Intelligent control system and method for ore extraction

Abstract

The invention discloses an intelligent control system and method for ore extraction, and belongs to the technical field of ore extraction. The intelligent control system comprises an unmanned mine car, intelligent extraction equipment and a remote controller; a driving device, a GPS positioning device, a first laser radar, a first camera and a sensor are arranged on the unmanned mine car; the intelligent mining equipment comprises a drilling device, a blasting device, a second camera and a second laser radar; the remote controller comprises a data acquisition module, a data processing module and a data transmission module. According to the intelligent control system and method for ore extraction, the drilling path of the drilling device and the moving path of the unmanned mine car can be planned through the ant colony algorithm, autonomous drilling of the drilling device and autonomous navigation and real-time adjustment of the unmanned mine car are achieved, simulation optimization is carried out through the digital twin technology, and the drilling speed is improved. And the explosive loading amount and the detonating sequence can be adjusted.

Description

Technical Field

[0001] This invention relates to the field of ore mining technology, and in particular to an intelligent control system and method for ore mining. Background Technology

[0002] In traditional mining, core processes such as drilling, blasting, and mine car transportation are highly dependent on manual operation. The harsh environment inside mines (such as high concentrations of dust, methane and other harmful gases, low visibility, and complex terrain) exposes workers directly to safety hazards such as collapses, explosions, and poisoning, resulting in a high rate of workplace injuries. Drilling and blasting processes rely heavily on manual experience and judgment. The determination of drilling locations and paths based on subjective experience easily leads to drilling deviations, affecting subsequent blasting results. Furthermore, the lack of scientific calculation of charge quantity and detonation sequence can result in insufficient or excessive blasting, leading to low resource utilization.

[0003] Traditional mining trucks rely on manual driving. Mining routes are complex and may have temporary obstacles. Manual route planning is prone to problems such as detours and congestion, resulting in low transportation efficiency. At the same time, there is a lack of real-time data linkage between drilling, blasting and transportation during mining. It is impossible to dynamically adjust the transportation plan according to the amount of ore after blasting and changes in the mine environment, resulting in poor overall process coordination. Summary of the Invention

[0004] The purpose of this invention is to provide an intelligent control system and method for ore mining. The ant colony algorithm can be used to plan the drilling path of the drilling device and the movement path of the unmanned mining vehicle, realizing autonomous drilling of the drilling device and autonomous navigation and real-time adjustment of the unmanned mining vehicle. Digital twin technology is used for simulation optimization, which can adjust the charge amount and detonation sequence.

[0005] To achieve the above objectives, the present invention provides an intelligent control system for ore mining, comprising an unmanned mining truck, intelligent mining equipment, and a remote controller.

[0006] The driverless mining truck is equipped with a drive unit, a GPS positioning device, a first lidar, a first camera, and sensors.

[0007] Intelligent mining equipment includes drilling devices, blasting devices, a second camera, and a second lidar.

[0008] The remote controller includes a data acquisition module, a data processing module, and a data transmission module.

[0009] Preferably, the data acquisition module is connected to the GPS positioning device, the first lidar, the first camera and sensors on the unmanned mining truck, as well as the second camera and the second lidar of the intelligent mining equipment;

[0010] The data processing module processes the data collected by the data acquisition module and transmits the processing results to the data transmission module.

[0011] The data transmission module is connected to the drive unit on the unmanned mining truck and the drilling and blasting devices of the intelligent mining equipment, and transmits the data processing results to the drive unit on the unmanned mining truck and the drilling and blasting devices of the intelligent mining equipment.

[0012] This invention provides an intelligent control method for ore mining, employing the aforementioned intelligent control system for ore mining, comprising the following steps:

[0013] S1. The remote controller controls the intelligent mining equipment to drill holes and blast the ore, and to crush the ore.

[0014] S2. The remote controller controls the unmanned mining truck to transport the crushed ore out of the mine.

[0015] Preferably, S1 specifically includes the following steps:

[0016] S1.1 The data acquisition module acquires data from the second camera and the second lidar on the intelligent mining equipment and transmits it to the data processing module;

[0017] S1.2 The data processing module constructs a three-dimensional model of the ore based on the collected data;

[0018] S1.3. Based on the constructed 3D model, the drilling location and drilling path are determined by the ant colony algorithm. The data processing module transmits the planning results to the drilling device through the data transmission module, and the drilling device drills according to the planning results.

[0019] S1.4 The data acquisition module acquires the drilling positions obtained by the second camera and the second lidar on the intelligent mining equipment, and the data processing module adjusts the three-dimensional model according to the drilling positions;

[0020] S1.5 The data processing module uses digital twin technology to simulate and optimize the adjusted 3D model to determine the optimal charge amount and detonation sequence. The determined charge amount and detonation sequence are then transmitted to the blasting device by the data transmission module. The blasting device crushes the ore according to the charge amount and detonation sequence.

[0021] Preferably, S2 specifically includes the following steps:

[0022] S2.1 Input the mine tunnel model into the data processing module. The data acquisition module collects the real-time location of the unmanned mining truck obtained by the GPS positioning device on the unmanned mining truck and transmits it to the data processing module.

[0023] S2.2 The data processing module plans the driving route of the unmanned mining truck based on the mine tunnel model and the obtained GPS positioning of the unmanned mining truck using the ant colony algorithm. The data processing module transmits the planned driving route to the drive device through the data transmission module. The drive device drives the unmanned mining truck to travel according to the planned driving route.

[0024] The S2.3 data acquisition module collects real-time positioning information and information on temporary obstacles in the mine tunnel from the GPS positioning device, the first lidar, the first camera, and sensors during the operation of the unmanned mining truck.

[0025] S2.4 The data processing module uses the ant colony algorithm to replan the driving route based on real-time positioning information and information on temporary obstacles in the mine tunnel. The data processing module transmits the replanned driving route to the drive unit via the data transmission module. The drive unit then drives the unmanned mining truck to travel according to the replanned driving route.

[0026] Preferably, when constructing the 3D model of the ore in S1.2, the ICP algorithm is used to register the point cloud data acquired by the second lidar and the second camera. The calculation formula of the objective function of the ICP algorithm is as follows:

[0027]

[0028] Where R is the rotation matrix, c is the translation vector, and p i For the i-th point in the target point cloud, q i For the source point cloud and p i The corresponding i-th point, where n is the total number of points in the point cloud;

[0029] The formula for calculating point cloud coordinate transformation is shown below:

[0030] p i =R·q i +c;

[0031] In the digital twin technology simulation optimization process of S1.5, when blasting ore as a whole, the volumetric method is used to determine the charge amount, and the calculation formula is as follows:

[0032] Q = K·V;

[0033] Where Q is the total charge, K is the explosive consumption per unit volume, and V is the total volume of the ore;

[0034] During the simulated blasting process, the deformation of the ore under blasting stress is described by the stress-strain relationship of the ore. The calculation formula is shown below:

[0035] σ = E·ε;

[0036] Where σ is the stress on the ore, E is the elastic modulus of the ore, and ε is the strain;

[0037] The shock wave intensity at different distances is calculated using the peak overpressure formula for blasting shock waves, thereby assessing the impact of blasting on the surrounding rock mass and optimizing the charge amount and detonation sequence. The calculation formula is shown below:

[0038]

[0039] Where ΔP is the peak overpressure of the shock wave at a distance from the blast source A, A is the distance from the blast source, and B and α are the site coefficients.

[0040] Preferably, the ant colony algorithm in S1.3, S2.2, and S2.4 includes a path selection probability formula and a pheromone update formula, wherein the path selection probability formula is as follows:

[0041]

[0042] in, Let τ be the probability that the k-th ant moves from node a to node b at time t. ab (t) represents the pheromone concentration from node a to node b at time t, and η ab This is heuristic information, typically the reciprocal of the path distance, η. ab =1 / d ab d ab Let be the distance from node a to node b, β be the pheromone importance factor, γ be the heuristic information importance factor, and allowed. k Let be the set of nodes that the k-th ant has not visited;

[0043] The pheromone update formula is shown below:

[0044]

[0045] Where ρ is the pheromone evaporation coefficient, and m is the total number of ants. Let be the pheromone concentration when the k-th ant passes through path (a, b). D represents the total amount of pheromones, L k Let be the total path length of the k-th ant.

[0046] Therefore, the present invention adopts the above-mentioned intelligent control system and method for ore mining. The ant colony algorithm can be used to plan the drilling path of the drilling device and the movement path of the unmanned mining car, realizing the autonomous drilling of the drilling device and the autonomous navigation and real-time adjustment of the unmanned mining car. The digital twin technology is used for simulation optimization, which can adjust the charge amount and detonation sequence.

[0047] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0048] Figure 1 This is a schematic diagram of the structure of an intelligent control system for ore mining according to the present invention. Detailed Implementation

[0049] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0050] Unless otherwise defined, the technical or scientific terms used in this invention shall have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0051] Example 1

[0052] like Figure 1 As shown, the present invention provides an intelligent control system for ore mining, comprising an unmanned mining truck, intelligent mining equipment, and a remote controller.

[0053] The driverless mining truck is equipped with a drive unit, a GPS positioning device, a first lidar, a first camera, and sensors.

[0054] Intelligent mining equipment includes drilling devices, blasting devices, a second camera, and a second lidar.

[0055] The remote controller includes a data acquisition module, a data processing module, and a data transmission module.

[0056] The data acquisition module is connected to the GPS positioning device, the first lidar, the first camera and sensors on the unmanned mining truck, as well as the second camera and the second lidar of the intelligent mining equipment.

[0057] The data processing module processes the data collected by the data acquisition module and transmits the processing results to the data transmission module.

[0058] The data transmission module is connected to the drive unit on the unmanned mining truck and the drilling and blasting devices of the intelligent mining equipment, and transmits the data processing results to the drive unit on the unmanned mining truck and the drilling and blasting devices of the intelligent mining equipment.

[0059] This invention provides an intelligent control method for ore mining, employing the aforementioned intelligent control system for ore mining, comprising the following steps:

[0060] S1. The remote controller controls the intelligent mining equipment to drill and blast the ore, and to crush the ore; specifically, it includes the following steps:

[0061] S1.1 The data acquisition module acquires data from the second camera and the second lidar on the intelligent mining equipment and transmits it to the data processing module;

[0062] S1.2 The data processing module constructs a three-dimensional model of the ore based on the collected data;

[0063] When constructing the 3D model of the ore in S1.2, the ICP algorithm is used to register the point cloud data acquired by the second lidar and the second camera. The calculation formula of the objective function of the ICP algorithm is as follows:

[0064]

[0065] Where R is the rotation matrix, c is the translation vector, and p i For the i-th point in the target point cloud, q i For the source point cloud and p i The corresponding i-th point, where n is the total number of points in the point cloud;

[0066] The formula for calculating point cloud coordinate transformation is shown below:

[0067] p i =R·q i +c;

[0068] S1.3. Based on the constructed 3D model, the drilling location and drilling path are determined by the ant colony algorithm. The data processing module transmits the planning results to the drilling device through the data transmission module, and the drilling device drills according to the planning results.

[0069] Ant colony optimization includes a path selection probability formula and a pheromone update formula, where the path selection probability formula is shown below:

[0070]

[0071] in, Let τ be the probability that the k-th ant moves from node a to node b at time t. ab(t) represents the pheromone concentration from node a to node b at time t, and η ab This is heuristic information, typically the reciprocal of the path distance, η. ab =1 / d ab d ab Let be the distance from node a to node b, β be the pheromone importance factor, γ be the heuristic information importance factor, and allowed. k Let be the set of nodes that the k-th ant has not visited;

[0072] The pheromone update formula is shown below:

[0073]

[0074] Where ρ is the pheromone evaporation coefficient, and m is the total number of ants. Let be the pheromone concentration when the k-th ant passes through path (a, b). D represents the total amount of pheromones, L k Let be the total path length of the k-th ant.

[0075] S1.4 The data acquisition module acquires the drilling positions obtained by the second camera and the second lidar on the intelligent mining equipment, and the data processing module adjusts the three-dimensional model according to the drilling positions;

[0076] S1.5 The data processing module uses digital twin technology to simulate and optimize the adjusted 3D model to determine the optimal charge amount and detonation sequence. The determined charge amount and detonation sequence are then transmitted to the blasting device by the data transmission module. The blasting device crushes the ore according to the charge amount and detonation sequence.

[0077] In the digital twin technology simulation optimization process of S1.5, when blasting ore as a whole, the volumetric method is used to determine the charge amount, and the calculation formula is as follows:

[0078] Q = K·V;

[0079] Where Q is the total charge, K is the explosive consumption per unit volume, and V is the total volume of the ore;

[0080] During the simulated blasting process, the deformation of the ore under blasting stress is described by the stress-strain relationship of the ore. The calculation formula is shown below:

[0081] σ = E·ε;

[0082] Where σ is the stress on the ore, E is the elastic modulus of the ore, and ε is the strain;

[0083] The shock wave intensity at different distances is calculated using the peak overpressure formula for blasting shock waves, thereby assessing the impact of blasting on the surrounding rock mass and optimizing the charge amount and detonation sequence. The calculation formula is shown below:

[0084]

[0085] Where ΔP is the peak overpressure of the shock wave at a distance from the blast source A, A is the distance from the blast source, and B and α are the site coefficients.

[0086] S2. The remote controller controls the unmanned mining truck to transport the crushed ore out of the mine; specifically, it includes the following steps:

[0087] S2.1 Input the mine tunnel model into the data processing module. The data acquisition module collects the real-time location of the unmanned mining truck obtained by the GPS positioning device on the unmanned mining truck and transmits it to the data processing module.

[0088] S2.2 The data processing module plans the driving route of the unmanned mining truck based on the mine tunnel model and the obtained GPS positioning of the unmanned mining truck using the ant colony algorithm. The data processing module transmits the planned driving route to the drive device through the data transmission module. The drive device drives the unmanned mining truck to travel according to the planned driving route.

[0089] Ant colony optimization includes a path selection probability formula and a pheromone update formula, where the path selection probability formula is shown below:

[0090]

[0091] in, Let τ be the probability that the k-th ant moves from node a to node b at time t. ab (t) represents the pheromone concentration from node a to node b at time t, and η ab This is heuristic information, typically the reciprocal of the path distance, η. ab =1 / d ab d ab Let be the distance from node a to node b, β be the pheromone importance factor, γ be the heuristic information importance factor, and allowed. k Let be the set of nodes that the k-th ant has not visited;

[0092] The pheromone update formula is shown below:

[0093]

[0094] Where ρ is the pheromone evaporation coefficient, and m is the total number of ants. Let be the pheromone concentration when the k-th ant passes through path (a, b). D represents the total amount of pheromones, Lk Let be the total path length of the k-th ant.

[0095] The S2.3 data acquisition module collects real-time positioning information and information on temporary obstacles in the mine tunnel from the GPS positioning device, the first lidar, the first camera, and sensors during the operation of the unmanned mining truck.

[0096] S2.4 The data processing module uses the ant colony algorithm to replan the driving route based on real-time positioning information and information on temporary obstacles in the mine tunnel. The data processing module transmits the replanned driving route to the drive unit via the data transmission module. The drive unit then drives the unmanned mining truck to travel according to the replanned driving route.

[0097] Ant colony optimization includes a path selection probability formula and a pheromone update formula, where the path selection probability formula is shown below:

[0098]

[0099] in, Let τ be the probability that the k-th ant moves from node a to node b at time t. ab (t) represents the pheromone concentration from node a to node b at time t, and η ab This is heuristic information, typically the reciprocal of the path distance, η. ab =1 / d ab d ab Let be the distance from node a to node b, β be the pheromone importance factor, γ be the heuristic information importance factor, and allowed. k Let be the set of nodes that the k-th ant has not visited;

[0100] The pheromone update formula is shown below:

[0101]

[0102] Where ρ is the pheromone evaporation coefficient, and m is the total number of ants. Let be the pheromone concentration when the k-th ant passes through path (a, b). D represents the total amount of pheromones, L k Let be the total path length of the k-th ant.

[0103] Therefore, the present invention adopts the above-mentioned intelligent control system and method for ore mining. The ant colony algorithm can be used to plan the drilling path of the drilling device and the movement path of the unmanned mining car, realizing the autonomous drilling of the drilling device and the autonomous navigation and real-time adjustment of the unmanned mining car. The digital twin technology is used for simulation optimization, which can adjust the charge amount and detonation sequence.

[0104] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. An intelligent control system for ore mining, characterized in that: This includes driverless mining trucks, intelligent mining equipment, and remote controllers; The driverless mining truck is equipped with a drive unit, a GPS positioning device, a first lidar, a first camera, and sensors. Intelligent mining equipment includes drilling devices, blasting devices, a second camera, and a second lidar. The remote controller includes a data acquisition module, a data processing module, and a data transmission module.

2. The intelligent control system for ore mining according to claim 1, characterized in that: The data acquisition module is connected to the GPS positioning device, the first lidar, the first camera and sensors on the unmanned mining truck, as well as the second camera and the second lidar of the intelligent mining equipment. The data processing module processes the data collected by the data acquisition module and transmits the processing results to the data transmission module. The data transmission module is connected to the drive unit on the unmanned mining truck and the drilling and blasting devices of the intelligent mining equipment, and transmits the data processing results to the drive unit on the unmanned mining truck and the drilling and blasting devices of the intelligent mining equipment.

3. An intelligent control method for ore mining, characterized in that: The intelligent control system for ore mining according to any one of claims 1-2 includes the following steps: S1. The remote controller controls the intelligent mining equipment to drill holes and blast the ore, and to crush the ore. S2. The remote controller controls the unmanned mining truck to transport the crushed ore out of the mine.

4. The intelligent control method for ore mining according to claim 3, characterized in that: S1 specifically includes the following steps: S1.1 The data acquisition module acquires data from the second camera and the second lidar on the intelligent mining equipment and transmits it to the data processing module; S1.2 The data processing module constructs a three-dimensional model of the ore based on the collected data; S1.

3. Based on the constructed 3D model, the drilling location and drilling path are determined by the ant colony algorithm. The data processing module transmits the planning results to the drilling device through the data transmission module, and the drilling device drills according to the planning results. S1.4 The data acquisition module acquires the drilling positions obtained by the second camera and the second lidar on the intelligent mining equipment, and the data processing module adjusts the three-dimensional model according to the drilling positions; S1.5 The data processing module uses digital twin technology to simulate and optimize the adjusted 3D model to determine the optimal charge amount and detonation sequence. The determined charge amount and detonation sequence are then transmitted to the blasting device by the data transmission module. The blasting device crushes the ore according to the charge amount and detonation sequence.

5. The intelligent control method for ore mining according to claim 4, characterized in that: S2 specifically includes the following steps: S2.1 Input the mine tunnel model into the data processing module. The data acquisition module collects the real-time location of the unmanned mining truck obtained by the GPS positioning device on the unmanned mining truck and transmits it to the data processing module. S2.2 The data processing module plans the driving route of the unmanned mining truck based on the mine tunnel model and the obtained GPS positioning of the unmanned mining truck using the ant colony algorithm. The data processing module transmits the planned driving route to the drive device through the data transmission module. The drive device drives the unmanned mining truck to travel according to the planned driving route. The S2.3 data acquisition module collects real-time positioning information and information on temporary obstacles in the mine tunnel from the GPS positioning device, the first lidar, the first camera, and sensors during the operation of the unmanned mining truck. S2.4 The data processing module uses the ant colony algorithm to replan the driving route based on real-time positioning information and information on temporary obstacles in the mine tunnel. The data processing module transmits the replanned driving route to the drive unit via the data transmission module. The drive unit then drives the unmanned mining truck to travel according to the replanned driving route.

6. The intelligent control method for ore mining according to claim 5, characterized in that: When constructing the 3D model of the ore in S1.2, the ICP algorithm is used to register the point cloud data acquired by the second lidar and the second camera. The calculation formula of the objective function of the ICP algorithm is as follows: Where R is the rotation matrix, c is the translation vector, and p i For the i-th point in the target point cloud, q i For the source point cloud and p i The corresponding i-th point, where n is the total number of points in the point cloud; The formula for calculating point cloud coordinate transformation is shown below: p i =R·q i +c; In the digital twin technology simulation optimization process of S1.5, when blasting ore as a whole, the volumetric method is used to determine the charge amount, and the calculation formula is as follows: Q = K·V; Where Q is the total charge, K is the explosive consumption per unit volume, and V is the total volume of the ore; During the simulated blasting process, the deformation of the ore under blasting stress is described by the stress-strain relationship of the ore. The calculation formula is shown below: σ = E·ε; Where σ is the stress on the ore, E is the elastic modulus of the ore, and ε is the strain; The shock wave intensity at different distances is calculated using the peak overpressure formula for blasting shock waves, thereby assessing the impact of blasting on the surrounding rock mass and optimizing the charge amount and detonation sequence. The calculation formula is shown below: Where ΔP is the peak overpressure of the shock wave at a distance from the blast source A, A is the distance from the blast source, and B and α are the site coefficients.

7. The intelligent control method for ore mining according to claim 6, characterized in that: The ant colony algorithm in S1.3, S2.2, and S2.4 includes the path selection probability formula and the pheromone update formula, where the path selection probability formula is shown below: in, Let τ be the probability that the k-th ant moves from node a to node b at time t. ab (t) represents the pheromone concentration from node a to node b at time t, and η ab This is heuristic information, typically the reciprocal of the path distance, η. ab =1 / d ab d ab Let be the distance from node a to node b, β be the pheromone importance factor, γ be the heuristic information importance factor, and allowed. k Let be the set of nodes that the k-th ant has not visited; The pheromone update formula is shown below: Where ρ is the pheromone evaporation coefficient, and m is the total number of ants. Let be the pheromone concentration when the k-th ant passes through path (a, b). D represents the total amount of pheromones, L k Let be the total path length of the k-th ant.

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