Cutting trajectory adaptive planning method based on digital twin system of fully mechanized coal mining face

Abstract

The application provides a cutting track self-adaptive planning method based on a fully-mechanized coal face digital twin system, and comprises the following steps: taking a coal seam interface prediction sequence output by an adaptive iterative state reconstruction filter as a feedforward reference, obtaining a current coal winning machine state, constructing a rolling optimization objective function, setting a constraint condition, linearizing a nonlinear objective function at a current working point and converting the nonlinear objective function into a standard quadratic programming form, solving to obtain an optimal control sequence, executing only a first control amount in the optimal control sequence in each control period, converting the first control amount into a hydraulic cylinder height adjustment pulse instruction and issuing the hydraulic cylinder height adjustment pulse instruction, updating a measurement of the adaptive iterative state reconstruction filter, and until the coal winning machine reaches an end point of the coal face. The application can realize accurate perception, intelligent decision and collaborative control of a whole production process of the fully-mechanized coal face, and improve coal winning efficiency, equipment reliability and safety.

Description

Technical Field

[0001] This invention relates to the field of path planning, specifically to an adaptive planning method for cutting trajectories based on a digital twin system of a fully mechanized mining face. Background Technology

[0002] Fully mechanized coal mining is currently the mainstream method of coal mining in my country. A fully mechanized mining face consists of three core pieces of equipment: a coal mining machine, a hydraulic support system, and a scraper conveyor. The coordinated operation of these three machines is fundamental to safe and efficient mining. With the deepening of intelligent coal mine construction, digital twin technology has been gradually introduced into fully mechanized mining faces. This aims to achieve real-time monitoring, predictive simulation, and intelligent decision-making of the coal mining process by constructing a virtual mirror of the physical working face. However, although digital twin technology has been proposed for the visual monitoring of fully mechanized mining faces, existing solutions mostly remain at the level of three-dimensional geometric reconstruction and data display, lacking a systematic approach to deeply integrate digital twins with advanced control algorithms. In particular, how to achieve high-precision state fusion of multi-source heterogeneous data, rolling time-domain adaptive planning of cutting trajectories, multi-objective collaborative control of support groups, and accurate prediction of equipment degradation trends within the digital twin framework remain pressing technical challenges. Summary of the Invention

[0003] This invention proposes an adaptive planning method for cutting trajectories based on a digital twin system of a fully mechanized mining face to solve the problems mentioned in the background. The technical solution provided by this invention is as follows:

[0004] The adaptive planning method for cutting trajectory based on the digital twin system of fully mechanized mining face includes the following steps:

[0005] Step 1: Reconstruct the coal seam interface prediction sequence output by the adaptive iterative state reconstructing filter. As a feedforward reference, where The time domain length is set based on the operating speed of the coal mining machine on site;

[0006] Step 2, obtain the current status of the coal mining machine Including the longitudinal position along the working face Horizontal offset traction speed and roller height ;

[0007] Step 3, Construct the rolling optimization objective function ;

[0008] Step 4: Set constraints, including the roller safety height boundary constraint, single-step maximum height adjustment constraint, traction speed safety range constraint, and maximum allowable acceleration constraint.

[0009] Step 5: Linearize the nonlinear objective function at the current operating point and transform it into a standard quadratic programming form, then solve for the optimal control sequence. ,in To control the time domain length, the setting is based on the computing power of the field controller;

[0010] Step 6: In each control cycle, only the first control variable in the optimal control sequence is executed. This is converted into a hydraulic cylinder height adjustment pulse command and sent out.

[0011] Step 7: Update the adaptive iterative state reconstruction filter measurement, return to step 1 for the next round of planning, until the coal mining machine reaches the end of the working face.

[0012] Preferably, step 3 involves rolling optimization of the objective function. The specific formula is as follows:

[0013]

[0014] in Let this be the roller height deviation vector for the (k+j)th step. The weighting matrix for tracking deviation is adjusted according to the degree of undulation of the coal seam on site, and its value range is [value range missing]. , To control the energy consumption weight matrix, adjustments are made based on the response characteristics of the on-site hydraulic system, with a value range of [value range missing]. , The velocity constraint weight matrix is... This is the speed constraint penalty coefficient, which is adjusted according to the on-site requirements for constraint tightness, and its value ranges from 100 to 10000.

[0015] Preferably, the nonlinear objective function in step 5 is solved using the interior point method.

[0016] Compared with existing technologies, the beneficial effects achieved by this invention are: it can provide technical support for accurate perception, intelligent decision-making and collaborative control of the entire production process of fully mechanized mining faces, thereby improving coal mining efficiency, equipment reliability and safety. Attached Figure Description

[0017] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0018] Figure 1 This is a flowchart of the trajectory adaptive planning method proposed in this invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] To make the above-mentioned objectives, features and effects of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0021] Example 1: An adaptive planning method for cutting trajectory based on a digital twin system of a fully mechanized mining face, such as... Figure 1 As shown, it includes the following steps:

[0022] Step 1: Reconstruct the coal seam interface prediction sequence output by the adaptive iterative state reconstructing filter. As a feedforward reference, where To predict the time domain length, the value is set according to the operating speed of the coal mining machine on site, with a range of 10 to 50 steps;

[0023] Step 2, obtain the current status of the coal mining machine Including the longitudinal position along the working face (Unit: m) Lateral offset (Unit: m) Traction speed (Unit: m / min) and roller height (Unit: m);

[0024] Step 3, Construct the rolling optimization objective function The specific formula is as follows:

[0025]

[0026] in Let be the roller height deviation vector at step k+j (unit: m). The weighting matrix for tracking deviation is adjusted according to the degree of undulation of the coal seam on site, and its value range is [value range missing]. , To control the energy consumption weight matrix, adjustments are made based on the response characteristics of the on-site hydraulic system, with a value range of [value range missing]. , The velocity constraint weight matrix is... This is the speed constraint penalty coefficient, which is adjusted according to the on-site requirements for constraint tightness, and its value ranges from 100 to 10000. This is the slack amount for the soft constraint. Single-step height adjustment limit (unit: mm);

[0027] Step 4, set the constraints, the specific formula is as follows:

[0028]

[0029]

[0030]

[0031]

[0032] in and This refers to the safety height boundary of the roller (unit: m). This represents the maximum single-step height adjustment (unit: mm). and The safe range for traction speed (unit: m / min). Maximum permissible acceleration (unit: m / s²) 2 );

[0033] Step 5: Linearize the nonlinear objective function at the current operating point and transform it into a standard quadratic programming form. Solve using the interior-point method to obtain the optimal control sequence. ,in To control the length of the time domain, The settings are based on the computing power of the field controller, and the value range is 3 to 15 steps.

[0034] Step 6: In each control cycle, only the first control variable in the optimal control sequence is executed. This is then converted into a hydraulic cylinder height adjustment pulse command and issued.

[0035] Step 7: Update the adaptive iterative state reconstruction filter measurement, return to step 1 for the next round of planning, until the coal mining machine reaches the end of the working face.

[0036] In this embodiment, the fully mechanized mining face digital twin system includes a three-layer physical architecture: a physical sensing layer, a data transmission layer, and a digital twin layer. The physical sensing layer and the data transmission layer are connected and transmit data via a fieldbus, and the data transmission layer and the digital twin layer are connected and transmit data via an industrial wireless network or fiber optic Ethernet.

[0037] In this embodiment, the physical sensing layer includes a coal mining machine sensor group, a hydraulic support sensor group, a scraper conveyor sensor group, a geological exploration sensor group, and an environmental parameter sensor group.

[0038] In this embodiment, the data transmission layer includes a 5G industrial wireless network, fiber optic Ethernet, edge computing nodes, and a time-series database.

[0039] In this embodiment, the digital twin layer includes a multi-source data fusion and reconstruction module, a cutting trajectory adaptive planning module, a hydraulic support group collaborative follow-up control module, and an equipment degradation life prediction module.

[0040] Example 2: The computer-readable storage medium of this example stores a computer program that, when executed by a processor, implements the steps in the adaptive planning method for cutting trajectory based on a digital twin system of a fully mechanized mining face in Example 1.

[0041] The computer-readable storage medium in this embodiment can be an internal storage unit of the terminal, such as the terminal's hard disk or memory; the computer-readable storage medium in this embodiment can also be an external storage device of the terminal, such as a plug-in hard disk, smart memory card, secure digital card, flash memory card, etc. equipped on the terminal; furthermore, the computer-readable storage medium can include both the terminal's internal storage unit and external storage devices.

[0042] The computer-readable storage medium of this embodiment is used to store computer programs and other programs and data required by the terminal. The computer-readable storage medium can also be used to temporarily store data that has been output or will be output.

[0043] Example 3: The computer device of this example includes a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps in the adaptive planning method for cutting trajectory based on the digital twin system of the fully mechanized mining face in Example 1.

[0044] In this embodiment, the processor can be a central processing unit, or other general-purpose processors, digital signal processors, application-specific integrated circuits, off-the-shelf programmable gate arrays or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor, etc. The memory can include read-only memory and random access memory, and provides instructions and data to the processor. A portion of the memory can also include non-volatile random access memory. For example, the memory can also store device type information.

[0045] Those skilled in the art will clearly understand that each implementation can be achieved using software plus the necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0046] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An adaptive planning method for cutting trajectory based on a digital twin system of a fully mechanized mining face, characterized in that, Includes the following steps: Step 1: Reconstruct the coal seam interface prediction sequence output by the adaptive iterative state reconstructing filter. As a feedforward reference, where The time domain length is set based on the operating speed of the coal mining machine on site; Step 2, obtain the current status of the coal mining machine Including the longitudinal position along the working face Horizontal offset traction speed and roller height ; Step 3, Construct the rolling optimization objective function ; Step 4: Set constraints, including the roller safety height boundary constraint, single-step maximum height adjustment constraint, traction speed safety range constraint, and maximum allowable acceleration constraint. Step 5: Linearize the nonlinear objective function at the current operating point and transform it into a standard quadratic programming form, then solve for the optimal control sequence. ,in To control the time domain length, the setting is based on the computing power of the field controller; Step 6: In each control cycle, only the first control variable in the optimal control sequence is executed. This is converted into a hydraulic cylinder height adjustment pulse command and sent out. Step 7: Update the adaptive iterative state reconstruction filter measurement, return to step 1 for the next round of planning, until the coal mining machine reaches the end of the working face.

2. The adaptive planning method for cutting trajectory based on a digital twin system of a fully mechanized mining face according to claim 1, characterized in that, Step 3: Rolling optimization of the objective function The specific formula is as follows: ； in Let this be the roller height deviation vector for the (k+j)th step. The weighting matrix for tracking deviation is adjusted according to the degree of undulation of the coal seam on site, and its value range is [value range missing]. , To control the energy consumption weight matrix, adjustments are made based on the response characteristics of the on-site hydraulic system, with a value range of [value range missing]. , The velocity constraint weight matrix is... This is the speed constraint penalty coefficient, which is adjusted according to the on-site requirements for constraint tightness, and its value ranges from 100 to 10000.

3. The adaptive planning method for cutting trajectory based on a digital twin system of a fully mechanized mining face according to claim 2, characterized in that, Step 5: Solve the nonlinear objective function using the interior point method.

4. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps in the adaptive planning method for cutting trajectory based on a fully mechanized mining face digital twin system as described in any one of claims 1-3.

5. A computer device comprising a processor, a memory, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps in the adaptive planning method for cutting trajectory based on a fully mechanized mining face digital twin system as described in any one of claims 1-3.

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