Method and device for optimizing hob layout parameters of full-face tunnel boring machine

Abstract

The invention relates to a method and device for optimizing hob layout parameters of a full-face tunnel boring machine (TBM), and the method comprises the steps: obtaining target parameter data which comprises geological parameter data of rock and initial layout parameter data of a TB M hob; based on the target parameter data, determining to-be-optimized layout parameters, a target function and constraint conditions for hob layout optimization under the composite rock stratum geology; and according to the target function and the constraint condition, optimizing the to-be-optimized layout parameter based on a genetic algorithm to obtain optimal layout parameter data of the TBM hob, thereby comprehensively considering the influence of rocks with different properties, constructing the target function and the constraint condition for hob layout optimization under the composite rock stratum geology, and improving the layout optimization efficiency. According to the method, the optimal layout parameter data of the TBM hob suitable for the composite rock stratum geology can be obtained, the mechanical property of the TBM hob is improved by obtaining the optimal layout parameter data, and the tunneling efficiency and adaptability of the TBM are improved.

Description

Technical Field

[0001] The present disclosure relates to the technical field of roller cutter layout, and in particular to a method and device for optimizing roller cutter layout parameters of a full-face tunnel boring machine. Background Art

[0002] The full-section tunnel boring machine (TBM) is a factory-based construction equipment that integrates mechanics, electricity, hydraulics, and control. It can realize the simultaneous operation of rock breaking, mucking, support, ventilation and other processes. At present, TBM is widely used in tunnel construction such as railways, highways, water conservancy, and municipal construction.

[0003] The cutterhead is located at the front end of the TBM. The cutters installed on the cutterhead are the direct tools for achieving the rock breaking function. Different installation layouts of the cutters on the cutterhead will directly affect the overall rock breaking effect of the cutterhead. In related technologies, the cutter layout is often designed only based on the rock with the greatest hardness. However, TBMs often face composite rock formations with complex structures and large differences in hardness. The entire TBM excavation geology cannot be simply described by a single geological model. Therefore, the accuracy of the optimal cutter layout parameters obtained by designing the cutter layout only based on the rock with the greatest hardness is low. Summary of the invention

[0004] The present invention provides a method, device and electronic device for optimizing layout parameters of a full-face tunnel boring machine cutter, so as to at least solve the problem of low accuracy of optimal layout parameter data of a TBM cutter in the related art.

[0005] The technical solution of the present disclosure is as follows:

[0006] According to a first aspect of an embodiment of the present disclosure, a method for optimizing layout parameters of a full-face tunnel boring machine cutter is provided, the method comprising: acquiring target parameter data, wherein the target parameter data comprises geological parameter data of rock and initial layout parameter data of a full-face tunnel boring machine (TBM) cutter; based on the target parameter data, determining layout parameters to be optimized, objective functions and constraints for cutter layout optimization under composite rock formation geology; and optimizing the layout parameters to be optimized based on a genetic algorithm according to the objective function and the constraints, so as to obtain optimal layout parameter data of the TBM cutter.

[0007] According to a second aspect of an embodiment of the present disclosure, a device for optimizing layout parameters of a full-face tunnel boring machine cutter is provided, comprising: an acquisition module for acquiring target parameter data, wherein the target parameter data comprises geological parameter data of rock and initial layout parameter data of the cutter of a full-face tunnel boring machine (TBM); a determination module for determining, based on the target parameter data, layout parameters to be optimized, objective functions and constraints for optimizing the cutter layout under composite rock formation geology; and an optimization module for optimizing the layout parameters to be optimized based on a genetic algorithm according to the objective function and the constraints, so as to obtain the optimal layout parameter data of the TBM cutter.

[0008] According to the third aspect of an embodiment of the present disclosure, there is provided an electronic device, comprising: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to execute the instructions to implement the method for optimizing the layout parameters of the cutter of a full-face tunnel boring machine as described in the first aspect of the embodiment of the present disclosure.

[0009] According to the fourth aspect of an embodiment of the present disclosure, a computer-readable storage medium is provided. When the instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to execute the method for optimizing the layout parameters of the cutter of a full-face tunnel boring machine as described in the first aspect of the embodiment of the present disclosure.

[0010] The technical solution provided by the embodiments of the present disclosure brings at least the following beneficial effects:

[0011] The embodiment of the present disclosure obtains target parameter data, wherein the target parameter data includes geological parameter data of rocks and initial layout parameter data of the cutter of a full-face tunnel boring machine (TBM). Based on the target parameter data, the layout parameters to be optimized, the objective function and the constraint conditions for optimizing the cutter layout under composite rock formation geology are determined. According to the objective function and the constraint conditions, the layout parameters to be optimized are optimized based on a genetic algorithm to obtain the optimal layout parameter data of the TBM cutter. Therefore, the present disclosure comprehensively considers the influence of rocks of different properties, constructs the objective function and the constraint conditions for optimizing the cutter layout under composite rock formation geology, and can obtain the optimal layout parameter data of the TBM cutter suitable for composite rock formation geology. By obtaining the optimal layout parameter data, the mechanical properties of the TBM cutter head are improved, and the excavation efficiency and adaptability of the TBM are improved.

[0012] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The drawings herein are incorporated into and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the description are used to explain the principles of the present disclosure, and do not constitute improper limitations on the present disclosure.

[0014] Figure 1 The present invention is a flow chart of a method for optimizing layout parameters of a roller cutter of a full-face tunnel boring machine according to an exemplary embodiment.

[0015] Figure 2 It is a flow chart of a method for optimizing layout parameters of a full-face tunnel boring machine cutter according to another exemplary embodiment.

[0016] Figure 3 It is a schematic diagram of an initial cutter layout diagram corresponding to a TBM cutter according to an exemplary embodiment.

[0017] Figure 4 The figure is a schematic diagram of a process of optimization based on a genetic algorithm according to an exemplary embodiment.

[0018] Figure 5 It is a schematic diagram of an optimal cutter layout diagram corresponding to a TBM cutter according to an exemplary embodiment.

[0019] Figure 6 It is a block diagram of a device for optimizing layout parameters of a roller cutter of a full-face tunnel boring machine according to an exemplary embodiment.

[0020] Figure 7 is a block diagram of an electronic device according to another exemplary embodiment. DETAILED DESCRIPTION

[0021] In order to enable ordinary persons in the art to better understand the technical solutions of the present disclosure, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the accompanying drawings.

[0022] It should be noted that the terms "first", "second", etc. in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Instead, they are merely examples of devices and methods consistent with some aspects of the present disclosure as detailed in the appended claims.

[0023] The following embodiments describe in detail the method, device and electronic equipment for optimizing the layout parameters of the cutter of a full-face tunnel boring machine proposed in the present disclosure.

[0024] Figure 1 A schematic flow chart of a method for optimizing layout parameters of a full-face tunnel boring machine cutter according to an embodiment of the present disclosure.

[0025] like Figure 1 As shown, the method for optimizing the layout parameters of the cutter of a full-face tunnel boring machine proposed in this embodiment includes the following steps:

[0026] S101, obtaining target parameter data, wherein the target parameter data includes geological parameter data of rocks and initial layout parameter data of a cutter of a full-face tunnel boring machine (TBM).

[0027] Among them, the full-section tunnel boring machine (TBM) is a large-scale and efficient tunnel construction machinery that integrates multiple functions such as tunneling, mucking, guiding, support, ventilation and dust prevention. TBM mainly relies on the rollers on the rotating cutter head to squeeze, shear and break the rocks.

[0028] It should be noted that geological parameter data include but are not limited to the tensile strength δ of various rocks. t , compressive strengthσ c And geological parameter data such as shear strength τ.

[0029] It should be noted that the initial layout parameter data of the TBM cutter include but are not limited to the diameter of the cutter disc, the cutter disc speed and cutter penetration, the number, position and size of the mucking grooves, the cutter type, the cutter spacing, the number of cutters, etc.

[0030] S102, based on the target parameter data, determining the layout parameters to be optimized, the target function and the constraint conditions for the cutter layout optimization under the composite rock formation geology.

[0031] It should be noted that the layout of the cutters generally needs to meet the following layout principles: (1) the principle of geometric non-interference, that is, the cutters cannot interfere with each other geometrically, and the cutters and the mucking trough cannot interfere with each other geometrically; (2) the principle of center of mass coincidence, that is, the total center of mass of the cutter group should coincide with the geometric center of the cutter disc as much as possible; (3) the principle of force and moment balance, that is, the radial force and overturning moment generated by the cutter group when breaking rock should be minimized. Among them, for composite rock geology, the cutters need to meet the above layout principles under different rock properties. The corresponding objective function is a multi-objective optimization problem under multiple rock properties, which can be converted into a single optimization target through the weighted coefficient method and compromise planning method.

[0032] Optionally, the layout parameter to be optimized in the hob layout optimization may be the polar diameter and / or polar angle of the hob.

[0033] In the embodiment of the present disclosure, the expression of the objective function of the hob layout optimization is:

[0034]

[0035] Among them, C(pos) is the objective function, pos is the position of the cutter group, l is the number of rock types, ω j is the weight value corresponding to the jth rock, p is the penalty factor, C j is the objective function corresponding to the jth rock, is the objective function value obtained by optimization under the jth rock condition, is the objective function value before optimization under the jth rock condition.

[0036] In the embodiment of the present disclosure, the objective function C corresponding to the jth rock j The expression is:

[0037]

[0038] Among them, jk is the weight factor of the objective function corresponding to the jth rock, f k is the kth objective function, f k0 is the kth objective function value corresponding to the initial layout of the hob.

[0039] It should be noted that when k = 1, Among them, F X is the radial force in the x direction, F Y is the radial force in the y direction; when k = 2, Among them, M X is the resultant moment in the x direction, M Y is the resultant moment in the y direction; when k = 3, Among them, d X is the position coordinate of the center of mass of the hob group in the x direction, d Y is the position coordinate of the center of mass of the hob group in the y direction.

[0040] in,

[0041] in,

[0042] in,

[0043] in,

[0044] in,

[0045] in,

[0046] Where, N is the total number of hobs, F siis the lateral force of the i-th hob, F ri is the rolling force of the i-th hob, F vi is the vertical force of the i-th hob, θ i is the polar angle of the i-th hob, α i is the installation inclination angle of the i-th hob, m is the mass of the hob, ω is the cutter head speed, ρ i is the polar diameter of the i-th hob.

[0047] It should be noted that the lateral force is much smaller than the rolling force and the vertical force, so the influence of the lateral force can be ignored in the calculation.

[0048] In the embodiment of the present disclosure, the constraint condition can be constructed based on the principle of geometric non-interference, and the constraint condition for the hob layout optimization is expressed as:

[0049] A i ∩A othee =0

[0050] Among them, A i is the geometric position occupied by the i-th hob, A other It is the geometric position occupied by other rollers and mucking grooves.

[0051] In the embodiment of the present disclosure, after the layout parameters to be optimized, the objective function and the constraints are obtained, a mathematical model for hob layout optimization can be constructed according to the layout parameters to be optimized, the objective function and the constraints.

[0052] For example, if the parameters to be optimized are the polar diameter and polar angle of the hob, the mathematical model for hob layout optimization is constructed as follows:

[0053]

[0054] S103, optimizing the layout parameters to be optimized based on a genetic algorithm according to the objective function and the constraint conditions, so as to obtain the optimal layout parameter data of the TBM cutter.

[0055] In the disclosed embodiment, the objective function can be solved based on a genetic algorithm under constraint conditions to optimize the layout parameters to be optimized and obtain the optimal layout parameter data of the TBM cutter.

[0056] In summary, the method for optimizing the layout parameters of the cutter of a full-section tunnel boring machine provided by the embodiment of the present disclosure obtains target parameter data, wherein the target parameter data includes the geological parameter data of the rock and the initial layout parameter data of the cutter of a full-section tunnel boring machine TBM. Based on the target parameter data, the layout parameters to be optimized, the objective function and the constraints for optimizing the layout of the cutter under composite rock formation geology are determined. According to the objective function and the constraints, the layout parameters to be optimized are optimized based on a genetic algorithm to obtain the optimal layout parameter data of the TBM cutter. Therefore, the present disclosure comprehensively considers the influence of rocks of different properties, constructs the objective function and the constraints for optimizing the layout of the cutter under composite rock formation geology, and can obtain the optimal layout parameter data of the TBM cutter suitable for composite rock formation geology. By obtaining the optimal layout parameter data, the mechanical properties of the TBM cutter head are improved, and the excavation efficiency and adaptability of the TBM are improved.

[0057] The following is an explanation of the specific process of optimizing the layout parameters to be optimized based on the genetic algorithm according to the objective function and the constraints to obtain the optimal layout parameter data of the TBM cutter.

[0058] As a possible implementation, Figure 2 As shown, based on the above embodiment, according to the objective function and the constraint conditions, the specific process of optimizing the layout parameters to be optimized based on the genetic algorithm to obtain the optimal layout parameter data of the TBM cutter includes the following steps:

[0059] S201, encoding the initial layout parameter data according to a preset encoding rule to generate an initial random population.

[0060] It should be noted that before optimizing the layout parameters to be optimized, the genetic optimization parameters preset by the genetic algorithm can be obtained, such as: crossover probability, mutation probability, number of individuals, genetic generation gap, encoding rules, etc.

[0061] For example, the encoding rule may be binary encoding.

[0062] In the embodiment of the present disclosure, after the preset encoding rule is acquired, the initial layout parameter data may be encoded according to the preset encoding rule to generate an initial random population.

[0063] S202, eliminating individuals that do not meet the constraint conditions in the initial random population to generate a first population, and performing a first optimization on the first population based on a first fitness value of the first population to obtain a second population after the first optimization.

[0064] In an embodiment of the present disclosure, after obtaining the initial random population, it can be determined whether the individuals in the initial random population satisfy the constraints, and the individuals in the initial random population that do not satisfy the constraints can be eliminated, and the individuals in the initial random population that satisfy the constraints can be retained to generate a first population, and based on the first fitness value of the first population, the first population is optimized for the first time to obtain a second population after the first optimization.

[0065] Optionally, the objective function can be used as a fitness function, a first fitness value of the first population can be determined according to the objective function, and according to the first fitness value, selection operation, crossover operation and mutation operation can be performed on the first population in sequence to perform a first optimization on the first population to obtain a second population after the first optimization.

[0066] S203, continue to eliminate individuals that do not meet the constraints in the second population to generate a third population, optimize the third population for the second time based on the second fitness value of the third population, and obtain a fourth population after the second optimization, and repeat the above steps until the optimization end condition is met to obtain the optimal population after the last optimization, so as to determine the optimal layout parameter data.

[0067] Optionally, the objective function can be used as a fitness function, and the second fitness value of the third population can be determined according to the objective function. According to the second fitness value, the third population can be sequentially subjected to selection, crossover and mutation operations to perform a second optimization on the third population, thereby obtaining a fourth population after the second optimization. The above steps can be repeated until the optimization end condition is met to obtain the optimal population after the last optimization, thereby determining the optimal layout parameter data.

[0068] It should be noted that the present disclosure does not limit the setting of the optimization end condition, which can be set according to actual conditions.

[0069] Optionally, the optimization termination condition is that the number of optimizations reaches a maximum number of optimizations; optionally, the optimization termination condition is that the fitness value converges.

[0070] In the disclosed embodiment, after obtaining the optimal layout parameter data of the TBM cutter, in order to obtain the optimal layout parameter data more intuitively, an optimal cutter layout diagram corresponding to the TBM cutter may be generated based on the optimal layout parameter data.

[0071] In summary, the optimization method for the layout parameters of the full-section tunnel boring machine cutter provided in the embodiment of the present disclosure encodes the initial layout parameter data according to a preset coding rule, generates an initial random population, removes individuals that do not meet the constraints in the initial random population to generate a first population, performs a first optimization on the first population based on a first fitness value of the first population to obtain a second population after the first optimization, continues to remove individuals that do not meet the constraints in the second population to generate a third population, performs a second optimization on the third population based on the second fitness value of the third population to obtain a fourth population after the second optimization, repeats the above steps until the optimization end condition is met to obtain the optimal population after the last optimization, so as to determine the optimal layout parameter data. Therefore, the present disclosure improves the accuracy and efficiency of obtaining the optimal layout parameter data of TBM cutters suitable for composite rock strata geology.

[0072] The specific process of the method for optimizing the layout parameters of the cutter of a full-face tunnel boring machine provided in the embodiment of the present disclosure is explained below.

[0073] For example, for water conveyance tunnel project A, from the previous geological survey report, it can be known that the TBM construction section mainly has the following two types of rocks with different properties, namely rock type I and rock type II. Among them, the tensile strength δ of rock type I is t1 =6MPa, compressive strengthσ c1 = 88MPa and shear strength = 10MPa; tensile strength of rock type II δ t2 =7MPa, compressive strengthσ c2 =74MPa and shear strength τ2=6MPa. In order to improve the excavation efficiency of TBM, the cutter penetration is set to 10mm / r for rock type I, and 12mm / r for rock type II. The cutter head speed is set to 10rpm. According to the actual situation, the TBM cutter head diameter is determined to be 7793mm, and a 19-inch cutter is selected. The mass of a single cutter is 200kg. The total number of cutters is 51, including 8 center cutters, 33 positive cutters and 10 side cutters.

[0074] For example, when only the influence of the hardest rock is considered, the initial cutter layout diagram corresponding to the TBM cutter is as follows: Figure 3 As shown, the diamond is the edge hob, the square is the positive hob, and the circle is the center hob.

[0075] It should be noted that since the positive cutters are the largest in number and are the main cutters for TBM excavation, the layout of the positive cutters can be optimized. There is an optimal cutter spacing range when the cutters break rock. In order to improve the calculation efficiency, the parameters to be optimized for the cutter layout optimization can be determined as the polar angles of each cutter. The rolling force and vertical force on each cutter can be calculated by the CSM cutter force prediction formula. The value of each weight factor is 1. Under the constraints, the process of optimizing the layout parameters based on the genetic algorithm is as follows: Figure 4 As shown in the figure, the optimal layout parameter data of the TBM hob is obtained. Based on the optimal layout parameter data, the optimal hob layout diagram corresponding to the TBM hob is shown in Figure 5 As shown, the diamond is the edge hob, the square is the positive hob, and the circle is the center hob. The genetic optimization parameters preset by the genetic algorithm are: the crossover probability is 0.70, the mutation probability is 0.05, the number of individuals is 200, the genetic generation gap is 0.90, and the number of binary coding bits of the variable is 12. The optimization solution process of the genetic algorithm will not be repeated here, and please refer to the above embodiment.

[0076] It should be noted that the comparison of the radial force, overturning moment and center of mass offset of the hob group before and after optimization is shown in Table 1.

[0077] Table 1

[0078]

[0079] It should be noted that, as shown in Table 1, the performance of the optimized cutterhead has been significantly improved in terms of radial force, overturning moment and centroid offset of the cutter group. For rock type I, the radial load of the cutterhead decreased from 39.050 kN to 9.896 kN, a decrease of 74.66%, the overturning moment decreased from 264.974 kNm to 26.086 kNm, a decrease of 90.16%, and the centroid deviation of the cutter group decreased from 69.947 mm to 25.323 mm, a decrease of 63.80%; for rock type II, the radial load of the cutterhead decreased from 39.146 kN to 12.653 kN, a decrease of 67.68%, and the overturning moment decreased from 263.650 kNm to 25.956 kNm, a decrease of 90.16%.

[0080] In summary, the optimization method for the layout parameters of the cutter of a full-face tunnel boring machine provided in the embodiment of the present disclosure comprehensively considers the influence of rocks of different properties, constructs the objective function and constraint conditions for optimizing the layout of the cutter under composite rock formation geology, and obtains the optimal layout parameter data of the TBM cutter suitable for composite rock formation geology based on the genetic algorithm. By obtaining the optimal layout parameter data, the mechanical properties of the TBM cutterhead are significantly improved, and the problems of uneven force on the cutterhead, severe wear of the cutter, and excessive vibration are avoided, thereby improving the excavation efficiency and adaptability of the TBM, and is conducive to increasing the life of the cutterhead main bearing.

[0081] Figure 6 1 is a block diagram of a device for optimizing layout parameters of a full-face tunnel boring machine cutter according to an exemplary embodiment. Figure 6 As shown, the optimization device 600 for the layout parameters of the roller cutter of a full-face tunnel boring machine according to an embodiment of the present disclosure may specifically include: an acquisition module 601 , a determination module 602 and an optimization module 603 .

[0082] An acquisition module 601 is used to acquire target parameter data, wherein the target parameter data includes geological parameter data of rocks and initial layout parameter data of a full-face tunnel boring machine (TBM) cutter;

[0083] A determination module 602 is used to determine the layout parameters to be optimized, the objective function and the constraint conditions for performing the layout optimization of the cutter under the composite rock formation geology based on the target parameter data;

[0084] The optimization module 603 is used to optimize the layout parameters to be optimized based on the genetic algorithm according to the objective function and the constraint conditions, so as to obtain the optimal layout parameter data of the TBM cutter.

[0085] In one embodiment of the present disclosure, the objective function of the hob layout optimization is expressed as:

[0086]

[0087] Among them, C(pos) is the objective function, pos is the position of the cutter group, l is the number of rock types, ω j is the weight value corresponding to the jth rock, p is the penalty factor, C j is the objective function corresponding to the jth rock, is the objective function value obtained by optimization under the jth rock condition, is the objective function value before optimization under the jth rock condition.

[0088] In one embodiment of the present disclosure, the objective function C corresponding to the j-th rock is j The expression is:

[0089]

[0090] Among them, jk is the weight factor of the objective function corresponding to the jth rock, f k is the kth objective function, f k0 is the kth objective function value corresponding to the initial layout of the hob.

[0091] In one embodiment of the present disclosure, the constraint condition for the hob layout optimization is expressed as:

[0092] S i ∩A other =0

[0093] Among them, A i is the geometric position occupied by the i-th hob, A other It is the geometric position occupied by other rollers and mucking grooves.

[0094] In one embodiment of the present disclosure, the optimization module 603 is further used to: encode the initial layout parameter data according to a preset encoding rule to generate an initial random population; eliminate individuals that do not meet the constraint conditions in the initial random population to generate a first population, and optimize the first population for the first time based on a first fitness value of the first population to obtain a second population after the first optimization; continue to eliminate individuals that do not meet the constraint conditions in the second population to generate a third population, and optimize the third population for the second time based on the second fitness value of the third population to obtain a fourth population after the second optimization, and repeat the above steps until the optimization end condition is met to obtain the optimal population after the last optimization to determine the optimal layout parameter data.

[0095] In one embodiment of the present disclosure, the optimization module 603 is also used to: use the objective function as a fitness function, and determine a first fitness value of the first population according to the objective function; and according to the first fitness value, perform selection operations, crossover operations, and mutation operations on the first population in sequence to perform a first optimization on the first population to obtain a second population after the first optimization.

[0096] In one embodiment of the present disclosure, the device 600 is further used to generate an optimal cutter layout diagram corresponding to the TBM cutter based on the optimal layout parameter data.

[0097] In summary, the optimization device for the layout parameters of the cutter of a full-section tunnel boring machine provided by the embodiment of the present disclosure obtains target parameter data, wherein the target parameter data includes the geological parameter data of the rock and the initial layout parameter data of the cutter of a full-section tunnel boring machine TBM. Based on the target parameter data, the layout parameters to be optimized, the objective function and the constraint conditions for optimizing the cutter layout under composite rock formation geology are determined. According to the objective function and the constraint conditions, the layout parameters to be optimized are optimized based on a genetic algorithm to obtain the optimal layout parameter data of the TBM cutter. Therefore, the present disclosure comprehensively considers the influence of rocks of different properties, constructs the objective function and the constraint conditions for optimizing the cutter layout under composite rock formation geology, and can obtain the optimal layout parameter data of the TBM cutter suitable for composite rock formation geology. By obtaining the optimal layout parameter data, the mechanical properties of the TBM cutter head are improved, and the excavation efficiency and adaptability of the TBM are improved.

[0098] Figure 7 is a block diagram of an electronic device 700 according to an exemplary embodiment.

[0099] like Figure 7 As shown, the electronic device 700 includes:

[0100] The memory 701 and the processor 702, a bus 703 connecting different components (including the memory 701 and the processor 702), the memory 701 stores a computer program, and when the processor 702 executes the program, the method for optimizing the layout parameters of the cutter of the full-section tunnel boring machine of the embodiment of the present disclosure is implemented.

[0101] Bus 703 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor or a local bus using any of a variety of bus structures. For example, these architectures include but are not limited to Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MAC) bus, Enhanced ISA bus, Video Electronics Standards Association (VESA) local bus and Peripheral Component Interconnect (PCI) bus.

[0102] The electronic device 700 typically includes a variety of electronic device readable media. These media can be any available media that can be accessed by the electronic device 700, including volatile and non-volatile media, removable and non-removable media.

[0103] The memory 701 may also include computer system readable media in the form of volatile memory, such as random access memory (RAM) 704 and / or cache memory 705. The electronic device 700 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the storage system 706 may be used to read and write non-removable, non-volatile magnetic media ( Figure 7 not shown, usually called a "hard drive"). Although Figure 7 Not shown in the figure, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk"), and an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, a DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to the bus 703 via one or more data medium interfaces. The memory 701 may include at least one program product having a set (e.g., at least one) of program modules that are configured to perform the functions of the various embodiments of the present disclosure.

[0104] A program / utility 708 having a set (at least one) of program modules 707 may be stored, for example, in the memory 701, such program modules 707 including but not limited to an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment. The program modules 707 generally perform the functions and / or methods of the embodiments described in the present disclosure.

[0105] The electronic device 700 may also communicate with one or more external devices 709 (e.g., keyboard, pointing device, display 711, etc.), one or more devices that enable a user to interact with the electronic device 700, and / or any device that enables the electronic device 700 to communicate with one or more other computing devices (e.g., network card, modem, etc.). Such communication may be performed through an input / output (I / O) interface 712. Furthermore, the electronic device 700 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through a network adapter 713. Figure 7 As shown, the network adapter 713 communicates with other modules of the electronic device 700 via the bus 703. It should be understood that, although not shown in the figure, other hardware and / or software modules can be used in conjunction with the electronic device 700, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0106] The processor 702 executes various functional applications and data processing by running the programs stored in the memory 701 .

[0107] It should be noted that the implementation process and technical principles of the electronic device of this embodiment refer to the aforementioned explanation of the method for optimizing the layout parameters of the cutter of the full-section tunnel boring machine in the embodiment of the present disclosure, and will not be repeated here.

[0108] The electronic device provided by the embodiment of the present disclosure obtains target parameter data, wherein the target parameter data includes geological parameter data of rocks and initial layout parameter data of the cutter of a full-face tunnel boring machine (TBM). Based on the target parameter data, the layout parameters to be optimized, the objective function and the constraint conditions for optimizing the cutter layout under composite rock formation geology are determined. According to the objective function and the constraint conditions, the layout parameters to be optimized are optimized based on a genetic algorithm to obtain the optimal layout parameter data of the TBM cutter. Therefore, the present disclosure comprehensively considers the influence of rocks of different properties, constructs the objective function and the constraint conditions for optimizing the cutter layout under composite rock formation geology, and can obtain the optimal layout parameter data of the TBM cutter suitable for composite rock formation geology. By obtaining the optimal layout parameter data, the mechanical properties of the TBM cutter head are improved, and the excavation efficiency and adaptability of the TBM are improved.

[0109] In order to implement the above embodiments, the present disclosure also proposes a computer-readable storage medium.

[0110] When the instructions in the computer-readable storage medium are executed by the processor of the electronic device, the electronic device can execute the above-mentioned method for optimizing the layout parameters of the roller cutter of the full-face tunnel boring machine. Optionally, the computer-readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0111] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the invention disclosed herein. The present disclosure is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The description and examples are to be considered exemplary only, and the true scope and spirit of the present disclosure are indicated by the following claims.

[0112] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A method for optimizing the layout parameters of a full-face tunnel boring machine cutter, characterized in that: The method comprises: Acquiring target parameter data, wherein the target parameter data includes geological parameter data of rocks and initial layout parameter data of a full-face tunnel boring machine (TBM) cutter; Based on the target parameter data, determine the layout parameters to be optimized, the target function and the constraint conditions for the cutter layout optimization under the composite rock formation geology; According to the objective function and the constraint conditions, the layout parameters to be optimized are optimized based on a genetic algorithm to obtain optimal layout parameter data of the TBM cutter.

2. The method according to claim 1, characterized in that: The expression of the objective function of the hob layout optimization is: Among them, C(pos) is the objective function, pos is the position of the cutter group, l is the number of rock types, ω j is the weight value corresponding to the jth rock, p is the penalty factor, C j is the objective function corresponding to the jth rock, is the objective function value obtained by optimization under the jth rock condition, is the objective function value before optimization under the jth rock condition.

3. The method according to claim 2, characterized in that The objective function C corresponding to the j-th rock j The expression is: Among them, jk is the weight factor of the objective function corresponding to the jth rock, f k is the kth objective function, f k0 is the kth objective function value corresponding to the initial layout of the hob.

4. The method according to claim 1, characterized in that: The constraint condition for the hob layout optimization is expressed as: A i ∩A other =0 Among them, A i is the geometric position occupied by the i-th hob, A other It is the geometric position occupied by other rollers and mucking grooves.

5. The method according to claim 1, characterized in that The optimizing the layout parameters to be optimized based on the genetic algorithm according to the objective function and the constraint conditions to obtain the optimal layout parameter data of the TBM cutter includes: According to the preset coding rules, the initial layout parameter data is encoded to generate an initial random population; Eliminating individuals that do not satisfy the constraint condition in the initial random population to generate a first population, and performing a first optimization on the first population based on a first fitness value of the first population to obtain a second population after the first optimization; Continue to eliminate individuals that do not meet the constraints in the second population to generate a third population, optimize the third population for the second time based on the second fitness value of the third population, and obtain a fourth population after the second optimization. Repeat the above steps until the optimization end condition is met to obtain the optimal population after the last optimization, so as to determine the optimal layout parameter data.

6. The method according to claim 5, characterized in that The first population is optimized for the first time based on the first fitness value of the first population to obtain a second population after the first optimization, and further includes: Taking the objective function as a fitness function, and determining a first fitness value of the first population according to the objective function; According to the first fitness value, a selection operation, a crossover operation and a mutation operation are sequentially performed on the first population to perform a first optimization on the first population to obtain a second population after the first optimization.

7. The method according to claim 1, characterized in that After obtaining the optimal layout parameter data of the TBM cutter, the method further includes: Based on the optimal layout parameter data, an optimal cutter layout diagram corresponding to the TBM cutter is generated.

8. A device for optimizing the layout parameters of the cutter of a full-face tunnel boring machine, characterized in that: The device comprises: An acquisition module, used for acquiring target parameter data, wherein the target parameter data includes geological parameter data of rocks and initial layout parameter data of a full-face tunnel boring machine (TBM) cutter; A determination module, for determining, based on the target parameter data, the layout parameters to be optimized, the target function and the constraint conditions for performing the layout optimization of the cutter under the composite rock formation geology; The optimization module is used to optimize the layout parameters to be optimized based on the genetic algorithm according to the objective function and the constraint conditions, so as to obtain the optimal layout parameter data of the TBM cutter.

9. An electronic device, characterized in that: include: processor; a memory for storing executable instructions for the processor; The processor is configured to execute the instructions to implement the method for optimizing the layout parameters of the cutter of a full-face tunnel boring machine as described in any one of claims 1-7.

10. A computer-readable storage medium, when the instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to execute the method for optimizing the layout parameters of the cutter of a full-face tunnel boring machine as described in any one of claims 1 to 7.

Images