A method and device for selecting a terminal counter cabinet foot with optimal compressive strength, and an electronic device

Abstract

The application provides a terminal counter cabinet foot compression strength optimization method and device and electronic equipment, and relates to the technical field of network communication. The method comprises the following steps: constructing a compression strength model of a terminal counter cabinet foot; and solving the compression strength model of the terminal counter cabinet foot by using a new bat algorithm to obtain optimal parameters of the terminal counter cabinet foot. The bat algorithm is improved in the application, the compression strength of the terminal counter cabinet foot is optimized, and the compression strength of the terminal counter cabinet foot is ensured to be strong.

Description

Technical Field

[0001] This invention relates to the field of artificial intelligence technology, and in particular to a method, apparatus, and electronic device for selecting the optimal compressive strength of terminal counter legs. Background Technology

[0002] The Bat Algorithm (BA) is a stochastic search algorithm that simulates the ability of bats in nature to detect prey and avoid obstacles using sonar. It simulates the basic detection and localization capabilities of bats using ultrasound and links this to the optimization objective. The biomimetic principle of the BA algorithm maps a population of 10 bats to NP feasible solutions in a D-dimensional problem space. The optimization and search processes are simulated as the movement and prey-hunting processes of individual bats. A fitness function is used to evaluate the quality of a bat's position, and the survival of the fittest is analogous to the iterative process of replacing less feasible solutions with better ones during optimization and search. In the Bat Algorithm, to simulate bats detecting prey and avoiding obstacles, the following three approximate or idealized rules are assumed:

[0003] 1) All bats use echolocation to sense distance, and they employ a clever method to distinguish prey from background obstacles.

[0004] 2) The bat is at position x i With speed v i Random flight at a fixed frequency f min Bats use variable wavelengths λ and volume A0 to search for prey. They automatically adjust the emitted pulse wavelength (or frequency) and pulse emission rate r (which belongs to the range [0,1]) based on their proximity to the target.

[0005] 3) Although there are multiple ways to change the volume, in the bat algorithm, it is assumed that the volume A changes from a maximum value A0 (an integer) to a fixed minimum value A. min .

[0006] By using an improved bat algorithm to effectively control the compressive strength of the terminal counter legs, not only can the quality of the terminal counter legs be guaranteed, but also the losses from trial and error during the production process of the terminal counter legs can be reduced.

[0007] Therefore, a method, device, and electronic equipment for selecting the optimal compressive strength of terminal counter legs are proposed. Summary of the Invention

[0008] This specification provides a method, device, and electronic device for selecting the optimal compressive strength of terminal counter legs. This application improves the bat algorithm to achieve optimal selection of the compressive strength of terminal counter legs, ensuring that the compressive strength of the terminal counter legs is strong.

[0009] The method for selecting the compressive strength of terminal counter legs provided in this application adopts the following technical solution, including:

[0010] Construct a compressive strength model for the legs of the terminal counter;

[0011] A novel bat algorithm is used to solve the compressive strength model of the terminal counter legs, obtaining the optimal parameters of the terminal counter legs. The specific steps include the following:

[0012] The parameter information of the terminal counter feet is initialized by low-bias sequence to obtain the initial population of the terminal counter feet;

[0013] The parameter initialization of the novel bat algorithm specifically includes: setting the population size and number of bats, the number of algorithm iterations, and initializing the impulse loudness of each bat. Pulse intensity Set the pulse frequency f i Randomly initialize population positions

[0014] Update the speed of each bat and location

[0015] For each individual bat, a random number rand(0, 1) needs to be generated, which is uniformly distributed between [0, 1]. rand(0, 1) represents a real number randomly generated in a uniform distribution within [0, 1]. If the condition is satisfied... Let be the pulse frequency of bat individual i at time t; perform a small-range perturbation near the current optimal bat individual position to generate a new bat individual position, and perform out-of-bounds normalization processing on the new bat individual position;

[0016] Calculate and sort the fitness values ​​of all bats, and find the current optimal solution x. * and the optimal value F(T) s T h (R, L) * ;

[0017] For each individual bat, generate a uniformly distributed random number rand(0, 1). renew and Let be the impulse intensity of bat individual i at time t. Let i be the position of individual bat i at time t;

[0018] Find the current global optimal solution x * The optimal parameters for the terminal counter feet are obtained.

[0019] Optionally, the construction of the compressive strength model for the terminal counter legs includes:

[0020] Let the shell thickness be T. s The shell thickness is T. h Inner radius of the shell R, shell length L, compressive strength F (T) s T h (R, L);

[0021] Construct a compressive strength model for the legs of the terminal counter:

[0022] F(Ts,Th,R,L)=0.6224TsRL+1.7781T h R 2 +3.1661Ts 2 L+19.84T h 2 L

[0023] Optionally, the speed of each bat is updated. and location include:

[0024] The speed of bat i at time t and location Update according to the following formula:

[0025]

[0026]

[0027] in, Let be the flight speed of bat individual i at time t. Let be the flight speed of bat individual i at time t-1; Let represent the position of individual bat i at time t. x represents the position of individual bat i at time t-1; * It is the current global optimal solution in the bat population, with frequency f. i Let be the pulse frequency emitted by individual bat i when searching for a target.

[0028] Optionally, the frequency f i The pulse frequency emitted by bat individual i when searching for a target includes:

[0029] f i =f min +(f max -f min )β

[0030] β∈[0,1] is a random vector in a uniform distribution; f max f is the maximum value of the pulse frequency.min The minimum pulse frequency is adjusted according to the size of the problem domain; initially, each bat is randomly assigned to a position in [f]. min f max The frequency values ​​of a uniform distribution.

[0031] Optionally, all individual bats need to generate a random number rand(0,1) uniformly distributed between [0,1]. rand(0,1) represents a real number randomly generated in a uniform distribution within [0,1]. If the condition is met... Given the pulse frequency of bat individual i at time t, a small-range perturbation is performed near the current optimal bat individual position to generate a new position, and out-of-bounds normalization processing is performed on it, including:

[0032] For local search, whenever a new optimal solution x is found... * Upon appearance, each bat will randomly generate a new coordinate position near the optimal solution:

[0033]

[0034] ∈ is a random value located in [-1, 1], A t It is the average loudness of all bats at time t; at this time, the loudness A i and pulse frequency r i It will also be continuously updated as it iterates:

[0035]

[0036]

[0037] Where, r t It is the pulse frequency at time t, r ∞ It is the final value of the pulse frequency, r 0 This is the initial value of the pulse frequency, t. max This is the moment of maximum iteration, where δ is a positive number approaching 0; A t A is the loudness at time t. ∞ It is the final value of loudness, A. 0 This is the initial value of loudness.

[0038] The preferred device for determining the compressive strength of terminal counter legs provided in this application adopts the following technical solution, including:

[0039] The model building module is used to build a compressive strength model of the terminal counter legs;

[0040] The solution module is used to solve the compressive strength model of the terminal counter legs using a novel bat algorithm, and to obtain the optimal parameters of the terminal counter legs. Specifically, it includes the following steps:

[0041] The first initialization unit is used to initialize the parameter information of the terminal counter feet through a low-bias sequence to obtain the initial population of the terminal counter feet.

[0042] The second initialization unit is used to initialize the parameter information of the novel bat algorithm, specifically including: setting the population size and number of bats, the number of algorithm iterations, and initializing the impulse loudness of each bat. Pulse intensity Set the pulse frequency f i Randomly initialize population positions

[0043] The first update unit is used to update the speed of each bat. and location

[0044] The processing unit generates a random number rand(0,1) uniformly distributed between [0,1] for each individual bat. rand(0,1) represents a real number randomly generated in a uniform distribution within [0,1]. If the condition is met... Let be the pulse frequency of bat individual i at time t; perform a small-range perturbation near the current optimal bat individual position to generate a new bat individual position, and perform out-of-bounds normalization processing on the new bat individual position;

[0045] The sorting unit is used to calculate and sort the fitness values ​​of all bats, and to find the current optimal solution x. * and the optimal value F(T) s T h (R, L) * ;

[0046] The second update unit is used to generate a uniformly distributed random number rand(0, 1) for each individual bat. renew and Let be the impulse intensity of bat individual i at time t. Let i be the position of individual bat i at time t;

[0047] The final parameter unit is used to find the current global optimal solution x. * The optimal parameters for the terminal counter feet are obtained.

[0048] Optionally, the model building module includes:

[0049] Let the shell thickness be T. s The shell thickness is T. h Inner radius of the shell R, shell length L, compressive strength F (T) s Th (R, L);

[0050] Construct a compressive strength model for the legs of the terminal counter:

[0051] F(Ts, Th, R, L) = 0.6224T s RL+1.7781T h R 2 +3.1661T s 2 L+19.84T h 2 L

[0052] Optionally, the first update unit includes:

[0053] The speed of bat i at time t and location Update according to the following formula:

[0054]

[0055]

[0056] in, Let be the flight speed of bat individual i at time t. Let be the flight speed of bat individual i at time t-1; Let represent the position of individual bat i at time t. x represents the position of individual bat i at time t-1; * It is the current global optimal solution in the bat population, with frequency f. i Let be the pulse frequency emitted by individual bat i when searching for a target.

[0057] Optionally, the frequency f i The pulse frequency emitted by bat individual i when searching for a target includes:

[0058] f i =f min +(f max -f min )β

[0059] β∈[0,1] is a random vector in a uniform distribution; f max f is the maximum value of the pulse frequency. min The minimum pulse frequency is adjusted according to the size of the problem domain; initially, each bat is randomly assigned to a position in [f]. min f max The frequency values ​​of a uniform distribution.

[0060] Optionally, the processing unit includes:

[0061] For local search, whenever a new optimal solution x is found... * Upon appearance, each bat will randomly generate a new coordinate position near the optimal solution:

[0062]

[0063] ∈ is a random value located in [-1, 1], A t It is the average loudness of all bats at time t; at this time, the loudness A i and pulse frequency r i It will also be continuously updated as it iterates:

[0064]

[0065]

[0066] Where, r t It is the pulse frequency at time t, r ∞ It is the final value of the pulse frequency, r 0 This is the initial value of the pulse frequency, t. max This is the moment of maximum iteration, where δ is a positive number approaching 0; A t A is the loudness at time t. ∞ It is the final value of loudness, A. 0 This is the initial value of loudness.

[0067] This specification also provides an electronic device, wherein the electronic device includes:

[0068] Processor; and,

[0069] A memory that stores computer-executable instructions, which, when executed, cause the processor to perform any of the methods described above.

[0070] This specification also provides a computer-readable storage medium that stores one or more programs that, when executed by a processor, implement any of the methods described above.

[0071] This application utilizes an improved bat algorithm to effectively control the compressive strength of terminal counter legs, ensuring not only the quality of the counter legs but also reducing losses from trial and error during production. The improved bat algorithm optimizes the compressive strength of the terminal counter legs, ensuring high compressive strength. Attached Figure Description

[0072] Figure 1This is a schematic diagram of a system structure for selecting the compressive strength of the legs of a terminal counter according to an embodiment of the present invention;

[0073] Figure 2 A flowchart illustrating a method for selecting the optimal compressive strength of terminal counter legs according to an embodiment of the present invention;

[0074] Figure 3 A schematic diagram of a device for selecting the compressive strength of a terminal counter leg provided in an embodiment of the present invention;

[0075] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0076] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0077] This application constructs a compressive strength model for the legs of a terminal counter and employs a novel bat algorithm to solve the model, obtaining the optimal parameters for the counter legs. This application utilizes the improved bat algorithm to effectively control the compressive strength of the terminal counter legs, ensuring not only the quality of the legs but also reducing losses from trial and error during production. This application improves the bat algorithm to achieve optimal selection of the compressive strength of the terminal counter legs, ensuring high compressive strength.

[0078] To better understand the above technical solutions, exemplary embodiments of this application will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of this application are shown in the drawings, it should be understood that this application can be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of this application and to fully convey the scope of this application to those skilled in the art.

[0079] Those skilled in the art will understand that Figure 1 The preferred system structure for the compressive strength of the terminal counter legs shown does not constitute a limitation on a system with an expanded body relationship. A preferred system for the compressive strength of the terminal counter legs may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0080] As one implementation method, it can be as follows Figure 1 As shown, Figure 1 This is a schematic diagram of a system structure for selecting the optimal compressive strength of the cabinet legs of a terminal counter, according to an embodiment of the present invention.

[0081] Processor 1100 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by integrated logic circuits in the hardware of processor 1100 or by instructions in software form. Processor 1100 may be a general-purpose processor, digital signal processor (DSP), application-specific integrated circuit (ASIC), off-the-shelf programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this invention. The general-purpose processor may be a microprocessor or any conventional processor. The software module may reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. This storage medium is located in memory 1200. Processor 1100 reads information from memory 1200 and, in conjunction with its hardware, completes the steps of the above method.

[0082] It is understood that the memory 1200 in the embodiments of the present invention can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), Synchronous DRAM (SDRAM), Double Data Rate Synchronous DRAM (DDRSDRAM), Enhanced Synchronous DRAM (ESDRAM), Synchlink DRAM (SLDRAM), and Direct Rambus RAM (DRRAM). The memory 1200 of the systems and methods described in the embodiments of the present invention is intended to include, but is not limited to, these and any other suitable types of memory.

[0083] In this embodiment of the application, the processor 1100 can be used to call the selection program for the compressive strength of the terminal counter legs stored in the memory 1200, and execute the following method:

[0084] Construct a compressive strength model for the legs of the terminal counter;

[0085] A novel bat algorithm is used to solve the compressive strength model of the terminal counter legs, obtaining the optimal parameters of the terminal counter legs. The specific steps include the following:

[0086] The parameter information of the terminal counter feet is initialized by low-bias sequence to obtain the initial population of the terminal counter feet;

[0087] The parameter initialization of the novel bat algorithm specifically includes: setting the population size and number of bats, the number of algorithm iterations, and initializing the impulse loudness of each bat. Pulse intensity Set the pulse frequency f i Randomly initialize population positions

[0088] Update the speed of each bat and location

[0089] For each individual bat, a random number rand(0, 1) needs to be generated, which is uniformly distributed between [0, 1]. rand(0, 1) represents a real number randomly generated in a uniform distribution within [0, 1]. If the condition is satisfied... Let be the pulse frequency of bat individual i at time t; perform a small-range perturbation near the current optimal bat individual position to generate a new bat individual position, and perform out-of-bounds normalization processing on the new bat individual position;

[0090] Calculate and sort the fitness values ​​of all bats, and find the current optimal solution x. * and the optimal value F(T) s T h (R, L) * ;

[0091] For each individual bat, generate a uniformly distributed random number rand(0, 1). renew and Let be the impulse intensity of bat individual i at time t. Let i be the position of individual bat i at time t;

[0092] Find the current global optimal solution x * The optimal parameters for the terminal counter feet are obtained.

[0093] In one embodiment, the processor 1100 can be used to invoke a selection program for the compressive strength of the terminal counter legs stored in the memory 1200, and perform the following operations:

[0094] Let the shell thickness be T. s The shell thickness is T. h Inner radius of the shell R, shell length L, compressive strength F (T) s T h (R, L);

[0095] Construct a compressive strength model for the legs of the terminal counter:

[0096] F(Ts,Th,R,L)=0.6224TsRL+1.7781T h R 2 +3.1661Ts 2 L+19.84T h 2 L

[0097] In one embodiment, the processor 1100 can be used to invoke a selection program for the compressive strength of the terminal counter legs stored in the memory 1200, and perform the following operations:

[0098] The speed of bat i at time t and location Update according to the following formula:

[0099]

[0100]

[0101] in, Let be the flight speed of bat individual i at time t. Let be the flight speed of bat individual i at time t-1; Let represent the position of individual bat i at time t. x represents the position of individual bat i at time t-1; * It is the current global optimal solution in the bat population, with frequency f. i Let be the pulse frequency emitted by individual bat i when searching for a target.

[0102] In one embodiment, the processor 1100 can be used to invoke a selection program for the compressive strength of the terminal counter legs stored in the memory 1200, and perform the following operations:

[0103] The frequency f i The pulse frequency emitted by bat individual i when searching for a target includes:

[0104] f i =fmin +(f max -f min )β

[0105] β∈[0,1] is a random vector in a uniform distribution; f max f is the maximum value of the pulse frequency. min The minimum pulse frequency is adjusted according to the size of the problem domain; initially, each bat is randomly assigned to a position in [f]. min f max The frequency values ​​of a uniform distribution.

[0106] In one embodiment, the processor 1100 can be used to invoke a selection program for the compressive strength of the terminal counter legs stored in the memory 1200, and perform the following operations:

[0107] For local search, whenever a new optimal solution x is found... * Upon appearance, each bat will randomly generate a new coordinate position near the optimal solution:

[0108]

[0109] ∈ is a random value located in [-1, 1], A t It is the average loudness of all bats at time t; at this time, the loudness A i and pulse frequency r i It will also be continuously updated as it iterates:

[0110]

[0111]

[0112] Where, r t It is the pulse frequency at time t, r ∞ It is the final value of the pulse frequency, r 0 This is the initial value of the pulse frequency, t. max This is the moment of maximum iteration, where δ is a positive number approaching 0; A t A is the loudness at time t. ∞ It is the final value of loudness, A. 0 This is the initial value of loudness.

[0113] Since the preferred device for selecting the compressive strength of the terminal counter legs provided in this application embodiment is the preferred device for selecting the compressive strength of the terminal counter legs used in implementing the method of this application embodiment, those skilled in the art can understand the specific structure and variations of the preferred device for selecting the compressive strength of the terminal counter legs based on the method described in this application embodiment, and therefore will not be repeated here. All preferred devices for selecting the compressive strength of the terminal counter legs used in the method of this application embodiment fall within the scope of protection of this application. The sequence numbers of the above embodiments of the present invention are merely for description and do not represent the superiority or inferiority of the embodiments.

[0114] For software implementation, the techniques described in the embodiments of the present invention can be implemented through modules (e.g., procedures, functions, etc.) that perform the functions described in the embodiments of the present invention. The software code can be stored in memory and executed by a processor. The memory can be implemented in the processor or externally.

[0115] Based on the above structure, embodiments of the present invention are proposed.

[0116] Reference Figure 2 , Figure 2 A flowchart illustrating the first embodiment of a method for selecting the optimal compressive strength of terminal counter legs provided by an embodiment of the present invention includes the following steps:

[0117] Step S110: Construct a compressive strength model for the legs of the terminal counter;

[0118] Step S120: The novel bat algorithm is used to solve the compressive strength model of the terminal counter legs to obtain the optimal parameters of the terminal counter legs. This specifically includes the following steps:

[0119] Step S1201: Initialize the parameter information of the terminal counter feet using a low-bias sequence to obtain the initial population of the terminal counter feet.

[0120] Step S1202 involves initializing the parameter information of the novel bat algorithm, specifically including: setting the population size and number of bats, the number of algorithm iterations, and initializing the impulse loudness of each bat. Pulse intensity Set the pulse frequency f i Randomly initialize population positions

[0121] Step S1203: Update the speed of each bat. and location

[0122] Step S1204: For each individual bat, a random number rand(0,1) needs to be generated that is uniformly distributed between [0,1]. rand(0,1) represents a real number randomly generated in a uniform distribution within [0,1]. If the condition is met... Let be the pulse frequency of bat individual i at time t; perform a small-range perturbation near the current optimal bat individual position to generate a new bat individual position, and perform out-of-bounds normalization processing on the new bat individual position;

[0123] Step S1205: Calculate and sort the fitness values ​​of all bats, and find the current optimal solution x. * and the optimal value F(T) s T h (R, L) * ;

[0124] Step S1206: Generate a uniformly distributed random number rand(0, 1) for each individual bat. renew and Let be the impulse intensity of bat individual i at time t. Let i be the position of individual bat i at time t;

[0125] Step S1207: Find the current global optimal solution x * The optimal parameters for the terminal counter feet are obtained.

[0126] Optionally, the construction of the compressive strength model for the terminal counter legs includes:

[0127] Let the shell thickness be T. s The shell thickness is T. h Inner radius of the shell R, shell length L, compressive strength F (T) s T h (R, L);

[0128] Construct a compressive strength model for the legs of the terminal counter:

[0129] F(Ts,Th,R,L)=0.6224TsRL+1.7781T h R 2 +3.1661Ts 2 L+19.84T h 2 L

[0130] Optionally, the speed of each bat is updated. and location include:

[0131] The speed of bat i at time t and location Update according to the following formula:

[0132]

[0133]

[0134] in, Let be the flight speed of bat individual i at time t. Let be the flight speed of bat individual i at time t-1; Let represent the position of individual bat i at time t. x represents the position of individual bat i at time t-1; * It is the current global optimal solution in the bat population, with frequency f. i Let be the pulse frequency emitted by individual bat i when searching for a target.

[0135] Optionally, the frequency f i The pulse frequency emitted by bat individual i when searching for a target includes:

[0136] f i =f min +(f max -f min )β

[0137] β∈[0,1] is a random vector in a uniform distribution; f max f is the maximum value of the pulse frequency. min The minimum pulse frequency is adjusted according to the size of the problem domain; initially, each bat is randomly assigned to a position in [f]. min f max The frequency values ​​of a uniform distribution.

[0138] Optionally, all individual bats need to generate a random number rand(0,1) uniformly distributed between [0,1]. rand(0,1) represents a real number randomly generated in a uniform distribution within [0,1]. If the condition is met... Given the pulse frequency of bat individual i at time t, a small-range perturbation is performed near the current optimal bat individual position to generate a new position, and out-of-bounds normalization processing is performed on it, including:

[0139] For local search, whenever a new optimal solution x is found... * Upon appearance, each bat will randomly generate a new coordinate position near the optimal solution:

[0140]

[0141] ∈ is a random value located in [-1, 1], A t It is the average loudness of all bats at time t; at this time, the loudness A i and pulse frequency r i It will also be continuously updated as it iterates:

[0142]

[0143]

[0144] Where, r t It is the pulse frequency at time t, r ∞ It is the final value of the pulse frequency, r 0 This is the initial value of the pulse frequency, t. max This is the moment of maximum iteration, where δ is a positive number approaching 0; A t A is the loudness at time t. ∞ It is the final value of loudness, A. 0 This is the initial value of loudness.

[0145] This application utilizes an improved bat algorithm to effectively control the compressive strength of terminal counter legs, ensuring not only the quality of the counter legs but also reducing losses from trial and error during production. The improved bat algorithm optimizes the compressive strength of the terminal counter legs, ensuring high compressive strength.

[0146] Based on the same inventive concept, the present invention also provides a device for selecting the compressive strength of the legs of a terminal counter, such as... Figure 3 As shown, Figure 3 This is a schematic diagram of a device for expanding solid-state relationships provided in an embodiment of the present invention. The device includes a model building module 10 and a solution module 20.

[0147] Model building module 10 is used to build a compressive strength model of the terminal counter legs;

[0148] The solution module 20 is used to solve the compressive strength model of the terminal counter legs using a novel bat algorithm, and to obtain the optimal parameters of the terminal counter legs. Specifically, it includes the following steps:

[0149] The first initialization unit 201 is used to initialize the parameter information of the terminal counter feet through a low-bias sequence to obtain the initial population of the terminal counter feet.

[0150] The second initialization unit 202 is used to initialize the parameter information of the novel bat algorithm, specifically including: setting the population size and number of bats, the number of algorithm iterations, and initializing the impulse loudness of each bat. Pulse intensity Set the pulse frequency f i Randomly initialize population positions

[0151] The first update unit, 203, is used to update the speed of each bat. and location

[0152] Processing unit 204 is used to generate a random number rand(0,1) uniformly distributed between [0,1] for each individual bat. rand(0,1) represents a real number randomly generated in a uniform distribution within [0,1]. If the condition is met... Let be the pulse frequency of bat individual i at time t; perform a small-range perturbation near the current optimal bat individual position to generate a new bat individual position, and perform out-of-bounds normalization processing on the new bat individual position;

[0153] Sorting unit 205 is used to calculate and sort the fitness values ​​of all bats, and find the current optimal solution x. * and the optimal value F(T) s T h (R, L) * ;

[0154] The second update unit 206 is used to generate a uniformly distributed random number rand(0, 1) for each individual bat. renew and Let be the impulse intensity of bat individual i at time t. Let i be the position of individual bat i at time t;

[0155] The final parameter unit 207 is used to find the current global optimal solution x. * The optimal parameters for the terminal counter feet are obtained.

[0156] Optionally, the model building module 10 includes:

[0157] Let the shell thickness be T. s The shell thickness is T. h Inner radius of the shell R, shell length L, compressive strength F (T) s T h (R, L);

[0158] Construct a compressive strength model for the legs of the terminal counter:

[0159] F(Ts, Th, R, L) = 0.6224T s RL+1.7781T h R 2 +3.1661T s 2 L+19.84T h 2 L

[0160] Optionally, the first update unit 203 includes:

[0161] The speed of bat i at time t and location Update according to the following formula:

[0162]

[0163]

[0164] in, Let be the flight speed of bat individual i at time t. Let be the flight speed of bat individual i at time t-1; Let represent the position of individual bat i at time t. x represents the position of individual bat i at time t-1; * It is the current global optimal solution in the bat population, with frequency f. i Let be the pulse frequency emitted by individual bat i when searching for a target.

[0165] Optionally, the frequency f i The pulse frequency emitted by bat individual i when searching for a target includes:

[0166] f i =f min +(f max -f min )β

[0167] β∈[0,1] is a random vector in a uniform distribution; f max f is the maximum value of the pulse frequency. min The minimum pulse frequency is adjusted according to the size of the problem domain; initially, each bat is randomly assigned to a position in [f]. min f max The frequency values ​​of a uniform distribution.

[0168] Optionally, the processing unit 204 includes:

[0169] For local search, whenever a new optimal solution x is found... * Upon appearance, each bat will randomly generate a new coordinate position near the optimal solution:

[0170]

[0171] ∈ is a random value located in [-1, 1], A t It is the average loudness of all bats at time t; at this time, the loudness A i and pulse frequency r i It will also be continuously updated as it iterates:

[0172]

[0173]

[0174] Where, r t It is the pulse frequency at time t, r ∞ It is the final value of the pulse frequency, r 0 This is the initial value of the pulse frequency, t. max This is the moment of maximum iteration, where δ is a positive number approaching 0; A t A is the loudness at time t. ∞ It is the final value of loudness, A. 0 This is the initial value of loudness.

[0175] The functions of the apparatus in this embodiment have been described in the above method embodiments. Therefore, for any parts not detailed in this embodiment, please refer to the relevant descriptions in the foregoing embodiments, which will not be repeated here.

[0176] Based on the same inventive concept, this application also provides a computer-readable storage medium storing a program for expanding entity relations. When the program for expanding entity relations is executed by a processor, it implements the various steps of the method for expanding entity relations as described above and achieves the same technical effect. To avoid repetition, it will not be described again here.

[0177] Since the computer-readable storage medium provided in the embodiments of this application is a computer-readable storage medium used to implement the methods of the embodiments of this application, those skilled in the art can understand the specific structure and variations of the computer-readable storage medium based on the methods described in the embodiments of this application, and therefore will not be repeated here. All computer-readable storage media used in the methods of the embodiments of this application fall within the scope of protection of this application.

[0178] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0179] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0180] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0181] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0182] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0183] It should be noted that any reference signs placed between parentheses in the claims should not be construed as limiting the claims. The word "comprising" does not exclude the presence of components or steps not listed in the claims. The word "a" or "an" preceding a component does not exclude the presence of a plurality of such components. The invention can be implemented by means of hardware comprising several different components and by means of a suitably programmed computer. In a unit claim enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third, etc., does not indicate any order. These words can be interpreted as names.

[0184] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0185] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for selecting the optimal compressive strength of terminal counter legs, characterized in that, Comprise: Set Shell body thickness is T s , shell head thickness is T h , shell inner radius R, shell length L, compressive strength F(T s , T h , R, L); construct the compressive strength model of terminal counter cabinet feet: F(Ts, Th, R, L)=0.6224TsRL+1.7781T h R 2 +3.1661Ts 2 L+19.84T h 2 L; The new bat algorithm is used to solve the compression strength model of the terminal counter cabinet foot, and optimal parameters of the terminal counter cabinet foot are obtained, and the specific steps include the following steps: The parameter information of the terminal counter cabinet foot is initialized by a low bias sequence to obtain an initialized population of the terminal counter cabinet foot; The parameter information initialization of the new bat algorithm specifically includes: setting the population size and the number of bats, the iteration number of the algorithm, initializing the pulse loudness of each bat pulse intensity setting the pulse frequency f i ; randomly initializing the population position updating the speed of each bat and position All bats individuals need to generate a random number rand(0, 1) uniformly distributed between [0, 1], rand(0, 1) represents a real number randomly generated on a uniform distribution in [0, 1], if the condition is the pulse frequency of the bat individual i at time t; a small range of disturbance is carried out near the current optimal bat individual position to generate a new bat individual position, and the new bat individual position is normalized to the boundary. Compute fitness value of all bats and sort them, find the current best solution x * and the best value F(T s , T h , R, L) * ; Generate a random number rand(0, 1) uniformly distributed for each bat individual, if rand(0, 1) < r, then Update and is the pulse sound intensity of bat individual i at time t, is the position of bat individual i at time t. finding a current global optimal solution x * obtaining optimal parameters of the terminal counter cabinet feet.

2. The method for selecting the optimal compressive strength of the terminal counter legs as described in claim 1, characterized in that, said updating the speed of each bat and position comprising: The velocity of the bat i at time t and position is updated according to the following equation: wherein, is the flight speed of the bat individual i at time t, is the flight speed of the bat individual i at time t-1; is the position of the bat individual i at time t, is the position of the bat individual i at time t-1; x * is the current global optimum solution in the bat population, frequency f i is the frequency of the emitted pulses of the bat individual i while searching for the target.

3. The method for selecting the optimal compressive strength of the terminal counter legs as described in claim 2, characterized in that, The frequency f i The frequency f of the pulses emitted by the bat individual i while searching for a target, comprises: f i = f min + (f max -f min )β β ∈ [0, 1] is a random vector uniformly distributed; f max is the maximum value of the pulse frequency, f min is the minimum value of the pulse frequency, adjusted according to the size of the problem domain; initially each bat is randomly assigned a frequency value uniformly distributed in [f min , f max ].

4. The method for selecting the optimal compressive strength of the terminal counter legs as described in claim 1, characterized in that, All the bats need to generate a random number rand(0, 1) uniformly distributed between [0, 1], rand(0, 1) represents a real number randomly generated on a uniform distribution in [0, 1], if the condition is the pulse frequency of the bat individual i at time t, a small range of disturbance is carried out near the current optimal bat individual position to generate a new position, and the out-of-bound normalization processing is carried out, including: For the local search, whenever there is a new optimal solution x * occurs, each bat will randomly generate a new coordinate position around the optimal solution: is a random value in [-1, 1], A t is the average of all bat loudness at time t; the loudness A i and pulse rate r i are also updated with each iteration: where r t is the pulse rate at time t, r ∞ is the final value of the pulse rate, r 0 is the initial value of the pulse rate, t max is the time of the maximum iteration, and δ is a positive number that tends to 0; A t is the loudness at time t, A ∞ is the final value of the loudness, A 0 is the initial value of the loudness.

5. A terminal counter cabinet foot pressure resistance strength optimization device, characterized in that, Comprise: Model building module, to set the shell body thickness T s , shell head thickness T h , shell inner radius R, shell length L, compressive strength F(T s , T h , R, L); build the compressive strength model of the terminal counter cabinet foot: F(Ts, Th, R, L) = 0.6224TsRL + 1.7781T h R 2 +3.1661Ts 2 L+19.84T h 2 L; The solving module is used to solve the compression strength model of the terminal counter cabinet foot by using the new bat algorithm, and optimal parameters of the terminal counter cabinet foot are obtained, and the specific steps include the following steps: The first initialization unit is used to initialize the parameter information of the terminal counter cabinet foot by a low bias sequence to obtain an initialized population of the terminal counter cabinet foot; A second initialization unit is configured to initialize parameter information of the new bat algorithm, specifically including: setting a population size and a number of bats, an iteration number of the algorithm, initializing a pulse loudness of each bat pulse intensity setting a pulse frequency f i ; randomly initializing population positions a first updating unit configured to update the speed of each bat and position The processing unit is configured to generate a random number rand(0, 1) uniformly distributed between 0 and 1 for each bat individual, rand(0, 1) represents a real number randomly generated on a uniform distribution between 0 and 1, and if the condition rand(0, 1) < fbest(i) / f(i) is met, the bat individual i is replaced by the current best bat individual is the pulse frequency of the bat individual i at time t; a small range of disturbance is performed around the position of the current best bat individual to generate a new bat individual position, and the new bat individual position is subjected to boundary normalization processing. A sorting unit is used to calculate the fitness value of all bats and sort them to find the current optimal solution x * and the optimal value F(T s , T h , R, L) * ; A second updating unit is configured to generate a uniformly distributed random number rand(0, 1) for each bat individual to regenerate. updating and is the pulse sound intensity of the bat individual i at the t time; is the position of the bat individual i at the t time. a final parameter unit for finding a current global optimal solution x * to obtain the optimal parameters of the terminal counter feet.

6. The terminal counter cabinet foot pressure resistance strength optimization device of claim 5, wherein, The first updating unit comprises: The velocity of the bat i at time t and position is updated according to the following equation: wherein, is the flight speed of bat individual i at time t, is the flight speed of bat individual i at time t-1; is the position of bat individual i at time t, is the position of bat individual i at time t-1; x * is the current global optimum solution in the bat population, frequency f i is the frequency of the emitted pulses by bat individual i while searching for the target.

7. An electronic device, comprising: The electronic device comprises: A processor; and A memory storing computer-executable instructions that, when executed, cause the processor to perform the method of any of claims 1-4.

8. A computer readable storage medium, wherein, The computer-readable storage medium stores one or more programs that, when executed by a processor, implement the method of any of claims 1-4.

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