A mining shovel trajectory planning method based on full-process asymmetric multi-segment S-shaped curve

Abstract

A method for trajectory planning of a mining electric shovel based on a full-process asymmetric multi-segment S-curve belongs to the field of trajectory planning technology. First, material surface point cloud data is acquired and processed. Second, a mathematical model of the mining electric shovel motor based on the full-process asymmetric multi-segment S-curve speed control algorithm is established. Third, the trajectory equation of the bucket tooth tip of the mining electric shovel is constructed. Fourth, a constraint boundary is given. Fifth, a mathematical model of the objective function and constraint function based on the full-process asymmetric multi-segment S-curve speed control algorithm is constructed. Sixth, the initial optimization value is input and the optimization is performed. Seventh, the optimal excavation trajectory is output and the optimization is ended. The present invention can optimize and adjust the maximum speed of the mining electric shovel motor and the running time of each segment of the motor on the basis of the traditional seven-segment S-curve speed control algorithm, realize online planning and energy consumption optimization of the mining electric shovel excavation trajectory; can effectively capture the complex motion characteristics during the excavation process, and ensure efficient operation under various working conditions.

Description

Technical Field

[0001] The present invention belongs to the technical field of trajectory planning, and relates to a mining electric shovel trajectory planning method based on a full-process asymmetric multi-segment S-shaped curve, specifically a method integrating an asymmetric seven-segment S-shaped curve speed control algorithm into excavation trajectory planning. Background Art

[0002] Electric mining shovels, essential equipment for open-pit mining, currently rely primarily on manual operation. However, limited visibility and complex terrain pose safety risks, increasing energy consumption and wear. With increasing efficiency and safety requirements, intelligent excavation is becoming a trend, with trajectory planning being a key technology, as it directly impacts efficiency and energy consumption.

[0003] Currently, numerical interpolation and fitting are commonly used methods for trajectory planning. These methods offer advantages such as high accuracy, flexibility, and energy efficiency optimization. However, they are computationally complex, lack robustness, and struggle to adapt to rapidly changing operating scenarios. Another approach, based on kinematic and dynamic models, offers the advantage of accurately describing the motion characteristics of excavating equipment, optimizing energy efficiency and stability. However, these methods are complex, making algorithm implementation and debugging challenging, and their adaptability in complex operating environments requires further verification. An asymmetric multi-segment S-curve velocity planning algorithm optimizes the bucket motion of an electric mining shovel, achieving smooth control and reducing energy consumption. This algorithm has the potential to address the computational complexity and model adaptability limitations of these methods, improving system stability and reducing excavation energy consumption. The front-end working mechanisms of an electric mining shovel primarily consist of a bucket, push rod, push mechanism, hoist rope, hoist mechanism, boom, and overhead sheave. Electric mining shovels are also equipped with lidar and inclination sensors.

[0004] This paper combines an asymmetric multi-segment S-curve velocity planning algorithm with genetic algorithm optimization to construct an analytically based kinematic model for the bucket of an electric mining shovel. This model optimizes the maximum excavation energy consumption by optimizing the maximum motor speed and the control time of each segment of the electric mining shovel to achieve an optimal velocity curve. This avoids mechanical shock and vibration caused by rapid deceleration, ensuring a smooth and continuous excavation trajectory. The key value of this invention lies in its improved stability and reduced energy consumption during the excavation process, providing an efficient and accurate optimization solution for trajectory planning of electric mining shovels. Summary of the Invention

[0005] In response to the problems existing in the prior art, the present invention provides a mining electric shovel trajectory planning method based on a full-process asymmetric multi-segment S-shaped curve.

[0006] The technical solution adopted in the present invention is:

[0007] A mining shovel trajectory planning method based on a full-process asymmetric multi-segment S-curve is proposed. This method integrates an asymmetric seven-segment S-curve speed control algorithm into excavation trajectory planning. The method for implementing the asymmetric seven-segment S-curve speed control algorithm in excavation trajectory planning is as follows: First, acquire and process material surface point cloud data. First, obtain the material surface point cloud data, including its 3D coordinates and contour information. Then, use MATLAB to segment the point cloud data, remove noise and irrelevant areas, extract valid 3D shape and contour information, and fit the material surface using Polynomial Response Surface (PRS) technology, providing a basis for subsequent excavation volume calculation. Second, establish a mathematical model for the mining shovel motor based on the full-process asymmetric multi-segment S-curve speed control algorithm. Using the maximum speeds of the mining shovel's push and hoist motors and the operating time of each segment as optimization variables, combined with kinematic knowledge, mathematical models for the mining shovel's push and hoist motors based on the full-process asymmetric multi-segment S-curve speed control algorithm are constructed. Third, construct the trajectory equation for the mining shovel's bucket tooth tip. Based on the mathematical models of the electric mining shovel's push and hoist motors constructed in step 4, and combined with knowledge of kinematics, trigonometric functions, and the front working mechanism of the electric mining shovel, the trajectory equations from the push and hoist motors to the bucket tooth tip of the electric mining shovel are established. Step 4: Define constraint boundaries. Based on the actual maximum speeds of the push and hoist motors and the total excavation time, determine the constraint boundaries of the corresponding optimization variables. Step 5: Construct a mathematical model for the objective function and constraint functions of the full-process asymmetric multi-segment S-curve speed control algorithm. The objective function aims to minimize energy consumption, and the constraint functions include constraints on the electric mining shovel's motors, structural constraints on the front working mechanism, and safe height constraints during excavation. Step 6: Input initial optimization values ​​and perform optimization. Based on the given initial values, a genetic algorithm (GA) is used for global optimization. The optimization variables are iteratively updated through selection, crossover, and mutation operations until the objective function converges or the set number of iterations is reached, ultimately generating the optimal excavation trajectory. Step 7: Output the optimal excavation trajectory, concluding the optimization. The specific steps include:

[0008] The first step is to obtain and process the material surface point cloud data. The details are as follows:

[0009] LiDAR collects 3D coordinates and contour information of the material surface within the excavation range of a mining shovel. Due to factors such as reduced density and resolution when the laser beam scans the edges, the material surface point cloud data must be segmented to improve fitting accuracy and reduce computational burden. Noise points and irrelevant areas are removed to extract valid 3D shape and contour information from the material surface point cloud. The segmented material surface point cloud has an x-axis coordinate range of [-6, 6], a y-axis range of [6, 25], and a z-axis range of [0, 15], all in meters. Finally, the segmented material surface point cloud is fitted using PRS.

[0010] The second step is to establish a mathematical model for the full-process asymmetric multi-segment S-curve speed control algorithm for the mining shovel motor. Using the maximum speeds of the mining shovel's push and hoist motors and the operating time of each segment as optimization variables, combined with kinematics knowledge, a mathematical model for the full-process asymmetric multi-segment S-curve speed control algorithm for the mining shovel's push and hoist motors is constructed. The details are as follows:

[0011] In the asymmetric multi-segment S-curve speed, in order to ensure the stability of the excavation process, the initial velocity and the final velocity are set to 0. Assume that the time periods from the first to the last are T1, T2, T3, T4, T5, T6, and T7 respectively, and the total time is T, t i (i=1,2,…,7) represents the end time node of each time period, and the maximum speed of the motor is v max , the motor acceleration expression is derived as:

[0012]

[0013] The acceleration expression is:

[0014]

[0015] Where J1, J3, J5, and J7 are the jerks in the 1st, 3rd, 5th, and 7th stages, respectively.

[0016] The speed expression is:

[0017]

[0018] Where v1, v2, v5, and v6 represent the final velocities of stages 1, 2, 5, and 6, respectively.

[0019] The displacement expression is:

[0020]

[0021] Where S1, S2, S3, S4, S5, and S6 represent the final displacements of stages 1, 2, 3, 4, 5, and 6, respectively. The maximum velocity v in formula (1) is maxAnd the motor running time T i (i=1,2,…,7) refers to the pushing motor and lifting motor of the mining electric shovel and their corresponding operating time.

[0022] Furthermore, when calculating the acceleration formula (2) for the mining shovel motor, the asymmetric multi-segment S-curve speed control algorithm is designed more precisely during the start-stop phase. By adjusting the time allocation and acceleration curve shape of each phase, residual vibration can be effectively reduced or eliminated, achieving a smoother motion process. This optimized control strategy helps improve the motion quality and overall efficiency of the mining shovel.

[0023] The asymmetric multi-segment S-curve speed control algorithm flexibly adjusts acceleration and jerk during different motion phases, resulting in a smoother and more flexible speed curve. This algorithm effectively optimizes complex operating conditions, reducing motor load and power peaks during acceleration and deceleration, thereby lowering the system's overall energy consumption. Through rational speed curve planning, the asymmetric multi-segment S-curve speed control algorithm ensures smooth motion while avoiding unnecessary shock and vibration, improving system efficiency and reliability.

[0024] The third step is to construct the trajectory equation of the mining shovel's bucket tooth tip. Based on the mathematical models of the mining shovel's push and lift motors constructed in the second step, combined with knowledge of kinematics, trigonometric functions, and the mining shovel's front-end working mechanism, the trajectory equation from the mining shovel's push and lift motors to the mining shovel's bucket tooth tip is established. The details are as follows:

[0025] Step 3.1) First, based on the working principle of a mining shovel, the rich excavation trajectory is directly formed by the combined action of the shovel's push and lift motors. In other words, when the shovel's push and lift mechanisms are set at different speeds, different push rod displacements Δl are obtained. gan and the lifting rope displacement Δl rope The push rod and the arm will form different arc angles θ in the same plane, which directly affects the shape of the excavation trajectory. The trajectory equation of the push motor and the lifting motor of the mining electric shovel to the bucket tooth tip of the mining electric shovel is first solved by the above angle θ, and the relationship between the angle θ and the push rod displacement Δl of the mining electric shovel is found. gan and the lifting rope displacement Δl rope The function f is:

[0026] θ=f(Δl gan ,Δl rope ) (5)

[0027] Step 3.2) Next, based on the structural relationship between the front-end working mechanisms of the mining shovel, find the trajectory equation of the mining shovel bucket tooth tip. This equation can be expressed as follows:

[0028]

[0029] Where x(t) and y(t) are the horizontal and vertical trajectory equations of the mining shovel bucket tooth tip, respectively; g and h are the functional relationships of the horizontal and vertical coordinates of the mining shovel bucket tooth tip over time, respectively; x is the vertical coordinate of the mining shovel bucket tooth tip over time; s It is a collection of the structural dimensions of the mining shovel bucket, push rod, hoist rope, boom, and sheave, as well as the height from the sheave to the horizontal ground and the initial height from the mining shovel bucket to the horizontal ground during initial excavation.

[0030] Step 3.3) Finally, the volume of material inside the bucket of the mining shovel during excavation is calculated based on the vertical trajectory equation of the mining shovel's bucket tooth tip obtained in step 3.2):

[0031]

[0032] Where w is the width of the mining shovel bucket, y is w (t) is the material surface prediction equation, Δx represents the two adjacent discrete points x in the horizontal excavation trajectory x(t). i (t) and x i+1 The displacement difference within time Δt is x(Δt+t)-x(t). The calculation of excavation volume is the basis for the subsequent calculation of the bucket fill rate constraint.

[0033] The fourth step is to assign constraint boundaries. The speed change of the electric shovel's motor is divided into seven time periods. By applying speed and time constraints, the motor speed and the running time of each period are controlled to obtain the optimal excavation trajectory and the lowest excavation energy consumption. The details are as follows:

[0034] According to the maximum motor speed of the mining shovel and its corresponding running time in each section in the above formula (1), all the optimization variables of the present invention are determined as follows:

[0035]

[0036] Where x is the decision variable, v pmax 、v hmax are the maximum speeds of the pushing motor and lifting motor of the mining electric shovel, T pi (i=1,2,…,7) and T hj(j = 1, 2, …, 6) are the operating times of the push and hoist motors of the electric mining shovel, respectively. Here, only the first six periods of the hoist motor are needed. The final hoist period is obtained by subtracting the sum of the six periods of the hoist motor from the sum of the seven periods of the push motor.

[0037] The following are the value ranges of all optimization variables of the present invention corresponding to formula (8):

[0038] Pushing motor speed v pmax (m / s): 3650-6100; increase motor speed v hmax (m / s): 3650-6000; Pushing the first time period T p1 / s: 0.5-4; push the second time period T p2 / s: 0.5-4; push the third time period T p3 / s: 0.5-4; push the fourth time period T p4 / s: 0.3-3; push the fifth time period T p5 / s: 0.5-4; push the sixth time period T p6 / s: 0.5-4; push the seventh time period T p7 / s: 0.5-4; improve the first time period T h1 / s: 0.5-4; increase the second time period T h2 / s: 0.5-4; improve the third time period T h3 / s: 0.5-4; improve T in the fourth time period h4 / s: 0.5-4; improve the fifth time period T h5 / s: 0.5-4; improve T in the sixth time period h6 / s:0.5-4.

[0039] The fifth step is to construct a mathematical model of the objective function and constraint function based on the full-process asymmetric multi-segment S-curve speed control algorithm. High energy consumption means high operating costs; secondly, it means waste of resources and low efficiency; finally, high energy consumption will also shorten the life of the equipment. Optimizing excavation energy consumption can reduce the operating temperature of the equipment, extend the life of the equipment, and improve the stability and reliability of the equipment. Therefore, the objective function of the present invention is defined as the energy consumption per unit excavation volume, and the constraint function includes the motor constraint of the mining electric shovel, the structural constraint of the front working mechanism of the mining electric shovel, and the safety height constraint during excavation. The details are as follows:

[0040] Step 5.1) Determine the objective function. The objective function of the present invention is expressed as:

[0041] J=(E p +E h ) / V (9)

[0042] Where, E p and E h Respectively represent the energy consumption of the pushing motor and lifting motor of the mining electric shovel during the excavation process, specifically:

[0043]

[0044] Where, F p and F h Represent the pushing force and lifting force respectively, v gan and v rope They represent the speed of the push rod and the speed of the hoist rope of the mining electric shovel respectively.

[0045] Step 5.2) Determine the constraint function. To ensure that the mining shovel meets the necessary structural and reliability requirements during excavation, it is necessary to constrain the motor's operating speed and time to ensure that the shovel's movement under actual working conditions meets engineering requirements. In addition, the structural constraints of the mining shovel's front working mechanism must be considered to ensure its structural reliability and stability. Specifically:

[0046] (1) For the excavation performance constraint, consider that the instantaneous output force and instantaneous output power of the mining shovel motor are less than the allowable limit value, that is:

[0047]

[0048] Where, F pmax and F hmax are the maximum output forces of the pushing motor and lifting motor of the mining electric shovel, P p and P h are the instantaneous output power of the pushing motor and the lifting motor of the mining electric shovel, P pmax and P hmax They are the maximum power of the pushing motor and lifting motor of the mining electric shovel respectively.

[0049] (2) Considering the stability of the excavation process, the speed of the push rod and the lifting rope cannot be less than 0 during excavation, that is:

[0050]

[0051] (3) To ensure excavation efficiency, according to the excavation experience of experienced mining shovel operators, a single excavation cycle must be kept within a reasonable range, namely:

[0052]

[0053] (4) To avoid energy waste during under-excavation and low efficiency during over-excavation, it is necessary to constrain the full bucket rate. Here, the full bucket rate range for a single excavation is set to 90%-110%, that is:

[0054]

[0055] Where V n Stands for rated load volume.

[0056] (5) For structural constraints, the maximum extension of the push rod Less than the maximum allowed length l max ,Right now:

[0057]

[0058] (6) To ensure that the mining shovel can contact the material during excavation, the initial horizontal position of the excavation trajectory is greater than the bottom of the material pile, that is:

[0059] g6=[Htanθ0-x(t0)≤0](17)

[0060] (7) In order to prevent the bottom of the bucket from scraping the ground at the beginning of excavation and to ensure that the equipment is not damaged, the minimum initial height of the bottom of the bucket is higher than the ground height, that is:

[0061] g7=[H0-y Mmin ≤0](18)

[0062] (8) In order to ensure that the overall structure of the bucket is basically or completely separated from the material surface at the end of excavation and to ensure the smoothness of excavation, the end of the excavation end position is greater than the set value and greater than the material surface height, that is:

[0063] g8=[Y e -y tip_end (t)≤0](19)

[0064] Where Y e It is a set value based on the material surface height.

[0065] After sorting out the above constraints, all the constraints of the asymmetric multi-segment S-curve speed control algorithm can be obtained as follows:

[0066]

[0067] Step 6: Input the initial optimization values ​​and optimize. Given the initial values ​​of the optimization variables, use GA to globally optimize the objective function (Equation (9)) of the mining shovel. When the design variables converge to a stable value or the number of iterations exceeds the maximum allowed number of iterations, set the end condition. If the end condition is not met, modify the initial optimization values ​​and repeat step 6; otherwise, terminate the loop and generate the optimal mining trajectory.

[0068] Step 7: Output the optimal mining trajectory and end the optimization.

[0069] Beneficial effects of the present invention:

[0070] The present invention can perform online planning of the excavation trajectory of a mining electric shovel. On the basis of the traditional seven-segment S-curve speed control algorithm, it optimizes and adjusts the maximum speed of the motor of the mining electric shovel and the running time of each segment of the motor, thereby realizing online planning of the excavation trajectory of the mining electric shovel and energy consumption optimization. At the same time, the proposed method has significant characteristics. By introducing a full-process asymmetric multi-segment S-curve speed control algorithm, it can achieve fine control of speed, acceleration and jerk in trajectory planning, effectively capturing the complex motion characteristics of the excavation process. In addition, the algorithm flexibly adjusts the motion trajectory of the pushing motor and lifting motor of the mining electric shovel, and takes excavation energy consumption as the optimization target, making the excavation trajectory smoother and more stable, reducing impact and vibration, and ensuring the efficient operation of the mining electric shovel under various working conditions. The present invention will improve the intelligence level of the mining electric shovel and provide a new architecture for the trajectory planning of the mining electric shovel. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 The present invention is a flow chart of a method for trajectory planning of a mining electric shovel based on a full-process asymmetric multi-segment S-shaped curve.

[0072] Figure 2 This is a comparison chart of the thrust motor power of the WK12 model mining electric shovel based on the method of trajectory planning for a mining electric shovel based on a full-process asymmetric multi-segment S-shaped curve of the present invention, the T-shaped curve speed control algorithm, and the symmetric S-shaped curve speed control algorithm.

[0073] Figure 3 This is a comparison chart of the hoisting motor power of the WK12 model mining electric shovel based on the method of mining electric shovel trajectory planning based on the full-process asymmetric multi-segment S-shaped curve of the present invention, the T-shaped curve speed control algorithm, and the symmetric S-shaped curve speed control algorithm. DETAILED DESCRIPTION

[0074] The present invention is further described below with reference to the accompanying drawings and specific embodiments.

[0075] The method of the convolutional cyclic deep learning model designed by the present invention for predicting digging force by integrating physical priors is as follows: Figure 1 As shown, the specific implementation is as follows:

[0076] The first step is to use LiDAR to collect the 3D coordinates and contour information of the material surface within the mining range of the mining shovel. The segmented material surface point cloud is then set to have an x-axis coordinate range of [-6, 6], a y-axis range of [6, 25], and a z-axis range of [0, 15], all in meters. Finally, a PRS fit is applied to the segmented material surface point cloud to provide a material surface foundation for subsequent trajectory planning.

[0077] The second step is to establish a mathematical model for the full-process asymmetric multi-segment S-curve speed control algorithm for the mining shovel motor. The maximum speeds of the mining shovel's push and lift motors and their respective operating times are used as optimization variables. Combined with kinematics knowledge, and to ensure the smoothness of the excavation process, the initial and final velocities are set to 0. The jerk (Formula (1)), acceleration (Formula (2)), velocity (Formula (3)), and displacement (Formula (4)) of the mining shovel's push and lift motors based on the full-process asymmetric multi-segment S-curve speed control algorithm are constructed.

[0078] The third step is to build the trajectory equation of the mining shovel bucket tooth tip based on the mathematical models of the pushing motor and lifting motor of the mining shovel constructed in the second step, combined with knowledge of kinematics, trigonometric functions, and the front working mechanism of the mining shovel. Follow the steps below:

[0079] First, according to the working principle of mining electric shovel, when the pushing mechanism and lifting mechanism of mining electric shovel are given different speeds (Formula (3)), different push rod displacements Δl will be obtained. gan and the lifting rope displacement Δl rope , the push rod and the big arm will form different arc angles θ in the same plane, determine the angle θ and the push rod displacement Δl of the mining electric shovel gan and the lifting rope displacement Δl rope The function f is formula (5).

[0080] Secondly, based on the structural dimensions of the mining shovel bucket, push rod, hoist rope, boom, and sheave, as well as the height from the sheave to the horizontal ground and the initial height from the mining shovel bucket to the horizontal ground during initial excavation, the horizontal and vertical trajectory equations of the mining shovel bucket tooth tip are determined, namely, formula (6).

[0081] Finally, based on the vertical trajectory equation of the bucket tooth tip of the mining electric shovel, the material volume inside the bucket of the mining electric shovel during excavation is calculated, laying the foundation for the subsequent bucket full rate objective function.

[0082] The fourth step is to define the constraint boundaries. The speed change of the mining shovel's motor is divided into seven time periods. By optimizing the maximum speed of the mining shovel's push and lift motors and the corresponding motor operating time in each period, the optimal excavation trajectory and the lowest excavation energy consumption are obtained. The value ranges of all optimization variables in the optimization process of this invention are as follows:

[0083] Pushing motor speed v pmax (m / s): 3650-6100; increase motor speed v hmax (m / s): 3650-6000; Pushing the first time period T p1 / s: 0.5-4; push the second time period Tp2 / s: 0.5-4; push the third time period T p3 / s: 0.5-4; push the fourth time period T p4 / s: 0.3-3; push the fifth time period T p5 / s: 0.5-4; push the sixth time period T p6 / s: 0.5-4; push the seventh time period T p7 / s: 0.5-4; improve the first time period T h1 / s: 0.5-4; increase the second time period T h2 / s: 0.5-4; improve the third time period T h3 / s: 0.5-4; improve T in the fourth time period h4 / s: 0.5-4; improve the fifth time period T h5 / s: 0.5-4; improve T in the sixth time period h6 / s:0.5-4.

[0084] The fifth step is to construct a mathematical model for the objective function and constraint functions of the full-process asymmetric multi-segment S-curve speed control algorithm, using the energy consumption per unit excavation volume as the objective function of the present invention, and the motor constraints of the mining shovel, the structural constraints of the front working mechanism of the mining shovel, and the safe height constraints during excavation as the constraint functions. The specific implementation is as follows:

[0085] First, the objective function (Formula (9)) is determined based on the energy consumption of the pushing motor and lifting motor of the mining electric shovel during the excavation process (Formulas (10) and (11)).

[0086] Next, determine the constraint function. To ensure that the mining shovel meets the necessary structural and reliability requirements during excavation, it is necessary to constrain the motor's operating speed and time to ensure that the shovel's movement under actual working conditions meets engineering requirements. Furthermore, the structural constraints of the mining shovel's front-end working mechanism must be considered to ensure its structural reliability and stability. The specific constraints are summarized in Equation (20).

[0087] Step 6: Input the initial optimization value and optimize. Given a set of initial values ​​of the optimization variables, use GA to perform global optimization on the objective function of the mining electric shovel. When the design variables converge to a stable value or the number of iterations exceeds the maximum allowed number of iterations, set the end condition. If the end condition is not met, modify the initial optimization value and repeat step 6; otherwise, terminate the loop and generate the optimal mining trajectory. Here, the maximum number of iterations of GA is set to 400, the population size is set to 500, and the function tolerance is set to 10 -6 .

[0088] Step 7: Output the optimal mining trajectory and end the optimization.

[0089] Figure 2 and Figure 3 A comparison chart shows the power of the push and hoist motors of a WK12 electric mining shovel using the present invention's method for trajectory planning based on a full-process asymmetric multi-segment S-curve, a T-curve speed control algorithm, and a symmetric S-curve speed control algorithm. The chart shows that both the push and hoist motor power of the present invention's method are lower than those of the other two methods. This measured data can be used for comparison with the prediction method proposed in this patent.

[0090] The above-described embodiments merely express the implementation methods of the present invention, but should not be understood as limiting the scope of the patent of the present invention. It should be pointed out that for those skilled in the art, several variations and improvements can be made without departing from the concept of the present invention, and these all fall within the scope of protection of the present invention.

Claims

1. A mining shovel trajectory planning method based on a full-process asymmetric multi-segment S-shaped curve, characterized in that: The mining electric shovel trajectory planning method is a method that integrates an asymmetric seven-segment S-curve speed control algorithm into the excavation trajectory planning, and the steps are as follows: The first step is to obtain and process the material surface point cloud data; The second step is to establish a mathematical model for the speed control algorithm of the electric shovel motor based on the full-process asymmetric multi-segment S-shaped curve. The maximum speed of the pushing motor and the lifting motor of the electric shovel and the operating time of each segment are used as optimization variables. In combination with kinematics knowledge, a mathematical model for the speed control algorithm of the pushing motor and the lifting motor of the electric shovel based on the full-process asymmetric multi-segment S-shaped curve is constructed. The third step is to construct the trajectory equation from the pushing motor and the lifting motor of the mining electric shovel to the bucket tooth tip of the mining electric shovel based on the mathematical model of the pushing motor and the lifting motor of the mining electric shovel constructed in the second step; The fourth step is to give the constraint boundaries. According to the actual maximum speed of the pushing motor and lifting motor of the mining shovel and the total excavation time, the constraint boundaries of the corresponding optimization variables are determined. The fifth step is to construct a mathematical model for the objective function and constraint function of the full-process asymmetric multi-segment S-curve speed control algorithm. The objective function aims to minimize energy consumption, and the constraint function includes constraints on the mining shovel's motor, the structure of the shovel's front-end working mechanism, and safe height constraints during excavation. The sixth step is to input the initial optimization value and optimize. Based on the given initial value, global optimization is performed and the optimization variables are iteratively updated until the objective function converges or the set number of iterations is reached, and finally the optimal mining trajectory is generated. Step 7: Output the optimal mining trajectory and end the optimization.

2. The mining shovel trajectory planning method based on the full-process asymmetric multi-segment S-shaped curve according to claim 1 is characterized in that: The first step is to obtain and process the material surface point cloud data; the details are as follows: First, the laser radar collects the three-dimensional coordinates and contour information of the material surface within the excavation range of the mining electric shovel; secondly, MATLAB is used to segment the point cloud data, eliminate noise and irrelevant areas, and extract the effective three-dimensional shape and contour information of the material surface point cloud. The x-axis coordinate range of the segmented material surface point cloud is [-6, 6], the y-axis range is [6, 25], and the z-axis range is [0, 15], all in meters; finally, the segmented material surface point cloud is fitted using PRS.

3. The mining shovel trajectory planning method based on the full-process asymmetric multi-segment S-shaped curve according to claim 2 is characterized in that: The second step is to establish a mathematical model of the mining shovel motor based on the full-process asymmetric multi-segment S-curve speed control algorithm; the details are as follows: In the asymmetric multi-segment S-curve speed, let the initial velocity and final velocity be 0; assume that the time intervals from the first to the last are T1, T2, T3, T4, T5, T6, and T7, respectively, and the total time is T, t i (i=1,2,…,7) represents the end time node of each time period, and the maximum speed of the motor is v max , the motor acceleration expression is derived as: The acceleration expression is: Where J1, J3, J5, and J7 are the jerks of the 1st, 3rd, 5th, and 7th stages, respectively; The speed expression is: Where v1, v2, v5, and v6 represent the final velocities of stages 1, 2, 5, and 6, respectively; The displacement expression is: Where S1, S2, S3, S4, S5, and S6 represent the final displacements of stages 1, 2, 3, 4, 5, and 6, respectively; the maximum velocity v in formula (1) is max And the motor running time T i (i=1,2,…,7) refers to the pushing motor and lifting motor of the mining electric shovel and their corresponding operating time.

4. The mining shovel trajectory planning method based on the full-process asymmetric multi-segment S-shaped curve according to claim 3 is characterized in that: The third step is to construct the trajectory equation from the pushing motor and the lifting motor of the mining electric shovel to the bucket tooth tip of the mining electric shovel; the details are as follows: Step 3.1) First, during the mining process of the mining shovel, its excavation trajectory is directly formed by the combined action of the pushing motor and the lifting motor of the mining shovel; when the pushing mechanism and the lifting mechanism of the mining shovel are given different speeds, different push rod displacements Δl will be obtained. gan and the lifting rope displacement Δl rope , the push rod and the arm will form different arc angles θ in the same plane, which directly affects the shape of the excavation trajectory; the trajectory equation of the pushing motor and the lifting motor of the mining electric shovel to the bucket tooth tip of the mining electric shovel is derived to obtain the angle θ, which is related to the push rod displacement Δl of the mining electric shovel. gan and the lifting rope displacement Δl rope The function f is: θ=f(Δl gan ,Δl rope ) (5) Step 3.2) Secondly, based on the structural relationship between the front-end working mechanisms of the mining electric shovel, the trajectory equation of the mining electric shovel bucket tooth tip is obtained as shown in formula (6), that is: Where x(t) and y(t) are the horizontal and vertical trajectory equations of the mining shovel bucket tooth tip, respectively; g and h are the functional relationships of the horizontal and vertical coordinates of the mining shovel bucket tooth tip over time, respectively; x is the vertical coordinate of the mining shovel bucket tooth tip over time; s It is the combination of the structural dimensions of the mining shovel bucket, push rod, hoist rope, boom, and sheave, the height from the sheave to the horizontal ground, and the initial height from the mining shovel bucket to the horizontal ground during initial excavation. Step 3.3) Finally, the volume of material inside the bucket of the mining shovel during excavation is calculated based on the vertical trajectory equation of the mining shovel bucket tooth tip obtained in step 3.2), that is: Where w is the width of the mining shovel bucket, y is w (t) is the material surface prediction equation, Δx represents the two adjacent discrete points x in the horizontal excavation trajectory x(t). i (t) and x i+1 (t) The displacement difference within time Δt is x(Δt+t)-x(t).

5. The mining shovel trajectory planning method based on the full-process asymmetric multi-segment S-shaped curve according to claim 4 is characterized in that: The fourth step is to set constraint boundaries. The speed change of the electric shovel motor is divided into seven time periods. By applying speed and time boundary constraints, the motor speed and the running time of each section of the motor are controlled to obtain the optimal excavation trajectory and the lowest excavation energy consumption. The details are as follows: According to the maximum motor speed of the mining shovel and its corresponding running time in each section in formula (1), all optimization variables are determined as follows: Where x is the decision variable, v pmax 、v hmax are the maximum speeds of the pushing motor and lifting motor of the mining electric shovel, T pi (i=1,2,…,7) and T hj (j=1, 2, ..., 6) are the operating time of each section of the pushing motor and the lifting motor of the mining electric shovel respectively; Here, only the first 6 time periods of the lifting motor of the mining electric shovel are taken, and the sum of the 7 time periods of the pushing motor of the mining electric shovel is subtracted from the sum of the 6 time periods of the lifting motor of the mining electric shovel to obtain the last time period of lifting.

6. The mining shovel trajectory planning method based on the full-process asymmetric multi-segment S-shaped curve according to claim 5 is characterized in that: The value ranges of all optimization variables are as follows: Pushing motor speed v pmax (m / s): 3650-6100; increase motor speed v hmax (m / s): 3650-6000; Pushing the first time period T p1 / s: 0.5-4; push the second time period T p2 / s: 0.5-4; push the third time period T p3 / s: 0.5-4; push the fourth time period T p4 / s: 0.3-3; push the fifth time period T p5 / s: 0.5-4; push the sixth time period T p6 / s: 0.5-4; push the seventh time period T p7 / s: 0.5-4; improve the first time period T h1 / s: 0.5-4; increase the second time period T h2 / s: 0.5-4; improve the third time period T h3 / s: 0.5-4; improve T in the fourth time period h4 / s: 0.5-4; improve the fifth time period T h5 / s: 0.5-4; improve T in the sixth time period h6 / s:0.5-4.

7. The mining shovel trajectory planning method based on the full-process asymmetric multi-segment S-shaped curve according to claim 5 is characterized in that: The fifth step is to construct a mathematical model of the objective function and constraint function based on the full-process asymmetric multi-segment S-curve speed control algorithm; the details are as follows: Step 5.1) Determine the objective function: J=(E p +E h ) / V (9) Where, E p and E h Respectively represent the energy consumption of the pushing motor and lifting motor of the mining electric shovel during the excavation process; Step 5.2) Determine the constraint function as follows: (1) For the excavation performance constraint, consider that the instantaneous output force and instantaneous output power of the mining shovel motor are less than the allowable limit value, that is: Where, F pmax and F hmax are the maximum output forces of the pushing motor and lifting motor of the mining electric shovel, P p and P h are the instantaneous output power of the pushing motor and the lifting motor of the mining electric shovel, P pmax and P hmax They are the maximum power of the pushing motor and lifting motor of the mining electric shovel respectively; (2) Considering the stability of the excavation process, the speed of the push rod and the lifting rope cannot be less than 0 during excavation, that is: (3) In order to ensure mining efficiency, a single mining cycle must be kept within a reasonable range, that is: (4) Constraining the full bucket rate; Here, the full bucket rate range of a single excavation is set to 90%-110%, that is: Where V n Represents the rated loading volume; (5) For structural constraints, the maximum extension of the push rod l O2F Less than the maximum allowed length l max ,Right now: (6) To ensure that the mining shovel can contact the material during excavation, the initial horizontal position of the excavation trajectory is greater than the bottom of the material pile, that is: g6=[Htanθ0-x(t0)≤0] (17) (7) In order to prevent the bottom of the bucket from scraping the ground at the beginning of excavation and to ensure that the equipment is not damaged, the minimum initial height of the bottom of the bucket is higher than the ground height, that is: g7=[H0-y Mmin ≤0] (18) (8) At the end of excavation, the entire structure of the bucket is basically or completely separated from the material surface, and the end of the excavation end position is greater than the set value and greater than the material surface height, that is: g8=[Y e -and tip_end (t)≤0] (19) Where Y e It is based on the set value of the material surface height; After sorting out the above constraints, all the constraints of the asymmetric multi-segment S-curve speed control algorithm are obtained as follows:

8. The mining shovel trajectory planning method based on the full-process asymmetric multi-segment S-shaped curve according to claim 7 is characterized in that: In the step 5.1), E p and E h Specifically: Where, F p and F h Represent the pushing force and lifting force respectively, v gan and v rope They represent the speed of the push rod and the speed of the hoist rope of the mining electric shovel respectively.

9. The mining electric shovel trajectory planning method based on the full-process asymmetric multi-segment S-shaped curve according to claim 7 is characterized in that: The sixth step is to input the initial optimization value for optimization, as follows: Given the initial values ​​of the optimization variables, GA is used to perform global optimization on the objective function of the mining electric shovel as shown in formula (9); when the design variables converge to a stable value or the number of iterations exceeds the maximum allowed number of iterations, the end condition is set; if the end condition is not met, the initial optimization value is modified and the sixth step is repeated; Otherwise, the loop is terminated and the optimal mining trajectory is generated.

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