Large-particle mineral screening system and method based on mechanical arm

Abstract

The invention relates to the technical field of crushed ore sorting, and discloses a large-particle mineral screening system and method based on a mechanical arm, and the method comprises the steps that the conveying speed of a conveying belt and the operation parameters of the mechanical arm are obtained, and the operation range of the mechanical arm is determined according to the conveying speed and the operation parameters. Mineral information of the conveying belt is obtained, all minerals are evaluated according to the mineral information, and a to-be-screened target is determined according to the evaluation result. And according to the number of the to-be-screened targets, the structure information of the to-be-screened targets and the operation parameters of the mechanical arm, the moving speed of the mechanical arm is determined. By comprehensively considering the speed of the conveying belt, the operation parameters of the mechanical arm, mineral information and structural evaluation, accurate screening target determination and optimization of the moving speed of the mechanical arm are achieved, and therefore automation, efficiency and precision of the mineral screening process are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of ore crushing and sorting, and in particular to a large-particle mineral screening system and method based on a mechanical arm. Background Art

[0002] When selecting ore minerals, the first thing to do is to crush them. Among them, the coarse crushing operation is usually carried out by jaw crusher. According to the feeding particle size requirements of the jaw crusher, the average particle size of the ore entering the crushing chamber should be smaller than the width of the feed port of the jaw crusher to prevent large-sized ore blocks from getting stuck in the feeding port of the crushing chamber. Therefore, the coarse particle size screening of minerals is an important part of the mineral processing process.

[0003] At present, the traditional coarse-grained screening methods are manual screening and mechanical grating screening. Manual screening is to select unqualified large-sized minerals by visual estimation; mechanical grating screening is to place a fixed grating above the heavy plate feeder of the jaw crusher to screen unqualified large-sized minerals, and the unqualified large-sized minerals on the screen are removed manually. Manual screening is labor-intensive, inefficient, and has low manual visual estimation accuracy; the mechanical grating is a fixed grating, which is inconvenient to disassemble and clean, so large-sized minerals often block the grating and cause damage to the belt and the subsequent coarse material bin port and crushing chamber feed port, which is extremely detrimental to the smooth operation of the crushing operation.

[0004] In summary, the traditional screening method has high labor intensity, low screening efficiency, and low screening accuracy, which often causes the grid screen, coarse material bin opening and crushing chamber feed opening to be blocked. The unqualified large-particle minerals that enter by mistake may even cause damage to the jaw crusher and belt, resulting in poor continuity in mineral processing work. Summary of the invention

[0005] In view of this, the present invention proposes a large-particle mineral screening system and method based on a robotic arm, aiming to solve the problems of low efficiency and accuracy of mineral screening in current technology, high labor intensity, and equipment damage and processing interruption due to blockage or misentry of large-particle minerals.

[0006] The present invention proposes a large-particle mineral screening method based on a robotic arm, comprising: Obtain the conveying speed of the conveyor belt and the operating parameters of the robot arm, and determine the operating range of the robot arm according to the conveying speed and the operating parameters; Obtain the mineral information of the conveyor belt, evaluate each mineral according to the mineral information, and determine the target to be screened according to the evaluation results; The moving speed of the robotic arm is determined according to the number of objects to be screened, the structural information of each object to be screened, and the operating parameters of the robotic arm.

[0007] Furthermore, obtaining the conveyor belt transport speed and the robot arm operation parameters includes: The operating parameters of the robot arm include the load capacity of the robot arm, the working radius of the robot arm, the joint angles and joint degrees of freedom of the robot arm, the movement speed of the robot arm, the movement acceleration of the robot arm and the response time of the robot arm.

[0008] Furthermore, when determining the operating range of the robot arm according to the transport speed and operating parameters, it includes: According to the transport speed, the moving time of the minerals on the conveyor belt is determined, and according to the operating parameters, the working range of the robot arm is determined, among which: Determine the working path of the robot operation based on the moving time, the robot's movement speed, load capacity, working radius and acceleration: ; Where Pr is the working path length of the robot arm operation, is the conveyor belt transport speed, The destination of the mineral transport. is the starting position of the mineral, and D is the distance between the mineral and the starting position of the robotic arm; Determine the operating range of the robot arm based on the length of the working path.

[0009] Furthermore, the mineral information of the conveyor belt is obtained, and the evaluation of each mineral is carried out according to the mineral information, including: The mineral information specifically refers to the image information of the mineral; Preprocessing each image information, and determining the mineral type and structural information of each mineral in the conveyor belt according to the processed image information, wherein the structural information is specifically the mineral size information; Evaluate each mineral based on its mineral type and textural information; Among them, the preprocessing of each image information includes: Removing noise from the image information based on a pre-configured filter, and graying the image information after the noise is removed; According to the grayscale processed image information, a calibration plate image of the mineral transportation route is obtained, and an internal parameter matrix and distortion parameters of an image acquisition device of the image information are obtained; ; D = [k1, k2, p1, p2, k3]; Among them, f x is the focal length of the image acquisition device in the x direction, f y is the focal length of the image acquisition device in the y direction, c x and c y is the projection center of the principal optical axis of the image acquisition device on the image plane, k1, k2, k3 are radial distortion coefficients, and p1, p2 are tangential distortion coefficients; The image information is dedistorted according to the intrinsic parameter matrix and distortion parameters.

[0010] Further, when determining the mineral type of each mineral in the conveyor belt according to the processed image information, it includes: Obtaining mineral image information of various types of minerals, the mineral image information includes texture information and color information, and establishing a mineral image correlation formula of the mineral type based on the texture information and the color information; According to the mineral image correlation formula of each mineral type, the texture feature vector and color feature vector of each mineral type are obtained; The texture information and color information of the mineral in the processed image information are extracted, and the type of the mineral is determined according to the relationship between the texture information and color information and the texture feature vector and color feature vector of each mineral type.

[0011] Further, when evaluating each mineral based on mineral type and structural information, it includes: Determines whether a mineral is evaluated based on its mineral type in relation to the configured preset mineral types: When the mineral type is consistent with the preset mineral type, the mineral is evaluated and the qualified score of the mineral is determined based on the structural information; When there is no consistency between the mineral type and the preset mineral type, it is determined that the mineral is not to be evaluated, and the qualified score of the mineral is determined to be 0.

[0012] Furthermore, the qualified time of the mineral is determined based on the structural information, including: Obtain the size difference between the size information of the mineral and the configured preset mineral size information, and determine the qualified score of the mineral according to the relationship between the size difference and the configured first preset size difference and second preset size difference: When the size difference is less than the first preset size difference, the qualified score of the mineral is determined to be D3; When the size difference is greater than or equal to the first preset size difference, and the size difference is less than the second preset size difference, the qualified score of the mineral is determined to be D2; When the size difference is greater than or equal to the second preset size difference, the qualified score of the mineral is determined to be D1; The first preset size difference is smaller than the second preset size difference, and D3<D2<D1.

[0013] Furthermore, when determining the target to be screened based on the evaluation results, it includes: Determine whether the mineral is a target to be screened based on the relationship between the mineral's qualified score and the configured preset qualified score: When the qualified score of the mineral is greater than or equal to the preset qualified score, it is determined that the mineral is not a target to be screened; When the qualified score of the mineral is less than the preset qualified score, the mineral is determined to be the target to be screened.

[0014] Further, according to the number of objects to be screened, the structural information of each object to be screened, and the operation parameters of the robot arm, the moving speed of the robot arm is determined, including: Obtain the distance information between the targets to be screened, and determine the moving speed of the robotic arm based on the relationship between the distance information, the structural information of the targets to be screened, the mineral moving speed, and the operating parameters of the robotic arm: ; Among them, Ur is the moving speed of the robot arm, dij is the distance between the targets to be screened, Tm is the time the mineral moves on the conveyor belt, S is the structural complexity of the target to be screened, W is the load of the robot arm, and A is the acceleration of the robot arm. The structural complexity of the target to be screened is obtained as follows: S = z1·F+z2·E+z3·H; Among them, F is the geometric data of the target to be screened, E is the volume of the target to be screened, and H is the weight of the target to be screened.

[0015] Compared with the prior art, the beneficial effect of the present invention is that by comprehensively considering the transportation speed of the conveyor belt, the operating parameters of the mechanical arm and the specific information of the mineral, the operating range and moving speed of the mechanical arm can be accurately determined. This method not only improves the accuracy and efficiency of the screening process, but also can effectively adapt to different mineral characteristics, ensuring that the mechanical arm can accurately capture the target mineral during the screening process. By evaluating the mineral information, the number and structural characteristics of the screening target are fully considered, so that the mechanical arm can optimize its operating parameters under the conditions of a variety of different mineral characteristics. Secondly, the speed of the mechanical arm is adjusted according to the structural information of the mineral. The weight, shape and volume of the mineral will directly affect the screening efficiency. Heavier or complex-shaped minerals may require the speed and strength of the mechanical arm to be appropriately adjusted to ensure the accuracy and stability of the mechanical arm. Through this structured information evaluation, the moving speed of the mechanical arm is accurately controlled, avoiding screening errors caused by too fast or too slow speed, thereby improving the success rate of screening. Finally, by obtaining the mineral information of the conveyor belt and evaluating each mineral individually, it can be ensured that the operation of the mechanical arm is not affected by the differences in the characteristics of different minerals. This personalized processing method effectively avoids the situation that the same speed may not be able to adapt to all minerals in traditional screening methods. Through this flexible adjustment mechanism, the screening efficiency and accuracy of the robot arm have been significantly improved, especially when processing large-particle minerals, which can greatly reduce the screening time and reduce the burden on the robot arm, thereby extending its service life.

[0016] On the other hand, the present application also provides a large-particle mineral screening system based on a robotic arm, comprising: A data acquisition module is electrically connected to the conveyor belt and the robotic arm, respectively, and is configured to obtain the transport speed of the conveyor belt and the operating parameters of the robotic arm, and determine the operating range of the robotic arm according to the transport speed and the operating parameters; An image acquisition module is configured to obtain mineral information of the conveyor belt, evaluate each mineral according to the mineral information, and determine a target to be screened according to the evaluation result; The central control module is electrically connected to the data acquisition module and the image acquisition module respectively, and is configured to determine the moving speed of the robotic arm according to the number of objects to be screened, the structural information of each object to be screened, and the operating parameters of the robotic arm.

[0017] It can be understood that the large-particle mineral screening system and method based on a robotic arm in the above-mentioned embodiments of the present invention have the same beneficial effects and will not be described in detail. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings: Figure 1 A flowchart of a method for screening large-particle minerals based on a robotic arm provided in an embodiment of the present invention; Figure 2 A functional block diagram of a large-particle mineral screening system based on a robotic arm provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0019] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art. It should be noted that, in the absence of conflict, the embodiments of the present invention and the features described in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0020] like Figure 1 As shown, in some embodiments of the present application, this embodiment provides a large-particle mineral screening method based on a robotic arm, comprising: Step S100: Acquire the transport speed of the conveyor belt and the operating parameters of the robot arm, and determine the operating range of the robot arm according to the transport speed and the operating parameters.

[0021] Specifically, when obtaining the conveyor belt transportation speed and the robot arm operating parameters, it includes: the robot arm operating parameters include the load capacity of the robot arm, the working radius of the robot arm, the joint angle of the robot arm and the joint degree of freedom of the robot arm, the movement speed of the robot arm, the movement acceleration of the robot arm and the response time of the robot arm.

[0022] Specifically, when determining the operating range of the robot arm according to the transport speed and operating parameters, it includes: determining the moving time of the minerals on the conveyor belt according to the transport speed, and determining the working range of the robot arm according to the operating parameters, wherein: determining the working path of the robot arm operation according to the moving time, the movement speed, load capacity, working radius and acceleration of the robot arm: .

[0023] Where Pr is the working path length of the robot arm operation, is the conveyor belt transport speed, The destination of the mineral transport. is the starting position of the mineral, and D is the distance between the mineral and the starting position of the robot. According to the length of the working path, the operating range of the robot is determined.

[0024] It is understandable that obtaining the conveyor belt transportation speed and the operating parameters of the robot arm is the premise for ensuring that the screening task can proceed smoothly. Specifically, the operating parameters of the robot arm include multiple factors such as load capacity, working radius, joint angle, joint degree of freedom, movement speed, acceleration and response time. These parameters jointly determine the performance and working range of the robot arm. Only on the basis of a comprehensive understanding of these parameters can the working state of the robot arm be flexibly adjusted in actual work to meet different mineral screening needs. Secondly, the transportation speed plays a vital role in determining the operating range of the robot arm. By calculating the movement time of the mineral on the conveyor belt, a reasonable time window is determined for the robot arm to ensure that the robot arm can complete the screening task before the mineral reaches the target position. At this time, the starting position and target position of the mineral, as well as the transportation speed of the conveyor belt, need to be accurately considered. The transportation speed directly affects the movement time of the mineral, which is one of the key factors for the robot arm to complete the operation in the screening task. Then, based on the movement time of the mineral, the parameters such as the movement speed, load capacity, working radius and acceleration of the robot arm are used to calculate the working path of the robot arm. Specifically, the working path length (Pr) of the robot arm is determined by the combination of these parameters. The length of the working path is closely related to the distance between the target position of the mineral, the movement ability of the robot arm, and the flow speed of the mineral. Only when these parameters are balanced can the robot arm complete the screening task efficiently. Finally, by calculating the length of the working path of the robot arm, the actual operating range of the robot arm can be further determined. According to the comparison between the working path length and the maximum working radius of the robot arm, it can be judged whether the robot arm can cover the required area and effectively screen the minerals. In this way, the operating range of the robot arm is optimized with the actual work requirements, ensuring that the screening task can be completed within the specified time, while maximizing the working efficiency and screening accuracy of the robot arm.

[0025] It can be seen that by fully acquiring the conveyor belt transportation speed and the operating parameters of the manipulator, the range of capabilities of the manipulator in actual work can be accurately evaluated. Specifically, the operating parameters of the manipulator include factors in multiple dimensions such as load capacity, working radius, movement speed, acceleration, etc., combined with the conveyor belt transportation speed, it can achieve comprehensive scheduling and control of the mineral screening task. By accurately determining these parameters, the efficiency and accuracy of the manipulator in the screening process can be ensured, and the inefficiency or errors caused by parameter mismatch during operation can be avoided. Secondly, through a comprehensive analysis based on the transportation speed and operating parameters, it is ensured that the manipulator can efficiently complete the mineral screening task within the specified time. By calculating the movement time of the mineral and the length of the working path of the manipulator, the operating range of the manipulator can be effectively predicted, avoiding the operation delay or insufficiency caused by the too fast or too slow movement speed of the manipulator in the task. This accurate prediction can help the manipulator respond quickly during the mineral transportation process and adjust its motion state in time to ensure that the task is completed on time. In addition, determining the actual operating range of the manipulator according to the length of the working path can optimize the working area of ​​the manipulator to ensure that it can cover all targets to be screened. Especially in complex mineral screening tasks, the flexibility and adaptability of the robot arm are critical. By adjusting the working path according to the movement time and distance information of the mineral, the robot arm can successfully complete the screening task within its maximum working radius, thereby improving work efficiency and reducing resource waste. Finally, by dynamically adjusting the movement speed and working path of the robot arm, it ensures that it can maintain good performance when facing minerals of different shapes, weights, and volumes.

[0026] Step S200: Obtain mineral information of the conveyor belt, evaluate each mineral according to the mineral information, and determine the target to be screened according to the evaluation result.

[0027] Specifically, the mineral information of the conveyor belt is obtained, and when each mineral is evaluated according to the mineral information, it includes: the mineral information is specifically the image information of the mineral. Each image information is preprocessed, and the mineral type and structural information of each mineral on the conveyor belt are determined according to the processed image information, and the structural information is specifically the mineral size information. Each mineral is evaluated according to the mineral type and structural information. Among them, when each image information is preprocessed, it includes: removing the noise in the image information based on the pre-configured filter, and graying the image information after the noise is removed. According to the grayed image information, the calibration plate image of the mineral transportation route is obtained, and the intrinsic parameter matrix and distortion parameters of the image acquisition device of the image information are obtained.

[0028] .

[0029] D=[k1,k2,p1,p2,k3].

[0030] Among them, f x is the focal length of the image acquisition device in the x direction, f y is the focal length of the image acquisition device in the y direction, c x and c y is the projection center of the main optical axis of the image acquisition device on the image plane, k1, k2, k3 are radial distortion coefficients, and p1, p2 are tangential distortion coefficients. The image information is dedistorted according to the intrinsic parameter matrix and distortion parameters.

[0031] Specifically, when determining the mineral type of each mineral on the conveyor belt based on the processed image information, it includes: obtaining the mineral image information of each type of mineral, the mineral image information includes texture information and color information, and establishing the mineral image association formula of the mineral type based on the texture information and the color information. According to the mineral image association formula of each mineral type, the texture feature vector and the color feature vector of each mineral type are obtained. The texture information and color information of the mineral in the processed image information are extracted, and the type of the mineral is determined based on the relationship between the texture information and the color information and the texture feature vector and the color feature vector of each mineral type.

[0032] Specifically, when evaluating each mineral based on the mineral type and structural information, it includes: determining whether to evaluate the mineral based on the relationship between the mineral type of the mineral and the preset mineral type configured: when the mineral type is consistent with the preset mineral type, it is determined that the mineral is evaluated, and the qualified score of the mineral is determined based on the structural information. When the mineral type is inconsistent with the preset mineral type, it is determined that the mineral is not evaluated, and the qualified score of the mineral is determined to be 0.

[0033] Specifically, when determining the qualified score of a mineral based on structural information, it includes: obtaining the size difference between the size information of the mineral and the configured preset mineral size information, and determining the qualified score of the mineral based on the relationship between the size difference and the configured first preset size difference and second preset size difference: when the size difference is less than the first preset size difference, the qualified score of the mineral is determined to be D3. When the size difference is greater than or equal to the first preset size difference, and the size difference is less than the second preset size difference, the qualified score of the mineral is determined to be D2. When the size difference is greater than or equal to the second preset size difference, the qualified score of the mineral is determined to be D1. Among them, the first preset size difference is less than the second preset size difference, and D3<D2<D1.

[0034] Specifically, when determining the target to be screened according to the evaluation results, it includes: determining whether the mineral is the target to be screened according to the relationship between the qualified score of the mineral and the preset qualified score configured: when the qualified score of the mineral is greater than or equal to the preset qualified score, it is determined that the mineral is not the target to be screened. When the qualified score of the mineral is less than the preset qualified score, it is determined that the mineral is the target to be screened.

[0035] It is understandable that the process of obtaining mineral information involves image acquisition of minerals on the conveyor belt, and the type and structural information of the minerals are obtained through image information. The processing of image information is the basis of the entire technical process. By filtering and denoising the mineral image, graying it, and combining the internal parameter matrix and distortion parameters of the image acquisition device for dedistortion processing, the distortion and noise in the image can be eliminated, and the accuracy and reliability of the image can be improved. This process ensures the accuracy of the mineral image data, thereby providing high-quality input for subsequent mineral evaluation. Then, the type of mineral is determined through the processed image information. The image information of the mineral includes texture information and color information, which are the key to the classification of mineral types. Using the correlation between texture and color, the system can establish an association model for the mineral image and match the texture and color features in the image with the preset mineral type. Based on the extracted texture and color feature vectors, the type of mineral can be accurately determined. This process relies on the accurate extraction of the texture and color features of the mineral, making the mineral classification more accurate. After determining the type of mineral, the next technical principle is to further evaluate the mineral through structural information. The structural information of minerals, especially the size information, is an important basis for evaluating whether the minerals meet the screening criteria. The difference in size information is compared with the preset standard size difference to determine whether the mineral meets the predetermined screening criteria. By calculating the relationship between the size difference and the first and second preset size differences, the system can assign a qualified score to the mineral to indicate the degree to which it meets the standard. This scoring method based on the size difference ensures the accuracy and standardization of mineral screening. In addition, the qualified score of the mineral is determined based on the size difference and the preset size difference range. When the size difference of the mineral is less than the first preset size difference, the mineral will obtain a higher qualified score; if the size difference is between the two preset size differences, the qualified score is lower; and when the size difference exceeds the second preset size difference, the mineral has the lowest qualified score. This evaluation mechanism subdivides different qualified grades according to the size difference of the mineral, thereby helping to achieve more accurate screening and screen out unqualified minerals. Finally, based on the relationship between the qualified score of the mineral and the preset qualified score, it is finally determined which minerals are the targets to be screened. The key to this screening mechanism lies in the setting of the preset qualified score. If the qualified score of a mineral is greater than or equal to the preset qualified score, the mineral will not be selected as a target for screening; if the qualified score is lower than the preset qualified score, the mineral will be marked as a target for screening. This screening method based on qualified scores is not only highly automated, but can also accurately screen out minerals that meet the requirements according to the preset standards, effectively improving the efficiency and accuracy of mineral screening.

[0036] It can be seen that through filtering, denoising and graying, interference factors in the image can be eliminated, ensuring that subsequent mineral classification and evaluation are based on more accurate image data. De-distortion processing is based on the intrinsic matrix and distortion parameters of the image acquisition device, which can effectively eliminate image distortion caused by camera lens distortion, further improve the accuracy of mineral images, and ensure the stability and reliability of the image processing process. Secondly, the mineral classification method based on image texture and color information can effectively distinguish different types of minerals. By establishing a mineral image association formula, the texture and color features in the image are associated with the preset mineral types, and the type of mineral can be quickly and accurately determined. This classification method avoids subjective errors in traditional manual screening methods and improves classification efficiency and screening accuracy. By extracting texture feature vectors and color feature vectors, the classification of minerals becomes more objective and automated, thereby improving the intelligence of the screening system. On the basis of mineral classification, this method also combines the structural information of minerals for accurate evaluation. By comparing the size information of minerals and combining the preset standard size difference, a qualified score can be assigned to each mineral. In this way, the screening criteria for minerals no longer rely solely on the type, but also take into account the degree of size matching, ensuring the multi-dimensional accuracy of the screening process. The correspondence between the size difference and the qualified score makes the mineral evaluation more detailed and scientific, avoiding the rough processing of the size information evaluation. In addition, by comparing the mineral type with the preset type, the screening system can decide whether to evaluate the mineral. Only when the mineral type is consistent with the preset type, the qualified assessment will be carried out, otherwise the qualified score is set to zero. This pre-screening mechanism avoids the misassessment of irrelevant minerals, optimizes the screening process, and improves the processing efficiency. The screening range is clear, and only qualified minerals are evaluated in detail, which effectively reduces the calculation burden. Through the evaluation of structural information and size difference, the qualified score of the mineral is scientifically calculated. According to the size difference, the minerals are divided into different qualified grades, and D3, D2, and D1 represent different qualified degrees. This evaluation mechanism provides flexible screening criteria, which enables the screening strategy to be adjusted according to the characteristics of different minerals, maximizing the accuracy and flexibility of mineral screening. Through the refined qualified score division, minerals with different size differences can be treated differently. Finally, based on the relationship between the qualified score and the preset qualified score, this technical solution can accurately identify the target to be screened. By judging the qualified score of the mineral, it is possible to automatically screen out the minerals that meet the requirements and exclude the minerals that do not meet the requirements, ensuring the efficient completion of the screening task. This mechanism avoids errors caused by manual intervention, improves the level of automation in screening, and demonstrates high efficiency and high precision in large-scale mineral screening. This method not only improves the overall efficiency of mineral screening, but also greatly reduces the workload of manual screening and ensures the stability and consistency of the screening results.

[0037] Step S300: Determine the moving speed of the robot arm according to the number of objects to be screened, the structural information of each object to be screened, and the operating parameters of the robot arm.

[0038] Specifically, when determining the moving speed of the robotic arm according to the number of targets to be screened, the structural information of each target to be screened, and the operating parameters of the robotic arm, the method includes: obtaining the distance information between each target to be screened, and determining the moving speed of the robotic arm according to the relationship between the distance information, the structural information of each target to be screened, the mineral moving speed, and the operating parameters of the robotic arm: .

[0039] Among them, Ur is the moving speed of the robot arm, dij is the distance between the targets to be screened, Tm is the time the mineral moves on the conveyor belt, S is the structural complexity of the target to be screened, W is the load of the robot arm, and A is the acceleration of the robot arm. The structural complexity of the target to be screened includes: S=z1·F+z2·E+z3·H. Among them, F is the geometric data of the target to be screened, E is the volume of the target to be screened, and H is the weight of the target to be screened.

[0040] It can be understood that the moving speed of the manipulator is determined by the relationship between the distance information and the structural complexity between the targets to be screened. In order to ensure the efficiency of the screening process, the moving speed of the manipulator must be dynamically adjusted according to the relative distance between the targets to be screened. By obtaining the distance information between the targets to be screened, the system can calculate the necessary movement time of the manipulator during the screening process, thereby determining the optimal operating speed of the manipulator so that the minerals can be grasped and processed in time. This method is based on the accurate calculation of the spatial relationship between the targets, ensuring that the manipulator can flexibly respond to the needs of different screening tasks. Secondly, the operating parameters of the manipulator, including load, acceleration, etc., need to be combined with the structural complexity of the target to be screened to optimize the motion control of the manipulator. The structural complexity of the target to be screened is obtained by a weighted combination of geometric data, volume and weight. Specifically, the structural complexity S is calculated by the formula S=z1·F+z2·E+z3·H, where F is the geometric data of the target, E is the volume, H is the weight, z1, z2, and z3 are the weight coefficients of these factors, and z1, z2, and z3 are not zero. By combining these factors, the robot arm can adjust the movement speed and load capacity according to the structural characteristics of each target to be screened, ensuring that targets of different complexity can be effectively processed. Finally, the time (Tm) that the mineral moves on the conveyor belt is also an important parameter that affects the movement speed of the robot arm. By considering the movement time of the mineral, the speed of the robot arm can be synchronized with the transmission progress of the mineral, thereby avoiding the robot arm from operating too early or too late. This comprehensive consideration of the conveyor belt speed, the mineral movement time and the robot arm operating parameters enables the robot arm to grasp and screen targets more accurately, improve the automation level and work efficiency of the entire screening system, and ensure the efficiency and stability of the mineral screening process.

[0041] In the above embodiment, by comprehensively considering the transport speed of the conveyor belt, the operating parameters of the mechanical arm and the specific information of the mineral, the operating range and moving speed of the mechanical arm can be accurately determined. This method not only improves the accuracy and efficiency of the screening process, but also can effectively adapt to different mineral characteristics, ensuring that the mechanical arm can accurately capture the target mineral during the screening process. By evaluating the mineral information, the number and structural characteristics of the screening target are fully considered, so that the mechanical arm can optimize its operating parameters under the conditions of a variety of different mineral characteristics. Secondly, the speed of the mechanical arm is adjusted according to the structural information of the mineral. The weight, shape and volume of the mineral will directly affect the screening efficiency. Heavier or complex-shaped minerals may require the speed and strength of the mechanical arm to be appropriately adjusted to ensure the accuracy and stability of the mechanical arm. Through this structured information evaluation, the moving speed of the mechanical arm is accurately controlled, avoiding screening errors caused by too fast or too slow speed, thereby improving the success rate of screening. Finally, by obtaining the mineral information of the conveyor belt and evaluating each mineral separately, it can ensure that the operation of the mechanical arm is not affected by the differences in the characteristics of different minerals. This personalized processing method effectively avoids the situation that the same speed may not be able to adapt to all minerals in the traditional screening method. Through this flexible adjustment mechanism, the screening efficiency and accuracy of the robotic arm have been significantly improved, especially when processing large-particle minerals, which can greatly reduce the screening time and reduce the burden on the robotic arm, thereby extending its service life.

[0042] In another preferred embodiment based on the above embodiment, this embodiment provides a large-particle mineral screening system based on a robotic arm, including: a data acquisition module, an image acquisition module and a central control module.

[0043] Specifically, the data acquisition module is electrically connected to the conveyor belt and the robotic arm, respectively. The data acquisition module is configured to obtain the conveyor belt's transportation speed and the robotic arm's operating parameters, and determine the robotic arm's operating range based on the transportation speed and the operating parameters. The image acquisition module is configured to obtain the mineral information of the conveyor belt, evaluate each mineral based on the mineral information, and determine the target to be screened based on the evaluation result. The central control module is electrically connected to the data acquisition module and the image acquisition module, respectively. The central control module is configured to determine the moving speed of the robotic arm based on the number of targets to be screened, the structural information of each target to be screened, and the operating parameters of the robotic arm.

[0044] It can be understood that the large-particle mineral screening system and method based on a robotic arm in the above-mentioned embodiments of the present invention have the same beneficial effects and will not be described in detail.

[0045] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0046] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0047] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0048] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0049] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for screening large-particle minerals based on a robotic arm, characterized in that: include: Obtain the conveying speed of the conveyor belt and the operating parameters of the robot arm, and determine the operating range of the robot arm according to the conveying speed and the operating parameters; Obtain the mineral information of the conveyor belt, evaluate each mineral according to the mineral information, and determine the target to be screened according to the evaluation results; The moving speed of the robotic arm is determined according to the number of objects to be screened, the structural information of each object to be screened, and the operating parameters of the robotic arm.

2. The method for screening large-particle minerals based on a robotic arm as claimed in claim 1, characterized in that: When obtaining the conveyor belt's transport speed and the robot arm's operating parameters, include: The operating parameters of the robot arm include the load capacity of the robot arm, the working radius of the robot arm, the joint angles and joint degrees of freedom of the robot arm, the movement speed of the robot arm, the movement acceleration of the robot arm and the response time of the robot arm.

3. The method for screening large-particle minerals based on a robotic arm as claimed in claim 2, characterized in that: When determining the operating range of the robot arm based on the transport speed and operating parameters, include: According to the transport speed, the moving time of the minerals on the conveyor belt is determined, and according to the operating parameters, the working range of the robot arm is determined, among which: Determine the working path of the robot operation based on the moving time, the robot's movement speed, load capacity, working radius and acceleration: ； Where Pr is the working path length of the robot arm operation, is the conveyor belt transport speed, The destination of the mineral transport. is the starting position of the mineral, and D is the distance between the mineral and the starting position of the robotic arm; Determine the operating range of the robot arm based on the length of the working path.

4. The method for screening large-particle minerals based on a robotic arm as claimed in claim 3, characterized in that: Obtain the mineral information of the conveyor belt and evaluate each mineral based on the mineral information, including: The mineral information specifically refers to the image information of the mineral; Preprocessing each image information, and determining the mineral type and structural information of each mineral in the conveyor belt according to the processed image information, wherein the structural information is specifically the mineral size information; Evaluate each mineral based on its mineral type and textural information; Among them, the preprocessing of each image information includes: Removing noise from the image information based on a pre-configured filter, and graying the image information after the noise is removed; According to the grayscale processed image information, a calibration plate image of the mineral transportation route is obtained, and an internal parameter matrix and distortion parameters of an image acquisition device of the image information are obtained; ； D = [k1, k2, p1, p2, k3]; Among them, f x is the focal length of the image acquisition device in the x direction, f y is the focal length of the image acquisition device in the y direction, c x and c y is the projection center of the principal optical axis of the image acquisition device on the image plane, k1, k2, k3 are radial distortion coefficients, and p1, p2 are tangential distortion coefficients; The image information is dedistorted according to the intrinsic parameter matrix and distortion parameters.

5. The method for screening large-particle minerals based on a robotic arm as claimed in claim 4, characterized in that: According to the processed image information, the mineral type of each mineral in the conveyor belt is determined, including: Obtaining mineral image information of various types of minerals, the mineral image information includes texture information and color information, and establishing a mineral image correlation formula of the mineral type based on the texture information and the color information; According to the mineral image correlation formula of each mineral type, the texture feature vector and color feature vector of each mineral type are obtained; The texture information and color information of the mineral in the processed image information are extracted, and the type of the mineral is determined according to the relationship between the texture information and color information and the texture feature vector and color feature vector of each mineral type.

6. The method for screening large-particle minerals based on a robotic arm as claimed in claim 5, characterized in that: When evaluating each mineral based on mineral type and structural information, this includes: Determines whether a mineral is evaluated based on its mineral type in relation to the configured preset mineral types: When the mineral type is consistent with the preset mineral type, the mineral is evaluated and the qualified score of the mineral is determined based on the structural information; When there is no consistency between the mineral type and the preset mineral type, it is determined that the mineral is not to be evaluated, and the qualified score of the mineral is determined to be 0.

7. The method for screening large-particle minerals based on a robotic arm as claimed in claim 6, characterized in that: Determine the qualified grade of minerals based on structural information, including: Obtain the size difference between the size information of the mineral and the configured preset mineral size information, and determine the qualified score of the mineral according to the relationship between the size difference and the configured first preset size difference and second preset size difference: When the size difference is less than the first preset size difference, the qualified score of the mineral is determined to be D3; When the size difference is greater than or equal to the first preset size difference, and the size difference is less than the second preset size difference, the qualified score of the mineral is determined to be D2; When the size difference is greater than or equal to the second preset size difference, the qualified score of the mineral is determined to be D1; The first preset size difference is smaller than the second preset size difference, and D3<D2<D1.

8. The method for screening large-particle minerals based on a robotic arm as claimed in claim 7, characterized in that: When determining the targets to be screened based on the evaluation results, the following should be included: Determine whether the mineral is a target to be screened based on the relationship between the mineral's qualified score and the configured preset qualified score: When the qualified score of the mineral is greater than or equal to the preset qualified score, it is determined that the mineral is not a target to be screened; When the qualified score of the mineral is less than the preset qualified score, the mineral is determined to be the target to be screened.

9. The method for screening large-particle minerals based on a robotic arm as claimed in claim 8, characterized in that: According to the number of objects to be screened, the structural information of each object to be screened, and the operation parameters of the robot arm, the moving speed of the robot arm is determined, including: Obtain the distance information between the targets to be screened, and determine the moving speed of the robotic arm based on the relationship between the distance information, the structural information of the targets to be screened, the mineral moving speed, and the operating parameters of the robotic arm: ； Among them, Ur is the moving speed of the robot arm, dij is the distance between the targets to be screened, Tm is the time the mineral moves on the conveyor belt, S is the structural complexity of the target to be screened, W is the load of the robot arm, and A is the acceleration of the robot arm. The structural complexity of the target to be screened is obtained as follows: S = z1·F+z2·E+z3·H; Among them, F is the geometric data of the target to be screened, E is the volume of the target to be screened, and H is the weight of the target to be screened.

10. A large-particle mineral screening system based on a robotic arm, applicable to the large-particle mineral screening method based on a robotic arm as claimed in any one of claims 1 to 9, characterized in that: include: A data acquisition module is electrically connected to the conveyor belt and the robotic arm, respectively, and is configured to obtain the transport speed of the conveyor belt and the operating parameters of the robotic arm, and determine the operating range of the robotic arm according to the transport speed and the operating parameters; An image acquisition module is configured to obtain mineral information of the conveyor belt, evaluate each mineral according to the mineral information, and determine a target to be screened according to the evaluation result; The central control module is electrically connected to the data acquisition module and the image acquisition module respectively, and is configured to determine the moving speed of the robotic arm according to the number of objects to be screened, the structural information of each object to be screened, and the operating parameters of the robotic arm.

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