Spline interpolation based simulation method of digital twin of scraper on fully-mechanized coal mining face

Abstract

The application provides a kind of fully mechanized coal face digital system twin scraper simulation method based on spline interpolation method, obtains the working face advancing data corresponding to the scraper, removes the error data from the end data, the data is replaced by null data, the appropriate data is calculated by using spline interpolation method to replace null data, to form new advancing data, the new scraper advancing data is filtered using Kalman filtering algorithm, the three-dimensional coordinates of the scraper are calculated combined with terrain data, the heading angle of each scraper is calculated according to the three-dimensional coordinates of each scraper, the pitch angle and roll angle of the scraper are obtained combined with the terrain, and the three-dimensional coordinates of each scraper are calibrated combined with inertial navigation data. Using the conventional support advancing stroke sensor and inertial navigation system data, the position and attitude of the scraper are simulated without additional sensors, truly realizing the restoration of the position and inclination of the working face scraper, and further improving the reliability of the unmanned mining simulation.

Description

Technical Field

[0001] This invention relates to a twin scraper simulation method for a fully mechanized mining face digital system based on spline interpolation. Background Technology

[0002] Longwall mining is the most efficient method of underground coal mining. A longwall mining face is a complex system of mechanical equipment and coal-rock interaction, consisting of over 300 pieces of equipment, including drum shearers, hydraulic supports, scraper conveyors, crushers, bridge transfer conveyors, and belt conveyors. To improve production efficiency and reduce the workload of miners, automated and intelligent mining has become a common goal pursued by the global coal mining industry.

[0003] The intelligent fully mechanized mining face production process is based on fully mechanized mining equipment. By integrating advanced sensing technology, it achieves reliable coal cutting and loading by the mining machine, while maintaining the geometric relationship of the working face and reliable roof support. Therefore, regardless of the principles, methods, and technical paths adopted, the goal of an intelligent fully mechanized mining face is to autonomously achieve reliable coal cutting, maintain the geometric relationship of the working face, and ensure reliable roof support. Reliable coal cutting mainly refers to controlling the mining equipment to adaptively cut within the coal seam; maintaining the geometric relationship of the working face means that the mining equipment maintains a certain straightness during continuous advancement; and reliable roof support means that the hydraulic supports and the surrounding rock maintain a stable coupling relationship.

[0004] Ge Shirong et al. defined a smart mining face as a production system that independently completes coal mining operations to varying degrees without human intervention. They pointed out that the foundation of a smart mining face is intelligent machinery, its characteristic is autonomous sensing and control, its function is independent operation, and its purpose is unmanned mining. They clarified that a smart mining face should possess five intelligent elements: autonomous perception, autonomous decision-making, autonomous control, autonomous collaboration, and autonomous interaction. Based on the differences in the level of these intelligent elements, they classified smart mining faces into primary, intermediate, and advanced levels. Xie Jiacheng et al. proposed a design and operation mode for a fully mechanized mining face production system based on digital twins, achieving optimal configuration of the production system and the goal of safe and efficient collaborative mining. Zhang Xuhui et al. proposed a digital twin-driven remote intelligent control technology framework for tunneling equipment. By constructing a digital twin of the tunneling face, they presented information related to underground personnel, equipment, and the environment in the digital space, achieving virtual-real integration and mutual intelligent driving, thus realizing the goal of intelligent collaboration between digital and physical tunneling and solving the safety problem of human-machine-environment symbiosis in tunneling construction. Mao Shanjun et al. proposed and studied several key technologies, including geodetic coordinate transmission for measuring robots, establishment and dynamic correction of transparent working face systems, 5G communication, adaptive coupling between coal mining machines and geological models, and an integrated management and control platform based on a temporal geographic information system (TGIS). They completed the development of a multi-dimensional visualization software system and the high integration of hardware systems, achieving precise positioning of fixed or moving target points such as coal mining machines and scraper conveyors at the millimeter or centimeter level.

[0005] The above research provides some directions for the construction of intelligent fully mechanized mining faces, but a complete solution for simulating the position and attitude of the scraper conveyor has not yet been proposed. Currently, the industry relies on inertial navigation equipment mounted on the coal mining machine to output data such as the position and inclination angle of the coal mining machine at the current time point, thereby calculating the position and inclination angle of the scraper.

[0006] Current scraper simulation methods utilize inertial navigation systems to calculate scraper data and update the current scraper position each time the coal mining machine passes the corresponding scraper. This method results in a lag in scraper data, failing to reflect the real-time straightness of the scraper machine on the working face and the real-time pushing process of the scraper on the actual working face, thus reducing the simulation effect and affecting production decisions. The fully mechanized mining face environment is harsh and involves numerous pieces of equipment. Simply adding more sensors to perceive the state of the fully mechanized mining face and ensure reliable operation has limited effect and may even reduce reliability and increase investment costs. Therefore, installing an inertial navigation system on each scraper machine is clearly not advisable. Moreover, a fully mechanized mining face digital twin system is a system that simulates the real fully mechanized mining face through 3D modeling and real-time data-driven simulation. Currently, there are relatively complete solutions for simulating the position and attitude of equipment such as the working face support and coal mining machine, but there is no mature solution for simulating the position and angle of the scraper. Without simulation of the scraper position and angle, it is impossible to simulate the working face pushing process, and there will also be problems such as distortion of the coal mining machine position and even three-machine cross-modeling. Summary of the Invention

[0007] To address the aforementioned technical problems, this invention provides a twin scraper simulation method for a fully mechanized mining face digital system based on spline interpolation.

[0008] A method for simulating the twin scraper of a fully mechanized mining face digital system based on spline interpolation includes:

[0009] Obtain working face displacement data that corresponds one-to-one with the scraper conveyor;

[0010] Error data is removed starting from the beginning of the data stream, and the data is replaced with empty data.

[0011] The spline interpolation method is used to calculate suitable data to replace the empty data, thereby forming new scraper displacement data;

[0012] The new scraper push data is filtered using the Kalman filter algorithm;

[0013] The filtered scraper push data is output, and the three-dimensional coordinates of the scraper are calculated by combining the terrain data. The heading angle of each scraper is calculated based on the three-dimensional coordinates of each scraper. The pitch angle and roll angle of the scraper are then obtained by combining the three-dimensional coordinates of the scraper with the terrain.

[0014] The three-dimensional coordinates of each scraper are calibrated using inertial navigation data.

[0015] Preferably, after obtaining the working face movement data corresponding to the scraper conveyor, the data is arranged in order from left to right facing the coal wall.

[0016] Preferably, erroneous data is removed starting from the end data, including:

[0017] The displacement data is considered as the scraper position. For the nth scraper, the line connecting the nth scraper and the (n-1)th scraper forms the first line segment, and the line connecting the (n-2)th scraper and the (n-1)th scraper forms the second line segment. The included angle between the first and second line segments is obtained, and the included angle is compared with a preset scraper limit angle threshold. If the included angle exceeds the preset scraper limit angle threshold, the displacement data corresponding to the nth scraper is determined to be unreasonable. The bracket number corresponding to the nth scraper is recorded, and the displacement data corresponding to the nth scraper is removed.

[0018] Preferably, the spline interpolation method is quadratic spline interpolation or cubic spline interpolation.

[0019] Preferably, the three-dimensional coordinates of each scraper are calibrated in conjunction with inertial navigation data, including: when inertial navigation data of a scraper for the most recent time period is received, the shift data of that time period is compared with the inertial navigation data, the error is corrected, and the three-dimensional coordinates, pitch angle, roll angle and heading angle of the scraper are updated.

[0020] The present invention has the following beneficial effects: The present invention proposes a simulation solution for a working face scraper conveyor. Using conventional support travel sensors and inertial navigation system data, the position and attitude of the scraper conveyor are simulated without adding additional sensors, so as to truly realize the restoration of the position and inclination angle of the working face scraper conveyor and further improve the reliability of unmanned mining simulation. Attached Figure Description

[0021] Figure 1 A flowchart of a twin scraper simulation method for a fully mechanized mining face digital system based on spline interpolation provided by the present invention;

[0022] Figure 2 A flowchart illustrating the process of removing erroneous data. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0024] The terms "first," "second," etc., used in the specification and claims of this invention are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class and the number of objects is not limited; for example, a first object can be one or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0025] The travel distance data of the longwall mining face is measured by displacement sensors at the support push rod. This data can be used to calculate the distance between the support and the scraper connected to it. During mining, the support and scraper gradually advance, i.e., pushing and pulling. Pushing involves fixing the support, extending the push rod, increasing the travel distance data, and advancing the scraper forward; pulling involves fixing the scraper, retracting the push rod, decreasing the travel distance data, and pulling the support forward. The travel distance sensor can detect the extension and retraction distance of the push rod in real time.

[0026] Inertial navigation data can be used to determine the position and pitch angle of the scraper at a given time. Combined with changes in the push-stroke data, the forward distance of the scraper can be calculated, i.e., the real-time position of the scraper. However, the push-stroke sensor transmits data at certain time intervals. This can cause some push rods to have already moved without the data being updated, further leading to the uneven "S-curve" exhibited by the scraper during the pushing process. Furthermore, in actual mining operations at the longwall face, issues such as varying sensor sensitivity or malfunctions often arise, affecting the accuracy of the overall scraper position simulation. Therefore, it is necessary to introduce algorithms to determine the reliability of sensor data, eliminate erroneous data, and introduce spline interpolation to interpolate erroneous data. There are many numerical processing methods; the interpolation method was chosen because the connection effect between scrapers on the actual working face is very similar to that of spline interpolation.

[0027] In a fully mechanized mining face, the mining operation advances along the face towards the coal face. Excluding minor backward movement of the scrapers during the setup phase, the scrapers do not move backward after advancing. Therefore, this backward movement can be eliminated in the simulation. This is achieved by using a Kalman filter algorithm to exclude possible scraper backward movement during interpolation. Then, combined with terrain data, the pitch angle, roll angle, yaw angle, and three-dimensional coordinates of each scraper are output. Simulation errors within a few cuts using this method are usually negligible. However, as the working face advances, the positional error of the scrapers gradually accumulates. Therefore, it is necessary to calibrate the scraper position every cut using inertial navigation data to ensure that the real-time positioning error of the scrapers is within ±5cm, thus achieving a digital twin simulation of the scraper in the fully mechanized mining face.

[0028] like Figure 1 As shown, this embodiment provides a method for simulating a twin scraper in a fully mechanized mining face digital system based on spline interpolation, including the following steps:

[0029] Step 1: Obtain the working face movement data corresponding to each scraper conveyor:

[0030] Acquire and save the working face movement data, and map it one-to-one with the scraper conveyor, arranging them in order from left to right facing the coal face. In this embodiment, the scraper movement data can be fitted using polynomial interpolation, piecewise low-order interpolation, piecewise cubic Hermite interpolation, polynomial fitting, power function, logarithmic function, exponential function, or least squares fitting methods.

[0031] Step 2: Starting from the beginning of the data, remove erroneous data and replace it with empty data.

[0032] Erroneous data refers to data that is significantly inconsistent with the actual field conditions due to sensor malfunction or other reasons. Under current measurement conditions, the end-point data is relatively accurate and can be considered reliable. The rejection criteria are as follows: Figure 2 Treating the shift data as the scraper position, for the nth scraper ( Figure 2 The scraper n), the nth scraper and the (n-1)th scraper ( Figure 2 The line connecting the scrapers n-1 in the middle forms the first line segment ( Figure 2 Line segment 1), the (n-2)th scraper ( Figure 2 The line connecting the scraper n-2 and the (n-1)th scraper forms the second line segment ( Figure 2In line segment 2), obtain the angle between the first line segment and the second line segment. A preset limit angle threshold is established, which is the limit angle specified in production, typically 2-3°. This angle is compared with the preset limit angle threshold. If the angle exceeds the threshold, the pushing data corresponding to the nth scraper is deemed unreasonable. The bracket number corresponding to the nth scraper is recorded, and the pushing data corresponding to the nth scraper is discarded.

[0033] Furthermore, in this embodiment, the extreme included angle of the scraper is taken as 3°, from Figure 2 If the data shows that the movement data of the nth scraper is unreasonable, then the movement data of the nth scraper is discarded, and the movement data of the (n+1)th scraper is judged. When judging the movement data of the (n+1)th scraper, the (n-1)th scraper should be used as the standard. The extreme angle is multiplied by the number of data intervals between the (n+1)th scraper and the (n-1)th scraper (the number of data intervals is 2) as the basis, until the data meets the judgment conditions. Figure 2 In the middle, the line connecting the (n+1)th scraper and the (n-1)th scraper forms the third line segment ( Figure 2 In line segment 3), the angle between the third line segment and the second line segment is 4°, which is less than the product of the limit angle 3° and the number of data intervals 2, which is 6°. When the pushing data of the nth scraper meets this condition and there is erroneous data before this judgment, n needs to be temporarily assigned a value to facilitate the judgment of the pushing data of the (n+2)th scraper. The assignment is based on a certain ratio of the previous reliable data and the nth scraper.

[0034] Step 3: Use spline interpolation to calculate suitable data to replace the empty data, thereby forming new scraper displacement data:

[0035] The discarded data is calculated using spline interpolation, and new scraper displacement data is generated. In this embodiment, the spline interpolation method is either quadratic or cubic spline interpolation.

[0036] Step 4: Use the Kalman filter algorithm to filter the new scraper push data:

[0037] The Kalman filter algorithm is used to further filter the new scraper movement data, filtering out data that has decreased compared to the previous data to prevent the scraper from moving backward. As other implementation methods, outliers can also be removed using the Laida criterion or the Chauville criterion.

[0038] Step 5: Output the filtered blade movement data, calculate the three-dimensional coordinates of the blades by combining them with the terrain data, calculate the heading angle of each blade based on its three-dimensional coordinates, and then derive its pitch and roll angles by combining the blade's three-dimensional coordinates with the terrain data.

[0039] The filtered scraper movement data is output, and the three-dimensional coordinates of the scraper are calculated by combining the terrain data. These are the real-time three-dimensional coordinates. The heading angle of each scraper is calculated based on its three-dimensional coordinates. The pitch angle and roll angle are then derived from the three-dimensional coordinates of the scraper combined with the terrain.

[0040] Step 6: Calibrate the three-dimensional coordinates of each scraper using inertial navigation data:

[0041] The three-dimensional coordinates of each scraper are calibrated by combining inertial navigation data. The calibration method is to compare the progress data of a certain scraper in the most recent time period with the inertial navigation data when the inertial navigation data of that time period is received, correct the error, and update the three-dimensional coordinates, pitch angle, roll angle and heading angle of the scraper.

[0042] This embodiment provides a twin scraper simulation method for a fully mechanized mining face digital system based on spline interpolation. It uses the support's travel data to calculate the scraper's three-dimensional coordinates, employs spline interpolation and Kalman filtering to filter the scraper travel data, and uses inertial navigation data to calibrate the current simulation results, preventing cumulative errors from causing significant simulation deviations. Without adding new sensors, it can simulate the three-dimensional coordinates of the scraper conveyor in real time, and can simulate the scraper's pushing and moving process.

[0043] Finally, it should be noted that in this embodiment, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0044] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of each example have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0045] The technical content provided by the present invention has been described in detail above. Specific examples have been used in this embodiment to illustrate the principle and implementation of the present invention. The description of the above embodiments is only for the purpose of helping to understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation and application scope based on the idea of ​​the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for simulating twin scrapers in a fully mechanized mining face digital system based on spline interpolation, characterized in that, include: Obtain working surface displacement data corresponding to the scraper conveyor; the working surface displacement data is the data measured by the displacement sensor at the support push rod, which is used to calculate the distance between the support and the scraper connected to it; Error data is removed starting from the beginning of the data stream, and the data is replaced with empty data. The spline interpolation method is used to calculate suitable data to replace the empty data, thereby forming new scraper displacement data; The Kalman filter algorithm is used to filter the new scraper push data to remove data that is smaller than the previous data, thus preventing the scraper from moving backward. The filtered scraper push data is output, and the three-dimensional coordinates of the scraper are calculated by combining the terrain data. The heading angle of each scraper is calculated based on the three-dimensional coordinates of each scraper. The pitch angle and roll angle of the scraper are then obtained by combining the three-dimensional coordinates of the scraper with the terrain. The three-dimensional coordinates of each scraper are calibrated using inertial navigation data; Remove erroneous data starting from the header data, including: The displacement data is considered as the scraper position. For the nth scraper, the line connecting the nth scraper and the (n-1)th scraper forms the first line segment, and the line connecting the (n-2)th scraper and the (n-1)th scraper forms the second line segment. The included angle of the first line segment and the second line segment is obtained. The included angle is compared with a preset scraper limit included angle threshold. If the included angle exceeds the preset scraper limit included angle threshold, the displacement data corresponding to the nth scraper is determined to be unreasonable. The bracket number corresponding to the nth scraper is recorded, and the displacement data corresponding to the nth scraper is removed. The three-dimensional coordinates of each scraper are calibrated by combining inertial navigation data. The calibration method is as follows: when inertial navigation data of a scraper for the most recent time period is received, the shift data of that time period is compared with the inertial navigation data, the error is corrected, and the three-dimensional coordinates, pitch angle, roll angle and heading angle of the scraper are updated.

2. The method for simulating a twin scraper in a fully mechanized mining face digital system based on spline interpolation as described in claim 1, characterized in that, After obtaining the working face movement data corresponding to each scraper conveyor, the data is arranged in order from left to right facing the coal face.

3. The method for simulating a twin scraper in a fully mechanized mining face digital system based on spline interpolation as described in claim 1, characterized in that, Spline interpolation can be either quadratic or cubic.

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