Fully mechanized coal mining face cutting planning method and device

Abstract

The invention discloses a cutting planning method and device for a fully mechanized coal mining face. Firstly, a fully mechanized coal mining equipment and cutting space constraint model is established; then, traversing coordinate points in DEM data by utilizing fully-mechanized coal mining equipment and a cutting space constraint model, and establishing an optimization model; and finally, solving an abnormal point elevation value which not only can meet the constraint condition mathematical model, but also can meet the requirements of the gangue cutting amount and the mining leakage amount in the optimization model, and forming cutting coordinate point DEM data by the optimized abnormal points and the normal coordinate points together. Similarly, DEM data of coal seam roof and floor cutting coordinate points are obtained, and the coal seam roof cutting planning path and the coal seam floor cutting planning path jointly form a fully mechanized coal mining face cutting planning path. According to the fully mechanized coal mining face cutting planning method and device, the cutting planning path can be automatically generated according to the coal seam top and bottom plate digital elevation model, and abnormal points which do not meet fully mechanized coal mining equipment attitude and cutting space constraints can be reasonably eliminated on the basis of considering the gangue cutting amount and the mining leakage amount.

Description

Technical Field

[0001] The invention relates to a method and a device for planning cutting of a fully mechanized mining face, which automatically generates a cutting path of a fully mechanized mining face according to a digital elevation model (DEM) of a coal seam roof and floor, and belongs to the technical field of unmanned mining in coal mines. Background Art

[0002] Unmanned mining of coal mines is an important issue in the field of coal mining in my country, and is an important way to achieve safe coal mining and reduce casualties. At present, the commonly used coal mining technology in automated fully mechanized mining faces is memory cutting technology. Its principle is: the coal mining machine driver controls the coal mining machine to make the drum complete the first cutting along the undulations of the coal seam, and the control system of the coal mining machine will record the stroke position and cutting height of the coal mining machine. In the subsequent cutting process, the coal mining machine will complete automatic cutting according to the stroke position and cutting height of the coal mining machine recorded by the control system. However, when the inclination of the coal seam changes significantly, the coal mining machine driver must re-control the coal mining machine to complete the cutting, and the control system will re-record the stroke position and cutting height of the coal mining machine as the basis for subsequent cutting. Although the memory cutting technology is simple, it is more suitable for situations where the coal seam does not change much. When the coal seam conditions are complex, frequent human-computer interaction is required, and true unmanned mining cannot be achieved.

[0003] In order to solve the above problems, many scholars have proposed a series of cutting path planning methods for comprehensive mining working faces. However, the current cutting path planning methods for comprehensive mining working faces all optimize the paths in the working face direction or the working face advancement direction separately, and cannot make the two direction paths simultaneously meet the constraints of comprehensive mining equipment and cutting space. Therefore, it is necessary to provide an improved technical solution to address the above-mentioned deficiencies in the existing technology.

[0004] The Chinese patent with the patent number: ZL 202110680453.3 aims at the problem that the existing coal cutting path optimization method of the coal mining machine is poorly adaptable to the spatial morphological changes of the coal seam in front of the fully-mechanized working face. A method for adaptive cutting path planning of the rear drum of the coal mining machine in the fully-mechanized working face is provided. This technology can determine the optional advancement path of the scraper conveyor trough according to a series of constraints, and plan the optimal cutting path by combining the optional advancement path with the vertical curvature of the scraper trough.

[0005] The Chinese patent with patent number: ZL 202310347385.8 addresses the technical problem that the number of trajectory points in coal mining path planning affects the production efficiency and production quality of the coal mining machine during the coal mining process. It discloses a method for planning the height path of the cutting drum of a coal mining machine. This technology can construct a cutting feature vector based on the coal seam boundary information and cluster it to obtain a series of boundary intervals. The optimal trajectory points are obtained through optimization algorithm optimization, and then the cutting drum height path planning is performed.

[0006] The Chinese invention patent with patent number: ZL 201910291920.6 overcomes the shortcomings of the existing memory cutting technology and provides an unmanned coal mining path planning method based on a high-precision navigation model of the working face, which is simple in method, easy to operate, and can automatically generate the optimal path for unmanned coal mining.

[0007] However, none of the above patents solve the technical problem of comprehensive mining working face cutting path planning that integrates four dimensions: coal seam floor, coal seam roof, working face direction, and working face advancement direction. Summary of the invention

[0008] Purpose of the invention: In view of the above-mentioned technical problems, the present invention proposes a cutting planning method and device for a comprehensive mining working face which can automatically generate a cutting planning path according to a digital elevation model (DEM) of the coal seam roof and floor. This method can reasonably eliminate abnormal points that do not meet the constraints of the comprehensive mining equipment posture and cutting space on the basis of considering the amount of cut gangue and the amount of missed mining.

[0009] In order to achieve the above technical objectives, the present invention adopts the following technical means:

[0010] A method for planning cutting of a fully mechanized mining face comprises the following steps:

[0011] S1. Establish a comprehensive mining equipment and cutting space constraint model based on the comprehensive mining equipment posture and cutting space constraint;

[0012] S2. Regrid the original coal seam DEM data according to the length of the middle slot of the scraper conveyor and the cutting depth of the coal mining machine to obtain the coal seam roof and floor grid DEM data with the length of the middle slot of the scraper conveyor and the cutting depth of the coal mining machine as the grid size. Use the fully mechanized mining equipment and cutting space constraint model to traverse the coordinate points in the DEM data, classify the coordinate points into normal coordinate points and coal seam floor abnormal points, and then divide the coal seam floor abnormal points into different areas. In each area, the optimized elevation value of each coal seam floor abnormal point is regarded as a decision variable. The posture of the fully mechanized mining equipment and the cutting space constraint are regarded as constraint conditions, and the minimum gangue cutting amount and the minimum missed mining amount are regarded as objective functions, and the coal seam floor optimization model is established; the NSGA2 algorithm is used to solve the elevation value of the coal seam floor abnormal point that can meet both the constraint condition mathematical model and the requirements of gangue cutting amount and missed mining amount. The optimized coal seam floor abnormal point and the coal seam floor normal coordinate point together constitute the coal seam floor cutting coordinate point DEM data, and the DEM data are connected in sequence along the working face direction to obtain the coal seam floor cutting planning path;

[0013] S3, re-gridding the coal seam bottom plate cutting coordinate point DEM data and the original coal seam roof DEM data obtained in step S2 according to the hydraulic support center distance and the hydraulic support length, and obtaining the coal seam top and bottom plate grid DEM data with the hydraulic support center distance and the hydraulic support length as the grid size, and using the comprehensive mining equipment and cutting space constraint model to traverse the coordinate points in the DEM data, and classify the coordinate points into normal coordinate points and coal seam roof abnormal points, and then divide the coal seam roof abnormal points into different areas, and in each area, regard the optimized elevation value of each coal seam roof abnormal point as a decision variable, and compare the comprehensive mining equipment posture with the cutting space constraint model. The time constraint is regarded as a constraint condition, the minimum gangue cutting amount and the minimum missed mining amount are regarded as the objective function, and a coal seam roof optimization model is established. The NSGA2 algorithm is used to solve the elevation value of the coal seam roof abnormal point that can meet both the constraint condition mathematical model and the requirements of gangue cutting amount and missed mining amount. The optimized coal seam roof abnormal point and the coal seam roof normal coordinate point together constitute the coal seam roof cutting coordinate point DEM data. The cutting coordinate point DEM data are connected in sequence along the working face direction to obtain the coal seam roof cutting planning path. The coal seam roof cutting planning path and the coal seam floor cutting planning path together constitute the comprehensive mining working face cutting planning path.

[0014] Step S1 specifically includes the following sub-steps:

[0015] S11. To solve the problem that the middle groove of the scraper conveyor cannot exceed the maximum vertical curvature, the maximum vertical curvature of the middle groove of the scraper conveyor is used as the threshold to limit the angle difference Kw of the curves of adjacent cutting bottom plates in the working face direction;

[0016]

[0017] Where, Zf is the elevation value of the cutting bottom plate coordinate point, unit is m; subscript j is the coordinate number of the working face direction; subscript i is the coordinate number of the working face advancement direction; l1 is the length of the middle trough of the scraper conveyor (m); Kw max is the maximum vertical curvature of the scraper conveyor (°), which is 3°;

[0018] S12, in order to solve the problem of unsmooth movement of the scraper conveyor, the maximum step height M of the coal seam max The threshold limits the elevation difference M between two adjacent cutting bottom plate coordinate points in the advancing direction of the working face;

[0019]

[0020] Where y is the ordinate value of the grid coordinate point of the cutting floor, in m; l2 is the cutting depth of the coal mining machine, in m; M max The maximum step height of the cutting bottom plate, in m, is 0.1 m;

[0021] S13. To solve the problem of hydraulic support biting and gangue leakage, the distance between the tops of adjacent hydraulic supports is calculated through the geometric relationship between the angle between adjacent cutting bottom plate curves and the height, width and other parameters of the hydraulic support. The maximum distance A between the tops of the hydraulic supports is calculated. max With minimum spacing A min Limit the top spacing of hydraulic supports;

[0022]

[0023] Where Zr is the elevation of the cutting top plate coordinate point, in m; s is the horizontal spacing of the hydraulic supports; A max A is the maximum spacing at the top of the hydraulic support, in m, and is taken as 0.2 m; min The minimum spacing at the bottom of the hydraulic support, in m, is 0m;

[0024] S14, in order to solve the problem of vertical cutting of the top and bottom plates of the hydraulic support, max The angle difference θ between the top plate cutting curve and the bottom plate cutting curve along the working face direction at the same coordinate position is limited by the threshold;

[0025]

[0026] Where, l3 is the center distance of the hydraulic support, unit is m; θ max The maximum tilt angle of the hydraulic support is 5°;

[0027] S15. To solve the problem that the pitch angle of the top beam of the hydraulic support cannot be too large, γ max The angle difference γ between the top plate cutting curve and the bottom plate cutting curve in the advancing direction of the working face at the same coordinate position is limited by the threshold;

[0028]

[0029] Where, l4 is the length of the hydraulic support, in m; γ max The maximum pitch angle of the top beam of the hydraulic support is 7°;

[0030] Formulas (1)-(5) together constitute the comprehensive mining equipment and cutting space constraint model.

[0031] Step S2 specifically includes the following sub-steps:

[0032] S21, grid the original coal seam roof and floor DEM data with the scraper conveyor length l1 and the coal mining machine cutting depth l2 as the grid size, and start from the origin of the coal seam floor coordinates, and substitute the first, second, and third coordinate points of the coal seam floor along the working face direction into formula (1). If the conditions are met, the second coordinate point is marked as a normal coordinate point. If not, the second coordinate point is marked as an abnormal point of the coal seam floor. By analogy, all abnormal points of the coal seam floor that exceed the maximum vertical curvature of the scraper conveyor are traversed; Similarly, the first, second and third coordinate points of the coal seam floor along the working face direction and the second coordinate point of the coal seam roof along the working face direction are substituted into formula (3) to traverse all coal seam floor abnormal points that cause the hydraulic support to leak gangue and bite the frame; similarly, the first, second and third coordinate points of the coal seam floor along the working face advancement direction are substituted into formula (2) to traverse all coal seam floor abnormal points that cause the scraper conveyor to move unsmoothly; for the coal seam floor abnormal points searched out, according to the principle that the difference in coordinate sequence number is less than or equal to 3, the coal seam floor abnormal points are divided into regions to obtain a series of floor abnormal regions;

[0033] S22. If the optimized elevation value of the abnormal point of the coal seam floor is greater than the original elevation value, this part is defined as the floor missed mining volume. If it is less than, it is defined as the floor gangue cutting volume. The coal seam floor optimization objective function is established in this way:

[0034]

[0035] In the formula, g f The amount of waste cut from the bottom plate; l f is the floor leakage volume; Zf is the original elevation value of the floor abnormal point; Zf* is the optimized elevation value of the coal seam floor abnormal point; j(m1,...,m p ) is the horizontal coordinate grid number of the abnormal point on the coal seam floor; i(n1,…,n q ) is the ordinate grid number of the abnormal point on the coal seam floor;

[0036] S23, in the abnormal area divided in step S21, in a certain abnormal area, it is assumed that there are u coal seam floor abnormal points, and the elevation values ​​are Zf ji , where j = m1...m p , i=n1......n q , formula (6) is defined as the objective function, formulas (1)-(3) are constraint conditions, and the optimal elevation value Zf of u coal seam floor abnormal points is ji * , where j = m1...m p , i=n1......n q As decision variables, an optimization model of abnormal points in the coal seam floor of u dimension is established;

[0037]

[0038] S24, optimize each floor abnormal area divided in step S21 in turn, establish the corresponding optimization model of formula (7) in the same abnormal area, and solve the model using the non-dominated sorting genetic algorithm. If the optimal value is found, continue to optimize the coal seam floor abnormal points in the next floor abnormal area until all areas are optimized; if not found, expand the floor abnormal area to the surrounding area by a grid length and optimize again until the optimal solution is found; the optimized coal seam floor abnormal points and normal coordinate points together constitute the coal seam floor cutting coordinate point DEM data, and the DEM data are connected in sequence along the working face direction to obtain the coal seam floor cutting planning path;

[0039] Step S3 specifically includes the following sub-steps:

[0040] S31, re-grid the DEM data of the cut coordinate points of the coal seam roof and bottom plates obtained in step S31 and the original DEM data of the coal seam roof according to the hydraulic support center distance l3 and the hydraulic support width l4 as the grid size, and obtain the top and bottom plate grid DEM data with the hydraulic support center distance and the hydraulic support length as the grid size; substitute the first and second coordinate points of the coal seam bottom plate along the working face direction and the first and second coordinate points of the coal seam roof along the working face direction into formula (4), if satisfied, mark the second coordinate point as a normal coordinate point, if not satisfied, mark the second coordinate point as a normal coordinate point. The coordinate points are marked as coal seam roof abnormal points; and so on, all coal seam floor abnormal points with excessive deflection of hydraulic supports are traversed. Similarly, the first and second coordinate points of the coal seam floor along the advancing direction of the working face and the first and second coordinate points of the coal seam roof along the advancing direction of the working face are substituted into formula (5), and all coal seam roof abnormal points exceeding the maximum pitch angle of the hydraulic support are traversed. For the coal seam roof abnormal points searched out, the coal seam roof abnormal point areas are divided according to the principle that the difference in coordinate sequence number is less than or equal to 3 as the same area, and a series of roof abnormal areas are obtained;

[0041] S32. If the optimized elevation value of the abnormal point of the coal seam roof is less than the original elevation value, this part is defined as the roof missed mining volume. If it is greater, it is defined as the roof gangue cutting volume. The coal seam roof optimization objective function is established as follows:

[0042]

[0043] In the formula, g r is the amount of gangue cut from the roof; l r is the amount of roof leakage; Zr is the original elevation value of the abnormal point on the coal seam roof; Zr* is the optimized elevation value of the abnormal point on the coal seam roof; subscript j(O1,…,O a ) is the horizontal coordinate grid number of the abnormal point on the coal seam roof; the subscript i (S1,…,St ) is the ordinate grid number of the abnormal point on the coal seam roof;

[0044] S33, in the roof abnormality area divided in step S31, in a certain roof abnormality area, it is assumed that there are v coal seam roof abnormality points, and the elevation values ​​are Zr ji , where j = O1...O a , i=S1......S t , formula (8) is defined as the objective function, formulas (4)-(5) are constraint conditions, and the optimal elevation value Zr of v coal seam roof abnormal points is ji * (where j = O1...O a , i=S1......S t ) is the decision variable, and the v-dimensional coal seam roof abnormal point optimization model is established:

[0045]

[0046] S34, optimize each roof abnormality area divided in step S31 in turn, establish the corresponding optimization model of formula (9) in the same roof abnormality area, and use the multi-objective optimization algorithm to solve the optimization model of formula (9). If the optimal value is found, continue to optimize the coal seam roof abnormality points in the next roof area until all areas are optimized; if not found, expand the roof abnormality area to the surrounding area by a grid length and re-optimize until the optimal solution is found; the optimized coal seam roof abnormality points and normal coordinate points constitute the coal seam roof cutting coordinate point DEM data, and the DEM data coordinate points are connected in sequence along the working face direction to obtain the coal seam roof cutting planning path, and the coal seam roof cutting planning path and the coal seam floor cutting planning path obtained in step S24 together constitute the comprehensive mining working face cutting planning path.

[0047] The present invention further discloses a fully mechanized mining face cutting planning device, which is applied to the fully mechanized mining face cutting planning method. The cutting planning device is integrally installed in a control box in the middle of a coal mining machine, and comprises:

[0048] Industrial computer, inertial navigation, lithium battery, shaft encoder and external display screen, among which:

[0049] The shaft encoder is installed on the traveling part and the rocker arm of the coal mining machine respectively;

[0050] The industrial computer is connected with the hydraulic support controller, the coal mining machine controller, the external display screen and the shaft encoder signal;

[0051] When the cutting planning device is connected to an external power supply, the inertial navigation device and the industrial computer are powered directly by the external power supply. When the cutting planning device is not connected to an external power supply, the inertial navigation device and the industrial computer are powered by a lithium battery.

[0052] Beneficial effects:

[0053] The present invention proposes a method and device for planning cutting of a fully mechanized mining working face. The method establishes a fully mechanized mining equipment posture and cutting space constraint model according to the layout posture of hydraulic supports and scraper conveyors and the equipment passability and other restrictive conditions. The model can be used to obtain the coordinate distribution of abnormal points on the roof and floor of the coal seam. According to the distribution of abnormal points on the roof and floor of the coal seam, the abnormal points are divided into a series of areas, and each abnormal area is optimized separately, thereby improving the optimization efficiency of the abnormal points.

[0054] In addition, this method takes the minimum amount of cut gangue and the minimum amount of missed mining as the optimization objectives, and the posture of the fully-mechanized mining equipment and the cutting space constraint model as the constraint conditions. It uses the non-dominated sorting genetic algorithm (NSGA-Ⅱ) to optimize the abnormal points in each area. It can reasonably eliminate the abnormal points that do not meet the posture and cutting space constraints of the fully-mechanized mining equipment on the basis of considering the amount of cut gangue and the amount of missed mining. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 It is a specific flow chart of the fully mechanized mining working face cutting planning method of the present invention;

[0056] Figure 2 It is a schematic diagram of the maximum vertical curvature of the scraper conveyor;

[0057] Figure 3 It is a schematic diagram of the scraper conveyor movement;

[0058] Figure 4 It is a schematic diagram of a hydraulic support bite frame;

[0059] Figure 5 This is a schematic diagram of hydraulic support leakage;

[0060] Figure 6 This is a schematic diagram of the hydraulic support being skewed;

[0061] Figure 7 Schematic diagram of the pitch angle of the top beam of the hydraulic support;

[0062] Figure 8 It is the abnormal area optimization flow chart;

[0063] Fig. 9 It is a structural schematic diagram of a fully mechanized mining working face cutting planning device of the present invention;

[0064] In the figure: 1. Coal seam bottom plate; 2. Coal seam roof plate; 3. Scraper conveyor; 4. Hydraulic support. DETAILED DESCRIPTION

[0065] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0066] The purpose of the present invention is to provide a method and device for comprehensive mining working face cutting planning, which can automatically generate cutting planning paths according to the digital elevation model (DEM) of the coal seam roof and floor, so as to solve or alleviate the problems existing in the above-mentioned prior art.

[0067] The comprehensive mining face cutting planning method of the present invention is as follows: Figure 1 As shown in the figure, firstly, the comprehensive mining equipment and cutting space constraint model is established according to the comprehensive mining equipment posture and cutting space constraint. Then, the original coal seam DEM data is re-gridded according to the length of the middle slot of the scraper conveyor and the cutting depth of the coal mining machine, and the coal seam roof and floor grid DEM data with the length of the middle slot of the scraper conveyor and the cutting depth of the coal mining machine are obtained. The coordinate points in the DEM data are traversed using the comprehensive mining equipment and cutting space constraint model, and the coordinate points are classified into normal coordinate points and coal seam floor abnormal points. Then, the coal seam floor abnormal points are divided into different areas. In each area, the optimized elevation value of each coal seam floor abnormal coordinate point is regarded as a decision variable, the comprehensive mining equipment posture and cutting space constraint are regarded as constraints, and the minimum cutting amount and the minimum leakage amount are regarded as objective functions to establish an optimization model. Finally, in the optimization model, the NSGA2 algorithm is used to solve the elevation value of the coal seam floor abnormal point that can meet both the constraint condition mathematical model and the requirements of the cutting amount and the missed mining amount. The optimized coal seam floor abnormal point and normal coordinate point together constitute the coal seam floor cutting coordinate point DEM data. Similarly, the coal seam floor cutting coordinate point DEM data and the original coal seam DEM data are re-grid according to the hydraulic support center distance and hydraulic support length to obtain the top and bottom plate grid DEM data with the hydraulic support center distance and hydraulic support length as the grid size. Repeat the above steps to obtain the coal seam top and bottom plate cutting coordinate point DEM data, and connect the DEM data in sequence along the working face direction to obtain the cutting planning path. The specific steps include:

[0068] (1) Figure 2 As shown, in order to solve the problem that the middle groove of the scraper conveyor cannot exceed the maximum vertical curvature, the maximum vertical curvature of the middle groove of the scraper conveyor is used as the threshold to limit the angle difference Kw of the adjacent cutting bottom plate curves in the working face direction (Formula 1).

[0069]

[0070] Where Zf is the elevation of the cutting bottom plate coordinate point (m); subscript j is the coordinate number of the working face direction; subscript i is the coordinate number of the working face advancement direction; l1 is the length of the middle trough of the scraper conveyor (m); Kw max The maximum vertical curvature of the scraper conveyor (°) is 3°.

[0071] like Figure 3 As shown in the figure, in order to solve the problem of unsmooth movement of the scraper conveyor, the maximum step height M of the coal seam is max The threshold limits the elevation difference M between two adjacent cutting bottom plate coordinate points in the advancing direction of the working face (Formula 2).

[0072]

[0073] Where y is the ordinate value of the grid coordinate point of the cutting floor (m); l2 is the cutting depth of the coal mining machine (m); M max The maximum step height of the cutting bottom plate (m) is taken as 0.1m.

[0074] like Figure 4 , 5 As shown in the figure, in order to solve the problem of hydraulic support biting and gangue leakage, the distance between the tops of adjacent hydraulic supports is calculated through the geometric relationship between the angle between the adjacent cutting bottom plate curves and the height, width and other parameters of the hydraulic support. max With minimum spacing A min Limit the top spacing of hydraulic supports (Formula 3).

[0075]

[0076] Where Zr is the elevation of the cutting top plate coordinate point (m); s is the horizontal spacing of the hydraulic support; A max A is the maximum spacing of the hydraulic support top (m), take 0.2m; min is the minimum spacing at the bottom of the hydraulic support (m), take 0m.

[0077] like Figure 6 As shown in the figure, for the problem that the hydraulic support needs to vertically cut the top and bottom plates, θ max The angle difference θ between the top plate cutting curve and the bottom plate cutting curve along the working face direction at the same coordinate position is limited by the threshold (Formula 4).

[0078]

[0079] Where, l3 is the center distance of the hydraulic support (m); θ max is the maximum tilt angle of the hydraulic support (°), which is 5°.

[0080] like Figure 7 As shown in the figure, in order to solve the problem that the pitch angle of the top beam of the hydraulic support cannot be too large, γ max The angle difference γ between the top plate cutting curve and the bottom plate cutting curve in the advancing direction of the working face at the same coordinate position is the threshold limit (Formula 5).

[0081]

[0082] Where, l4 is the length of the hydraulic support (m); γ max is the maximum pitch angle of the top beam of the hydraulic support (°), which is 7°.

[0083] Formulas (1)-(5) together constitute the comprehensive mining equipment and cutting space constraint model.

[0084] (2) The original coal seam roof and floor DEM data are gridded with the scraper conveyor length l1 and the coal mining machine cutting depth l2 as the grid size. The roof and floor grid DEM data with the scraper length and the coal mining machine cutting depth as the grid size, starting from the origin of the coal seam floor coordinate, the first, second, and third coordinate points of the coal seam floor along the working face direction are substituted into formula (1). If satisfied, the second coordinate point is marked as a normal coordinate point. If not satisfied, the second coordinate point is marked as an abnormal point of the coal seam floor. By analogy, all abnormal points of the coal seam floor that exceed the maximum vertical curvature of the scraper conveyor are traversed. Similarly, the first, second, and third coordinate points of the coal seam floor along the working face direction and the second coordinate point of the coal seam roof along the working face direction are substituted into formula (3) to traverse all abnormal points of the coal seam floor that cause the hydraulic support to leak gangue and bite the frame. Similarly, the first, second, and third coordinate points of the coal seam floor along the advancing direction of the working face are substituted into formula (2) to traverse all the coal seam floor abnormal points that make the scraper conveyor move unsmoothly. For the coal seam floor abnormal points searched out, according to the principle that the difference in coordinate sequence number is less than or equal to 3, the coal seam floor abnormal points are divided into regions to obtain a series of floor abnormal regions.

[0085] (3) If the optimized elevation value of the abnormal point of the coal seam floor is greater than the original elevation value, then this part is defined as the floor missed mining volume; if it is less than, then it is defined as the floor cut waste volume. In this way, the coal seam floor optimization objective function (Formula 6) is established:

[0086]

[0087] In the formula, g f The amount of waste cut from the bottom plate; l f is the amount of missed mining at the bottom plate; Zf is the original elevation value of the bottom plate abnormal point; Zf* is the optimized elevation value of the bottom plate abnormal point; j(m1,…,m p ) is the grid number of the abscissa of the abnormal coordinate point; i(n1,…,n q ) is the ordinate grid number of the abnormal coordinate point.

[0088] In the coal seam floor abnormal area divided in step (2), under a coal seam floor abnormal area, it is assumed that there are u coal seam floor abnormal points, and their elevation values ​​are Zf ji (where j = m1...m p , i=n1......n q), formula (6) is the objective function, formulas (1)-(3) are the constraints, and the optimal elevation value Zf of u coal seam floor abnormal points is ji * (where j = m1...m p , i=n1......n q ), as the decision variable, the u-dimensional coal seam floor abnormal point optimization model is established (Formula 7).

[0089]

[0090] (4) Optimize each abnormal area of ​​the coal seam floor divided in step (2) in turn, and establish the corresponding optimization model of formula (7) in the same abnormal area, such as Figure 8 As shown in the figure, the non-dominated sorting genetic algorithm (NSGA-Ⅱ) is used to solve this model. If the optimal value is found, the optimization of the abnormal points in the next area will continue until all areas are optimized. If not found, the abnormal area will be expanded to the surrounding area by a grid length and optimized again until the optimal solution is found. The optimized coal seam floor abnormal points and normal coordinate points together constitute the coal seam floor cutting coordinate point DEM data. The DEM data is connected in sequence along the working face direction to form the coal seam floor cutting planning path.

[0091] (5) The DEM data of the cut-off coordinate points of the coal seam floor obtained in step (4) and the original coal seam roof DEM data are re-grid according to the hydraulic support center distance l3 and the hydraulic support width l4 as the grid size, and the roof and bottom plate grid DEM data with the hydraulic support center distance and the hydraulic support length as the grid size are obtained. The first and second coordinate points of the coal seam floor along the working face direction and the first and second coordinate points of the coal seam roof along the working face direction are substituted into formula (4). If satisfied, the second coordinate point is marked as a normal coordinate point. If not satisfied, the second coordinate point is marked as an abnormal point of the coal seam roof. By analogy, all abnormal points of the coal seam roof with excessive deflection of the hydraulic support are traversed. Similarly, the first and second coordinate points of the coal seam floor along the working face advancement direction and the first and second coordinate points of the coal seam roof along the working face advancement direction are substituted into formula (5), and all abnormal points of the coal seam roof that exceed the maximum pitch angle of the hydraulic support are traversed. For the coal seam roof anomaly points that are searched out, the coal seam roof anomaly point areas are divided according to the principle that the difference in coordinate numbers is less than or equal to 3 as the same area, and a series of roof anomaly areas are obtained.

[0092] (6) If the optimized elevation value of the abnormal point of the coal seam roof is less than the original elevation value, then this part is defined as the roof missed mining volume; if it is greater, then it is defined as the roof cut gangue volume. In this way, the coal seam roof optimization objective function (Formula 8) is established:

[0093]

[0094] In the formula, g r is the amount of gangue cut from the roof; l r is the roof leakage volume; Zr is the original elevation value of the roof abnormal point; Zr* is the optimized elevation value of the roof abnormal point; subscript j (O1, ..., O a ) is the grid number of the abscissa of the abnormal coordinate point; the subscript i (S1,…,S t ) is the ordinate grid number of the abnormal coordinate point.

[0095] In the abnormal area divided in step (5), under a certain abnormal area, it is assumed that there are v abnormal points with elevation values ​​of Zr ji (where j = O1...O a , i=S1......S t ), formula (8) is the objective function, formulas (4)-(5) are the constraints, and the optimal elevation value Zr of v outliers is ji * (where j = O1...O a , i=S1......S t ) is taken as the decision variable, and the v-dimensional coal seam roof abnormal point optimization model is established (Formula 9).

[0096]

[0097] (7) Each abnormal roof area divided in step (5) is optimized in turn, and the corresponding optimization model of formula (9) is established in the same abnormal area, such as Figure 8 As shown, the non-dominated sorting genetic algorithm (NSGA-Ⅱ) is used to solve the optimization model of formula (9). If the optimal value is found, the optimization of the abnormal points in the next area will continue until all areas are optimized. If not found, the abnormal area is expanded to a grid length around and optimized again until the optimal solution is found. The optimized coal seam roof abnormal points and normal coordinate points together constitute the DEM data of the coal seam roof cutting coordinate points. The DEM data coordinate points are connected in sequence along the working face direction to obtain the coal seam roof cutting planning path. The coal seam roof cutting planning path and the coal seam floor cutting planning path obtained in step (4) together constitute the comprehensive mining working face cutting planning path.

[0098] like Fig. 9As shown, the comprehensive mining working face cutting planning device of the present invention includes an industrial computer, an inertial navigation, a lithium battery shaft encoder and an external display screen. The cutting planning device is installed in the middle control box of the coal mining machine, and the shaft encoder is installed on the walking part and the rocker arm of the coal mining machine. The industrial computer is connected to the hydraulic support controller, the coal mining machine controller, the external display screen and the shaft encoder. When the cutting planning device is connected to an external power supply, the inertial navigation device and the industrial computer are directly powered by the external power supply. When the cutting planning device is not connected to an external power supply, the inertial navigation device and the industrial computer are powered by a lithium battery. The comprehensive mining working face cutting planning is carried out in the industrial computer.

Claims

1. A fully mechanized mining face cutting planning method, characterized in that: The following steps are involved: S1. Establish a comprehensive mining equipment and cutting space constraint model based on the comprehensive mining equipment posture and cutting space constraint; S2. Regrid the original coal seam DEM data according to the length of the middle slot of the scraper conveyor and the cutting depth of the coal mining machine to obtain the coal seam roof and floor grid DEM data with the length of the middle slot of the scraper conveyor and the cutting depth of the coal mining machine as the grid size. Use the fully mechanized mining equipment and cutting space constraint model to traverse the coordinate points in the DEM data, classify the coordinate points into normal coordinate points and coal seam floor abnormal points, and then divide the coal seam floor abnormal points into different areas. In each area, the optimized elevation value of each coal seam floor abnormal point is regarded as a decision variable. The posture of the fully mechanized mining equipment and the cutting space constraint are regarded as constraint conditions, and the minimum gangue cutting amount and the minimum missed mining amount are regarded as objective functions, and the coal seam floor optimization model is established; the NSGA2 algorithm is used to solve the elevation value of the coal seam floor abnormal point that can meet both the constraint condition mathematical model and the requirements of gangue cutting amount and missed mining amount. The optimized coal seam floor abnormal point and the coal seam floor normal coordinate point together constitute the coal seam floor cutting coordinate point DEM data, and the DEM data are connected in sequence along the working face direction to obtain the coal seam floor cutting planning path; S3, re-gridding the coal seam bottom plate cutting coordinate point DEM data and the original coal seam roof DEM data obtained in step S2 according to the hydraulic support center distance and the hydraulic support length, and obtaining the coal seam top and bottom plate grid DEM data with the hydraulic support center distance and the hydraulic support length as the grid size, and using the comprehensive mining equipment and cutting space constraint model to traverse the coordinate points in the DEM data, and classify the coordinate points into normal coordinate points and coal seam roof abnormal points, and then divide the coal seam roof abnormal points into different areas, and in each area, regard the optimized elevation value of each coal seam roof abnormal point as a decision variable, and compare the comprehensive mining equipment posture with the cutting space constraint model. The time constraint is regarded as a constraint condition, the minimum gangue cutting amount and the minimum missed mining amount are regarded as the objective function, and a coal seam roof optimization model is established. The NSGA2 algorithm is used to solve the elevation value of the coal seam roof abnormal point that can meet both the constraint condition mathematical model and the requirements of gangue cutting amount and missed mining amount. The optimized coal seam roof abnormal point and the coal seam roof normal coordinate point together constitute the coal seam roof cutting coordinate point DEM data. The cutting coordinate point DEM data are connected in sequence along the working face direction to obtain the coal seam roof cutting planning path. The coal seam roof cutting planning path and the coal seam floor cutting planning path together constitute the comprehensive mining working face cutting planning path.

2. The method for planning cutting of fully mechanized mining working face according to claim 1 is characterized in that: Step S1 specifically includes the following sub-steps: S11. To solve the problem that the middle groove of the scraper conveyor cannot exceed the maximum vertical curvature, the maximum vertical curvature of the middle groove of the scraper conveyor is used as the threshold to limit the angle difference Kw of the curves of adjacent cutting bottom plates in the working face direction; Where, Zf is the elevation value of the cutting bottom plate coordinate point, unit is m; subscript j is the coordinate number of the working face direction; subscript i is the coordinate number of the working face advancement direction; l1 is the length of the middle trough of the scraper conveyor (m); Kw max is the maximum vertical curvature of the scraper conveyor (°), which is 3°; S12, in order to solve the problem of unsmooth movement of the scraper conveyor, the maximum step height M of the coal seam max The threshold limits the elevation difference M between two adjacent cutting bottom plate coordinate points in the advancing direction of the working face; Where y is the ordinate value of the grid coordinate point of the cutting floor, in m; l2 is the cutting depth of the coal mining machine, in m; M max The maximum step height of the cutting bottom plate, in m, is 0.1 m; S13. To solve the problem of hydraulic support biting and gangue leakage, the distance between the tops of adjacent hydraulic supports is calculated through the geometric relationship between the angle between adjacent cutting bottom plate curves and the height, width and other parameters of the hydraulic support. The maximum distance A between the tops of the hydraulic supports is calculated. max With minimum spacing A min Limit the top spacing of hydraulic supports; Where Zr is the elevation value of the cut top plate coordinate point, unit: m; s is the horizontal spacing of hydraulic supports; A max A is the maximum spacing at the top of the hydraulic support, in m, and is taken as 0.2 m; min The minimum spacing at the bottom of the hydraulic support, in m, is 0m; S14, in order to solve the problem of vertical cutting of the top and bottom plates of the hydraulic support, max The angle difference θ between the top plate cutting curve and the bottom plate cutting curve along the working face direction at the same coordinate position is limited by the threshold; Where, l3 is the center distance of the hydraulic support, unit is m; θ max The maximum tilt angle of the hydraulic support is 5°; S15. To solve the problem that the pitch angle of the top beam of the hydraulic support cannot be too large, γ max The angle difference γ between the top plate cutting curve and the bottom plate cutting curve in the advancing direction of the working face at the same coordinate position is limited by the threshold; Where, l4 is the length of the hydraulic support, in m; γ max The maximum pitch angle of the top beam of the hydraulic support is 7°; Formulas (1)-(5) together constitute the comprehensive mining equipment and cutting space constraint model.

3. The method for planning cutting of fully mechanized mining face according to claim 2, characterized in that: Step S2 specifically includes the following sub-steps: S21, grid the original coal seam roof and floor DEM data with the scraper conveyor length l1 and the coal mining machine cutting depth l2 as the grid size, and start from the origin of the coal seam floor coordinates, and substitute the first, second, and third coordinate points of the coal seam floor along the working face direction into formula (1). If the conditions are met, the second coordinate point is marked as a normal coordinate point. If not, the second coordinate point is marked as an abnormal point of the coal seam floor. By analogy, all abnormal points of the coal seam floor that exceed the maximum vertical curvature of the scraper conveyor are traversed; Similarly, the first, second and third coordinate points of the coal seam floor along the working face direction and the second coordinate point of the coal seam roof along the working face direction are substituted into formula (3) to traverse all coal seam floor abnormal points that cause the hydraulic support to leak gangue and bite the frame; similarly, the first, second and third coordinate points of the coal seam floor along the working face advancement direction are substituted into formula (2) to traverse all coal seam floor abnormal points that cause the scraper conveyor to move unsmoothly; for the coal seam floor abnormal points searched out, according to the principle that the difference in coordinate sequence number is less than or equal to 3, the coal seam floor abnormal points are divided into regions to obtain a series of floor abnormal regions; S22. If the optimized elevation value of the abnormal point of the coal seam floor is greater than the original elevation value, this part is defined as the floor missed mining volume. If it is less than, it is defined as the floor gangue cutting volume. The coal seam floor optimization objective function is established in this way: In the formula, g f The amount of waste cut from the bottom plate; l f is the floor leakage volume; Zf is the original elevation value of the floor abnormal point; Zf* is the optimized elevation value of the coal seam floor abnormal point; j(m1,...,m p ) is the horizontal coordinate grid number of the abnormal point on the coal seam floor; i(n1,…,n q ) is the ordinate grid number of the abnormal point on the coal seam floor; S23, in the abnormal area divided in step S21, in a certain abnormal area, it is assumed that there are u coal seam floor abnormal points, and the elevation values ​​are Zf ji , where j = m1...m p , i=n1......n q , formula (6) is defined as the objective function, formulas (1)-(3) are constraint conditions, and the optimal elevation value Zf of u coal seam floor abnormal points is ji * , where j = m1...m p , i=n1......n q As decision variables, an optimization model of abnormal points in the coal seam floor of u dimension is established; S24, optimize each floor abnormal area divided in the step S21 in turn, and in the same abnormal area, establish the corresponding optimization model of formula (7), and use the non-dominated sorting genetic algorithm to solve the model. If the optimal value is found, continue to optimize the coal seam floor abnormal points in the next floor abnormal area until all areas are optimized; if not found, expand the floor abnormal area to the surrounding area by a grid length and optimize again until the optimal solution is found; the optimized coal seam floor abnormal points and normal coordinate points together constitute the coal seam floor cutting coordinate point DEM data, and the DEM data are connected in sequence along the working face direction to obtain the coal seam floor cutting planning path.

4. The method for planning cutting of fully mechanized mining face according to claim 3 is characterized in that: Step S3 specifically includes the following sub-steps: S31, re-grid the DEM data of the cut coordinate points of the coal seam roof and bottom plates obtained in step S31 and the original DEM data of the coal seam roof according to the hydraulic support center distance l3 and the hydraulic support width l4 as the grid size, and obtain the top and bottom plate grid DEM data with the hydraulic support center distance and the hydraulic support length as the grid size; substitute the first and second coordinate points of the coal seam bottom plate along the working face direction and the first and second coordinate points of the coal seam roof along the working face direction into formula (4), if satisfied, mark the second coordinate point as a normal coordinate point, if not satisfied, mark the second coordinate point as a normal coordinate point. The coordinate points are marked as coal seam roof abnormal points; and so on, all coal seam floor abnormal points with excessive deflection of hydraulic supports are traversed. Similarly, the first and second coordinate points of the coal seam floor along the advancing direction of the working face and the first and second coordinate points of the coal seam roof along the advancing direction of the working face are substituted into formula (5), and all coal seam roof abnormal points exceeding the maximum pitch angle of the hydraulic support are traversed. For the coal seam roof abnormal points searched out, the coal seam roof abnormal point areas are divided according to the principle that the difference in coordinate sequence number is less than or equal to 3 as the same area, and a series of roof abnormal areas are obtained; S32. If the optimized elevation value of the abnormal point of the coal seam roof is less than the original elevation value, this part is defined as the roof missed mining volume. If it is greater, it is defined as the roof gangue cutting volume. The coal seam roof optimization objective function is established as follows: In the formula, g r is the amount of gangue cut from the roof; l r is the amount of roof leakage; Zr is the original elevation value of the abnormal point on the coal seam roof; Zr* is the optimized elevation value of the abnormal point on the coal seam roof; subscript j(O1,…,O a ) is the horizontal coordinate grid number of the abnormal point on the coal seam roof; the subscript i (S1,…,S t ) is the ordinate grid number of the abnormal point on the coal seam roof; S33, in the roof abnormal area divided in step S31, in a certain roof abnormal area, it is assumed that there are v coal seam roof abnormal points, and the elevation values ​​are Zr ji , where j = O1...O a , i=S1......S t , formula (8) is defined as the objective function, formulas (4)-(5) are constraint conditions, and the optimal elevation value Zr of v coal seam roof abnormal points is ji * (where j = O1...O a , i=S1......S t ) is the decision variable, and the v-dimensional coal seam roof abnormal point optimization model is established: S34, optimize each roof abnormality area divided in step S31 in turn, establish the corresponding optimization model of formula (9) in the same roof abnormality area, and use the multi-objective optimization algorithm to solve the optimization model of formula (9). If the optimal value is found, continue to optimize the coal seam roof abnormality points in the next roof area until all areas are optimized; if not found, expand the roof abnormality area to the surrounding area by a grid length and re-optimize until the optimal solution is found; the optimized coal seam roof abnormality points and normal coordinate points constitute the coal seam roof cutting coordinate point DEM data, and the DEM data coordinate points are connected in sequence along the working face direction to obtain the coal seam roof cutting planning path, and the coal seam roof cutting planning path and the coal seam floor cutting planning path obtained in step S24 together constitute the comprehensive mining working face cutting planning path.

5. A fully mechanized mining face cutting planning device, applied to the fully mechanized mining face cutting planning method according to any one of claims 1 to 4, the cutting planning device is integrally installed in the middle control box of the coal mining machine, characterized in that: include: Industrial computer, inertial navigation, lithium battery, shaft encoder and external display screen, among which: The shaft encoder is installed on the traveling part and the rocker arm of the coal mining machine respectively; The industrial computer is connected with the hydraulic support controller, the coal mining machine controller, the external display screen and the shaft encoder signal; When the cutting planning device is connected to an external power supply, the inertial navigation device and the industrial computer are powered directly by the external power supply. When the cutting planning device is not connected to an external power supply, the inertial navigation device and the industrial computer are powered by a lithium battery.

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