Wireless remote control and data return device of coal bunker cleaning machine

Abstract

The invention discloses a wireless remote control and data return device of a coal bunker cleaning machine, relates to the technical field of wireless remote control of coal bunker cleaning machines, aims to solve the technical problem that the type and residual quantity of coal accumulated in a traditional bunker cannot be accurately recognized, and comprises a field sensing module, a data processing module, a wireless transmission module and a remote control module. In a noise reduction unit of a data processing module, a wavelet noise reduction algorithm is used for processing image data acquired by an AI visual camera. Mist noise generated by dust interference can be effectively filtered out, key features such as edges and textures of coal deposits can be clearly reserved, the problem that viscous coal and wet powdery coal as well as blocky coal and viscous coal are difficult to distinguish manually is solved, the type difference and distribution condition of the coal deposits in the bin can be clearly captured, a high-quality image basis is provided for subsequent accurate recognition of the types of the coal deposits, and the coal deposits in the bin can be accurately recognized. And the coal deposit characteristics are judged no longer depending on subjective experience of operators. The problem that the type and residual quantity of accumulated coal in a bin cannot be accurately recognized is solved.

Description

Technical Field

[0001] This invention relates to the field of wireless remote control technology for coal bunker cleaning machines, and more specifically, to a wireless remote control and data transmission device for a coal bunker cleaning machine. Background Technology

[0002] In the coal industry, coal bunkers are core facilities for coal transfer and storage, and the safety and efficiency of coal cleaning operations inside them directly affect the continuity of the coal production process.

[0003] However, coal bunkers are prone to coal accumulation and sticking on their walls during use, affecting normal operation and coal transport efficiency. The type and amount of coal accumulated in the bunker (e.g., sticky coal, lump coal, powdery coal) cannot be accurately identified, requiring operators to rely on experience to plan the cleaning path. Furthermore, the physical properties of sticky coal, lump coal, and powdery coal differ significantly, necessitating drastically different cleaning methods: sticky coal has strong adhesion, requiring a robotic arm for powerful scraping and repeated sweeping; lump coal is hard and large, requiring prior crushing by a robotic arm before cleaning; and powdery coal is highly fluid and easily accumulates in bunker wall recesses or corners, necessitating targeted adjustments to the cleaning angle and force.

[0004] The dim lighting and dust inside the coal bunker make it difficult for operators to clearly distinguish the characteristics of the accumulated coal. For example, sticky coal and damp, powdery coal look similar, and sticky coal can easily be mistakenly treated with the light-force cleaning method used for powdery coal. This results in sticky coal not being able to be removed from the bunker wall, requiring repeated cleaning. On the other hand, if lumpy coal is mistakenly identified as sticky coal, the robotic arm may directly impact it with a strong scraping motion, potentially damaging the bunker wall or overloading the robotic arm joints. In view of this, we propose a wireless remote control and data transmission device for a coal bunker cleaning machine. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the prior art, adapt to practical needs, and provide a wireless remote control and data transmission device for a coal bunker cleaning machine, so as to solve the technical problem that the type and amount of coal accumulated in the bunker cannot be accurately identified.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a wireless remote control and data transmission device for a coal bunker cleaning machine. The device includes a field sensing module, a data processing module, a wireless transmission module, a remote control module, and an execution module for the coal bunker cleaning machine. The field sensing module is connected to the data processing module and is used to collect the operating status parameters, environmental parameters, and visual data of the coal bunker cleaning machine, and transmit the collected data to the data processing module. The data processing module is connected to the wireless transmission module and is used to process the data transmitted by the field sensing module and transmit the processed data to the wireless transmission module. The wireless transmission module is connected to the remote control module and is used to establish a wireless communication link between the data processing module and the remote control module to achieve bidirectional data transmission. The remote control module is connected to the execution module through the wireless transmission module and is used to receive and display the transmitted data, send control commands, and monitor the system operating status. The execution module of the coal bunker cleaning machine is connected to the execution control module and is used to receive control commands sent by the remote control module and drive the execution module of the coal bunker cleaning machine to perform actions.

[0007] Preferably, the on-site sensing module includes an explosion-proof pressure sensor, an explosion-proof position sensor, an AI vision camera, and an environmental sensor. The explosion-proof pressure sensor is used to collect pressure data from the hydraulic pump station and cutting motor of the coal bunker cleaning machine, with a range covering 15-30 MPa. The explosion-proof position sensor is used to monitor the rotation angle of the robotic arm from 0-360° and the lifting displacement of the hydraulic winch from 0-30m. The AI ​​vision camera is used to collect images of coal accumulation on the inner wall and residual coal at the bottom of the coal bunker. The environmental sensor is used to detect methane concentration, dust concentration, and temperature and humidity parameters inside the coal bunker.

[0008] Preferably, the data processing module includes a filtering unit and a noise reduction unit. The filtering unit uses the Kalman filter algorithm to perform real-time filtering on the pressure fluctuation data collected by the explosion-proof pressure sensor. The noise reduction unit uses the wavelet noise reduction algorithm to reduce the noise of the image data collected by the AI ​​vision camera and the environmental data collected by the environmental sensor, and outputs stable and reliable preprocessed data.

[0009] Preferably, the wireless transmission module adopts a mining-grade 5G / LoRa dual-mode gateway, wherein the 5G communication mode is used to transmit control commands and high-definition video data, and the LoRa communication mode is used to transmit sensor data and device status information.

[0010] Preferably, the execution control module includes a jack array, a hydraulic winch, a robotic arm, and a hydraulic pump station. The jack array is used to adjust the working posture and position of the cleaning machine, the hydraulic winch is used to control the lifting and lowering movement of the cleaning machine, the robotic arm is used to perform cleaning operations on the coal bunker wall, and the hydraulic pump station provides hydraulic power to each execution mechanism.

[0011] Preferably, the remote control module includes a touch screen, a remote control terminal, an AI coal quantity analysis module, a fault early warning unit, an emergency stop module, and an execution control module. The touch screen is used to display the working status of the coal bunker cleaning machine and the environmental parameters of the coal bunker. The remote control terminal is used to generate and send control commands. The AI ​​coal quantity analysis module is used to analyze the residual coal quantity in the images collected by the AI ​​vision camera and generate optimization suggestions for the cleaning path. The fault early warning unit is used to compare the pre-processed data with preset threshold values ​​for parameters such as pressure, position, and environment in real time. After the emergency stop module is triggered, it cuts off the power output of the hydraulic pump station within 0.1 seconds, stopping all actions of the coal bunker cleaning machine. The execution control module is used to parse and verify the remote control commands. After the verification is successful, it sends action commands to the coal bunker cleaning machine execution module and receives the status data fed back by the execution module, which is then transmitted back to the touch screen for display.

[0012] Preferably, the mining 5G / LoRa dual-mode gateway supports automatic link switching, automatically switching to LoRa communication mode when the 5G signal strength is lower than a set threshold.

[0013] Preferably, the AI ​​coal quantity analysis module uses a deep learning algorithm to identify coal deposits of different properties, including viscous coal, lump coal, and powdery coal, and generates a thermal map of coal deposit distribution in the coal bunker.

[0014] Compared with the prior art, the beneficial effects of the present invention are:

[0015] 1. This invention utilizes a wavelet denoising algorithm in the noise reduction unit of the data processing module to process image data acquired by an AI vision camera. This effectively filters out fog-like noise caused by dust interference, clearly preserving key features such as the edges and textures of accumulated coal. It solves the problem of manual differentiation between sticky coal and moist powdery coal, and between lump coal and sticky coal. It can clearly capture the differences in coal type and distribution within the storage bin, overcoming the difficulty of manual judgment due to dim lighting and dust in the bin. This provides a high-quality image foundation for accurate identification of coal type, eliminating reliance on operator subjective experience to judge coal characteristics. It also solves the problem of inaccurate identification of coal type and residual quantity within the storage bin.

[0016] 2. This invention also utilizes deep learning algorithms in the AI ​​coal quantity analysis module to generate a heat map of coal accumulation distribution in the coal bunker. This visually presents the amount of residual coal in different areas of the bunker, clearly locating areas prone to accumulation such as bunker wall depressions and corners. The system automatically plans the optimal cleaning path, eliminating the need for manual planning of the work sequence based on experience. It guides the robotic arm to operate according to the logic of prioritizing high-residue areas and concentrating similar types of accumulated coal, reducing unnecessary round trips by the robotic arm, shortening cleaning time, and avoiding over-cleaning that leads to equipment wear and resource waste, thus ensuring standardized cleaning quality. Attached Figure Description

[0017] Figure 1 This is a schematic block diagram of the system structure of the present invention. Detailed Implementation

[0018] Examples, such as Figure 1 As shown, this invention relates to a wireless remote control and data transmission device for a coal bunker cleaning machine. The device includes a field sensing module, a data processing module, a wireless transmission module, a remote control module, and an execution module for the coal bunker cleaning machine. The field sensing module is connected to the data processing module and is used to collect the operating status parameters, environmental parameters, and visual data of the coal bunker cleaning machine, and transmit the collected data to the data processing module. The data processing module is connected to the wireless transmission module and is used to process the data transmitted by the field sensing module and transmit the processed data to the wireless transmission module. The wireless transmission module is connected to the remote control module and is used to establish a wireless communication link between the data processing module and the remote control module to achieve bidirectional data transmission. The remote control module is connected to the execution module through the wireless transmission module and is used to receive and display the transmitted data, send control commands, and monitor the system's operating status. The execution module of the coal bunker cleaning machine is connected to the execution control module and is used to receive control commands sent by the remote control module and drive the execution module of the coal bunker cleaning machine to perform actions.

[0019] Preferably, the on-site sensing module includes an explosion-proof pressure sensor, an explosion-proof position sensor, an AI vision camera, and an environmental sensor. The explosion-proof pressure sensor is used to collect pressure data from the hydraulic pump station and cutting motor of the coal bunker cleaning machine, with a range covering 15-30 MPa. The explosion-proof position sensor is used to monitor the rotation angle of the robotic arm from 0-360° and the lifting displacement of the hydraulic winch from 0-30m. The AI ​​vision camera is used to collect images of coal accumulation on the inner wall and residual coal at the bottom of the coal bunker. The environmental sensor is used to detect methane concentration, dust concentration, and temperature and humidity parameters inside the coal bunker.

[0020] Preferably, the data processing module includes a filtering unit and a noise reduction unit. The filtering unit uses the Kalman filtering algorithm to perform real-time filtering on the pressure fluctuation data collected by the explosion-proof pressure sensor. The noise reduction unit uses the wavelet noise reduction algorithm to reduce the noise of the image data collected by the AI ​​vision camera and the environmental data collected by the environmental sensor, and outputs stable and reliable preprocessed data.

[0021] The data processing module targets continuous time-domain signals such as hydraulic pump station pressure, cutting motor pressure, robotic arm rotation angle, and hydraulic winch displacement of the coal bunker cleaning machine. Due to high-frequency noise easily generated by equipment vibration and hydraulic shock, an adaptive Kalman filter is used to dynamically suppress interference. The core algorithm automatically adjusts the filtering parameters by estimating the statistical characteristics of the signal and noise in real time, without requiring precise pre-modeling of system noise. The algorithm is implemented iteratively using the following formula:

[0022] State prediction: ,in This is the predicted pressure value. With a value set to 1.0, the hydraulic system pressure changes slowly, and the state transition matrix is ​​approximately an identity matrix. Set to 0.02, and calibrate based on the correlation characteristics between hydraulic pump flow control and pressure changes. For hydraulic pump flow control;

[0023] Covariance prediction: , Let be the state error covariance, initially set to 0.1. To mitigate process noise covariance, adjustments are made in real time based on pressure data fluctuations. When the pressure fluctuation amplitude exceeds 1 MPa, Set it to 0.05, otherwise set it to 0.01;

[0024] Kalman gain: , Set to 1.0, the observation matrix is ​​directly associated with the state matrix. To observe the noise covariance, a value of 0.02 is set based on the sensor accuracy.

[0025] Status Update: , The filtered pressure value is obtained from the actual pressure value collected by the sensor using this formula. ;

[0026] Covariance update: , The identity matrix is ​​used, and the updated covariance is used in the next round of filtering iteration;

[0027] The algorithm described above can reduce the pressure data fluctuation range from ±2MPa before filtering to within ±0.5MPa, ensuring the control accuracy of the hydraulic system.

[0028] For image data acquired by AI vision cameras and environmental data acquired by environmental sensors, wavelet noise reduction algorithm is used for processing. The specific steps and formulas are as follows:

[0029] Wavelet decomposition: for the original data The image pixel values / environment parameter values ​​are subjected to a 3-level discrete wavelet transform, using the db4 wavelet basis function. The decomposition formula is as follows: ,in These are approximate coefficients. For detail coefficients including noise, For scaling function, For wavelet functions, To decompose the scale, The translation coefficient;

[0030] Soft thresholding: For the detail coefficients obtained from decomposition A soft thresholding algorithm is used to suppress noise, and the threshold value is... The formula is determined using Stein's unbiased risk estimation: , For data length, The noise standard deviation is calculated from the first-level detail coefficient, and the processing formula is as follows: ,That It is a symbolic function;

[0031] Wavelet reconstruction: The processed detail coefficients With approximation coefficient Perform inverse wavelet transform to reconstruct the denoised data. The formula is: ;

[0032] After processing, the noise removal rate of the image data is ≥85%, and the dynamic standard deviation of the environmental data is reduced from... The VOL was reduced to ±0.05%, providing reliable data for subsequent analysis and control.

[0033] Preferably, the wireless transmission module adopts a mining-grade 5G / LoRa dual-mode gateway. The 5G communication mode is used to transmit control commands and high-definition video data, and the LoRa communication mode is used to transmit sensor data and equipment status information. The mining-grade 5G / LoRa dual-mode gateway supports automatic link switching. When the 5G signal strength is lower than a set threshold, it automatically switches to LoRa communication mode. The wireless transmission module adopts a mining-grade 5G / LoRa dual-mode gateway (model KT258-F), with an explosion-proof rating of ExdIMb and an operating voltage of 12-24VDC. The specific communication mode and link switching logic are as follows.

[0034] 5G communication mode: Adopts SA (Standalone) network mode, with the frequency band set at 2.6GHz. This frequency band is a dedicated frequency band for mining, and the downlink speed is... Uplink speed Transmission delay It is mainly used to transmit remote control commands and high-definition video data collected by AI vision cameras, ensuring the real-time nature of control commands and the smoothness of video footage;

[0035] LoRa communication mode: Uses the LoRaWAN protocol, operating frequency band 433MHz, spreading factor set to 12, bandwidth 125kHz, transmission rate... It is mainly used to transmit sensor data and equipment status information from the field sensing module, with a transmission distance of [missing information]. Bit error rate ≤ ;

[0036] Automatic link switching function: The gateway monitors the Reference Signal Received Power (RSRP) of the 5G signal in real time and sets a switching threshold. When detected When, it automatically switches to LoRa communication mode; when When switching back to 5G communication mode, the response time is adjusted. To ensure uninterrupted communication links.

[0037] Preferably, the execution control module includes a jack array, a hydraulic winch, a robotic arm, and a hydraulic pump station. The jack array is used to adjust the working posture and position of the cleaning machine, the hydraulic winch is used to control the lifting and lowering movement of the cleaning machine, the robotic arm is used to perform cleaning operations on the coal bunker wall, and the hydraulic pump station provides hydraulic power to each execution mechanism.

[0038] Preferably, the remote control module includes a touch screen, a remote control terminal, an AI coal quantity analysis module, a fault early warning unit, an emergency stop module, and an execution control module. The touch screen is used to display the working status of the coal bunker cleaning machine and the environmental parameters of the coal bunker. The remote control terminal is used to generate and send control commands. The AI ​​coal quantity analysis module is used to analyze the residual coal quantity in the images captured by the AI ​​vision camera. The AI ​​coal quantity analysis module uses a deep learning algorithm to identify coal accumulation of different properties, including sticky coal, lump coal, and powdery coal, and generates a thermal map of coal accumulation distribution in the coal bunker and generates optimization suggestions for the cleaning path. The fault early warning unit is used to compare pre-processed data in real time with preset threshold values ​​for parameters such as pressure, location, and environment. After the emergency stop module is triggered, it cuts off the power output of the hydraulic pump station within 0.1 seconds, stopping all actions of the coal bunker cleaning machine. The execution control module is used to parse and verify the remote control commands. After successful verification, it sends action commands to the coal bunker cleaning machine execution module and receives status data fed back by the execution module, which is then transmitted back to the touch screen for display.

[0039] The AI ​​coal quantity analysis module employs a deep learning algorithm based on a CNN (Convolutional Neural Network) model. The model training dataset contains 100,000 images of different types of coal deposits (clay coal, lump coal, and powdery coal). The specific implementation steps and formulas are as follows: Convolutional layer feature extraction: The input layer is a 1920×1080×3 RGB coal deposit image, processed through 3 convolutional layers with a 3×3 kernel size, a stride of 1, and padding set to "same". The activation function is... The formula is: ,in For the l-th convolutional layer Location feature value, For convolution kernel weights, For bias;

[0040] Fully connected layer classification: The output of the last convolutional layer is global average pooled to obtain a 2048-dimensional feature vector, which is then input to two fully connected layers, and the output layer uses... The activation function, with the formula: ,in For category Predicted probabilities for (sticky coal / lumpy coal / powdered coal) This is a global average pooling feature. For the weights of the fully connected layer, With bias, classification accuracy is ≥92%;

[0041] CAM Heatmap Generation: Based on the weights of the fully connected layers and the feature map of the last convolutional layer, a heatmap of coal accumulation distribution is generated. The formula is as follows: ,in For heat map Location response value (the higher the value, the higher the amount of coal residue), the heat map resolution is consistent with the input image, used to generate suggestions for optimizing the clearing path, and it is recommended to prioritize clearing areas with high response values.

[0042] The emergency stop module is installed on the remote control terminal panel, featuring a red mushroom-shaped button, a self-locking hardware emergency stop button, and a software emergency stop command via a virtual touchscreen button. Upon triggering the emergency stop, a signal is sent via both hardwired and wireless channels, cutting off the hydraulic pump station's power output within 0.1 seconds, de-energizing the hydraulic pump station's electromagnetic relief valve, depressurizing to atmospheric pressure, and stopping all actions of the cleaning machine. The emergency stop response time is determined by the formula... Verification, among which (Command transmission delay) ≤30ms (Instruction parsing delay) ≤10ms (Execution latency) ≤40ms, total response time It meets safety requirements.

[0043] The embodiments disclosed in this invention are preferred embodiments, but are not limited thereto. Those skilled in the art can easily understand the spirit of this invention based on the above embodiments and make different extensions and variations, but as long as they do not depart from the spirit of this invention, they are all within the protection scope of this invention.

Claims

1. A wireless remote control and data transmission device for a coal bunker cleaning machine, characterized in that, It includes a field sensing module, a data processing module, a wireless transmission module, and a remote control module; The on-site perception module includes an AI vision camera, which is used to collect images of coal accumulation on the inner wall of the coal bunker and residual coal at the bottom. The data processing module includes a filtering unit and a noise reduction unit. The filtering unit uses the Kalman filtering algorithm to perform real-time filtering processing on the pressure fluctuation data collected by the explosion-proof pressure sensor. The noise reduction unit uses a wavelet noise reduction algorithm to reduce the noise in the image data acquired by the AI ​​vision camera and outputs stable and reliable preprocessed data. The remote control module includes an AI coal quantity analysis module, which uses a deep learning algorithm to identify coal deposits of different properties, including viscous coal, lump coal, and powdery coal, and generates a thermal map of coal deposit distribution in the coal bunker.

2. The wireless remote control and data transmission device for a coal bunker cleaning machine according to claim 1, characterized in that, The data processing module is connected to the wireless transmission module and is used to process the data transmitted by the field sensing module and transmit the pre-processed data to the remote control module.

3. The wireless remote control and data transmission device for a coal bunker cleaning machine according to claim 2, characterized in that... The on-site sensing module is connected to the data processing module and is used to collect the operating status parameters, environmental parameters and visual data of the coal bunker cleaning machine, and transmit the collected data to the data processing module. The field sensing module also includes an explosion-proof pressure sensor, an explosion-proof position sensor, and an environmental sensor; The explosion-proof pressure sensor is used to collect pressure data from the hydraulic pump station and cutting motor of the coal bunker cleaning machine, with a range of 15-30MPa. The explosion-proof position sensor is used to monitor the rotation angle of the robotic arm from 0 to 360° and the lifting displacement of the hydraulic winch from 0 to 30m. The environmental sensor is used to detect methane concentration, dust concentration, and temperature and humidity parameters inside the coal bunker.

4. The wireless remote control and data transmission device for a coal bunker cleaning machine according to claim 2, characterized in that, The data processing module is used to perform spatiotemporal registration and feature-level fusion of multiple sensor data. The output of the data processing module includes sensor data, image data and fused data after filtering and noise reduction.

5. The wireless remote control and data transmission device for a coal bunker cleaning machine according to claim 1, characterized in that, The AI ​​coal quantity analysis module generates optimized suggestions for clearing paths based on the heat map of coal accumulation distribution. The AI ​​coal quantity analysis module can distinguish coal accumulation of different properties and evaluate the priority of clearing.

6. The wireless remote control and data transmission device for a coal bunker cleaning machine according to claim 1, characterized in that, The wireless transmission module adopts a dual-mode communication method of mining 5G and LoRa. The 5G communication mode is used to transmit control commands and high-definition video data; The LoRa communication mode is used to transmit sensor data and device status information; It has the function of automatically switching between 5G and LoRa modes based on signal strength.

7. The wireless remote control and data transmission device for a coal bunker cleaning machine according to claim 1, characterized in that, The remote control module also includes a touch screen, a remote control terminal, a fault warning unit, an emergency stop module, and an execution control module; The touchscreen is used to display the working status of the cleaning machine and environmental parameters of the coal bunker. The remote control terminal is used to generate and send control commands; The fault early warning unit is used to compare preprocessed data in real time with preset pressure, location, and environmental parameter thresholds. The emergency stop module cuts off the power output of the hydraulic pump station within 0.1 seconds after being triggered, stopping all actions of the clearing machine. The execution control module is used to parse and verify remote control commands, and at the same time receive status data fed back by the execution module and send it back to the touch screen for display.

8. The wireless remote control and data transmission device for a coal bunker cleaning machine according to claim 7, characterized in that, It also includes an execution module for a coal bunker cleaning machine, which is connected to an execution control module and is used to receive control commands sent by a remote control module to drive the execution module of the coal bunker cleaning machine to perform actions; The execution module of the coal bunker cleaning machine includes a jack array, a hydraulic winch, a robotic arm, and a hydraulic pump station; The jack array is used to adjust the working posture and position of the cleaning machine; The hydraulic winch is used to control the lifting and lowering movement of the cleaning machine; The robotic arm is used to perform cleaning operations on the walls of the coal bunker; The hydraulic pump station provides hydraulic power to each actuator.

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