Equipment mechanical parameters optimization method based on improved teaching and chicken swarm algorithm

Abstract

The present application discloses a method for optimizing the mechanical parameters of equipment based on improved teaching and chicken swarm algorithm, the method comprising: obtaining the initial mechanical parameters of the equipment; obtaining the positioning measurement values ​​of the equipment; creating a motion model of the equipment, and obtaining the target mechanical parameters according to the motion model, the initial mechanical parameters and the positioning measurement values; optimizing the target mechanical parameters using a hybrid intelligent algorithm based on the improved teaching algorithm and the improved chicken swarm algorithm to obtain the optimized mechanical parameters of the equipment. The method of the present invention has a fast convergence speed and a strong global exploration capability, and can correct the initial mechanical parameters of the equipment in a relatively short time, find the actual mechanical parameters of the equipment, and reduce the errors caused by inaccurate parameters during the use of the equipment.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment parameter calibration, and in particular to an equipment mechanical parameter optimization method, device, electronic device and computer-readable storage medium based on improved teaching and chicken swarm algorithm. Background Art

[0002] With the development of science and technology, the application of measuring equipment in industrial control is becoming more and more extensive, the application scenarios are becoming more and more complex, and the requirements for the precision control of measuring equipment are becoming higher and higher. In actual use, due to the installation error and control error of measuring equipment, the measurement accuracy of measuring equipment will usually decrease after a period of use. In order to ensure the measurement accuracy of the equipment, the actual mechanical parameters of the measuring equipment need to be calibrated.

[0003] Since the operating errors of the equipment have certain random characteristics and there is mutual coupling between the errors, the existing technology usually uses an error model to characterize the parameter error information of the equipment, and optimizes the mechanical parameters of the equipment according to the positioning measurement data of the equipment and the error model. However, for high-precision measurement equipment, the parameter optimization method of the existing technology has complicated operation steps and low parameter optimization accuracy, which still cannot meet the requirements of actual use.

[0004] Therefore, the existing technology for optimizing the mechanical parameters of equipment has the problems of complicated operation steps, high application cost and low optimization accuracy. It is impossible to accurately identify and optimize the mechanical parameters of the equipment, which affects the measurement accuracy of the measuring equipment. Summary of the invention

[0005] In view of this, it is necessary to provide a method, device, electronic device and computer-readable storage medium for optimizing equipment mechanical parameters based on improved teaching and chicken swarm algorithm to solve the problems of complicated operation steps, low optimization accuracy and long optimization time existing in the prior art.

[0006] In order to solve the above problems, the present invention provides a method for optimizing equipment mechanical parameters based on improved teaching and chicken swarm algorithm, comprising:

[0007] Obtaining initial mechanical parameters of the device and positioning measurement values ​​of the device;

[0008] Creating a motion model of the device, and obtaining target mechanical parameters based on the motion model, initial mechanical parameters, and positioning measurements;

[0009] The target mechanical parameters are optimized by using a hybrid intelligent algorithm based on an improved teaching algorithm and an improved chicken swarm algorithm to obtain the optimized mechanical parameters of the equipment.

[0010] Furthermore, the initial mechanical parameters of the equipment are obtained, including:

[0011] Obtain the nominal mechanical parameters of the equipment;

[0012] According to the nominal mechanical parameters, a constraint range is preset, and a plurality of initial mechanical parameters are generated within the constraint range using a random function.

[0013] Furthermore, a motion model of the device is created, and target mechanical parameters are obtained according to the motion model, initial mechanical parameters and positioning measurement values, including:

[0014] Obtaining mechanical coordinate data of the device corresponding to the positioning measurement value according to the motion model, the initial mechanical parameters and the positioning measurement value;

[0015] Calculating the fitness corresponding to the mechanical coordinate data according to the mechanical coordinate data;

[0016] Creating an objective function with the target mechanical parameter as an independent variable and the fitness as a dependent variable;

[0017] The objective function is solved by using a hybrid intelligent algorithm based on an improved teaching algorithm and an improved chicken swarm algorithm to obtain an optimal fitness, and the target mechanical parameters corresponding to the optimal fitness are optimized mechanical parameters.

[0018] Further, calculating the fitness corresponding to the mechanical coordinate data according to the mechanical coordinate data includes:

[0019] According to the mechanical coordinate data, a coordinate error value of the mechanical coordinate data is calculated using the Laida criterion;

[0020] The fitness corresponding to the mechanical coordinate data is obtained according to the coordinate error value.

[0021] Furthermore, the objective function is solved by using a hybrid intelligent algorithm based on an improved teaching algorithm and an improved chicken swarm algorithm, including:

[0022] The objective function is iteratively solved using an improved teaching algorithm to determine whether the number of iterations of the teaching algorithm meets a preset conversion condition; when the number of iterations does not meet the conversion condition, an improved chicken swarm algorithm is used to obtain new target mechanical parameters, and then the improved teaching algorithm is used to optimize the new target mechanical parameters to obtain optimized mechanical parameters.

[0023] Furthermore, the improved teaching algorithm is divided into a teaching phase and a learning phase, and the improved teaching algorithm is used to iteratively solve the objective function, including:

[0024] In the teaching stage, the initial mechanical parameters are used as the initial values ​​of the target mechanical parameters, and the fitness corresponding to the target mechanical parameters is calculated; the target mechanical parameters corresponding to the maximum fitness are selected as "teachers", and the remaining target mechanical parameters are used as "students";

[0025] In each iteration, the "student" is optimized by narrowing the gap between the average values ​​of the "teacher" and the "student". After the optimization is completed, the fitness of the optimized "student" and the "teacher" is calculated; the fitness of the "teacher" and the optimized "student" is compared, and the target mechanical parameter corresponding to the maximum fitness value is selected as the new "teacher";

[0026] During the learning phase, each iteration optimizes the target mechanical parameters by narrowing the gap between the “students”.

[0027] Furthermore, the narrowing of the gap between the average values ​​of the “teacher” and the “student” and the optimization of the “student” include:

[0028] Use the following formula to optimize "student":

[0029] X new,i =X old,i +Difference_Mean i +c×rand(gbest i -X old,i ),

[0030] Difference_Mean i =r i (M new -T F M i )

[0031] Among them, Xold,i represents the i-th "student", Xnew,i represents the i-th optimized "student", Difference_Mean i represents the difference between the “teacher” and the “student”, c is a random number between (0, 1), rand represents the generation of a random number; gbest represents the historical optimal target mechanical parameter, Mnew represents the current “teacher”, Mi represents the average value of the “student”; T F represents the learning factor, r i Is a random number.

[0032] The present invention also provides a device for optimizing equipment mechanical parameters based on improved teaching and chicken swarm algorithm, comprising:

[0033] Acquisition module: used to obtain the initial mechanical parameters of the equipment and the positioning measurement values ​​of the equipment;

[0034] Analysis module: used for creating a motion model of the device, and obtaining target mechanical parameters according to the motion model, initial mechanical parameters and positioning measurement values;

[0035] Optimization module: used to optimize the target mechanical parameters by using a hybrid intelligent algorithm based on an improved teaching algorithm and an improved chicken swarm algorithm to obtain the optimized mechanical parameters of the equipment.

[0036] The present invention also provides an electronic device, including a processor and a memory, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the equipment mechanical parameter optimization method based on improved teaching and chicken swarm algorithm as described in any of the above technical solutions is implemented.

[0037] The present invention also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for optimizing equipment mechanical parameters based on improved teaching and chicken swarm algorithm as described in any of the above technical solutions is implemented.

[0038] Compared with the prior art, the beneficial effects of the present invention include: first, obtaining the initial mechanical parameters of the device and the positioning measurement values ​​of the device; second, creating a motion model of the device, and obtaining the target mechanical parameters according to the motion model, the initial mechanical parameters and the positioning measurement values; finally, optimizing the target mechanical parameters using a hybrid intelligent algorithm based on an improved teaching algorithm and an improved chicken swarm algorithm to obtain the optimized mechanical parameters of the device. The method of the present invention has a fast convergence speed and a strong global exploration capability. It can correct the initial mechanical parameters of the device in a short time, find the actual mechanical parameters of the device, and reduce the errors caused by inaccurate parameters during the use of the device. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 A schematic diagram of a scenario of an embodiment of an application system of a method for optimizing equipment mechanical parameters based on improved teaching and chicken swarm algorithm provided by the present invention;

[0040] Figure 2 A schematic flow chart of an embodiment of a method for optimizing equipment mechanical parameters based on improved teaching and chicken swarm algorithm provided by the present invention;

[0041] FIG. 3( a ) is a flow chart of steps S301 to S316 in a process of an embodiment of a method for optimizing equipment mechanical parameters based on improved teaching and chicken swarm algorithm provided by the present invention;

[0042] FIG3( b ) is a flow chart of steps S317 to S333 in the process of an embodiment of a method for optimizing equipment mechanical parameters based on improved teaching and chicken swarm algorithm provided by the present invention;

[0043] Figure 4 A schematic diagram of the structure of an embodiment of a device for optimizing equipment mechanical parameters based on improved teaching and chicken swarm algorithm provided by the present invention;

[0044] Figure 5 It is a structural block diagram of an embodiment of an electronic device provided by the present invention. DETAILED DESCRIPTION

[0045] The preferred embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings, wherein the accompanying drawings constitute a part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not used to limit the scope of the present invention.

[0046] The present invention provides a method, device, electronic device and computer-readable storage medium for optimizing equipment mechanical parameters based on improved teaching and chicken swarm algorithm, which are described in detail below.

[0047] The embodiment of the present invention provides an application system of a method for optimizing equipment mechanical parameters based on improved teaching and chicken swarm algorithm. Figure 1 A schematic diagram of a scenario of an embodiment of an application system of a method for optimizing equipment mechanical parameters based on improved teaching and chicken swarm algorithm provided by the present invention. The system may include a server 101, in which an equipment mechanical parameter optimization device based on improved teaching and chicken swarm algorithm is integrated, such as Figure 1 Servers in .

[0048] In the embodiment of the present invention, the server 101 is mainly used for:

[0049] Obtaining initial mechanical parameters of the device and positioning measurement values ​​of the device;

[0050] Creating a motion model of the device, and obtaining target mechanical parameters based on the motion model, initial mechanical parameters, and positioning measurements;

[0051] The target mechanical parameters are optimized by using a hybrid intelligent algorithm based on an improved teaching algorithm and an improved chicken swarm algorithm to obtain the optimized mechanical parameters of the equipment.

[0052] In the embodiment of the present invention, the server 101 may be an independent server, or a server network or server cluster composed of servers. For example, the server 101 described in the embodiment of the present invention includes but is not limited to a computer, a network host, a single network server, a plurality of network server sets or a cloud server composed of a plurality of servers. The cloud server is composed of a large number of computers or network servers based on cloud computing.

[0053] It is to be understood that the terminal 102 used in the embodiment of the present invention may be a device including both receiving and transmitting hardware, that is, a device having receiving and transmitting hardware capable of performing two-way communication on a two-way communication link. Such a device may include: a cellular or other communication device having a single-line display or a multi-line display or a cellular or other communication device without a multi-line display. The specific terminal 102 may be a desktop computer, a portable computer, a network server, a PDA (Personal Digital Assistant), a mobile phone, a tablet computer, a wireless terminal device, a communication device, an embedded device, etc. The present embodiment does not limit the type of the terminal 102.

[0054] Those skilled in the art will understand that Figure 1 The application environment shown in the figure is only one application scenario of the solution of the present invention and does not constitute a limitation on the application scenario of the solution of the present invention. Other application environments may also include Figure 1 More or fewer terminals as shown in Figure 1 Only two terminals are shown in the figure. It can be understood that the application system of the equipment mechanical parameter optimization method based on improved teaching and chicken swarm algorithm can also include one or more other terminals, which are not specifically limited here.

[0055] In addition, if Figure 1 As shown, the application system of the equipment mechanical parameter optimization method based on improved teaching and chicken swarm algorithm can also include a first memory 103 for storing data, such as positioning measurement values, initial mechanical parameters, etc.

[0056] It should be noted that Figure 1 The scenario diagram of the application system of the equipment mechanical parameter optimization method based on improved teaching and chicken swarm algorithm is only an example. The application system and scenario of the equipment mechanical parameter optimization method based on improved teaching and chicken swarm algorithm described in the embodiment of the present invention are for more clearly illustrating the technical solution of the embodiment of the present invention, and do not constitute a limitation on the technical solution provided by the embodiment of the present invention. Ordinary technicians in this field can know that with the evolution of the application system of the equipment mechanical parameter optimization method based on improved teaching and chicken swarm algorithm and the emergence of new business scenarios, the technical solution provided by the embodiment of the present invention is also applicable to similar technical problems.

[0057] The embodiment of the present invention provides a method for optimizing equipment mechanical parameters based on improved teaching and chicken swarm algorithm, and its flow chart is as follows: Figure 2 As shown, the equipment mechanical parameter optimization method based on improved teaching and chicken swarm algorithm includes:

[0058] Step S201, obtaining initial mechanical parameters of the device and positioning measurement values ​​of the device;

[0059] Step S202: creating a motion model of the device, and obtaining target mechanical parameters according to the motion model, initial mechanical parameters and positioning measurement values;

[0060] Step S203: Utilize a hybrid intelligent algorithm based on an improved teaching algorithm and an improved chicken swarm algorithm to optimize the target mechanical parameters to obtain optimized mechanical parameters of the equipment.

[0061] Compared with the prior art, the device mechanical parameter optimization method based on improved teaching and chicken swarm algorithm provided in this embodiment first obtains the initial mechanical parameters of the device and the positioning measurement values ​​of the device; secondly, creates a motion model of the device, and obtains the target mechanical parameters according to the motion model, the initial mechanical parameters and the positioning measurement values; finally, optimizes the target mechanical parameters using a hybrid intelligent algorithm based on the improved teaching algorithm and the improved chicken swarm algorithm to obtain the optimized mechanical parameters of the device. The method of the present invention has a fast convergence speed and a strong global exploration capability. It can correct the initial mechanical parameters of the device in a short time, find the actual mechanical parameters of the device, and reduce the errors caused by inaccurate parameters during the use of the device.

[0062] As a specific embodiment, in step S201, the device is a 6-DOF articulated arm coordinate measuring machine.

[0063] As a preferred embodiment, in step S201, obtaining initial mechanical parameters of the device includes:

[0064] Obtain the nominal mechanical parameters of the equipment;

[0065] According to the nominal mechanical parameters, a constraint range is preset, and a plurality of initial mechanical parameters are generated within the constraint range using a random function.

[0066] As a specific embodiment, the initial mechanical parameters include rod length, joint length, joint torsion angle, and probe length.

[0067] As a specific embodiment, in step S201, obtaining the positioning measurement value of the device includes:

[0068] Fixing the conical hole on the operating platform, and then placing a small ball at the end of the probe of the articulated arm type coordinate measuring machine in the conical hole, so that the end of the probe matches the conical hole;

[0069] The articulated arm coordinate measuring machine is shaken to a plurality of different postures to obtain a plurality of sets of joint angle values, wherein the joint angle values ​​are positioning measurement values.

[0070] As a specific embodiment, in step S202, the preset motion model created is the MDH motion model.

[0071] As a preferred embodiment, in step S203, the target mechanical parameters are optimized by using a hybrid intelligent algorithm based on an improved teaching algorithm and an improved chicken swarm algorithm to obtain the optimized mechanical parameters of the equipment, including:

[0072] Obtaining mechanical coordinate data of the device corresponding to the positioning measurement value according to the motion model, the initial mechanical parameters and the positioning measurement value;

[0073] Calculating the fitness corresponding to the mechanical coordinate data according to the mechanical coordinate data;

[0074] Creating an objective function with the target mechanical parameter as an independent variable and the fitness as a dependent variable;

[0075] The objective function is solved by using a hybrid intelligent algorithm based on an improved teaching algorithm and an improved chicken swarm algorithm to obtain an optimal fitness, and the target mechanical parameters corresponding to the optimal fitness are optimized mechanical parameters.

[0076] As a preferred embodiment, calculating the fitness corresponding to the mechanical coordinate data according to the mechanical coordinate data includes:

[0077] According to the mechanical coordinate data, a coordinate error value of the mechanical coordinate data is calculated using the Laida criterion;

[0078] The fitness corresponding to the mechanical coordinate data is obtained according to the coordinate error value.

[0079] As a specific embodiment, the MDH motion model can be used to obtain: for mechanical parameters (a i ,d i ,α i ,l),i=1,2,...,6, a 6-DOF articulated arm coordinate measuring machine, given a set of joint angles (θ 1 ,θ 2 ,θ 3 ,θ 4 ,θ 5 ,θ 6 ), the coordinates of the end of the probe of the articulated arm coordinate measuring machine (ie, the mechanical coordinates) are:

[0080]

[0081] Where a represents the rod length, d represents the joint length, α represents the joint torsion angle, and l represents the probe length.

[0082] For any set of mechanical parameters X=(a i ,d i ,α i,l),i=1,2,...,6, the mechanical parameters and N groups of joint angles k θ m (k=1,2,...,N;m=1,2,...,6) into the calculation formula of mechanical coordinates, we can get N sets of mechanical coordinates ( k x, k y, k z),k=1,2,...,N.

[0083] The initial mechanical parameters are calculated as X = (a i ,d i ,α i ,l),i=1,2,...,6, the average value of the N sets of mechanical coordinates (x,y,z) can be obtained:

[0084]

[0085] According to the average value (x, y, z) of the mechanical coordinates, the accumulated deviation of each set of mechanical coordinates is calculated respectively, and the accumulated deviation of each set of mechanical coordinates is obtained as follows:

[0086]

[0087] According to the accumulated deviation of each set of mechanical coordinates, the accumulated average deviation of N sets of mechanical coordinates is obtained as follows:

[0088]

[0089] According to the cumulative average deviation of N groups of mechanical coordinates, the cumulative variance of the deviation can be obtained as:

[0090]

[0091] Applying the "3σ" principle (Laida's law), the coordinate error value of the mechanical coordinate data is obtained as follows:

[0092]

[0093] According to the coordinate error value, the fitness corresponding to the mechanical coordinate data is obtained as follows:

[0094]

[0095] Wherein, rea lim is a very small positive number, ensuring that the denominator is not 0 or infinitely close to 0; it can be seen that for each set of mechanical parameters, there is a fitness corresponding to the corresponding coordinate data. The larger the fitness, the smaller the coordinate error value is, and the closer the mechanical parameter is to the actual value; therefore, the optimization of mechanical parameters can be converted into: solving the optimal solution of the objective function with the target mechanical parameters as the independent variable and the fitness as the dependent variable.

[0096] As a preferred embodiment, the objective function is solved by using a hybrid intelligent algorithm based on an improved teaching algorithm and an improved chicken swarm algorithm, including:

[0097] The objective function is iteratively solved using an improved teaching algorithm to determine whether the number of iterations of the teaching algorithm meets a preset conversion condition; when the number of iterations does not meet the conversion condition, an improved chicken swarm algorithm is used to obtain new target mechanical parameters, and then the improved teaching algorithm is used to optimize the new target mechanical parameters to obtain optimized mechanical parameters.

[0098] As a preferred embodiment, the improved teaching algorithm is divided into a teaching phase and a learning phase, and the improved teaching algorithm is used to iteratively solve the objective function, including:

[0099] In the teaching stage, the initial mechanical parameters are used as the initial values ​​of the target mechanical parameters, and the fitness corresponding to the target mechanical parameters is calculated; the target mechanical parameters corresponding to the maximum fitness are selected as "teachers", and the remaining target mechanical parameters are used as "students";

[0100] In each iteration, the "student" is optimized by narrowing the gap between the average values ​​of the "teacher" and the "student". After the optimization is completed, the fitness of the optimized "student" and the "teacher" is calculated; the fitness of the "teacher" and the optimized "student" is compared, and the target mechanical parameter corresponding to the maximum fitness value is selected as the new "teacher";

[0101] During the learning phase, each iteration optimizes the target mechanical parameters by narrowing the gap between the “students”.

[0102] As a preferred embodiment, the narrowing of the gap between the average values ​​of the “teacher” and the “student” and the optimization of the “student” include:

[0103] Use the following formula to optimize "student":

[0104] X new,i =X old,i +Difference_Mean i +c×rand(gbest i -X old,i ), (1)

[0105] Difference_Mean i =r i (Mnew-T F M i ), (2)

[0106] Among them, Xold,i represents the i-th "student", Xnew,i represents the i-th optimized "student", Difference_Mean i represents the difference between the “teacher” and the “student”, c is a random number between (0, 1), rand represents the generation of a random number; gbest represents the historical optimal target mechanical parameters, Mnew represents the current “teacher”, M i represents the average value of "students"; T F represents the learning factor, r i Is a random number.

[0107] As a specific embodiment, calculating the fitness of the optimized "student" and "teacher"; comparing the fitness of the "teacher" and the optimized "student", and selecting the target mechanical parameter corresponding to the maximum fitness as the new "teacher" includes:

[0108] if but in, is the i-th optimized “student”, M new Is the "teacher" of the current generation, is the fitness of the optimized “student”, fitness(M new ) is the fitness corresponding to the current generation of “teacher”.

[0109] As a specific embodiment, the optimization formula of the "student" in the learning stage is:

[0110]

[0111] Among them, Xold,i represents the i-th "student", Xnew,i represents the i-th optimized "student", and X i and X j are all "students" in the current generation except X old,i Two "students" randomly selected from outside, fitness(X i ) means "student" X i Corresponding fitness; fitness(X j ) means "student" X j The corresponding fitness; gbest represents the historical optimal target mechanical parameters; rand is a random number between (0, 1), r 1 and r 2 is the learning factor, and in this embodiment, the value is 2.

[0112] As a specific embodiment, the improved chicken swarm algorithm includes:

[0113] Cock Update Formula:

[0114]

[0115]

[0116] Among them, pi represents the optimal individual, g represents the historical optimal individual, m 1 and m 2 is the learning factor, f i It's a cock The fitness value, f i A rooster randomly selected from the rooster population The fitness value of represents the updated mechanical parameters of the cock in the tth iteration, represents the mechanical parameters of the rooster in the tth iteration;

[0117] Hen update formula:

[0118]

[0119]

[0120]

[0121] in, It's a hen spouse, In the group of roosters and hens, except for the individual An individual randomly selected from other i 、f r1 、f r2 Individual The corresponding fitness value; represents the updated mechanical parameters of the hen in the tth iteration, represents the mechanical parameters of the hen in the tth iteration;

[0122] Chicken update formula:

[0123]

[0124] Wherein, FL is a learning factor, and in this embodiment, the value is 2; Indicates t The updated mechanical parameters of the chicken in the iteration, denotes the mechanical parameters of the chick in the tth iteration.

[0125] As a specific embodiment, the above solution is described in detail in combination with FIG. 3(a) and FIG. 3(b):

[0126] Step S301: Establish a "student" population with a total of I individuals, with initial mechanical parameters s X=( s a y , s d y , s α y , s l),s=1,2,...,I,y=1,2,..,6 as the initial values ​​of the target mechanical parameters;

[0127] Step S302: Input the obtained N sets of joint angle values n θ m (n=1,2,...,N;m=1,2,...,6), calculate the fitness corresponding to each set of target mechanical parameters according to N sets of joint angle values;

[0128] Step S303: Select the individual with the highest fitness as the “teacher” M new , the remaining mechanical parameters are "students", and the historical optimal target mechanical parameters gbest are initialized to gbest = M new ;

[0129] Step S304: setting the first iteration number k=1;

[0130] Step S305: setting the second iteration number i=1;

[0131] Step S306: Update the optimized mechanical parameters of the i-th “student” individual to X according to formulas (1), (2), and (3). new,i ;

[0132] Step S307: Determine the fitness (X) corresponding to the optimized "student" mechanical parameters new,i ) is greater than or equal to the teacher's fitness (M new ), if fitness(X new,i )≥fitness(M new ), go to step S308, if fitness(X new,i ) <fitness(M new ), proceed to step S309;

[0133] Step S308: Use the optimized “student” as the new “teacher”, that is, let M new =X new,i ;

[0134] Step S309: Determine the fitness (M) of the “teacher” new,i) greater than or equal to the fitness (gbest) corresponding to the historical optimal target mechanical parameters; if fitness(M new,i ) ≥ fitness(gbest), go to step S310; if fitness(M new,i ) < fitness(gbest), go to step S311;

[0135] Step S310: Set "teacher" as the historical optimal target mechanical parameters of the current iteration, that is: let gbest = M new,i ;

[0136] Step S311: Determine whether the first iteration number i is greater than the population size I. If i ≤ I, go to step S312; if i > I, go to step S313;

[0137] Step S312: Increment the first iteration number by 1, i = i + 1, and return to step S306;

[0138] Step S313: Determine whether the first iteration number i and the preset first threshold Q satisfy the conversion condition i Mod Q > 0, where Mod represents the remainder operation; if i Mod Q ≤ 0, go to step S314; if i Mod Q > 0, go to step S317;

[0139] Step S314: Determine whether the second iteration number k is greater than the first maximum iteration number K; if k > K, go to step S315, if k ≤ K, go to step S316;

[0140] Step S315: Output the current historical optimal target mechanical parameter gbest as the optimized mechanical parameter and end the operation;

[0141] Step S316: Increment the second iteration number by 1, k = k + 1, and return to step S305;

[0142] Step S317: Select the optimal individual p i and the historical optimal target mechanical parameter gbest according to the fitness of the current individual;

[0143] Step S318: Set the third iteration number t = 1;

[0144] Step S319: Determine whether the third iteration number t and the preset second threshold G satisfy the conversion condition t Mod G = 0; if t Mod G = 0, go to step S320; if t Mod G ≠ 0, go to step S321;

[0145] Step S320: Establish the chicken flock hierarchy, including roosters, hens, and chicks;

[0146] Step S321: Set the fourth iteration number s = 1;

[0147] Step S322: Update the rooster according to formula (4) to

[0148] Step S323: Update the hen according to formulas (5), (6), and (7) to

[0149] Step S324: Update the chick according to formula (8) to

[0150] Step S325: Determine whether the fourth iteration number s is greater than or equal to the total number of individuals I. If s < I, go to Step S326; if s ≥ I, go to Step S327;

[0151] Step S326: Increment the fourth iteration number s by 1, s = s + 1, and return to Step S322;

[0152] Step S327: Set the fifth iteration number n = 1;

[0153] Step S328: Determine whether the fitness of the updated individual and the current individual satisfies If go to Step S329; if go to Step S330;

[0154] Step S329: Take the updated individual as the optimal individual p n : Let

[0155] Step S330: Determine whether the fitness of the selected individual and the historical optimal mechanical parameters satisfies fitness(p n ) ≥ fitness(gbest); if fitness(p n ) ≥ fitness(gbest), go to Step S331; if fitness(p n ) < fitness(gbest), go to Step S332;

[0156] Step S331: Let the optimal individual p n be the historical optimal mechanical parameters, gbest = p n ;

[0157] Step S332: Determine whether the third iteration number t is greater than the preset second maximum iteration number T. If t ≤ T, go to Step S333; if t > T, return to Step S314;

[0158] Step S333: The third iteration number t is increased by 1, t=t+1, and the process returns to step S319.

[0159] The embodiment of the present invention provides a device for optimizing equipment mechanical parameters based on improved teaching and chicken swarm algorithm, and its structural block diagram is as follows: Figure 4 As shown, the equipment mechanical parameter optimization device 400 based on improved teaching and chicken swarm algorithm includes:

[0160] Acquisition module 401: used to acquire initial mechanical parameters of the device and positioning measurement values ​​of the device;

[0161] Analysis module 402: used to create a motion model of the device, and obtain target mechanical parameters according to the motion model, initial mechanical parameters and positioning measurement values;

[0162] Optimization module 403: used to optimize the target mechanical parameters by using a hybrid intelligent algorithm based on an improved teaching algorithm and an improved chicken swarm algorithm to obtain the optimized mechanical parameters of the equipment.

[0163] like Figure 5 As shown, the above-mentioned method for optimizing mechanical parameters of equipment based on improved teaching and chicken swarm algorithm, the present invention also provides an electronic device 500 accordingly, which can be a computing device such as a mobile terminal, a desktop computer, a notebook, a palm computer and a server. The electronic device includes a processor 501, a second memory 502 and a display 503.

[0164] In some embodiments, the second memory 502 may be an internal storage unit of a computer device, such as a hard disk or memory of a computer device. In other embodiments, the second memory 502 may also be an external storage device of a computer device, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the computer device. Further, the second memory 502 may also include both an internal storage unit of a computer device and an external storage device. The second memory 502 is used to store application software and various types of data installed on the computer device, such as program codes for installing the computer device. The second memory 502 may also be used to temporarily store data that has been output or is to be output. In one embodiment, a program 504 of a device mechanical parameter optimization method based on improved teaching and chicken swarm algorithm is stored on the second memory 502, and the program 504 of the device mechanical parameter optimization method based on improved teaching and chicken swarm algorithm can be executed by the processor 501, thereby realizing the device mechanical parameter optimization method based on improved teaching and chicken swarm algorithm of each embodiment of the present invention.

[0165] In some embodiments, the processor 501 can be a central processing unit (CPU), a microprocessor or other data processing chip, which is used to run the program code or process data stored in the second memory 502, such as executing an equipment mechanical parameter optimization program based on improved teaching and chicken swarm algorithm.

[0166] In some embodiments, the display 503 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, etc. The display 503 is used to display information on the computer device and to display a visual user interface. The components 5015603 of the computer device communicate with each other through a system bus.

[0167] This embodiment also provides a computer-readable storage medium, on which is stored a program for a method for optimizing equipment mechanical parameters based on improved teaching and chicken swarm algorithm. When the processor executes the program, the method for optimizing equipment mechanical parameters based on improved teaching and chicken swarm algorithm as described above is implemented.

[0168] The computer-readable storage medium and computing device provided according to the above-mentioned embodiments of the present invention can be implemented with reference to the specific description of the equipment mechanical parameter optimization method based on improved teaching and chicken swarm algorithm as described above according to the present invention, and have similar beneficial effects as the equipment mechanical parameter optimization method based on improved teaching and chicken swarm algorithm as described above, which will not be repeated here.

[0169] The present invention discloses a method, device, electronic device and computer-readable storage medium for optimizing equipment mechanical parameters based on improved teaching and chicken swarm algorithm. First, the initial mechanical parameters of the equipment and the positioning measurement values ​​of the equipment are obtained; secondly, a motion model of the equipment is created, and the target mechanical parameters are obtained according to the motion model, the initial mechanical parameters and the positioning measurement values; finally, the target mechanical parameters are optimized by using a hybrid intelligent algorithm based on the improved teaching algorithm and the improved chicken swarm algorithm to obtain the optimized mechanical parameters of the equipment.

[0170] The method of the present invention has fast convergence speed and strong global exploration capability, and can correct the initial mechanical parameters of the equipment in a short time, find the actual mechanical parameters of the equipment, and reduce the errors caused by inaccurate parameters during the use of the equipment.

[0171] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by any technician familiar with the technical field within the technical scope disclosed by the present invention should be covered within the protection scope of the present invention.

Claims

1. A method for optimizing equipment mechanical parameters based on improved teaching and chicken swarm algorithm, It is characterized in that include: Obtaining initial mechanical parameters of the device and positioning measurement values ​​of the device; Creating a motion model of the device, and obtaining target mechanical parameters based on the motion model, initial mechanical parameters, and positioning measurements; Utilizing a hybrid intelligent algorithm based on an improved teaching algorithm and an improved chicken swarm algorithm, the target mechanical parameters are optimized to obtain optimized mechanical parameters of the equipment; A motion model of the device is created, and target mechanical parameters are obtained according to the motion model, initial mechanical parameters, and positioning measurement values, including: Obtaining mechanical coordinate data of the device corresponding to the positioning measurement value according to the motion model, the initial mechanical parameters and the positioning measurement value; Calculating the fitness corresponding to the mechanical coordinate data according to the mechanical coordinate data; Creating an objective function with the target mechanical parameter as an independent variable and the fitness as a dependent variable; The objective function is solved by using a hybrid intelligent algorithm based on an improved teaching algorithm and an improved chicken swarm algorithm to obtain an optimal fitness, and the target mechanical parameters corresponding to the optimal fitness are optimized mechanical parameters; The objective function is solved by using a hybrid intelligent algorithm based on an improved teaching algorithm and an improved chicken swarm algorithm, including: Iteratively solving the objective function using an improved teaching algorithm to determine whether the number of iterations of the teaching algorithm meets a preset conversion condition; when the number of iterations does not meet the conversion condition, obtaining new target mechanical parameters using an improved chicken swarm algorithm, and then optimizing the new target mechanical parameters using the improved teaching algorithm to obtain optimized mechanical parameters; The improved teaching algorithm is divided into a teaching phase and a learning phase, and the improved teaching algorithm is used to iteratively solve the objective function, including: In the teaching stage, the initial mechanical parameters are used as the initial values ​​of the target mechanical parameters, and the fitness corresponding to the target mechanical parameters is calculated; the target mechanical parameters corresponding to the maximum fitness value are selected as "teachers", and the remaining target mechanical parameters are used as "students"; In each iteration, the "student" is optimized by narrowing the gap between the average values ​​of the "teacher" and the "student". After the optimization is completed, the fitness of the optimized "student" and the "teacher" is calculated; the fitness of the "teacher" and the optimized "student" is compared, and the target mechanical parameter corresponding to the maximum fitness value is selected as the new "teacher"; In the learning phase, each iteration optimizes the target mechanical parameters by narrowing the gap between the "students"; The narrowing of the gap between the average values ​​of the "teacher" and the "student" and optimizing the "student" includes: Use the following formula to optimize "student": ， in, Indicates "students", Indicates An optimized "student", Represents the difference between "teacher" and "student", is a random number between (0, 1). Indicates generating a random number; represents the historical optimal target mechanical parameters, Indicates the current "teacher", represents the average value of "students"; represents the learning factor, Is a random number.

2. The equipment mechanical parameter optimization method based on improved teaching and chicken swarm algorithm according to claim 1, It is characterized in that Obtain the initial mechanical parameters of the equipment, including: Obtain the nominal mechanical parameters of the equipment; According to the nominal mechanical parameters, a constraint range is preset, and a plurality of initial mechanical parameters are generated within the constraint range using a random function.

3. The equipment mechanical parameter optimization method based on improved teaching and chicken swarm algorithm according to claim 1, It is characterized in that Calculating the fitness corresponding to the mechanical coordinate data according to the mechanical coordinate data includes: According to the mechanical coordinate data, a coordinate error value of the mechanical coordinate data is calculated using the Laida criterion; The fitness corresponding to the mechanical coordinate data is obtained according to the coordinate error value.

4. A device for optimizing equipment mechanical parameters based on improved teaching and chicken swarm algorithm, applicable to the method for optimizing equipment mechanical parameters based on improved teaching and chicken swarm algorithm as described in any one of claims 1 to 3, It is characterized in that include: Acquisition module: used to obtain the initial mechanical parameters of the equipment and the positioning measurement values ​​of the equipment; Analysis module: used for creating a motion model of the device, and obtaining target mechanical parameters according to the motion model, initial mechanical parameters and positioning measurement values; Optimization module: used to optimize the target mechanical parameters by using a hybrid intelligent algorithm based on an improved teaching algorithm and an improved chicken swarm algorithm to obtain the optimized mechanical parameters of the equipment.

5. An electronic device, It is characterized in that It comprises a processor and a memory, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the equipment mechanical parameter optimization method based on improved teaching and chicken swarm algorithm as described in any one of claims 1 to 3 is implemented.

6. A computer-readable storage medium, It is characterized in that A computer program is stored thereon, and when the computer program is executed by a processor, the method for optimizing equipment mechanical parameters based on improved teaching and chicken swarm algorithm as described in any one of claims 1 to 3 is implemented.

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