above iterative process is repeated until the accuracy requirements are met.

[0028] In step 4, the multi-level alarm specifically refers to: When the coal level exceeds 80% of the design height, the temperature is greater than 60℃, or the methane concentration is 0.5-1%LEL, a level one alarm will be activated. When the coal level exceeds 90% of the design height, the temperature is greater than 80℃, or the methane concentration is 1-3%LEL, a level two alarm will be activated. When the coal level exceeds the design height, the temperature is greater than 100℃, or the methane concentration is 3%LEL, a level three alarm will be activated.

[0029] When a Level 3 alarm occurs, the inspection robot immediately stops its inspection, leaves the coal bunker, and the central control platform sends an emergency alarm signal to the remote end.

[0030] A control system for a coal bunker inspection robot, applicable to a control method for a coal bunker inspection robot, includes an inspection robot, a lifting drive module, and a centralized control platform. The centralized control platform is equipped with an instruction sending unit, a self-testing unit, a data receiving and processing unit, and a multi-level alarm unit. The instruction sending unit is used to send inspection instructions. The self-testing unit is used to detect the working status of the inspection robot, the operating status of the lifting drive module, and the readiness status of the linkage protection components after receiving the inspection instructions, to ensure that the inspection robot can operate normally and the lifting drive module can be driven normally. The inspection robot is equipped with a lidar, a thermal imaging camera, a visible light camera, and a multi-parameter gas sensor. The visible light camera and the thermal imaging camera are integrated on an infrared dual-light gimbal. The lidar is used to perform a panoramic scan after the inspection robot enters the coal bunker to collect spatial point cloud data of the coal bunker. The thermal imaging camera is used to acquire a thermal image of the inside of the coal bunker after the inspection robot enters the coal bunker. The multi-parameter gas sensor is used to detect the concentration of gas inside the coal bunker after the inspection robot enters the coal bunker. The lifting drive module is connected to the inspection robot and is used to drive the inspection robot into or out of the coal bunker. The data receiving and processing unit is used to receive spatial point cloud data collected by lidar, thermal imaging images acquired by thermal imaging cameras, and gas concentration data detected by multi-parameter gas sensors. It constructs a 3D point cloud model of the coal bunker based on the spatial point cloud data, analyzes the thermal imaging images to obtain the internal temperature of the coal bunker, and parses the gas concentration data to obtain the methane concentration value inside the coal bunker. The multi-level alarm unit is used to determine whether the alarm conditions are met based on the difference between the current coal level and the designed height of the coal bunker, the internal temperature of the coal bunker, and the methane concentration value obtained by the data receiving and processing unit. If the conditions are met, the corresponding level of alarm is triggered.

[0031] The control system of the coal bunker inspection robot also includes a linkage protection component, which includes an infrared positioning sensor and a bunker door drive motor. Infrared positioning sensors are used to detect the position of the inspection robot: when the inspection robot is detected to be at the docking position outside the warehouse, it sends a position signal to the intelligent control platform; when the inspection robot is detected to be at the docking position inside the warehouse, it also sends a position signal to the intelligent control platform. The intelligent centralized control platform also includes a warehouse door control unit. Upon receiving a signal from an infrared positioning sensor indicating that the inspection robot is at the docking position outside the warehouse opening, the warehouse door control unit sends an opening command to the warehouse door drive motor to open the warehouse door at the top of the coal warehouse, allowing the inspection robot to enter. After the inspection robot enters the coal warehouse, it sends a closing command to the warehouse door drive motor to close the warehouse door. Furthermore, upon receiving a signal from an infrared positioning sensor indicating that the inspection robot is at the docking position inside the warehouse opening, it sends an opening command to the warehouse door drive motor to open the warehouse door, allowing the inspection robot to exit the coal warehouse. After the inspection robot exits the coal warehouse, it sends a closing command to the warehouse door drive motor to close the warehouse door.

[0032] In this embodiment, the inspection robot body adopts a high-strength metal shell, weighs ≤65kg, and has dimensions of 350mm×400mm×700mm, with an IP67 protection rating. The front end integrates a lidar (the model selected meets ExibIMb explosion-proof standards, with a scanning frequency of 10Hz), and the side is equipped with an infrared dual-light gimbal, including a 640×480 thermal imaging camera and a 4-megapixel visible light camera. A multi-parameter gas sensor array (GD3 series, detection range: methane 0-100% LEL, carbon monoxide 0-1000ppm) is installed on the top.

[0033] The auxiliary equipment includes a lifting drive module, consisting of an explosion-proof and intrinsically safe winch (Ex(D)ibIMb), a φ8mm galvanized steel wire rope, and guide pulleys. The winch has a rated tensile force of 5000N and a lifting speed of 0.5m / s. The wire rope detector monitors the rope's broken wires and wear in real time using the principle of electromagnetic induction.

[0034] The door is an automatic protective cover with a track pulley structure, driven by an intrinsically safe mining motor. The opening / closing time is ≤10 seconds. It is linked to the robot's lifting command and the position status is confirmed by a limit switch.

[0035] The intelligent centralized control platform's interface displays a 3D model of the coal bunker, real-time data (coal level, temperature, gas concentration), and alarm information. The software integrates a coal level calculation algorithm, a trend analysis module (data curves for the past 72 hours), and an emergency linkage interface (supporting OPC protocol for connection with the coal mine's overall dispatch system).

[0036] Through the application of the solution in this embodiment, the data fusion of lidar and infrared camera reduces the coal level measurement error from ±5cm to ±3cm, and the temperature anomaly point location accuracy reaches 0.5m; the linkage design of automatic protective cover and robot reduces the exposure time of the silo opening by 90% compared with traditional manual operation, and the dust removal device effectively extends the working time of the sensor; local data preprocessing shortens the alarm response time from 200ms to 100ms, meeting the needs of handling sudden emergencies in coal silos; the system can be seamlessly connected with the existing comprehensive management and control platform of the coal mine and is suitable for various coal silo types such as round and square.

[0037] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the embodiments disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein.

[0038] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A control method of a coal bunker inspection robot, characterized by, The method comprises the following steps: Step 1, the centralized control platform sends an inspection instruction to perform self-checking on the inspection robot and the coal bunker to ensure that the inspection robot and its auxiliary equipment are working properly; Step 2, the inspection robot enters the coal bunker, performs panoramic scanning through a laser radar, obtains a thermal imaging image through a thermal imaging camera, and detects the concentration of gas in the coal bunker through a gas sensor; Step 3, the centralized control platform obtains a 3D point cloud model of the coal bunker according to the panoramic scanning, the 3D point cloud model comprising the outline and the current coal level of the coal bunker, analyzes the thermal imaging image to obtain the internal temperature of the coal bunker, and obtains the concentration value of methane in the gas; Step 4, the centralized control platform performs multi-level alarm according to whether the difference between the current coal level and the design height, the internal temperature, and the concentration value of methane are out of limits.

2. The control method of the coal bunker inspection robot according to claim 1, characterized in that, Further comprising step 5, after the inspection robot enters the coal bunker and descends to a set height, the laser radar scans the coal bunker wall to obtain 3D point cloud data, the thermal imaging camera obtains a thermal imaging image, and the visible light camera obtains a visible light image; the AI identifies and marks the coal sticking area and cracks of the coal bunker wall based on the 3D point cloud data, the thermal imaging image and the visible light image, and sends alarm information that the coal bunker wall has coal sticking or cracks.

3. The control method of the coal bunker inspection robot according to claim 1, characterized in that, In the step 2, the process that the inspection robot enters the coal bunker is specifically as follows: The infrared sensor detects whether the position of the inspection robot is at the connection position outside the bunker mouth, if the position of the inspection robot is at the connection position outside the bunker mouth, the infrared sensor feeds back a signal to the centralized control platform, the centralized control platform issues an instruction to the bunker door driving motor to open the bunker door located at the top of the coal bunker, the inspection robot enters the coal bunker, and then the bunker door is closed; The process that the inspection robot exits the coal bunker is specifically as follows: The infrared sensor detects whether the position of the inspection robot is at the connection position inside the bunker mouth, if the position of the inspection robot is at the connection position inside the bunker mouth, the infrared sensor feeds back a signal to the centralized control platform, the centralized control platform issues an instruction to the bunker door driving motor to open the bunker door located at the top of the coal bunker, the inspection robot exits the coal bunker, and then the bunker door is closed.

4. The control method of the coal bunker inspection robot according to claim 1, characterized in that, in After obtaining the 3D point cloud model of the coal bunker, the Kalman filtering model is further used to remove the noise points caused by dust, specifically as follows: The spatial coordinates and reflectivity of the coal bunker 3D point cloud are taken as state vectors, a state transition equation is constructed based on the geometric constraints of the coal bunker design structure diagram, and a filter gain coefficient is set to balance the weights of the Kalman filtering model prediction value and the actual observation value; The original point cloud data is iteratively processed through the Kalman filtering algorithm: the theoretical spatial distribution of the current frame point cloud is predicted based on the effective geometric features of the previous frame point cloud, and the discrete points exceeding the design geometric boundary of the coal bunker are excluded; the current frame original point cloud is compared with the prediction value, the residual error is calculated, the discrete points with a reflectivity lower than a threshold value and a residual error exceeding a set range are determined as dust noise points, and are filtered, and the continuous point cloud data conforming to the actual geometric features of the coal bunker is reserved.

5. The control method of the coal bunker inspection robot according to claim 4, characterized in that, The effect of removing the noise points caused by dust through the Kalman filtering model is further verified, specifically as follows: After the Kalman filtering is completed, a 3D point cloud model after noise removal is output, and the geometric accuracy of the 3D point cloud model is verified by comparison with a theoretical model of a coal bunker design structure diagram. Meanwhile, visual checking is performed in combination with the collected visible light image. If the profiles of the bunker walls and the boundaries of the coal level in the point cloud model are consistent with the actual shapes in the visible light image, it is determined that the noise removal is qualified, and the point cloud model can be used for subsequent coal level calculation and bunker wall anomaly identification. If there is a deviation, the noise covariance matrix of the Kalman filtering is adjusted again, and the above iteration process is repeated until the accuracy requirement is met.

6. The control method of the coal bunker inspection robot according to claim 1, characterized in that, In step 4, the multi-level alarm is specifically: When the coal level exceeds 80% of the design height, the temperature is greater than 60℃, or the methane concentration is 0.5-1% LEL, a first-level alarm is started; When the coal level exceeds 90% of the design height, the temperature is greater than 80℃, or the methane concentration is 1-3% LEL, a second-level alarm is started; When the coal level exceeds the design height, the temperature is greater than 100℃, or the methane concentration is 3% LEL, a third-level alarm is started.

7. The control method of the coal bunker inspection robot according to claim 6, characterized in that, When the third-level alarm occurs, the inspection robot immediately stops inspection and immediately leaves the coal bunker, and the centralized control platform sends an emergency alarm signal to a remote terminal.

8. A control system of a coal bunker inspection robot, which is suitable for the control method of the coal bunker inspection robot according to any one of claims 1 to 7, characterized in that, The system comprises an inspection robot, a lifting drive module and a centralized control platform. The centralized control platform is provided with an instruction sending unit, a self-checking unit, a data receiving and processing unit and a multi-level alarm unit. The instruction sending unit is used for sending an inspection instruction. The self-checking unit is used for detecting the working state of the inspection robot, the running state of the lifting drive module and the readiness state of the linkage protection assembly after receiving the inspection instruction, to ensure that the inspection robot can normally work and the lifting drive module can normally drive. The inspection robot is provided with a laser radar, a thermal imaging camera and a multi-parameter gas sensor. The laser radar is used for panoramic scanning after the inspection robot enters the coal bunker, to collect spatial point cloud data of the coal bunker. The thermal imaging camera is used for acquiring a thermal imaging image of the inside of the coal bunker after the inspection robot enters the coal bunker. The multi-parameter gas sensor is used for detecting the concentration of gas in the coal bunker after the inspection robot enters the coal bunker. The lifting drive module is connected with the inspection robot and is used for driving the inspection robot to enter or exit the coal bunker. The data receiving and processing unit is used for receiving the spatial point cloud data collected by the laser radar, the thermal imaging image acquired by the thermal imaging camera and the gas concentration data detected by the multi-parameter gas sensor, and constructing a 3D point cloud model of the coal bunker according to the spatial point cloud data, analyzing the thermal imaging image to acquire the temperature inside the coal bunker, and analyzing the gas concentration data to acquire the concentration value of methane in the coal bunker. The multi-level alarm unit is used for judging whether the alarm condition is met according to the difference between the current coal level and the design height of the coal bunker, the temperature inside the coal bunker and the concentration value of methane, and triggering the alarm of the corresponding level if the condition is met.

9. The control system of a coal bunker inspection robot according to claim 8, characterized in that, The linkage protection assembly comprises an infrared positioning sensor and a bunker door driving motor. The infrared positioning sensor is used for detecting the position of the inspection robot: when detecting that the inspection robot is at the external docking position of the bunker opening, a position signal is sent to the intelligent control platform; when detecting that the inspection robot is at the internal docking position of the bunker opening, a position signal is also sent to the intelligent control platform; The intelligent control platform is further provided with a bunker door control unit, which is used for issuing an opening instruction to the bunker door driving motor to control the opening of the bunker door at the top of the coal bunker to allow the inspection robot to enter the coal bunker after receiving the signal that the inspection robot is at the external docking position of the bunker opening sent by the infrared positioning sensor, and issuing a closing instruction to the bunker door driving motor to control the closing of the bunker door after the inspection robot enters the coal bunker; and is also used for issuing an opening instruction to the bunker door driving motor to control the opening of the bunker door to allow the inspection robot to exit the coal bunker after receiving the signal that the inspection robot is at the internal docking position of the bunker opening sent by the infrared positioning sensor, and issuing a closing instruction to the bunker door driving motor to control the closing of the bunker door after the inspection robot exits the coal bunker.

10. The control system of a coal bunker inspection robot according to claim 8, characterized in that, The inspection robot is further provided with a visible light camera, and the visible light camera and the thermal imaging camera are integrated on an infrared dual-light cloud platform.

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