Collaborative design method for mining electric shovel

Abstract

The invention provides a collaborative design method for mining electric shovels, and relates to the technical field of mining electric shovels. According to the collaborative design method for the mining electric shovel, the design period of the mining electric shovel can be shortened, the design efficiency is improved, and a better design result can be obtained. The collaborative design method for the mining electric shovel comprises the steps that structural design parameters and control design parameters are determined; the structural design parameters comprise parameters for limiting structural parts of the mining electric shovel, and the control design parameters comprise parameters for representing the motion state of a bucket of the mining electric shovel; screening the structural design parameters and the control design parameters, and determining core elements in the structural design parameters and / or core elements in the control design parameters; constructing an approximate model of the mining electric shovel based on the core elements; constructing a collaborative design framework based on the approximate model; and based on the collaborative design framework, constructing an optimization design process, and determining a design result of the mining electric shovel.

Description

Technical Field

[0001] This application relates to the technical field of mining electric shovels, and particularly to a collaborative design method for mining electric shovels. Background Art

[0002] Large mining electric shovels (also known as "mechanical front shovels") are one of the heavy equipment widely used in open-pit mining methods and play an important role in the excavation and loading operations of minerals. The rapid development of the open-pit mining industry has led to an increasing demand for large mining electric shovels, and higher performance requirements for electric shovels.

[0003] As a large excavation machine, the design process of a mining electric shovel involves multiple aspects such as structure, materials, and control. Each part is coupled and restricted with each other, and the overall performance has non-linear characteristics. The traditional design mode of mining electric shovels is serial, that is, the design process is divided into several stages, and different systems are designed and optimized in different stages. This design method does not consider the coupling relationship between systems, and requires multiple repeated modifications during the design process, resulting in a long design cycle. In addition, the result of this serial design method can make the local performance of the electric shovel optimal, but it is difficult to make the global performance of the electric shovel optimal. Therefore, the traditional design method has certain limitations in dealing with the integrated design problem of mining electric shovels, and thus a better method for designing and optimizing electric shovels is needed. Summary of the Invention

[0004] This application provides a collaborative design method for mining electric shovels, which can shorten the design cycle of mining electric shovels, improve design efficiency, and obtain better design results.

[0005] The collaborative design method for mining electric shovels provided by this application includes: determining structural design parameters and control design parameters; the structural design parameters include parameters that define the structural components of the mining electric shovel, and the control design parameters include parameters that characterize the motion state of the bucket of the mining electric shovel; screening the structural design parameters and control design parameters to determine the core elements in the structural design parameters and / or the core elements in the control design parameters; constructing an approximate model of the mining electric shovel based on the core elements; constructing a collaborative design framework based on the approximate model; constructing an optimization design process based on the collaborative design framework, and determining the design result of the mining electric shovel.

[0006] The collaborative design method of the electric mining shovel provided by this application can comprehensively consider the requirements in aspects such as structure, mechanics, and control during the design process of the electric mining shovel because it determines the structural design parameters and control design parameters simultaneously, which is conducive to optimizing the overall performance of the designed electric mining shovel. Moreover, by screening the structural design parameters and control design parameters, the core elements that have a greater impact on the overall performance of the electric mining shovel can be selected. In this way, during the design process of the electric mining shovel, the parameters with less impact on the overall performance of the electric mining shovel can be ignored, which is conducive to reducing the computational workload in the entire design process, thereby shortening the design cycle of the electric mining shovel. At the same time, constructing an approximate model, a collaborative design framework, and an optimization design process for the electric mining shovel can reduce the computational workload of collaborative design problems, which is conducive to improving the design efficiency. And through the optimization of the approximate model, a better design result can be obtained.

[0007] In a possible implementation manner of this application, determining the structural design parameters and control design parameters includes: establishing the excavation trajectory equation of the bucket; determining the initial state parameters of the bucket and the terminal state parameters of the bucket; and determining the control parameter design variables in the control design parameters based on the excavation trajectory equation, the initial state parameters, and the terminal state parameters.

[0008] In a possible implementation manner of this application, screening the structural design parameters and control design parameters to determine the core elements in the structural design parameters and / or control design parameters includes: determining the target output parameters based on the structural design parameters and the control parameter design variables; and verifying the structural design parameters and the control parameter design variables based on the target output parameters to determine the core elements.

[0009] In a possible implementation manner of this application, determining the target output parameters based on the structural design parameters and the control parameter design variables includes: establishing a parametric model of the electric mining shovel based on the structural design parameters; establishing a particle model of the excavated material; controlling the parametric model to simulate the excavation process of the particle model based on the control parameter design variables, and obtaining the target output parameters of the parametric model.

[0010] In a possible implementation manner of the present application, based on the target output parameters, the structure design parameters and the control parameter design variables are verified to determine the core elements, including: generating multiple groups of test sample points, and each group of test sample points includes a structure sample point and a control sample point; the structure sample point includes the value of at least one of the structure design parameters, and the control sample point includes the value of at least one of the control parameter design variables; based on the structure sample point, the parametric model is corrected to obtain a corrected parameter model; based on the control sample point, the corrected parameter model is controlled to simulate the excavation process of the particle model, and the target output parameter corresponding to each group of test sample points is obtained; based on the response of each group of test sample points to the target output parameter corresponding to each group of test sample points, the core elements are determined from the multiple groups of test sample points.

[0011] In a possible implementation manner of the present application, based on the response of each group of test sample points to the target output parameter corresponding to each group of test sample points, the core elements are determined from the multiple groups of test sample points, including: taking the structure design parameters and / or the control design parameters corresponding to the test sample points whose influence on the target output parameter is greater than a preset threshold among the multiple groups of test sample points as the core elements.

[0012] In a possible implementation manner of the present application, an approximate model of the electric shovel for mining is constructed based on the core elements, including: based on the core elements, an initial model of the electric shovel for mining is established by the response surface method and the Kriging method respectively; the verification sample points are used to verify the error of the initial model, and the fitting accuracy of the initial model is determined; the verification sample points are the test sample points that do not include the core elements among the multiple groups of test sample points; the initial model corresponding to the fitting accuracy greater than or equal to the preset fitting threshold is used as the approximate model.

[0013] In a possible implementation manner of the present application, a collaborative design framework is constructed based on the approximate model, including: determining the structure design variables, the structure design objectives and the structure design constraints of the structure subsystem model; determining the control design variables, the control design objectives and the control design constraints of the control subsystem model; taking the structure design variables as the input to the structure subsystem model, and taking the structure design constraints and the structure design objectives as the constraint limitations on the structure subsystem model; the structure subsystem model is the model part of the approximate model that includes the structural components of the electric shovel for mining; taking the control design variables as the input to the control subsystem model, and taking the control design constraints and the control design objectives as the constraint limitations on the control subsystem model; the control subsystem model is the part of the approximate model that characterizes the operating parameters of the electric shovel for mining.

[0014] In a possible implementation manner of the present application, based on the collaborative design framework, an optimized design process is constructed, and the design result of the electric shovel for mines is determined, including: constructing an optimized design process in Isight based on the structural subsystem model, structural design variables, structural design objectives, structural design constraints, control subsystem model, control design variables, control design constraints, and control design objectives; running the optimized design process to optimize the structural subsystem model and the control subsystem model until an optimal design parameter combination of the structural subsystem model and the control subsystem model is obtained. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 is the flowchart of the collaborative design method for the electric shovel for mines provided by the present application Figure 1 ;

[0016] Figure 2 is the schematic diagram of the parametric model of the electric shovel for mines provided by the present application;

[0017] Figure 3 is the schematic diagram of the joint simulation of the parametric model of the electric shovel for mines and the excavation material model provided by the present application;

[0018] Figure 4 is the flowchart of the collaborative design method for the electric shovel for mines provided by the present application Figure 2 ;

[0019] Figure 5 is the schematic diagram of the stacking surface of the excavation material provided by the present application;

[0020] Figure 6 is the flowchart of the collaborative design method for the electric shovel for mines provided by the present application Figure 3 ;

[0021] Figure 7 is the flowchart of the collaborative design method for the electric shovel for mines provided by the present application Figure 4 ;

[0022] Figure 8 is the flowchart of the collaborative design method for the electric shovel for mines provided by the present application Figure 5 ;

[0023] Figure 9 is the flowchart of the collaborative design method for the electric shovel for mines provided by the present application Figure 6 ;

[0024] Figure 10 is the flowchart of the collaborative design method for the electric shovel for mines provided by the present application Figure 7 ;

[0025] Figure 11 is the flowchart of the collaborative design method for the electric shovel for mines provided by the present application Figure 8 ;

[0026] Figure 12 Schematic diagram of the collaborative design framework of the electric shovel for mines provided in this application;

[0027] Figure 13 Flow of the collaborative design method of the electric shovel for mines provided in this application Figure 9 .

[0028] Explanation of reference numerals:

[0029] 1 - Boom; 2 - Arm; 3 - Bucket; 4 - Sheave; 5 - Travel mechanism; 6 - Rotary platform; 7 - Excavated material; L1 - Horizontal distance from the boom mounting hinge point to the rotary center; L2 - Height from the boom mounting hinge point to the ground; L3 - Distance from the boom mounting hinge point to the center of the push gear; L4 - Boom length; L5 - Arm length; R1 - Boom mounting angle; R2 - Initial excavation angle. Detailed implementation manners

[0030] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the following will further describe the specific technical solutions of this application in detail with reference to the accompanying drawings in the embodiments of this application. The following embodiments are used to illustrate this application, but are not used to limit the scope of this application.

[0031] In the embodiments of this application, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the embodiments of this application, unless otherwise specified, the meaning of "plural" is two or more.

[0032] In addition, in the embodiments of this application, orientation terms such as "upper", "lower", "left", and "right" are defined relative to the orientation of the components shown in the accompanying drawings. It should be understood that these directional terms are relative concepts, which are used for relative description and clarification, and they may change accordingly with the change of the orientation of the components placed in the accompanying drawings.

[0033] In the embodiments of this application, unless otherwise clearly specified and limited, the term "connection" should be understood in a broad sense. For example, "connection" may be a fixed connection, a detachable connection, or an integral body; it may be directly connected or indirectly connected through an intermediate medium.

[0034] In the embodiments of the present application, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the phrase "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or device comprising that element.

[0035] In the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design solution described as "exemplary" or "for example" in the embodiments of the present application should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Rather, the use of words such as "exemplary" or "for example" is intended to present relevant concepts in a specific manner.

[0036] The embodiments of the present application provide a collaborative design method for a mining electric shovel. This method comprehensively considers the requirements in aspects such as structure, granular mechanics, and control involved in the design process of the mining electric shovel, can achieve the global optimization of the overall performance of the electric shovel, and is beneficial to shortening the design cycle. Refer to Figure 1 and Figure 2 , Figure 1 is the flow of the collaborative design method for the mining electric shovel provided by the present application Figure 1 , Figure 2 is a schematic diagram of the parametric model of the mining electric shovel provided by the present application, Figure 3 is a schematic diagram of the joint simulation of the parametric model of the mining electric shovel and the excavation material model provided by the present application. As Figure 1 shown, the collaborative design method of this mining electric shovel includes the following steps S101 to S105.

[0037] S101. Determine the structural design parameters and the control design parameters; the structural design parameters include the parameters defining the structural members of the mining electric shovel, and the control design parameters include the parameters characterizing the motion state of the bucket of the mining electric shovel.

[0038] In some embodiments, as Figure 2 shown, the electric shovel consists of multiple parts. For example, it is composed of a power system, a transmission system, a slewing mechanism, a traveling mechanism 5, a slewing platform 6, a working device, a control system, etc. Among them, the working device includes a boom 1, a bucket 3, an arm 2, a sheave 4, etc. During the design process of the electric shovel, the parameters of each structural member in these components need to be determined according to the usage requirements to optimize the global performance of the electric shovel.

[0039] Exemplarily, as Figure 2As shown in the figure, the structural design parameters of the electric shovel may include the horizontal distance L1 from the boom mounting hinge point to the center of rotation, the height L2 from the boom mounting hinge point to the ground, the distance L3 from the boom mounting hinge point to the center of the push gear, the boom length L4, the dipperstick length L5, the diameter of the push gear, the diameter of the crown block, the boom mounting angle R1, the initial excavation angle R2, etc.

[0040] In some embodiments, during the operation of the electric shovel, the operating state of the bucket is particularly important, and the operating state of the bucket has a great impact on the excavation energy consumption, excavation efficiency, etc. of the electric shovel. The relevant parameters for controlling the operating state of the bucket can be used as control design parameters. For example, the control design parameters may include the movement speed, acceleration, starting position, and ending position of the bucket, etc.

[0041] S102. Screen the structural design parameters and control design parameters to determine the core elements in the structural design parameters and / or the core elements in the control design parameters.

[0042] In some embodiments, after determining the structural design parameters of each structural component of the electric shovel and the control design parameters characterizing the operating state of the bucket, a large number of structural design parameters and control design parameters can be screened. Parameters that have a greater impact on the global performance of the electric shovel are selected from multiple structural design parameters and / or control design parameters, while parameters that have a smaller impact on the global performance of the electric shovel are ignored. The structural design parameters and / or control design parameters that have a greater impact on the global performance of the electric shovel are used as the core elements for designing the electric shovel. In the subsequent design process, mainly consider how to determine the values of these core elements.

[0043] S103. Build an approximate model of the mine electric shovel based on the core elements.

[0044] In some embodiments, after determining the core elements for designing the electric shovel, an approximate model of the electric shovel can be built. For example, an approximate model of the electric shovel can be built based on multiple data in the core elements using simulation software, design platforms, etc.

[0045] Exemplarily, the approximate model can be built in the form of including a structural subsystem model and a control subsystem model. Among them, the structural subsystem model includes the structural components of each part of the electric shovel, and the control subsystem model includes data for controlling the bucket to simulate the excavation process, etc.

[0046] S104. Build a collaborative design framework based on the approximate model.

[0047] In some embodiments, after the construction of the approximate model of the electric shovel is completed, the approximate model needs to be further optimized. According to the constructed approximate model, that is, according to the parameters of each structural member in the obtained structural subsystem model and the data in the control subsystem model, etc., a collaborative design framework can be constructed. The association relationships between the parameters of each structural member in the structural subsystem model, the data in the control subsystem model, and the parameters of the whole machine system level of the electric shovel can be established in the collaborative design framework, so as to facilitate the synchronous optimization of each part in the structural subsystem model, the control subsystem model, and the approximate model.

[0048] S105. Based on the collaborative design framework, construct an optimization design process and determine the design result of the mining electric shovel.

[0049] In some embodiments, after the construction of the collaborative design framework of the electric shovel is completed, an optimization design process can be constructed. For example, according to the association relationships, influence relationships, etc. between each part in the structural subsystem model, the control subsystem model, and the approximate model in the collaborative design framework, determine the part that needs to be optimized first, and then determine other parts that need to be optimized until the optimization of all parts of the entire electric shovel is completed. Finally, screen and determine the optimal design result from the data set obtained during the optimization process of the approximate model of the electric shovel, so as to complete the collaborative design of the electric shovel.

[0050] The collaborative design method of the mining electric shovel provided by the embodiments of the present application, since the structural design parameters and the control design parameters are determined simultaneously, in the design process of the mining electric shovel, the requirements in aspects such as structure, mechanics, and control involved can be comprehensively considered, which is beneficial to making the global performance of the designed mining electric shovel reach the optimal. And by screening the structural design parameters and the control design parameters, the core elements that have a greater impact on the global performance of the mining electric shovel can be screened out. In this way, in the design process of the mining electric shovel, the parameters that have a smaller impact on the global performance of the mining electric shovel can be ignored, which is beneficial to reducing the calculation amount in the entire design process, thereby shortening the design cycle of the mining electric shovel. At the same time, constructing the approximate model, the collaborative design framework, and the optimization design process of the mining electric shovel can reduce the calculation amount of collaborative design problems, which is beneficial to improving the design efficiency, and through the optimization of the approximate model, a better design result can be obtained.

[0051] Refer to Figure 4 , Figure 4 which is the flow of the collaborative design method of the mining electric shovel provided by the present application Figure 2 . Based on Figure 1 , Figure 1 Step S101 in can be implemented through the following steps S1011 to S1013. The following is described in combination with Figure 4 .

[0052] S1011. Establish the excavation trajectory equation of the bucket.

[0053] In some embodiments, during the process of determining the control design parameters of the electric shovel, the required control design parameters can be determined through the simulation of the excavation trajectory of the bucket in the electric shovel.

[0054] In some embodiments, referring to Figure 5 , Figure 5 is a schematic diagram of the stacking surface of the excavated material provided by this application. During the excavation process of the bucket, the excavation trajectory of the bucket is usually a continuous curve movement, and during the excavation process, the bucket usually moves in a two-dimensional plane. Among them, for excavated materials with different stacking surface shapes, different excavation trajectory equations can be established.

[0055] Exemplarily, as Figure 5 shown, when the stacking surface of the excavated material is a typical stacking surface, that is, the stacking surface of the excavated material is relatively flat (can be approximated as a plane, and the stacking condition of the excavated material is good), the bucket can be excavated along the specified trajectory by directly controlling the speeds of the hoist motor and the crowd motor of the electric shovel. Both the hoist movement (the movement generated by the hoist motor driving the bucket) and the crowd movement (the movement generated by the crowd motor driving the bucket) during the excavation process of the bucket experience three stages: acceleration movement, uniform movement, and deceleration movement. For example, according to the control of the hoist motor and the crowd motor of the mining electric shovel, a first excavation trajectory equation can be established, and the first excavation trajectory equation is shown in the following formula (1):

[0056]

[0057] In the formula, l ti represents the hoist displacement of the bucket during the excavation process, a1 represents the acceleration of the hoist acceleration stage of the bucket during the excavation process, t1 represents the acceleration time of the hoist acceleration movement of the bucket during the excavation process, t2 represents the uniform movement time of the hoist uniform movement of the bucket during the excavation process, t3 represents the deceleration time of the hoist deceleration movement of the bucket during the excavation process; l tui represents the crowd displacement of the bucket during the excavation process, a2 represents the acceleration of the crowd acceleration stage of the bucket during the excavation process, t4 represents the acceleration time of the crowd acceleration movement of the bucket during the excavation process, t5 represents the uniform movement time of the crowd uniform movement of the bucket during the excavation process, t6 represents the deceleration time of the crowd deceleration movement of the bucket during the excavation process.

[0058] During the process of controlling the bucket to excavate the excavated material on a typical heap surface, the initial and final speeds of the lifting and pushing motions of the bucket are both 0, and the initial lifting displacement and initial pushing displacement are both 0. Therefore, for the excavated material on a typical heap surface, the control parameter design variables include the acceleration, acceleration time, constant-speed time, deceleration time of the lifting motion, and the acceleration, acceleration time, constant-speed time, deceleration time of the pushing motion during the bucket excavation process.

[0059] Another example is as Figure 5 shown. When the heap surface of the excavated material is a complex heap surface, that is, the stacking surface of the excavated material is uneven and there are large protrusions or pits (it cannot be approximated as a plane, and the stacking condition of the excavated material is poor), according to the control of the lifting motor and pushing motor of the electric shovel for mining, the excavation trajectory of the bucket can be represented by a polynomial interpolation equation, that is, the second excavation trajectory equation shown in the following formula (2) can be established.

[0060]

[0061] In the formula, s x (t) represents the abscissa of the bucket in the plane rectangular coordinate system, s y (t) represents the ordinate of the bucket in the plane rectangular coordinate system, a x0 , a x1 , a x2 , a x3 , a x4 , a x5 , a x6 are respectively the 6 coefficients of the 6th-degree polynomial interpolation equation representing the abscissa, a y0 , a y1 , a y2 , a y3 , a y4 , a y5 , a y6 are respectively the 6 coefficients of the 6th-degree polynomial interpolation equation representing the ordinate, and t represents the excavation time of the bucket.

[0062] During the process of controlling the bucket to excavate the excavated material on a complex heap surface, the initial speed, initial acceleration, final speed, and final acceleration of the bucket are all 0, and the initial excavation position of the bucket is at the coordinate origin. Therefore, the control parameter design variables are the coefficients of the 6th-degree polynomial interpolation equation, the final position of the bucket, and the excavation time of the bucket (the duration from the start of excavation to the completion of excavation and stop of the bucket).

[0063] S1012. Determine the initial state parameters and final state parameters of the bucket.

[0064] In some embodiments, during the excavation process, the initial state and the termination state of the bucket are easy to determine. For example, the origin of the plane rectangular coordinate system can be established at the initial excavation position of the bucket, and the initial velocity, initial acceleration, termination velocity, and termination acceleration of the bucket are all 0.

[0065] S1013. Determine the control parameter design variables in the control design parameters based on the excavation trajectory equation, the initial state parameters, and the termination state parameters.

[0066] In some embodiments, after establishing the excavation trajectory equation of the bucket and determining the initial state parameters and the termination state parameters of the bucket, the initial state parameters and the termination state parameters can be substituted into the excavation trajectory equation to solve the excavation trajectory equation, and the values of the coefficients, etc. in the excavation trajectory equation can be determined.

[0067] In the above embodiments, since the excavation trajectory equation of the bucket is established and the excavation trajectory equation is solved according to the initial state parameters and the termination state parameters of the bucket, the control data affecting the bucket can be determined.

[0068] Refer to Figure 6 , Figure 6 which is the flow of the collaborative design method for the electric shovel provided by this application. Figure 3 . Based on Figure 1 , Figure 1 step S102 in can be implemented through the following steps S1021 to S1022. The following is described in conjunction with Figure 6 .

[0069] S1021. Determine the target output parameters based on the structural design parameters and the control parameter design variables.

[0070] In some embodiments, after determining the structural design parameters and the control parameter design variables of the electric shovel, the target output parameters of the electric shovel can be determined according to the structural design parameters and the control parameter design variables, that is, the target output parameters that have a greater impact on the operation and performance of the electric shovel are determined according to the structural design parameters and the control parameter design variables.

[0071] Exemplarily, if the termination position of the bucket after completing the excavation process is different, the pushing force and lifting force applied to the electric shovel are different, the movement trajectory and excavation speed of the electric shovel are also different, and thus the excavation energy consumption is also different. For example, the excavation speed, excavation energy consumption, lifting force, pushing force, etc. can be used as the target output parameters of the electric shovel.

[0072] S1022. Verify the structural design parameters and the control parameter design variables based on the target output parameters to determine the core elements.

[0073] In some embodiments, after determining the target output parameters, the structural design parameters and control parameter design variables can be verified and screened according to the requirements for the target output parameters, so as to determine the core elements in the structural design parameters and / or control parameter design variables, that is, to determine the core elements in the structural design parameters and / or control parameter design variables that have a greater impact on the overall performance of the electric shovel.

[0074] Exemplarily, if it is usually required that the electric shovel has a faster digging speed and lower digging energy consumption, the structural design parameters and control parameter design variables that have a greater impact on the digging speed, digging energy consumption, etc. can be determined according to the limitations on the digging speed, digging energy consumption, etc.

[0075] In the above embodiments, since the target output parameters are determined according to the structural design parameters and control parameter design variables, and then the structural design parameters and control parameter design variables are verified according to the target output parameters, it is convenient to determine the core elements that have a greater impact on the target output parameters.

[0076] Refer to Figure 7 , Figure 7 for the flow of the collaborative design method of the mine electric shovel provided by this application. Figure 4 Based on Figure 6 , Figure 6 Step S1021 in Figure 7 can be implemented through the following steps S201 to S203, which will be described below in combination with

[0077] S201. Establish a parametric model of the mine electric shovel based on the structural design parameters.

[0078] In some embodiments, according to the determined structural design parameters, for example, the horizontal distance L1 from the boom mounting hinge point to the slewing center, the height L2 from the boom mounting hinge point to the ground, the distance L3 from the boom mounting hinge point to the center of the push gear, the boom length L4, the stick length L5, the diameter of the push gear, the diameter of the crown block, the boom mounting angle R1, the initial digging angle R2, etc., a parametric model of the electric shovel can be constructed.

[0079] Exemplarily, as shown in Figure 2 , a parametric model of the electric shovel can be established in the dynamics software Adams, and the parametric model can be controlled to move in a specified manner according to the control design parameters. For example, according to the control parameter design variables, the bucket can be controlled to dig along a specified digging trajectory.

[0080] S202. Establish a particle model of the excavated material.

[0081] In some embodiments, during the excavation process of an electric shovel, for different excavation materials, the excavation trajectory of the bucket of the electric shovel may be different, and the target output parameters of the electric shovel may also be different. Models of different excavation materials can be established to control the parametric model to simulate the excavation of different excavation materials.

[0082] Exemplarily, as Figure 3 shown, a particle model of the granular excavation material 7 can be established. For example, using the discrete element software Edem, a particle model of the excavation material can be established in the discrete element software Edem. Particle models with different particle sizes can be established.

[0083] S203. Based on the control parameter design variables, control the parametric model to simulate the excavation process of the particle model, and obtain the target output parameters of the parametric model.

[0084] In some embodiments, after establishing the parametric model of the electric shovel and the particle model of the material, the bucket can be controlled to simulate the excavation of the particle model according to different excavation trajectories, so as to obtain the target output parameters such as excavation speed, excavation energy consumption, pushing force, and lifting force during the simulated excavation process of the parametric model.

[0085] Exemplarily, as Figure 3 shown, through the co-simulation of Adams and Edem, the parametric model can be realized to simulate the excavation of the particle model, so as to obtain multiple sets of target output parameters.

[0086] In the above embodiments, since the parametric model of the mining electric shovel and the particle model of the excavation material are established, the parametric model can be controlled based on the control parameter design variables, so that the parametric model simulates the excavation of the particle model, thereby obtaining the target output parameters of the parametric model. Furthermore, the change situation of each output parameter of the electric shovel during the actual working process under the corresponding structural design parameters and control parameter design variables can be known, which is convenient for determining the core elements that have a greater impact on the overall performance of the electric shovel.

[0087] Referring to Figure 8 , Figure 8 is the flow chart of the collaborative design method for the mining electric shovel provided by this application. Figure 5 . Based on Figure 6 , Figure 6 the step S1022 in Figure 8 can be implemented through the following steps S301 to S304. The following will be described in conjunction with

[0088] S301. Generate multiple groups of test sample points. Each group of test sample points includes structural sample points and control sample points; the structural sample points include the values of at least one of the structural design parameters, and the control sample points include the values of at least one of the control parameter design variables.

[0089] In some embodiments, after constructing the parametric model of the electric shovel and the particle model of the excavated material, in order to reduce the computational amount and screen the core factors, and ignore the secondary factors with relatively low impact on the global performance of the electric shovel, Design of Experiments (DOE) can be carried out. For example, by changing at least one parameter among the structural design parameters and the control parameter design variables in the parametric model, and then by comparing the target output parameters, the core elements can be determined.

[0090] Exemplarily, multiple groups of experimental sample points can be generated. Based on the constructed parametric model, each group of experimental sample points is used to correct the parametric model. For example, multiple groups of experimental sample points can be generated by the optimal Latin hypercube design method. Each group of experimental sample points includes structural sample points and control sample points, and the values of at least one of the structural sample points and the control sample points in different groups of experimental sample points are different. All the structural sample points of the same structural member can be evenly distributed within the data set composed of all the structural sample points of this structural member. And all the control sample points of the same control parameter design variable can be evenly distributed within the data set composed of all the control sample points of this control parameter design variable.

[0091] S302. Based on the structural sample points, correct the parametric model to obtain a corrected parametric model.

[0092] In some embodiments, the set formed by the data of all the structural sample points and control sample points can be used as a design matrix to correct the parametric model.

[0093] Exemplarily, the structural sample points in a group of experimental sample points can be used to correct the parametric model, that is, to change the specific structure of the parametric model, so as to obtain a corrected parametric model.

[0094] S303. Based on the control sample points, control the corrected parametric model to simulate the excavation process of the particle model, and obtain the target output parameters corresponding to each group of experimental sample points.

[0095] In some embodiments, after completing one correction of the parametric model, the control sample points in the same group of experimental sample points can be used to control the corrected parametric model to simulate the execution of the excavation process of the particle model, so as to obtain the target output parameters corresponding to the corrected parametric model of this group of experimental sample points.

[0096] Exemplarily, the Simcode component can be used to drive the bat batch file to integrate the co-simulation model of Adams and Edem with the design platform Isight for experimental design, and the response of the structural design parameters and control design parameters to the target output parameters can be obtained.

[0097] S304. Determine the core elements from multiple groups of test sample points based on the response of each group of test sample points to the corresponding target output parameters.

[0098] In some embodiments, after using the design matrix to complete the correction of the parametric model, the target output parameters corresponding to each group of test sample points can be obtained. By comparing each group of target output parameters, the relatively optimal target output parameters can be determined.

[0099] Exemplarily, the structural design parameters and / or control design parameters corresponding to the test sample points in multiple groups of test sample points that have an impact on the target output parameter greater than a preset threshold can be used as core elements. For example, when the data in the test sample points changes significantly and the target output parameter also changes significantly, such as the change in the target output parameter exceeds ten percent and the target output parameter is within the actually acceptable range, the structural sample points and control sample points in this test sample point can be used as core elements. Among them, the core elements can include multiple groups of test sample points.

[0100] In the above embodiments, there are many design parameters for the electric shovel. Since the influence degree of different design parameters on the target output parameter can be evaluated through experimental design, the core elements of the electric shovel can be screened out, and the parameters with little influence on the global performance of the electric shovel can be ignored, which is beneficial to reducing the calculation cost. At the same time, when some design goals are contradictory to each other, using the experimental design method can clarify the law between the design parameters and the design goals, which is beneficial to finding a suitable solution that takes into account each goal.

[0101] Refer to Figure 9 , Figure 9 which is the flow of the collaborative design method for the mine electric shovel provided by this application. Figure 6 . Based on Figure 1 , Figure 1 , step S103 in Figure 9 can be implemented through the following steps S1031 to S1033. The following is described in combination with

[0102] S1031. Based on the core elements, establish an initial model of the mine electric shovel through the response surface method and the Kriging method.

[0103] In some embodiments, after determining the core elements of the electric shovel, the parameters with a greater impact on the global performance of the electric shovel can be limited within a limited range. The similarity of the approximate model can be determined according to a part of the test sample points in the determined core elements, and an initial model of the mine electric shovel can be established using the response surface method and the Kriging method.

[0104] S1032. Use the verification sample points to verify the error of the initial model and determine the fitting accuracy of the initial model; the verification sample points are the test sample points in multiple groups of test sample points that do not include the core elements.

[0105] In some embodiments, refer to Figure 10 , Figure 10 which is the flow of the collaborative design method for the electric mining shovel provided by this application Figure 7 . As Figure 10 shown, the established initial model can be verified for error using the verification sample points, that is, taking the control sample points in the verification sample points as the input, obtaining the corresponding target output parameters, and determining the fitting accuracy of the initial model by comparing the target output parameters with the target output parameters of the initial model.

[0106] S1033. Take the initial model corresponding to the fitting accuracy greater than or equal to the preset fitting threshold as the approximate model.

[0107] In some embodiments, as Figure 10 shown, for the established multiple initial models, error verification is respectively performed using the verification sample points, and the initial model corresponding to the fitting accuracy greater than or equal to the preset fitting threshold can be taken as the approximate model.

[0108] Refer to Figure 11 , Figure 11 which is the flow of the collaborative design method for the electric mining shovel provided by this application Figure 8 . Based on Figure 1 , Figure 1 step S104 in Figure 11 can be implemented through the following steps S1041 to S1044, which will be described below in combination with

[0109] S1041. Determine the structural design variables, structural design objectives, and structural design constraints of the structural subsystem model.

[0110] In some embodiments, after determining the approximate model of the electric shovel, the core elements of the structural part and the control part of the electric shovel have been determined, and a collaborative design framework for optimizing the design of the electric shovel can be constructed to improve the efficiency of optimizing the design of the electric shovel.

[0111] Exemplarily, the relevant parameters of the structural subsystem model can be determined. The structural subsystem model includes each structural member of the electric shovel in the approximate model. For example, the structural design variables of the structural subsystem model can be determined, such as the diameter of the crown block, the height from the boom mounting hinge point to the ground, the distance from the boom mounting hinge point to the center of the push gear, etc. The structural design objectives of the structural subsystem model can be determined, such as the excavation energy consumption, etc. The structural design constraints of the structural subsystem model can be determined, such as the full bucket rate of the bucket, so that the volume of the excavated particle model is within the bucket capacity range of the bucket.

[0112] S1042. Determine the control design variables, control design objectives, and control design constraints of the control subsystem model.

[0113] In some embodiments, relevant parameters of the control subsystem can be determined. The control subsystem includes various data during the bucket excavation process. For example, the control design variables of the control subsystem can be determined, such as the termination position of the bucket excavation. The control design objectives of the control subsystem can be determined, such as the excavation speed. The control design constraints of the control subsystem can be determined, such as both the pushing and lifting speeds need to be greater than 0 and less than the maximum rated speed of the motor, etc.

[0114] S1043. Take the structural design variables as the input to the structural subsystem model, and take the structural design constraints and structural design objectives as the constraint limitations on the structural subsystem model; the structural subsystem model is the model part including the structural components of the electric shovel for mining in the approximate model.

[0115] In some embodiments, refer to Figure 12 , Figure 12 is a schematic diagram of the collaborative design framework of the electric shovel for mining provided by this application. As Figure 12 shown, the structural design variables can be taken as the input to the structural subsystem model, that is, change the structural subsystem model according to the structural design variables, and take the structural design constraints and structural design objectives as the constraint limitations on the structural subsystem model. The structural subsystem can be optimized through the structural subsystem optimizer, and the optimized result of the structural subsystem is fed back to the optimizer at the whole machine system level. Among them, the structural subsystem optimizer can adopt an optimization algorithm for optimizing the structural subsystem.

[0116] S1044. Take the control design variables as the input to the control subsystem model, and take the control design constraints and control design objectives as the constraint limitations on the control subsystem model; the control subsystem model is the part in the approximate model that characterizes the operating parameters of the electric shovel for mining.

[0117] In some embodiments, as Figure 12 shown, the control design variables can be taken as the input to the control subsystem model, that is, change the control subsystem model according to the control design variables, and take the control design constraints and control design objectives as the constraint limitations on the control subsystem model. The control subsystem can be optimized through the control subsystem optimizer, and the optimized result of the control subsystem is fed back to the optimizer at the whole machine system level. Among them, the control subsystem optimizer can adopt an optimization algorithm for optimizing the control subsystem.

[0118] Exemplarily, under the condition of the derivative information passed by the overall system-level optimizer and the shared design variables at the overall system level, the structural subsystem optimizer and the control subsystem optimizer minimize the influence of the coupled state variables on the target parameters at the overall system level after multiple analyses, and transmit the coupled state variables, the optimization results of the structural subsystem, and the optimization results of the control subsystem back to the overall system-level optimizer. The overall system-level optimizer then coordinates the consistency differences between the structural subsystem model and the control subsystem model according to the coupled state variables returned by the structural subsystem optimizer and the control subsystem optimizer. Meanwhile, under the constraints of the overall system level, the target output parameters at the overall system level are minimized, and then the overall system-level optimizer determines whether the convergence criterion is met. If not, the shared design variables and derivative information are updated, and the updated shared design variables and derivative information are returned to the structural subsystem optimizer and the control subsystem optimizer to continue the optimization until the convergence criterion is met.

[0119] Among them, the derivative information is the degree of influence of the response of the subsystem on the target output parameters at the system level in the BLISCO method. The shared design variables include parameters that are relevant in both the structural subsystem model and the control subsystem model, such as the boom length and the distance from the boom mounting hinge point to the center of the push gear. The coupled state variables include the influence of the output of the structural subsystem as the input of the control subsystem on the control subsystem, or the influence of the output of the control subsystem as the input of the structural subsystem on the structural subsystem.

[0120] Refer to Figure 13 , Figure 13 is the flow of the collaborative design method for the electric shovel provided by this application Figure 9 . Based on Figure 1 , Figure 1 Step S105 in Figure 13 can be implemented through the following steps S1051 to S1052, which will be described below in combination with

[0121] S1051. Based on the structural subsystem model, structural design variables, structural design objectives, structural design constraints, control subsystem model, control design variables, control design constraints, and control design objectives in Isight, construct an optimization design process.

[0122] In some embodiments, after the establishment of the collaborative design framework for the electric shovel is completed, an optimization design process can be built in the design platform Isight based on the collaborative design framework. That is, based on the structural subsystem model, structural design variables, structural design objectives, structural design constraints, control subsystem model, control design variables, control design constraints, and control design objectives, etc., an optimization design process is constructed in Isight.

[0123] S1052. Run the optimization design process to optimize the structural subsystem model and the control subsystem model until the optimal design parameter combination of the structural subsystem model and the control subsystem model is obtained.

[0124] In some embodiments, after the construction of the optimization design process is completed, Isight can be used to run the optimization process to optimize the structural subsystem model and the control subsystem model of the electric shovel until the optimal design parameter combination of the structural subsystem model and the control subsystem model is obtained, that is, to determine the optimal data combination among the optimal parameter data of each structural component of the electric shovel and the optimal parameter data of the control of the bucket.

[0125] The collaborative design method of the mine electric shovel provided by the embodiments of the present application comprehensively considers the coupling relationships in aspects such as structure, mechanics, and control involved in the electric shovel design process, and realizes the collaborative design of the structural parameters and control parameters of the electric shovel. Compared with the traditional serial design method, it can shorten the design cycle, is beneficial to reducing the risk of making the overall performance of the electric shovel fall into a local optimum during design, and can improve the excavation efficiency of the electric shovel, reduce the excavation energy consumption and failure rate.

[0126] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application, and they should all be covered by the scope of the specification of the present application. In particular, as long as there is no structural conflict, the technical features mentioned in each embodiment can be combined in any way.

Claims

1. A collaborative design method for a mining electric shovel, characterized in that: include: Determine structural design parameters and control design parameters; The structural design parameters include parameters defining the structural components of the mining shovel, and the control design parameters include parameters characterizing the motion state of the bucket of the mining shovel; Screening the structural design parameters and the control design parameters to determine core elements in the structural design parameters and / or core elements in the control design parameters; Constructing an approximate model of the mining electric shovel based on the core elements; constructing a collaborative design framework based on the approximate model; Based on the collaborative design framework, an optimized design process is constructed, and a design result of the mining electric shovel is determined.

2. The collaborative design method for a mining electric shovel according to claim 1, characterized in that: The determining of structural design parameters and control design parameters includes: Establishing a digging trajectory equation for the bucket; Determining initial state parameters of the bucket and final state parameters of the bucket; Based on the mining trajectory equation, the initial state parameters and the terminal state parameters, control parameter design variables in the control design parameters are determined.

3. The collaborative design method for a mining electric shovel according to claim 2, characterized in that: The excavation trajectory equation of the bucket is established, including: When the pile surface of the excavated material is a typical pile surface, a first excavation trajectory equation is established based on the control of the lifting motor and the pushing motor of the mining electric shovel. The first excavation trajectory equation is shown in the following formula (1): Where, l ti represents the lifting displacement of the bucket during the excavation process, a1 represents the acceleration of the bucket during the acceleration phase of the lifting process, t1 represents the acceleration time of the bucket during the acceleration phase of the lifting process, t2 represents the uniform speed time of the bucket during the uniform speed of the lifting process, and t3 represents the deceleration time of the bucket during the deceleration phase of the lifting process; l tui represents the pushing displacement of the bucket during the excavation process, a2 represents the acceleration of the bucket during the acceleration phase of the pushing process, t4 represents the acceleration time of the bucket during the acceleration phase of the pushing process, t5 represents the uniform speed time of the bucket during the uniform speed motion during the pushing process, and t6 represents the deceleration time of the bucket during the deceleration phase of the pushing process; In the case where the pile surface of the excavated material is a complex pile surface, a second excavation trajectory equation is established based on the control of the lifting motor and the pushing motor of the mining electric shovel. The second excavation trajectory equation is shown in the following formula (2): Where s x (t) represents the horizontal coordinate of the bucket in the plane rectangular coordinate system, s y (t) represents the ordinate of the bucket in the plane rectangular coordinate system, a x0 、a x1 、a x2 、a x3 、a x4 、a x5 、a x6 are the six coefficients of the sixth-order polynomial interpolation equation representing the horizontal coordinate, a y0 、a y1 、a y2 、a y3 、a y4 、a y5 、a y6 are the six coefficients of the sixth-order polynomial interpolation equation representing the vertical coordinate, and t represents the digging time of the bucket.

4. The collaborative design method for a mining electric shovel according to claim 3, characterized in that: Screening the structural design parameters and the control design parameters to determine the core elements in the structural design parameters and / or the core elements in the control design parameters includes: Determining target output parameters based on the structural design parameters and the control parameter design variables; Based on the target output parameters, the structural design parameters and the control parameter design variables are verified to determine the core elements.

5. The collaborative design method for a mining electric shovel according to claim 4, characterized in that: The determining of target output parameters based on the structural design parameters and the control parameter design variables includes: Establishing a parameterized model of the mining electric shovel based on the structural design parameters; Build a particle model of the excavated material; Based on the control parameter design variables, the parameterized model is controlled to simulate a mining process of the particle model, and the target output parameter of the parameterized model is obtained.

6. The collaborative design method for a mining electric shovel according to claim 5, characterized in that: Based on the target output parameters, the structural design parameters and the control parameter design variables are verified to determine the core elements, including: generating a plurality of groups of test sample points, each group of the test sample points including structural sample points and control sample points; the structural sample points including the value of at least one of the structural design parameters, and the control sample points including the value of at least one of the control parameter design variables; Based on the structural sample points, the parameterized model is modified to obtain a modified parameter model; Based on the control sample points, controlling the modified parameter model to simulate the mining process of the particle model, and obtaining the target output parameters corresponding to each group of the test sample points; The core elements are determined from the multiple groups of test sample points based on the response of each group of test sample points to the target output parameters corresponding to each group of test sample points.

7. The collaborative design method for a mining electric shovel according to claim 6, characterized in that: Determining the core elements from the plurality of groups of test sample points based on the response of each group of test sample points to the target output parameter corresponding to each group of test sample points includes: The structural design parameters and / or the control design parameters corresponding to the test sample points in the plurality of groups of test sample points, whose influence on the target output parameter is greater than a preset threshold, are used as the core elements.

8. The collaborative design method for a mining electric shovel according to claim 6, characterized in that: An approximate model of the mining electric shovel is constructed based on the core elements, including: Based on the core elements, the initial model of the mining electric shovel is established by using the response surface method and the kriging method respectively; Using verification sample points to perform error verification on the initial model and determine the fitting accuracy of the initial model; the verification sample points are test sample points in the multiple groups of test sample points that do not include the core elements; The initial model corresponding to the fitting accuracy being greater than or equal to a preset fitting threshold is used as the approximate model.

9. The collaborative design method for a mining electric shovel according to claim 8, characterized in that: A collaborative design framework is constructed based on the approximate model, including: Determine the structural design variables, structural design objectives and structural design constraints of the structural subsystem model; Determine the control design variables, control design objectives and control design constraints of the control subsystem model; The structural design variables are used as inputs to a structural subsystem model, and the structural design constraints and the structural design objectives are used as constraints on the structural subsystem model; the structural subsystem model is a model portion of the structural components of the mining shovel included in the approximate model; The control design variables are used as inputs to a control subsystem model, and the control design constraints and the control design objectives are used as constraints on the control subsystem model; the control subsystem model is the part of the approximate model that characterizes the operating parameters of the mining electric shovel.

10. The collaborative design method for a mining electric shovel according to claim 9, characterized in that: Based on the collaborative design framework, an optimization design process is constructed and the design results of the mining electric shovel are determined, including: In Isight, the optimization design process is constructed based on the structural subsystem model, the structural design variables, the structural design objectives, the structural design constraints, the control subsystem model, the control design variables, the control design constraints, and the control design objectives; The optimization design process is run to optimize the structural subsystem model and the control subsystem model until an optimal design parameter combination of the structural subsystem model and the control subsystem model is obtained.

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