Mine stone crusher automatic operation system and method based on artificial intelligence

Abstract

The invention relates to the field of artificial intelligence, in particular to a mine stone crusher automatic operation system and method based on artificial intelligence. Comprising the steps of converting collected image data into a three-dimensional environment model, and performing equal-interval grid segmentation on the three-dimensional environment model through a grid segmentation method; a multi-objective optimization control algorithm is introduced, a comprehensive objective function value of each candidate strike point is calculated, and an optimal strike point is obtained; calculating a cost function of the path by using an adaptive multi-target path planning algorithm to obtain an optimal path; a self-adaptive hitting power control algorithm is used to calculate hitting power; and generating a control signal of the stone crusher by using a control signal generation algorithm. The technical problems that a control signal generation mode of an existing stone crusher is simple, non-linear changes in a complex working environment cannot be processed, and an unstable phenomenon is easily generated when a stone crushing hammer works at a high speed are solved.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence, and in particular to an automatic operation system and method of a mine crusher based on artificial intelligence. Background Art

[0002] In modern industry and infrastructure construction, crushers are widely used for crushing and processing of various stones. With the advancement of science and technology, the traditional operation mode of crushers has gradually revealed many shortcomings. The operation process of traditional crushers relies on manual control. Operators need to monitor and adjust the working status of crushers based on experience, which not only consumes a lot of manpower and time, but is also prone to misoperation, resulting in equipment damage or reduced production efficiency. In addition, due to the large differences in the hardness, particle size and other characteristics of stones, the control parameters of the crushing process need to be adjusted frequently, and manual adjustments are often difficult to accurately control, thus affecting the stability of product quality.

[0003] In addition, the existing automatic operation method of the stone crusher still has the following technical problems: the impact point selection is based on a preset fixed path or a simple surface analysis, which is difficult to accurately adapt to the complex conditions of the ore surface, resulting in inaccurate impact and even damage to the stone crushing hammer; due to the unstable impact angle of the stone crushing hammer, it is easy to cause uneven impact force or poor stone crushing effect; during the operation of the stone crusher, the path planning of the stone crushing hammer from the initial position to the impact point is inaccurate, which easily leads to energy waste and reduced control accuracy; the impact force adjustment mechanism is relatively lagging and cannot be dynamically adjusted according to the real-time operating environment changes, resulting in difficulty in accurately controlling the impact force and affecting the stone crushing effect; the control signal generation method of the stone crusher is relatively simple and cannot handle nonlinear changes in complex operating environments, which easily leads to instability of the stone crushing hammer when operating at high speed.

[0004] In order to solve the above problems, artificial intelligence technology has gradually been introduced into the automated control of crushers. The development of artificial intelligence technology has provided new ideas and methods for the intelligent and automated control of crushers. By embedding artificial intelligence algorithms into the control system of crushers, real-time monitoring and dynamic adjustment of the crushing process can be achieved, thereby improving production efficiency, reducing equipment failure rates, and reducing human intervention and potential safety risks. Summary of the invention

[0005] The present invention provides an automatic operation system and method for a mining rock crusher based on artificial intelligence, so as to solve the problems that the existing striking point selection is based on a preset fixed path or a simple surface analysis, which is difficult to accurately adapt to the complex conditions of the ore surface, resulting in inaccurate striking and even damage to the rock crushing hammer; the striking angle of the rock crushing hammer is unstable, which easily leads to uneven striking force or poor stone crushing effect; during the operation of the rock crusher, the path planning of the rock crushing hammer from the initial position to the striking point is inaccurate, which easily leads to energy waste and reduced control accuracy; the striking force adjustment mechanism is relatively lagging and cannot be dynamically adjusted according to the real-time operating environment changes, which makes it difficult to accurately control the striking force and affects the stone crushing effect; the control signal generation method of the rock crusher is relatively simple, which cannot handle the nonlinear changes in the complex operating environment, and easily leads to the technical problems of instability of the rock crushing hammer during high-speed operation.

[0006] The present invention provides an artificial intelligence-based automatic operation system and method for a mining rock crusher, which specifically includes the following technical solutions:

[0007] An automatic operation method of a mine crusher based on artificial intelligence comprises the following steps:

[0008] S1: Obtain the hardness of the ore surface based on the collected image data, and obtain the candidate striking points through the grid segmentation method; introduce the multi-objective optimization control algorithm, calculate the comprehensive objective function value of each candidate striking point, and obtain the optimal striking point, thereby obtaining the hardness of the ore surface at the optimal striking point;

[0009] S2: Based on the optimal striking point, the optimal path is calculated using an adaptive multi-objective path planning algorithm, and the speed and acceleration of the stone breaker at different times are calculated based on the optimal path;

[0010] S3: Calculate the striking force using an adaptive striking force control algorithm based on the speed of the rock breaker at different times, the acceleration of the rock breaker and the hardness of the ore surface at the optimal striking point;

[0011] S4: Generate a control signal for the rock crusher using a control signal generation algorithm based on the impact force, the rock crusher speed, the rock crusher acceleration and the hardness of the ore surface at the optimal impact point.

[0012] Preferably, the S1 specifically includes:

[0013] The image data is converted into a three-dimensional environmental model to obtain the hardness of the ore surface; the three-dimensional environmental model is segmented into equally spaced grids using the grid segmentation method, and the center point of the grid is the candidate impact point.

[0014] Preferably, the S1 specifically includes:

[0015] Based on the candidate striking points and the hardness of the ore surface, the comprehensive objective function value of each candidate striking point is obtained through a multi-objective optimization control algorithm, and the striking point with the largest comprehensive objective function value is the optimal striking point.

[0016] Preferably, the S2 specifically includes:

[0017] In the implementation process of the adaptive multi-objective path planning algorithm, the cost function of the path is calculated. The specific formula is:

[0018]

[0019] Where J(L(t)) represents the cost function of the path at time t; Represents from the start time t0 to the end time t f Integrate over time; a(t) represents the acceleration of the hammer at time t; It represents the rate of change of motor power fluctuation at time t; represents the position of the stone breaker at time t; p o Indicates the optimal striking point.

[0020] Preferably, the S2 specifically includes:

[0021] The cost function of the path is minimized through iterative optimization to obtain the optimal path.

[0022] Preferably, the S3 specifically includes:

[0023] The adaptive striking force control algorithm controls the striking force through the speed and acceleration of the stone breaker, and dynamically adjusts the striking force in combination with the hardness information of the ore striking point at the optimal striking point.

[0024] Preferably, the S4 specifically includes:

[0025] The calculation formula for generating the control signal of the lithotripsy is:

[0026]

[0027] Wherein, C(t) represents the control signal of the crusher at time t; γ represents the adjustment coefficient used to adjust the weight of the impact force in the control signal generation; F(t) represents the impact force of the crushing hammer at time t; v(t) represents the speed of the crushing hammer at time t; δ represents the adjustment coefficient used to adjust the influence of acceleration and the hardness of the ore surface at the optimal impact point on the control signal generation; H(p o ) represents the hardness of the ore surface at the optimal striking point.

[0028] An automatic operation system of a mine crusher based on artificial intelligence includes the following parts:

[0029] Image data acquisition module, three-dimensional environment model generation module, candidate strike point selection module, multi-objective optimization control algorithm module, path planning and optimization module, adaptive strike force control module, control signal generation module;

[0030] Image data acquisition module: by installing multi-camera machine vision cameras in the working area of ​​the crusher, image data from different angles are collected and output to the 3D environment model generation module;

[0031] 3D environment model generation module: based on the image data from the image data acquisition module, the image data is converted into a 3D environment model using a convolutional neural network and a 3D reconstruction algorithm, and the hardness of the ore surface is obtained. The 3D environment model is output to the candidate strike point selection module, and the hardness of the ore surface is output to the multi-objective optimization control algorithm module;

[0032] Candidate strike point selection module: Based on the three-dimensional environment model of the three-dimensional environment model generation module, the three-dimensional environment model is segmented into equally spaced grids by the grid segmentation method, and the center point of the grid is the candidate strike point, which is output to the multi-objective optimization control algorithm module;

[0033] Multi-objective optimization control algorithm module: Based on the candidate striking points of the candidate striking point selection module and the hardness of the ore surface of the three-dimensional environment model generation module, the multi-objective optimization control algorithm is used to obtain the comprehensive objective function value of each candidate striking point, and the striking point with the largest comprehensive objective function value is the optimal striking point; the hardness of the ore surface at the optimal striking point is obtained through the convolutional neural network and three-dimensional reconstruction algorithm of the three-dimensional environment model generation module; the optimal striking point is output to the path planning and optimization module, and the hardness of the ore surface at the optimal striking point is output to the adaptive striking force control module and the control signal generation module;

[0034] Path planning and optimization module: Based on the optimal striking point of the multi-objective optimization control algorithm module, the adaptive multi-objective path planning algorithm is used to calculate the cost function of the path, and the gradient descent method is used for iterative optimization to minimize the cost function and obtain the optimal path. The speed and acceleration of the stone breaker at different times are calculated according to the optimal path, and the speed and acceleration of the stone breaker at different times are output to the adaptive striking force control module and the control signal generation module;

[0035] Adaptive impact force control module: Based on the speed and acceleration of the stone breaker at different times and the hardness of the ore surface at the optimal impact point, the impact force is calculated in real time using an adaptive impact force control algorithm and output to the control signal generation module;

[0036] Control signal generation module: Based on the impact force of the adaptive impact force control module, the speed and acceleration of the crushing hammer at different moments of the path planning and optimization module, and the hardness of the ore surface at the optimal impact point of the multi-objective optimization control algorithm module, the control signal generation algorithm is used to generate the control signal of the crusher.

[0037] The beneficial effects of the technical solution of the present invention are:

[0038] 1. Through the multi-objective optimization control algorithm, the candidate striking points are comprehensively evaluated in terms of multiple indicators such as energy transfer efficiency, striking stability and ore hardness adaptability, and the comprehensive objective function value of each candidate striking point is obtained, which ensures the effective energy transfer, stable striking angle control and adaptive processing of ores of different hardness during the operation of the crusher, thereby reducing energy waste and extending the service life of the equipment, and ensuring the operation stability in complex ore environments. By maximizing the comprehensive objective function value, the optimal striking point is selected to achieve optimal control of the crusher operation.

[0039] 2. An adaptive multi-objective path planning algorithm is used to optimize the moving path of the stone crusher, and the cost function of the path is minimized through the gradient descent method, which significantly reduces the acceleration fluctuation and motor power fluctuation, ensuring that the stone crusher can stably move to the optimal striking point in a complex environment, avoiding unnecessary vibration or error, and ensuring the accuracy of the stone crusher's operation.

[0040] 3. Through the adaptive impact force control algorithm, the impact force is accurately adjusted according to the speed of the stone crushing hammer at different times, the acceleration of the stone crushing hammer and the surface hardness information of the ore at the optimal impact point, further improving the impact accuracy and operating effect of the crusher.

[0041] 4. The control signal generation algorithm is used to generate the control signal for controlling the operation process of the stone crusher, ensuring the stability of the stone crusher's impact process under different operating conditions. In particular, when the stone crushing hammer is moving at high speed, the square root and logarithmic processing of the speed and acceleration of the stone crushing hammer further reduces the fluctuation of the control signal, so that the stone crusher can still maintain stable operation in a complex operating environment, reducing the failure rate and maintenance cost of the equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 This is a structural diagram of an automatic operation system of a mine rock crusher based on artificial intelligence according to the present invention;

[0043] Figure 2 The present invention is a flowchart of an automatic operation method of a mine crusher based on artificial intelligence. DETAILED DESCRIPTION

[0044] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the technical scheme in the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiment of the present invention. Obviously, the described embodiment is only a part of the embodiment of the present invention, not all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0045] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0046] The specific scheme of an automatic operation system and method for a mining crusher based on artificial intelligence provided by the present invention is described in detail below with reference to the accompanying drawings.

[0047] See attached Figure 1 , which shows a structure diagram of an automatic operation system of a mine crusher based on artificial intelligence provided by an embodiment of the present invention, the system includes the following parts:

[0048] Image data acquisition module, three-dimensional environment model generation module, candidate strike point selection module, multi-objective optimization control algorithm module, path planning and optimization module, adaptive strike force control module, control signal generation module;

[0049] Image data acquisition module: by installing multi-camera machine vision cameras in the working area of ​​the crusher, image data from different angles are collected and output to the 3D environment model generation module;

[0050] 3D environment model generation module: based on the image data from the image data acquisition module, the image data is converted into a 3D environment model using a convolutional neural network and a 3D reconstruction algorithm, and the hardness of the ore surface is obtained. The 3D environment model is output to the candidate strike point selection module, and the hardness of the ore surface is output to the multi-objective optimization control algorithm module;

[0051] Candidate strike point selection module: Based on the three-dimensional environment model of the three-dimensional environment model generation module, the three-dimensional environment model is segmented into equally spaced grids by the grid segmentation method, and the center point of the grid is the candidate strike point, which is output to the multi-objective optimization control algorithm module;

[0052] Multi-objective optimization control algorithm module: Based on the candidate striking points of the candidate striking point selection module and the hardness of the ore surface of the three-dimensional environment model generation module, the multi-objective optimization control algorithm is used to obtain the comprehensive objective function value of each candidate striking point, and the striking point with the largest comprehensive objective function value is the optimal striking point; the hardness of the ore surface at the optimal striking point is obtained through the convolutional neural network and three-dimensional reconstruction algorithm of the three-dimensional environment model generation module; the optimal striking point is output to the path planning and optimization module, and the hardness of the ore surface at the optimal striking point is output to the adaptive striking force control module and the control signal generation module;

[0053] Path planning and optimization module: Based on the optimal striking point of the multi-objective optimization control algorithm module, the adaptive multi-objective path planning algorithm is used to calculate the cost function of the path, and the gradient descent method is used for iterative optimization to minimize the cost function and obtain the optimal path. The speed and acceleration of the stone breaker at different times are calculated according to the optimal path, and the speed and acceleration of the stone breaker at different times are output to the adaptive striking force control module and the control signal generation module;

[0054] Adaptive impact force control module: Based on the speed and acceleration of the stone breaker at different times and the hardness of the ore surface at the optimal impact point, the impact force is calculated in real time using an adaptive impact force control algorithm and output to the control signal generation module;

[0055] Control signal generation module: Based on the impact force of the adaptive impact force control module, the speed and acceleration of the crushing hammer at different moments of the path planning and optimization module, and the hardness of the ore surface at the optimal impact point of the multi-objective optimization control algorithm module, the control signal generation algorithm is used to generate the control signal of the crusher.

[0056] See attached Figure 2 , which shows a flow chart of an automatic operation method of a mine crusher based on artificial intelligence provided by an embodiment of the present invention, the method comprising the following steps:

[0057] S1. Obtain the hardness of the ore surface based on the collected image data, and obtain the candidate striking points through the grid segmentation method; introduce a multi-objective optimization control algorithm, calculate the comprehensive objective function value of each candidate striking point, obtain the optimal striking point, and thus obtain the hardness of the ore surface at the optimal striking point;

[0058] By installing a multi-camera machine vision camera in the working area of ​​the crusher, image data from different angles I = {I1, I2, ..., I n}, I jis the jth image data, and n is the number of image data; using a convolutional neural network and a three-dimensional reconstruction algorithm, the image data is converted into a three-dimensional environment model M (X, Y, Z), and the hardness of the ore surface is obtained; the convolutional neural network and the three-dimensional reconstruction algorithm are technical means well known to those skilled in the art and will not be described in detail here;

[0059] The three-dimensional environment model M (X, Y, Z) is segmented into equally spaced grids by the grid segmentation method, and the center point of the grid is the candidate strike point P = {p1, p2, ..., p N}, where p i (x i ,y i , z i ) represents the i-th candidate attack point, (x i ,y i , z i ) represents the position coordinates of the i-th candidate striking point, and N is the number of candidate striking points;

[0060] In order to achieve the optimal operation control effect of the crusher, based on the candidate striking points and the hardness of the ore surface, a multi-objective optimization control algorithm is used to obtain the comprehensive objective function value of each candidate striking point, to ensure the energy transfer efficiency and striking stability of the striking point during the operation of the crusher, and to adapt to the hardness of the ore surface. The striking point with the largest comprehensive objective function value is the optimal striking point.

[0061] The multi-objective optimization control algorithm performs a comprehensive evaluation based on the energy transfer efficiency, striking stability and ore hardness adaptability of the candidate striking points to obtain a comprehensive objective function value for each candidate striking point. The striking point with the largest comprehensive objective function value is the optimal striking point.

[0062] The core of the energy transfer efficiency is that when the rock crusher strikes the ore, the energy is transferred from the rock crusher's rock crusher hammer to the candidate striking point. The distance between the candidate striking point and the rock crusher hammer directly affects the effective transfer of energy. The first adjustment parameter is used to perform nonlinear weighting on the distance from the candidate striking point to the rock crusher hammer, which can flexibly adjust the influence of the distance from the candidate striking point to the rock crusher hammer on the energy transfer efficiency, and combined with the energy loss factor, it reflects the energy loss caused by the ore material factor in the energy transfer process;

[0063] The striking stability takes into account the striking angle and the hardness of the ore surface. The striking angle is adjusted by the hyperbolic tangent function, and the output range of the striking angle can be limited to The hardness of the surface of the candidate striking point directly affects the striking effect of the crusher, and the influence of the hardness of the ore surface at the candidate striking point on the striking stability is adjusted by the second adjustment parameter;

[0064] The adaptability of ore hardness adopts a piecewise function form to process the dynamic change of ore hardness, in order to adapt to the change of ore hardness. Ores of different hardness require different processing methods when selecting striking points. Low-hardness ores are easy to break, and are adjusted by exponential decay. High-hardness ores are not easy to break, and are adjusted by logarithmic functions to slow down the impact of increased hardness and ensure that high-hardness areas are not completely excluded from the selection range of striking points due to excessive hardness.

[0065] The calculation formula of the comprehensive objective function value is:

[0066]

[0067] Among them, O(p i ) represents the comprehensive objective function value, that is, the priority of the i-th candidate strike point, which is used to select the optimal strike point; d(p i ) represents the distance between the i-th candidate striking point and the crushing hammer of the stone breaker; k1 represents the first adjustment parameter, which is used to adjust the influence of the distance between the i-th candidate striking point and the crushing hammer of the stone breaker on the energy transfer efficiency; R(p i ) represents the energy loss factor, i.e., the energy loss at the i-th candidate strike point due to energy transfer, which can be set according to the specific implementation scenario and is not limited here; θ(p i ) represents the striking angle of the i-th candidate striking point, that is, the angle between the crushing hammer of the stone crusher and the i-th candidate striking point; tanh represents the hyperbolic tangent function, which is used to smooth the nonlinear effect of the striking angle of the i-th candidate striking point; H(p i ) represents the hardness of the ore surface at the i-th candidate striking point; k2 represents the second adjustment parameter, which is used to adjust the influence of the hardness of the ore surface at the i-th candidate striking point on the striking stability; It represents the ore hardness adaptability function, which is used to dynamically adjust the priority of the candidate striking point according to the hardness of the ore surface, reflecting the adaptability of the i-th candidate striking point to the hardness of the ore surface, and avoiding unnecessary energy consumption or damage to the crusher due to the excessive hardness of the candidate striking point surface. The specific calculation formula of the ore hardness adaptability function is:

[0068]

[0069] Among them, H0 represents the hardness threshold, which is used to distinguish the hardness of the ore; when H(p i )≤H0, the ore hardness adaptability is calculated by the exponential decay function, indicating that the increase of ore hardness will rapidly reduce the ore hardness adaptability; β1 represents the adjustment parameter used to control the influence of the exponential decay function on the ore hardness, adjusts the steepness of the ore hardness adaptability curve, and controls the decay speed of the exponential decay function; when H(pi )>H0, the ore hardness adaptability is calculated by a logarithmic function, and the ore hardness adaptability is adjusted smoothly to reduce the impact on the priority of the candidate strike points, and prevent the candidate strike points from being wrongly prioritized due to the high ore hardness; ∈ represents the minimum value, to prevent the denominator from being zero;

[0070]

[0071] Among them, p o represents the optimal strike point, which is the maximum value point selected after optimizing the comprehensive objective function among all candidate strike points; arg max represents a mathematical operator, which means selecting the candidate strike point p that makes the comprehensive objective function value reach the maximum value i ;

[0072] At the same time, based on the optimal striking point, the hardness of the ore surface at the optimal striking point is obtained through convolutional neural network and three-dimensional reconstruction algorithm;

[0073] S2. Based on the optimal striking point, an adaptive multi-objective path planning algorithm is used to calculate the optimal path, and the speed and acceleration of the stone crusher at different times are calculated according to the optimal path;

[0074] To ensure that the stone crusher can move accurately to the optimal striking point, an adaptive multi-objective path planning algorithm is used to calculate the cost function of the path based on the optimal striking point.

[0075] The goal of the adaptive multi-objective path planning algorithm is to plan the best path of the stone breaker from the initial position to the optimal striking point by optimizing acceleration, motor power fluctuation and position error, and further ensure energy efficiency, operational stability and operation accuracy in complex environments through multi-objective fusion and nonlinear processing, such as square root and logarithmic function processing;

[0076] The calculation formula of the path cost function is:

[0077]

[0078] Where J(L(t)) represents the cost function of the path at time t; Represents from the start time t0 to the end time t f The time is integrated to accumulate the cost at different time points in the whole process; a(t) represents the acceleration of the stone breaker at time t; |a(t)| 1 / 2 It means that energy consumption is controlled by the square root of acceleration, so that the increase of energy consumption remains gentle, thus reducing unnecessary energy consumption; It represents the rate of change of the motor power fluctuation at time t, reflecting the motor power fluctuation of the motor system during the operation of the rock crusher; It means using logarithmic function to smooth the motor power fluctuation, ensure the smooth operation of the motor system, and reduce the control instability caused by motor power fluctuation; represents the position of the stone crusher at time t; p o Indicates the optimal striking point; It represents the deviation between the position of the stone breaker and the optimal striking point at time t, i.e., the position error; It represents the position error suppression item, which ensures that the stone breaker hammer reaches the optimal striking point accurately and can make timely adjustments when position deviation occurs, so as to avoid unnecessary vibration or instability caused by over-correction;

[0079] By using the gradient descent method for iterative optimization to minimize the cost function of the path, the optimal path, that is, the optimal moving trajectory of the stone breaker from the initial position to the optimal striking point, is obtained; the speed and acceleration of the stone breaker at different times are calculated according to the optimal path; the gradient descent method is a technical means well known to those skilled in the art and will not be described in detail here;

[0080] During the path planning process, the path is adjusted according to the real-time updated position and speed of the rock breaker, which can maintain the optimal path selection in the changing working environment;

[0081] S3, using an adaptive striking force control algorithm to calculate the striking force based on the speed of the stone breaker hammer at different times, the acceleration of the stone breaker hammer and the hardness of the ore surface at the optimal striking point;

[0082] Based on the speed of the stone breaker hammer at different times, the acceleration of the stone breaker hammer and the hardness of the ore surface at the optimal striking point, an adaptive striking force control algorithm is used to calculate the striking force in real time; the adaptive striking force control algorithm accurately controls the striking force through the speed and acceleration of the stone breaker hammer, avoiding energy waste and striking errors caused by delayed adjustment of the striking force, and dynamically adjusts the striking force in combination with the hardness information of the ore striking point at the optimal striking point. In order to avoid unreasonable striking force when the speed is extremely small or even zero, a minimum value prevention mechanism is added to ensure the stability of the striking effect;

[0083] The calculation formula of striking force is:

[0084]

[0085] Where F(t) is the striking force at time t; m is the mass of the stone breaker; a(t) is the acceleration of the stone breaker at time t; H(p o) represents the hardness of the ore surface at the optimal striking point, which determines the transmission effect of the striking force and the degree of crushing of the stone; B represents the striking area of ​​the stone breaker, which is determined by the physical size of the stone breaker; v(t) represents the speed of the stone breaker at time t; ∈ represents the minimum value to prevent the denominator from being zero;

[0086] S4, generating a control signal for the rock crusher using a control signal generation algorithm based on the striking force, the speed of the rock crusher, the acceleration of the rock crusher, and the hardness of the ore surface at the optimal striking point;

[0087] Based on the striking force, the speed of the stone crushing hammer, the acceleration of the stone crushing hammer and the hardness of the ore surface at the optimal striking point, a control signal generating algorithm is used to generate a control signal for a stone crusher; the control signal generating algorithm combines the striking force, the speed of the stone crushing hammer, the acceleration of the stone crushing hammer and the hardness of the ore surface at the optimal striking point to generate a control signal for controlling the striking process of the stone crusher, and combines nonlinear function processing and an adaptive adjustment mechanism to ensure the stability of the striking process of the stone crusher under different operating conditions;

[0088] The square root and logarithmic processing of the speed of the rock breaker effectively reduces the signal fluctuation when the rock breaker moves at high speed; the square term of the hardness of the ore surface is used to reduce the excessive interference of extremely high hardness ore on the control signal, ensuring the stable output of the control signal;

[0089] The calculation formula for the control signal generation of the lithotripsy is:

[0090]

[0091] Wherein, C(t) represents the control signal of the stone crusher at time t, which is used to control the striking process of the stone crushing hammer; γ represents the adjustment coefficient used to adjust the weight of the striking force in the generation of the control signal; F(t) represents the striking force of the stone crushing hammer at time t; ln(1+|v(t)| 1 / 2 ) represents the smoothing function of the speed of the stone crushing hammer at time t, which is a combination of logarithmic function and square root. The logarithmic function can smoothly process the high-speed motion state of the stone crushing hammer, and the square root further slows down the rate of change; v(t) represents the speed of the stone crushing hammer at time t; δ represents the adjustment coefficient used to adjust the influence of the acceleration and the hardness of the ore surface at the optimal striking point on the generation of the control signal; a(t) represents the acceleration of the stone crushing hammer at time t; Represents the square root processing of acceleration to ensure that the control signal remains stable; H(p o ) represents the hardness of the ore surface at the optimal impact point; H(p o ) 2 Indicates the square term of the hardness of the ore surface, which is used to reduce the excessive interference of extremely high hardness ore on the control signal and ensure the stable output of the control signal;

[0092] According to the striking force, the speed of the rock breaker, the acceleration of the rock breaker and the hardness of the ore surface at the optimal striking point, a control signal generating algorithm is used to generate a control signal for the rock crusher to accurately control the striking action of the rock crusher.

[0093] In summary, an artificial intelligence-based automatic operation system and method for a mining crusher has been completed.

[0094] The order of the embodiments of the invention is for description only and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0095] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0096] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that the technical solutions described in the above embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention.

Claims

1. An automatic operation method of a mine crusher based on artificial intelligence, characterized in that: The following steps are involved: S1: Obtain the hardness of the ore surface based on the collected image data, and obtain the candidate striking points through the grid segmentation method; introduce the multi-objective optimization control algorithm, calculate the comprehensive objective function value of each candidate striking point, and obtain the optimal striking point, thereby obtaining the hardness of the ore surface at the optimal striking point; S2: Based on the optimal striking point, the optimal path is calculated using an adaptive multi-objective path planning algorithm, and the speed and acceleration of the stone breaker at different times are calculated based on the optimal path; S3: Calculate the striking force using an adaptive striking force control algorithm based on the speed of the rock breaker at different times, the acceleration of the rock breaker and the hardness of the ore surface at the optimal striking point; S4: Generate a control signal for the rock crusher using a control signal generation algorithm based on the impact force, the rock crusher speed, the rock crusher acceleration and the hardness of the ore surface at the optimal impact point.

2. The method for automatic operation of a mine rock crusher based on artificial intelligence according to claim 1, characterized in that: The S1 specifically includes: The image data is converted into a three-dimensional environmental model to obtain the hardness of the ore surface; the three-dimensional environmental model is segmented into equally spaced grids using the grid segmentation method, and the center point of the grid is the candidate impact point.

3. The method for automatic operation of a mine crusher based on artificial intelligence according to claim 2 is characterized in that: The S1 specifically includes: Based on the candidate striking points and the hardness of the ore surface, the comprehensive objective function value of each candidate striking point is obtained through a multi-objective optimization control algorithm, and the striking point with the largest comprehensive objective function value is the optimal striking point.

4. The method for automatic operation of a mine crusher based on artificial intelligence according to claim 1, characterized in that: The S2 specifically includes: In the implementation process of the adaptive multi-objective path planning algorithm, the cost function of the path is calculated. The specific formula is: Where J(L(t)) represents the cost function of the path at time t; Represents from the start time t0 to the end time t f Integrate over time; a(t) represents the acceleration of the hammer at time t; It represents the rate of change of motor power fluctuation at time t; represents the position of the stone breaker at time t; p o Indicates the optimal striking point.

5. The method for automatic operation of a mine crusher based on artificial intelligence according to claim 4 is characterized in that: The S2 specifically includes: The cost function of the path is minimized through iterative optimization to obtain the optimal path.

6. The method for automatic operation of a mine rock crusher based on artificial intelligence according to claim 1, characterized in that: The S3 specifically includes: The adaptive striking force control algorithm controls the striking force through the speed and acceleration of the stone breaker, and dynamically adjusts the striking force in combination with the hardness information of the ore striking point at the optimal striking point.

7. The method for automatic operation of a mine crusher based on artificial intelligence according to claim 1, characterized in that: The S4 specifically includes: The calculation formula for generating the control signal of the lithotripsy is: Wherein, C(t) represents the control signal of the crusher at time t; γ represents the adjustment coefficient used to adjust the weight of the impact force in the control signal generation; F(t) represents the impact force of the crushing hammer at time t; v(t) represents the speed of the crushing hammer at time t; δ represents the adjustment coefficient used to adjust the influence of acceleration and the hardness of the ore surface at the optimal impact point on the control signal generation; H(p o ) represents the hardness of the ore surface at the optimal striking point.

8. An artificial intelligence-based automatic operation system for a mine crusher, applied to the artificial intelligence-based automatic operation method for a mine crusher as claimed in claim 1, characterized in that: Includes the following parts: Image data acquisition module, three-dimensional environment model generation module, candidate strike point selection module, multi-objective optimization control algorithm module, path planning and optimization module, adaptive strike force control module, control signal generation module; Image data acquisition module: collects image data and outputs the image data to the three-dimensional environment model generation module; 3D environment model generation module: converts image data into a 3D environment model, obtains the hardness of the ore surface, outputs the 3D environment model to the candidate impact point selection module, and outputs the hardness of the ore surface to the multi-objective optimization control algorithm module; Candidate strike point selection module: The three-dimensional environment model is segmented into equally spaced grids by the grid segmentation method. The center point of the grid is the candidate strike point, and the candidate strike point is output to the multi-objective optimization control algorithm module; Multi-objective optimization control algorithm module: Based on the candidate striking points and the hardness of the ore surface, the multi-objective optimization control algorithm is used to obtain the optimal striking point; the hardness of the ore surface at the optimal striking point is obtained through the three-dimensional environment model generation module; the optimal striking point is output to the path planning and optimization module, and the hardness of the ore surface at the optimal striking point is output to the adaptive striking force control module and the control signal generation module; Path planning and optimization module: Based on the optimal striking point, the optimal path is obtained using an adaptive multi-objective path planning algorithm. The speed and acceleration of the stone breaker at different times are calculated according to the optimal path, and the speed and acceleration of the stone breaker at different times are output to the adaptive striking force control module and the control signal generation module; Adaptive impact force control module: Based on the speed and acceleration of the stone breaker at different times and the hardness of the ore surface at the optimal impact point, the impact force is calculated using an adaptive impact force control algorithm and output to the control signal generation module; Control signal generation module: Based on the impact force, the speed of the crushing hammer at different times, the acceleration and the hardness of the ore surface at the optimal impact point, the control signal generation algorithm is used to generate the control signal of the crusher.

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