Method and system for detecting advancing degree of fully mechanized coal mining face

Abstract

The invention provides a method and a system for detecting the advancing degree of a fully mechanized coal mining face. The method comprises the following steps: acquiring advancing bracket advancing distances measured by MEMS inertial navigation arranged on a head advancing bracket and a tail advancing bracket; acquiring a coal mining machine track detected by a coal mining machine encoder; and calculating the real-time propelling distance of each cutter of the coal mining machine according to the advancing distance of the forepoling and the track of the coal mining machine, and accumulating the real-time propelling distance with the historical propelling distance to obtain the propelling degree of the two roadways. The method does not depend on an external sensor, the accuracy of propulsion degree calculation is improved, and unnecessary interference influences are reduced.

Description

Technical Field

[0001] This invention relates to the field of fully mechanized mining face technology in underground coal mines, and more specifically, to a method and system for detecting the advance of a fully mechanized mining face. Background Technology

[0002] During production at the working face, it is necessary to understand the progress of the fully mechanized mining operation in real time for each shift. Accurate progress calculations not only effectively demonstrate the logical relationships of the production process and improve team collaboration efficiency, but also provide strong data support for managers. Through this data, managers can better integrate and optimize resources, control progress, and more efficiently maintain a balance between cost and quality, avoiding resource waste and efficiency decline.

[0003] Currently, existing solutions mainly rely on manual measurement, anchor bolt positioning, and sensor averaging to calculate the advance rate. Sensor averaging refers to calculating the advance rate using the average values ​​of the first three and last three sensors, along with the effective identification of the complete cutting tool. This method places high demands on sensor identification and the complete cutting tool. Infrared sensors are often exposed externally, and anchor bolt positioning requires high precision. See CN113008311A for a coal mine fully mechanized mining face advance rate detection system. The anchor bolt method requires extensive installation work upfront, deploying numerous anchor bolts and positioning tags in the roadway. These tags identify the stage distance, thus obtaining the overall face advance status. Installing anchor bolts is labor-intensive and time-consuming. Due to the complex underground working environment and high levels of coal dust, the effectiveness of the positioning tags is affected, thus impacting the accuracy of the advance rate calculation. Furthermore, the removal of anchor bolts after production also poses significant safety hazards, creating unnecessary trouble and risks for the entire fully mechanized mining operation.

[0004] In order to solve the above problems, people have been seeking an ideal technological solution. Summary of the Invention

[0005] Therefore, it is necessary to provide a method for detecting the advance of a fully mechanized mining face that does not rely on external sensors, thereby improving the accuracy of advance calculation and reducing unnecessary interference.

[0006] To achieve the above objectives, the first aspect of the present invention provides a method for detecting the advance rate of a fully mechanized mining face, comprising the following steps: Acquire the advance support displacement distance measured by MEMS inertial navigation systems installed on the nose and tail advance supports; Obtain the coal mining machine trajectory detected by the encoder; The real-time advance distance of each cutter of the coal mining machine is calculated based on the advance distance of the advance support and the trajectory of the coal mining machine, and then added to the historical advance distance to obtain the advance rate of the two roadways.

[0007] This invention, on the one hand, employs MEMS inertial navigation sensors integrated into the head and tail advance frames to capture subtle motion changes of the coal mining machine in real time. Compared to traditional technologies that require the deployment of numerous auxiliary sensors or positioning tags, this solution not only reduces initial installation costs but also simplifies subsequent maintenance, saving coal mining companies significant manpower and material resources. On the other hand, the innovative dynamic error correction algorithm continuously corrects system errors through periodic calibration and adaptive parameter updates, resulting in continuously optimized accuracy in advance calculation. This closed-loop control mechanism of measurement-calibration-learning-correction ensures that the system maintains excellent measurement accuracy under various operating conditions.

[0008] In one embodiment, the formula used to calculate the real-time advance distance of each cutter of the coal mining machine based on the advance support pushing distance, the tilt and pitch angles of the coal mining machine body, and the coal mining machine trajectory is as follows: L 每刀 =L 上一刀 +σ / N, where L 每刀 It is the advance support pushing distance measured by MEMS inertial navigation for each cut of the coal mining machine, σ is the total pushing deviation of the previous stage, N is the total number of cuts in the previous stage, and σ / N is the deviation value of each cut in the previous stage.

[0009] The dynamic error correction algorithm employed in this invention significantly improves the accuracy and reliability of working face advancement calculations through an innovative recursive correction mechanism. The core of this algorithm lies in its intelligent compensation of current measurements using historical deviation data, forming a self-optimizing calculation system.

[0010] Specifically, L 每刀代表当前刀 The actual propulsion distance, L 上一刀 The calculation benchmark ensures the continuity of the data, while the key compensation term σ / N reflects the intelligence of the algorithm—by using the ratio of the total deviation value σ in the previous stage to the total number of cuts N, the average correction amount per cut is calculated, thereby evenly distributing the historical deviation to the current calculation.

[0011] This algorithm can effectively eliminate systematic deviations such as sensor zero-point drift and installation errors, avoiding the accumulation and amplification of errors. Secondly, through phased deviation statistical analysis, the algorithm can adapt to changes in geological conditions and equipment performance degradation, maintaining long-term calculation accuracy. Furthermore, this mechanism reduces excessive reliance on single measurement data, improving the system's anti-interference ability and stability.

[0012] In one embodiment, before calculating the real-time advance distance of each cutter of the coal mining machine based on the advance support pushing distance and the coal mining machine trajectory, the pushing distance and the coal mining machine trajectory are filtered and denoised, and abnormal data such as jump supports and exceeding the preset range are removed.

[0013] By using data preprocessing technology, the collected advance support pushing distance and coal mining machine trajectory data are purified in a multi-level and intelligent manner, which significantly improves the quality and usability of the raw data and establishes a highly reliable data foundation for the calculation of the working face advance.

[0014] In one embodiment, before calculating the real-time advance distance of each cutter of the coal mining machine based on the advance support push distance and the coal mining machine trajectory, the difference between the current advance support push distance and the previous advance support push distance is calculated and recorded for the real-time collected advance support push distance. Simultaneously, based on the complete cutter identification of the coal mining machine trajectory detected by the encoder, when a complete cutter is determined to have been executed, the calculation of the advance of both roadways is triggered. The calculation formula is: L=L 历史推进距离 +L 每刀 .

[0015] The above scheme achieves full automation of the progress calculation, greatly reducing the need for manual intervention; through real-time difference recording and event triggering mechanisms, it ensures the timeliness and accuracy of the calculation results.

[0016] To achieve the above objectives, a second aspect of the present invention provides a fully mechanized mining face advance detection system, comprising: a MEMS inertial navigation system and a separate MEMS inertial navigation system, which are respectively installed in sealed shells on the head advance frame and the tail advance frame, for measuring the advance distance of the advance frame, and reporting to the centralized control data center through a controller; Encoders are used to detect the trajectory of coal mining machines; The data transmission module is used to transmit the pushing distance of the advanced support and the trajectory of the coal mining machine to the centralized control data center via a combination of CAN and Ethernet. The centralized control data center is configured to handle data reception and forwarding. At the business layer, the configuration is to calculate the real-time advance distance of each cutter of the coal mining machine based on the advance support's pushing distance and the coal mining machine's trajectory, and then add it to the historical advance distance to obtain the advance rate of the two roadways.

[0017] To achieve the above objectives, a third aspect of the present invention provides a computer device, including a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; the memory is used to store computer programs; and the processor is used to execute the program stored in the memory to implement the steps of the fully mechanized mining face advance detection method as described in the first aspect.

[0018] To achieve the above objectives, a fourth aspect of the present invention provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the fully mechanized mining face advance detection method as described in the first aspect.

[0019] The beneficial effects of this invention are as follows: This invention uses a high-precision inertial navigation sensor, which can measure each propulsion very accurately and continuously update and learn during the measurement process to make it even more accurate. It is easy to install and operate, and only requires installing the inertial navigation system on the front and rear leading frames. Attached Figure Description

[0020] Figure 1 This is a flowchart illustrating the fully mechanized mining face advancement detection method of the present invention.

[0021] Figure 2 This is a schematic diagram of the principle of the fully mechanized mining face advancement detection system of the present invention. Detailed Implementation

[0022] The technical solution of the present invention will be further described in detail below through specific embodiments.

[0023] Example 1 This embodiment provides a method for detecting the advance rate of a fully mechanized mining face, such as... Figure 1 As shown, it includes the following steps: Acquire the advance support displacement distance measured by MEMS inertial navigation systems installed on the nose and tail advance supports; Obtain the coal mining machine trajectory detected by the encoder; The real-time advance distance of each cutter of the coal mining machine is calculated based on the advance distance of the advance support and the trajectory of the coal mining machine, and then added to the historical advance distance to obtain the advance rate of the two roadways.

[0024] The formula used to calculate the real-time advance distance of each cutter of the coal mining machine, based on the advance support's moving distance, the machine's tilt and pitch angles, and the machine's trajectory, is as follows: L 每刀 =L 上一刀 +σ / N, where L 每刀 It is the advance support pushing distance measured by MEMS inertial navigation for each cut of the coal mining machine, σ is the total pushing deviation of the previous stage, N is the total number of cuts in the previous stage, and σ / N is the deviation value of each cut in the previous stage.

[0025] Under normal circumstances, each time the advanced support moves automatically, the MEMS inertial navigation system sends the measured value to the controller, and forwards it to the business layer for storage through the data relay center. The business layer will accumulate these data to obtain the advance of the two lanes L=l1+l2+l3...

[0026] Due to environmental factors, there may be some error values ​​σ at the head and tail of the machine during the calculation process. These data need to be manually calibrated in stages. The business continuously learns and updates based on the error values ​​to achieve the goal of accurate measurement.

[0027] Specifically, after each stage is completed, the total σ shift deviation and total number of cuts are updated. The compensation parameters are continuously optimized through regular manual calibration data, forming a self-improving calculation system. For example, multi-level calibration cycles can be set: rapid calibration every 10 cuts, standard calibration every 50 cuts, and comprehensive calibration every 200 cuts. The calibration cycle is dynamically adjusted according to the geological conditions of the working face, automatically shortening the calibration interval when geological changes are drastic.

[0028] It is understandable that before calculating the real-time advance of each cutter based on the advance support's moving distance, the coal mining machine's tilt and pitch angle data, and the coal mining machine's trajectory, the moving distance, the coal mining machine's tilt and pitch angle data, and the coal mining machine's trajectory are filtered and denoised, and abnormal data such as jump supports and data exceeding the preset range are removed.

[0029] Specifically, a composite filtering algorithm is used to denoise the original signal, combining Kalman filtering, median filtering, and low-pass filtering techniques to effectively eliminate random errors such as measurement noise and equipment vibration interference, while preserving the true motion characteristics of the working face. For the coal mining machine trajectory data, the system can apply the Savitzky-Golay smoothing algorithm to perfectly maintain the trajectory's morphological characteristics while removing noise, ensuring the accuracy of subsequent analysis.

[0030] In addition, threshold algorithms can be used to monitor sudden changes in the support displacement distance in real time, accurately identify and eliminate abnormal data points such as support jumps, and prevent equipment failures or operational abnormalities from contaminating the calculation results. At the same time, reasonable numerical range boundaries are set based on the actual working conditions of the working face, and outliers exceeding the physical range are automatically filtered to ensure that all data involved in the calculation conform to the actual engineering situation.

[0031] This refined data preprocessing mechanism has brought about significant technical benefits: the stability and reliability of the calculation results have been fundamentally improved, the system's anti-interference ability has been significantly enhanced, and miscalculations and misjudgments caused by data quality issues have been effectively avoided.

[0032] Specifically, when the real-time measured displacement distance of the advanced support changes, the difference in displacement distance for each measurement is obtained; based on the uploaded encoder data, a complete tool is identified, and when a complete tool is generated, the advance angle calculation is triggered: L=L 上一刀 +L 该阶段MEMS惯导行进值 +σ / N.

[0033] The above solution employs an event-triggered mechanism, achieving a dual improvement in the real-time performance and accuracy of working face advance calculation. Specifically, this solution combines difference recording with complete tool identification to construct a highly efficient and reliable automatic advance measurement system.

[0034] At the data acquisition level, the system monitors the changes in the advance support's movement distance in real time. By calculating and recording the difference between the current value and the previous value, it accurately captures the actual advancement dynamics of the support. This difference recording method effectively avoids the cumulative error of absolute values ​​and can sensitively reflect the instantaneous advancement status of the working face, providing a high-quality data foundation for subsequent calculations.

[0035] Regarding the event triggering mechanism, the system intelligently identifies the formation of a complete cutter based on the trajectory data detected by the coal mining machine's encoder, thereby automatically triggering the advance calculation process. This identification method based on physical motion characteristics is more accurate and reliable than traditional time-cycle or manual judgment methods.

[0036] Core calculation formula L=L 上一刀 +L 该阶段MEMS惯导行进值 +σ / N maintains data continuity and ensures real-time calculation. By continuously accumulating the advance distance of each cut into historical values, the system can continuously track the overall progress of the working face, providing accurate reference for production management.

[0037] Finally, the acquired data can be displayed in three dimensions. When a cut is completed, the progress bar is updated, and the contour curve of the entire working surface is drawn based on the angle of the previous cut and the progress.

[0038] It is understandable that displaying the acquired data in a three-dimensional form can more intuitively and concretely show the contour curve of the entire working face and provide strong data support for managers to make reasonable mining plans.

[0039] It should be understood that although the steps in the flowcharts of the above embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0040] Example 2 Based on the same inventive concept, this application also provides a fully mechanized mining face advance detection system for implementing the aforementioned fully mechanized mining face advance detection method. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations of one or more fully mechanized mining face advance detection system embodiments provided below can be found in the limitations of the fully mechanized mining face advance detection method described above, and will not be repeated here.

[0041] The fully mechanized mining face advancement detection system, such as Figure 2 As shown, it includes: MEMS inertial navigation systems are installed in sealed housings on the nose and tail forward supports, respectively, to measure the distance the forward supports are pushed and reported to the central control data center via the controller. Encoders are used to detect the trajectory of coal mining machines; The data transmission module is used to transmit the pushing distance of the advanced support and the trajectory of the coal mining machine to the centralized control data center through a combination of CAN and Ethernet. Among them, CAN has a fast and stable transmission speed and is mainly used for communication between supports, while Ethernet transmits these data to the centralized control data center for calculation and processing. The centralized control data center is configured to handle data reception and forwarding. At the business layer, the configuration is to calculate the real-time advance distance of each cutter of the coal mining machine based on the advance support's pushing distance and the coal mining machine's trajectory, and then add it to the historical advance distance to obtain the advance rate of the two roadways.

[0042] Furthermore, the fully mechanized mining face advance detection system also includes a posture sensor, which is installed on the coal mining machine body to collect the tilt and pitch angle data of the coal mining machine body; specifically, the sampling frequency can be 10Hz, so as to provide favorable data support for the three-dimensional display of the entire working face. The 3D display module is used to display the acquired coal mining machine trajectory, the inclination and pitch angles of the coal mining machine body, and the advance support pushing distance in a 3D form. After executing a complete cutter, it updates the advance rate of the two roadways and draws the contour curve of the entire working face based on the current cutter inclination angle and advance distance.

[0043] The workflow of the fully mechanized mining face advancement detection system is as follows: 1. MEMS Inertial Navigation System Power-On Initialization: Each time the support is powered on, the MEMS inertial navigation system needs to be initialized and self-calibrated to ensure the accuracy of subsequent measurements.

[0044] 2. Data Acquisition: The sensor uploads data every 100ms, including encoder values, coal mining machine body tilt angle, pitch angle, support disconnection communication and other key data.

[0045] 3. Data preprocessing: The collected data is filtered and denoised to remove outliers and abnormal data that exceed the acceptable range, in order to ensure the validity of the basic data.

[0046] 4. Business Algorithm Processing: Real-time acquisition of MEMS inertial navigation data; recording the difference each time the data changes; identifying complete cutting tools based on uploaded encoder data; triggering advance calculation when a complete cutting tool is generated: L=L 历史推进距离 +L per cut, L 每刀 =L 上一刀 +σ / N.

[0047] 5. Feedback Monitoring: When a cut is completed, the interface updates the progress bar, and the 3D graph also draws the outline curve of the entire working surface based on the angle of the previous cut and the progress.

[0048] Example 3 This embodiment provides a computer device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the steps of the fully mechanized mining face advance detection method described in Embodiment 1.

[0049] Example 4 Based on the above embodiments, this embodiment provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the fully mechanized mining face advance detection method described in Embodiment 1.

[0050] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them; although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications can still be made to the specific implementation of the present invention or equivalent substitutions can be made to some technical features without departing from the spirit of the technical solutions of the present invention, and all such modifications and substitutions should be covered within the scope of the technical solutions claimed in the present invention.

Claims

1. A method for detecting the advance rate of a fully mechanized mining face, characterized in that, Includes the following steps: Acquire the advance support displacement distance measured by MEMS inertial navigation systems installed on the nose and tail advance supports; Obtain the coal mining machine trajectory detected by the encoder; The real-time advance distance of each cutter of the coal mining machine is calculated based on the advance distance of the advance support and the trajectory of the coal mining machine, and then added to the historical advance distance to obtain the advance rate of the two roadways.

2. The method for detecting the advancement of a fully mechanized mining face according to claim 1, characterized in that, The formula used to calculate the real-time advance distance of each cutter of the coal mining machine based on the advance support's moving distance, the machine's tilt and pitch angles, and the machine's trajectory is as follows: L 每刀 =L 上一刀 +σ / N, where L 每刀 It is the advance support pushing distance measured by MEMS inertial navigation for each cut of the coal mining machine, σ is the total pushing deviation of the previous stage, N is the total number of cuts in the previous stage, and σ / N is the deviation value of each cut in the previous stage.

3. The method for detecting the advance rate of a fully mechanized mining face according to claim 2, characterized in that, Before calculating the real-time advance distance of each cutter of the coal mining machine based on the advance support pushing distance and the coal mining machine trajectory, the pushing distance and the coal mining machine trajectory are filtered and denoised, and abnormal data such as jump supports and exceeding the preset range are removed.

4. The method for detecting the advance rate of a fully mechanized mining face according to claim 3, characterized in that, Before calculating the real-time advance distance of each cutter of the coal mining machine based on the advance support's advance distance and the coal mining machine's trajectory, the difference between the current advance support's advance distance and the previous advance support's advance distance is calculated and recorded for the real-time acquired advance support's advance distance. Simultaneously, based on the complete cutter identification of the coal mining machine trajectory detected by the encoder, when a complete cutter is determined to have been executed, the calculation of the advance of both roadways is triggered. The calculation formula is: L=L 历史推进距离 +L 每刀 .

5. A method for detecting the advance rate of a fully mechanized mining face according to any one of claims 2-4, characterized in that, While acquiring the coal mining machine trajectory detected by the encoder, it also acquires the tilt and pitch angles of the coal mining machine body collected by the posture sensor installed on the coal mining machine body. By correlating the tilt and pitch angles of the coal mining machine with the trajectory of the coal mining machine, the angles on each trajectory of the fully mechanized mining face can be obtained.

6. The method for detecting the advance rate of a fully mechanized mining face according to claim 5, characterized in that, The acquired coal mining machine trajectory, the inclination and pitch angles of the coal mining machine body, and the advance support pushing distance are displayed in three dimensions. After a complete cut is executed, the advancement of the two roadways is updated, and the contour curve of the entire working face is drawn based on the current cutter inclination angle and advancement distance.

7. A fully mechanized mining face advancement detection system, characterized in that, include: MEMS inertial navigation systems are installed in sealed housings on the nose and tail forward supports, respectively, to measure the distance the forward supports are pushed and reported to the central control data center via the controller. Encoders are used to detect the trajectory of coal mining machines; The data transmission module is used to transmit the pushing distance of the advanced support and the trajectory of the coal mining machine to the centralized control data center via a combination of CAN and Ethernet. The centralized control data center is configured to handle data reception and forwarding. At the business layer, the configuration is to calculate the real-time advance distance of each cutter of the coal mining machine based on the advance support's pushing distance and the coal mining machine's trajectory, and then add it to the historical advance distance to obtain the advance rate of the two roadways.

8. The fully mechanized mining face advancement detection system according to claim 7, characterized in that, It also includes a posture sensor, which is installed on the body of the coal mining machine to collect data on the tilt and pitch angles of the coal mining machine. The 3D display module is used to display the acquired coal mining machine trajectory, the inclination and pitch angles of the coal mining machine body, and the advance support pushing distance in a 3D form. After executing a complete cutter, it updates the advance rate of the two roadways and draws the contour curve of the entire working face based on the current cutter inclination angle and advance distance.

9. A computer device, characterized in that: It includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; The processor, when executing a program stored in memory, implements the steps of the fully mechanized mining face advance detection method as described in any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the steps of the fully mechanized mining face advance detection method as described in any one of claims 1 to 6.

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