A system and method for controlling the online operation switching of the main axis based on big data

Abstract

The invention discloses an online operation switching spindle control system and method based on big data, and relates to the field of spindle control systems. The invention adds an intermediate shaft between the spindle and the slave shaft in a servo system to complete the spindle switching operation during the online operation of the servo system, thereby realizing the switching of the spindle of the equipment during operation; by collecting the stability influencing parameter data and the corresponding stability level data of the servo system, data support is provided for the subsequent construction of a final classification center data matrix that can reflect the corresponding relationship between the two; by constructing the final classification center data matrix, a classification basis is provided for the subsequent acquisition of the stability level of the servo system that is to be switched when switching the spindle; from the aspect of stability, it is reflected whether the currently constructed acceleration change equation meets the requirements; if not, the constructed acceleration change equation is optimized to ensure that the servo system can run stably.

Description

Technical Field

[0001] The present invention belongs to the field of spindle control systems, and in particular, relates to an online operation switching spindle control system and method based on big data. Background Art

[0002] When the servo system is switched between offline and online operation, the main axis followed by multiple slave axis cams is changed, and they either follow virtual main axis one or main axis two. However, the existing motion control function does not allow the main axis to be switched during operation. If it is changed, it will cause the equipment to stop unexpectedly, and there is no way to meet the requirements. Summary of the invention

[0003] In view of the problems in the related technology, the present invention proposes an online operation switching spindle control system and method based on big data to overcome the above-mentioned technical problems existing in the existing related technology.

[0004] In order to solve the above technical problems, the present invention is achieved through the following technical solutions:

[0005] The present invention is a method for controlling the online operation switching spindle based on big data, comprising the following steps:

[0006] S1, collecting the main axis and intermediate axis data of multiple servo systems;

[0007] S2, collecting stability influencing parameter data and corresponding stability level data of multiple servo systems at multiple historical time points; then obtaining and optimizing the classification center data of each type of data in the collected stability influencing parameter data according to the collected stability level data, and obtaining the final classification center data matrix;

[0008] S3, constructing acceleration change equations of the intermediate shafts of multiple servo systems during acceleration and deceleration processes; and obtaining stability influencing parameter data of the multiple servo systems based on the constructed acceleration change equations of the multiple servo systems to obtain a second stability influencing parameter data matrix set;

[0009] S4, classifying the stability level of the servo system corresponding to the second stability influencing parameter data matrix set according to the final classification center data matrix to obtain a stability level data matrix; and then determining whether it is necessary to optimize the acceleration change equation of the servo system according to the stability level data matrix to obtain a first final acceleration change equation set and a second final acceleration change equation set;

[0010] S5, determining whether to apply the first final acceleration change equation set and the second final acceleration change equation set to the servo system of the spindle to be switched according to the rotation angle data of the spindle and the intermediate shaft of the servo system of the spindle to be switched and the spindle and intermediate shaft data of multiple servo systems;

[0011] In this scheme, an intermediate shaft is added between the main shaft and the slave shaft in the servo system to complete the main shaft switching operation during the online operation of the servo system; firstly, by collecting the main shaft and intermediate shaft data of multiple servo systems, it provides data reference for the subsequent main shaft switching during the operation of the servo system to be switched, so as to select the corresponding most suitable acceleration change equation; by collecting the stability influencing parameter data and the corresponding stability level data of the servo system, it provides data support for the subsequent construction of the final classification center data matrix that can reflect the corresponding relationship between the two, and by constructing the final classification center data matrix, it provides data support for the stability of the servo system to be switched when switching the main shaft. The acquisition of grades provides a basis for classification; it reflects whether the currently constructed acceleration change equation meets the requirements from the aspect of the stability of multiple servos. If it does not meet the requirements, the constructed acceleration change equation is optimized to ensure that the servo system can operate stably when the constructed acceleration change equation is applied to the servo system; when the servo system of the spindle to be switched performs spindle switching, the spindle rotation angle data of the servo system of the spindle to be switched is compared with the spindle rotation angle data of multiple servo systems collected, so as to select which servo system's acceleration change equation is used to apply in the servo system of the spindle to be switched, so as to ensure the stability of the servo system of the spindle to be switched when the spindle is switched.

[0012] Preferably, the S1 comprises the following steps:

[0013] S11, set the servo system set a={a1,a2,...,a i ,...,a a′}, a i represents the set i-th servo system, a′ represents the total number of set servo systems; then the hardware of each servo system in the servo system set is set to obtain the servo spindle hardware matrix as follows,

[0014]

[0015] in, They respectively represent the virtual spindle 1, spindle 2 and intermediate axis of the i-th servo system in the servo system; a 2ij represents the jth slave axis of the i-th servo system in the servo system, Indicates the total number of slave axes in each servo system of the servo system;

[0016] S12, setting a first operating state and a second operating state; the first operating state means that each slave axis in each servo system in the servo system set follows the virtual master axis 1 to run synchronously; the second operating state means that each slave axis in each servo system in the servo system set follows the master axis 2 to run synchronously;

[0017] Each servo system in the servo system set is set to the first operating state and the second operating state respectively; after the setting is completed, an encoder with the same precision is used to measure the rotation angle data of the virtual spindle 1, spindle 2 and intermediate shaft in each servo system in the servo system set to obtain the first rotation angle data matrix b1 and the second rotation angle data matrix b2; respectively as follows,

[0018]

[0019] Among them, b 1i1 , b 1i2 , b 1i3 respectively represent the rotation angle data of the virtual spindle 1, spindle 2 and intermediate shaft after the i-th servo system in the servo system set is set to the first operating state; b 2i1 , b 2i2 , b 2i3 respectively represent the rotation angle data of the virtual spindle 1, the spindle 2 and the intermediate shaft after the i-th servo system in the servo system set is set to the second operating state;

[0020] By collecting the data of the main axes and intermediate axes of multiple servo systems in the servo system, the richness of the data is improved; since the situation when each slave axis in the servo system follows the virtual main axis 1 synchronously and the situation when each slave axis in the servo system follows the main axis 2 synchronously are different, the rotation angle data of the main axes and intermediate axes in these two operating states are collected, thereby improving the comprehensiveness of the collected data;

[0021] Preferably, S2 comprises the following steps:

[0022] S21, set the parameter type that affects the stability of the servo system operation, and obtain the parameter type set that affects the stability of the servo system operation. c i ′ represents the type of parameter that affects the stability of the servo system. Indicates the total number of parameter types that affect the stability of the servo system operation;

[0023] S22. Set multiple historical time points to obtain a set of historical time points represents the set i-th historical time point, d represents the total number of set historical time points; and then multiple servo system stability levels are set to obtain the stability level set d i ′ represents the set i-th stability level, The total number of stability levels set;

[0024] According to the historical time point set And the set of affected parameter types Acquisition servo system set a={a1,a2,...,a i ,...,a a′}, and the stability affecting parameter data and the corresponding stability level data of each servo system, and obtain the first stability affecting parameter data matrix set and stability level data matrix e; represents the stability influencing parameter data matrix at the i-th historical time point; and e are as follows,

[0025]

[0026] in, represents the kth stability influencing parameter data of the jth servo system at the ith historical time point, e ij represents the stability level data of the i-th servo system at the j-th historical time point;

[0027] S22, according to the stability level data matrix e, the first stability influencing parameter data matrix set The data is divided into two parts to obtain the classification matrix set of influencing parameter data. e i ′ represents the i-th influencing parameter data classification matrix divided as follows,

[0028]

[0029] Among them, e i ' jk represents the kth stability influencing parameter data of the jth servo system in the ith influencing parameter data classification matrix; represents the total number of stability influencing parameter data sets in the i-th influencing parameter data classification matrix;

[0030] S23, obtaining the influencing parameter data classification matrix set The optimal classification center data of each influencing parameter data classification matrix in the final classification center data matrix is ​​obtained as follows,

[0031]

[0032] in, Representing the jth stability influencing parameter data in the classification center data set of the i-th influencing parameter data classification matrix in the influencing parameter data classification matrix set;

[0033] Preferably, the influencing parameter types include vibration level, noise change, current, voltage, speed and position, etc.;

[0034] By collecting the stability influencing parameter data and the corresponding stability level data of each servo system in the servo system set, a classification model for classifying the stability level of a set of servo systems is constructed, so as to facilitate the subsequent evaluation of the pros and cons of each different acceleration change equation.

[0035] Preferably, the S23 comprises the following steps:

[0036] S231. Build the first pelican population f 1i represents the i-th pelican in the first pelican population, f1′ represents the size of the first pelican population; the maximum number of iterations of the first pelican population is set to The current number of iterations is They are respectively recorded as the first maximum number of iterations and the first current number of iterations; and the search space dimension of the first pelican population is set to

[0037] S232, multiple times from the influencing parameter data classification matrix set A row of data is randomly selected from each influencing parameter data classification matrix to form the initial position matrix of each pelican in the first pelican population, and the initial position matrix set is obtained. represents the initial position matrix of the i-th pelican in the first pelican population; as follows,

[0038]

[0039] in, express The kth stability influencing parameter data in the jth classification center data set;

[0040] S233, according to the initial position matrix set Construct the fitness function g of the first pelican population as follows:

[0041]

[0042] Among them, e i ' j′k represents the kth stability influencing parameter data of the j′th servo system in the i-th influencing parameter data classification matrix; represents the kth stability influencing parameter data in the i-th classification center data set in the initial position matrix of the i′th pelican in the first pelican population;

[0043] S234, start the iteration operation; before the iteration, set the first current iteration number of each iteration to 1; during each iteration, calculate the fitness value of each pelican in the first pelican population according to the fitness function g of the first pelican population to obtain a first fitness value set; take the maximum fitness in the first fitness value set as the first global optimal fitness and the corresponding pelican position as the first global optimal position;

[0044] Then, the position of each pelican in the first pelican population is updated according to the first global optimal position and the first global optimal fitness; after the update is completed, the first current iteration number is increased by 1 and the next round of iteration is entered;

[0045] S235, when When , the iteration is stopped and the first final global optimal position is output; the first final global optimal position is used as the final classification center data matrix

[0046] The Pelican optimization algorithm can effectively search the entire search space and find the optimal solution or the approximate optimal solution; at the same time, the search efficiency is high and a feasible solution can be found quickly; therefore, in this scheme, the Pelican optimization algorithm is used to perform multiple iterative optimizations on the classification center data of each influencing parameter data classification matrix in the influencing parameter data classification matrix set, and the overall discrete degree of the influencing parameter data classification matrix set is used as the criterion for measuring the quality of the current solution, and finally the overall discrete degree of the influencing parameter data classification matrix set is minimized.

[0047] Preferably, S3 comprises the following steps:

[0048] S31, in the first operating state, when the intermediate shaft of each servo system in the servo system is first accelerated and then decelerated;

[0049] Construct the acceleration change equation of each servo system in the servo system set during the acceleration process and the deceleration process, and obtain the first initial acceleration change equation set b1′={b1′1,b1′2,...,b1′ i ,...,b1′ a′} and the second initial acceleration change equation set b2′={b2′1,b2′2,...,b2′ i ,...,b2′ a′}; b1′ i and b2′ iThey respectively represent the acceleration variation equations of the i-th servo system in the servo system set during the acceleration process and the deceleration process; they are as follows,

[0050]

[0051] In the formula, Respectively represent b1′ i and b2′ i The first-order parameter in ; is the independent variable, indicating time; c 1i 、c 2i Respectively represent b1′ i and b2′ i The bias parameters in ;

[0052] S32, the first initial acceleration change equation set b1′={b1′1,b1′2,...,b1′ i ,...,b1′ a′} and the second initial acceleration change equation set b2′={b2′1,b2′2,...,b2′ i ,...,b2′ a′} is applied to the servo system set a={a1,a2,...,a i ,...,a a′} in each servo system and start online spindle switching;

[0053] Select the time points of the spindle switching process to obtain the switching time point set g i ′ represents the i-th time point selected during the spindle switching process, Indicates the total number of time points selected from the spindle switching process; according to the influencing parameter type set And switch time point set Acquisition servo system set a={a1,a2,...,a i ,...,a a′}, and obtain the second stability influencing parameter data matrix set represents the stability influencing parameter data matrix at the i-th switching time point; as follows,

[0054]

[0055] in, represents the kth stability influencing parameter data of the jth servo system at the i-th switching time point;

[0056] The constructed first initial acceleration change equation set and the second initial acceleration change equation set are applied to the corresponding servo system, and the stability influencing parameter data of the servo system are collected, providing data support for the subsequent classification of the stability level of the servo system.

[0057] Preferably, S4 comprises the following steps:

[0058] S41, respectively calculating the second stability influencing parameter data matrix set Each row of data in the final classification center data matrix The Euclidean distance of each row of data in the Euclidean distance matrix set is obtained. h i represents the Euclidean distance data matrix at the i-th switching time point; as follows,

[0059]

[0060] Among them, h ijk It means that at the i-th switching time point, the servo system set a={a1, a2, ..., a i ,...,a a′} from the stability influencing parameter data of the jth set of servo systems to the final classification center data matrix The Euclidean distance data between the k-th row of data;

[0061] S42, coordination stability level set The Euclidean distance matrix set The stability level data of the servo system corresponding to the minimum value of each row of data in each Euclidean distance matrix is ​​classified into the stability level corresponding to the classification center data, and the stability level data matrix h′ is obtained; as follows,

[0062]

[0063] Among them, h i ' jk It means that at the i-th switching time point, the servo system set a={a1, a2, ..., a i ,...,a a′}Stability level data of the jth servo system in;

[0064] S43, setting a stability level data threshold m1′; comparing each stability level data in the stability level data matrix h′ with the stability level data threshold m1′; when there is stability level data in the stability level data matrix h′ that is less than the stability level data threshold m1′, optimizing the first initial acceleration change equation and the second initial acceleration change equation of the servo system corresponding to the stability level data; after the optimization is completed, obtaining the first final acceleration change equation set and the second final acceleration change equation set; otherwise, there is no need to optimize the first initial acceleration change equation and the second initial acceleration change equation of the servo system corresponding to the stability level data;

[0065] The stability level of the servo system is classified to determine whether the corresponding first initial acceleration change equation and second initial acceleration change equation meet the requirements, thereby determining whether the first initial acceleration change equation and the second initial acceleration change equation need to be optimized.

[0066] Preferably, optimizing the first initial acceleration change equation and the second initial acceleration change equation of the servo system corresponding to the stability level data in S43 comprises the following steps:

[0067] S431, setting the first initial acceleration change equation to be optimized And the second initial acceleration change equation to be optimized They are as follows:

[0068]

[0069] In the formula, Respectively and The first-order parameter in; l1 and l2 represent and The bias parameters in ;

[0070] The first initial acceleration change equation to be optimized And the second initial acceleration change equation to be optimized Integrate the parameters in to get the initial parameter set

[0071] S432. Build a second pelican population f 2i represents the i-th pelican in the second pelican population, f2′ represents the size of the second pelican population; the maximum number of iterations of the second pelican population is set to The current number of iterations is Recorded as the second maximum number of iterations and the second current number of iterations respectively; and then setting the search space dimension of the second pelican population to 4;

[0072] S433: Based on the first initial parameter set The initial position of each pelican in the second pelican population is set by using sin chaotic mapping to obtain the first initial position matrix

[0073]

[0074] in, represent the components of the initial position of the i-th pelican in the second pelican population in the 1st, 2nd, 3rd, and 4th search space dimensions respectively;

[0075] The calculation formula of the sin chaotic map is as follows:

[0076]

[0077] In the formula, Respectively represent the result values ​​generated in the i-th round and the i+1-th round of iteration;

[0078] S434, constructing a fitness function matrix m of the second pelican population according to the stability level data threshold; as follows,

[0079]

[0080] In the formula, m′2 represents the first initial acceleration change equation to be optimized And the second initial acceleration change equation to be optimized The corresponding servo system stability level data;

[0081] S435, start iteration, before iteration, set the second current iteration number Set to 1; in each round of iteration, calculate the fitness value of each pelican in the second pelican population according to the fitness function matrix m of the second pelican population to obtain a second fitness value set; take the maximum fitness in the second fitness value set as the second global optimal fitness and the corresponding pelican position as the second global optimal position;

[0082] Then, the position of each pelican in the second pelican population is updated according to the second global optimal position and the second global optimal fitness; after the update is completed, the second current iteration number is increased by 1 and the next round of iteration is entered;

[0083] S436, when When , the iteration is stopped, and the second final global optimal position is output; the second final global optimal position is used as the final parameter set;

[0084] Substitute the final parameter set into the first initial acceleration change equation to be optimized And the second initial acceleration change equation to be optimized The first final acceleration change equation and the second final acceleration change equation are obtained; the first final acceleration change equation and the second final acceleration change equation are applied to the corresponding servo system to obtain an optimized servo system; the optimized servo system performs a spindle switching operation and switches the spindle according to the switching time point set. Calculate the stability level data of the optimized servo system at each switching time point to obtain an optimized stability level data set;

[0085] When each data in the optimized stability level data set is greater than or equal to the stability level data threshold m1′, the first final acceleration change equation and the second final acceleration change equation are substituted into the first initial acceleration change equation set b1′={b1′1, b1′2, ..., b1′ i ,...,b1′ a′} and the second set of initial acceleration change equations

[0086] b2′={b2′1,b2′2,...,b2′ i ,...,b2′ a′}, the first final acceleration change equation set and the second final acceleration change equation set are obtained; otherwise, return to S435 to continue iterating until each data in the optimized stability level data set is greater than or equal to the stability level data threshold m1′, and the first final acceleration change equation set and the second final acceleration change equation set are obtained;

[0087] The Pelican optimization algorithm is used to perform multiple iterative optimizations on the linear coefficients and bias parameters of the acceleration change equations that do not meet the requirements, and the corresponding servo system stability level is used as a criterion for measuring the quality of the current solution, so that the output first final acceleration change equation set and the second final acceleration change equation set can finally meet the requirements.

[0088] Preferably, the S5 comprises the following steps:

[0089] S51, setting a servo system of a spindle to be switched; using an encoder with the same precision to measure the rotation angle data of a virtual spindle 1, a spindle 2 and an intermediate shaft of the servo system of the spindle to be switched in a first operating state and a second operating state, to obtain a first spindle rotation angle data set and a second spindle rotation angle data set;

[0090] S52, calculating the Euclidean distance data between the first spindle rotation angle data set to be switched and the second spindle rotation angle data set to be switched and each row of data in the first rotation angle data matrix b1 and the second rotation angle data matrix b2, respectively, to obtain a first Euclidean distance data set and a second Euclidean distance data set;

[0091] S53, applying a first final acceleration change equation and a second final acceleration change equation of the servo system corresponding to the smallest Euclidean distance data set in the first Euclidean distance data set and the second Euclidean distance data set to the servo system of the spindle to be switched;

[0092] The rotation angle data of the spindle of the servo system of the spindle to be switched is compared with the spindle rotation angle data of multiple servo systems collected, so as to select which servo system's acceleration change equation to apply to the servo system of the spindle to be switched, thereby ensuring the stability of the servo system of the spindle to be switched when the spindle is switched.

[0093] An online operation switching spindle control system based on big data, comprising a servo system spindle data acquisition module, a first stability influencing parameter acquisition module, a classification center data optimization module, an initial acceleration change equation construction module, a second stability influencing parameter acquisition module, a classification module, an equation determination optimization module and a switching application determination module;

[0094] The servo system spindle data acquisition module is used to collect spindle and intermediate shaft data of multiple servo systems to obtain a first rotation angle data matrix and a second rotation angle data matrix;

[0095] The first stability influencing parameter acquisition module is used to acquire stability influencing parameter data and corresponding stability grade data of multiple servo systems at multiple historical time points to obtain a first stability influencing parameter data matrix set and a stability grade data matrix;

[0096] The classification center data optimization module is used to obtain and optimize the classification center data of each type of data in the first stability influencing parameter data matrix according to the stability level data matrix to obtain a final classification center data matrix;

[0097] The initial acceleration change equation construction module is used to construct acceleration change equations of multiple servo system intermediate shafts during acceleration and deceleration to obtain a first initial acceleration change equation set and a second initial acceleration change equation set;

[0098] The second stability influencing parameter acquisition module is used to acquire stability influencing parameter data of multiple servo systems based on the first initial acceleration change equation set and the second initial acceleration change equation set to obtain a second stability influencing parameter data matrix set;

[0099] The classification module is used to classify the stability level of the servo system corresponding to the second stability influencing parameter data matrix set according to the final classification center data matrix to obtain a stability level data matrix;

[0100] The equation determination optimization module is used to determine whether the acceleration change equation of the servo system needs to be optimized according to the stability level data matrix, so as to obtain a first final acceleration change equation set and a second final acceleration change equation set;

[0101] The switching application determination module is used to determine whether to apply the first final acceleration change equation set and the second final acceleration change equation set to the servo system of the main shaft to be switched according to the rotation angle data of the main shaft and the intermediate shaft of the servo system of the main shaft to be switched.

[0102] The present invention has the following beneficial effects:

[0103] 1. In the present invention, an intermediate shaft is added between the main shaft and the slave shaft in the servo system to complete the main shaft switching operation during the online operation of the servo system, thereby realizing the main shaft switching of the equipment during operation without any adverse effects on the equipment; wherein, by collecting the stability influencing parameter data and the corresponding stability level data of the servo system, data support is provided for the subsequent construction of a final classification center data matrix that can reflect the corresponding relationship between the two, and by constructing the final classification center data matrix, a classification basis is provided for the subsequent acquisition of the stability level of the servo system that switches the main shaft when switching the main shaft; from the aspect of stability, it reflects whether the currently constructed acceleration change equation meets the requirements. If it does not meet the requirements, the constructed acceleration change equation is optimized to ensure that when the constructed acceleration change equation is applied to the servo system, the servo system can operate stably.

[0104] 2. The present invention collects the rotation angle data of the main shaft and the intermediate shaft in two operating states: each slave shaft in the servo system follows the virtual main shaft one to run synchronously; and each slave shaft in the servo system follows the main shaft two to run synchronously, thereby improving the comprehensiveness of the collected data.

[0105] 3. In the present invention, the Pelican optimization algorithm is used to perform multiple iterative optimizations on the classification center data of each influencing parameter data classification matrix in the influencing parameter data classification matrix set, as well as the linear term coefficients and bias parameters of the acceleration change equation that do not meet the requirements, so that the corresponding optimal classification center and the optimal acceleration change equation parameters can be quickly obtained.

[0106] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0107] In order to more clearly illustrate the technical solutions of the embodiments of the invention, the drawings required for describing the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the invention. For ordinary technicians in this field, they can also obtain drawings based on these drawings without paying creative work.

[0108] Figure 1 The present invention is a flowchart of an online operation switching spindle control system based on big data for controlling online operation switching spindles. DETAILED DESCRIPTION

[0109] The following will be combined with the drawings in the embodiments of the invention to clearly and completely describe the technical solutions in the embodiments of the invention. Obviously, the described embodiments are only part of the embodiments of the invention, not all of the embodiments. Based on the embodiments in the invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the invention.

[0110] In the description of the present invention, it is necessary to understand that the terms "opening", "upper", "lower", "top", "middle", "inside" and the like indicating orientation or positional relationship are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the components or elements referred to must have a specific orientation, be constructed and operate in a specific orientation, and therefore cannot be understood as limiting the invention.

[0111] Embodiment 1

[0112] This embodiment is a method for controlling the online operation switching of the main axis based on big data, comprising the following steps:

[0113] 1. Receive a command to switch the spindle;

[0114] 2. When the actual position of virtual spindle 1 is greater than the actual position of spindle 2, the position deviation is the actual position of virtual spindle 1 minus the actual position of spindle 2; when the actual position of virtual spindle 1 is less than the actual position of spindle 2, the position deviation is the actual position of virtual spindle 1 minus the actual position of spindle 2 plus the cycle period of spindle 1.

[0115] 3. After calculating the position deviation, plan and execute the absolute positioning motion curve. The curve includes set position, set speed, set acceleration, and outputs the current position of the curve. After execution, the position output is 0; set position = position deviation; speed = catching up speed; acceleration = catching up acceleration;

[0116] Wherein, when setting the acceleration, the following steps are included:

[0117] S1, collecting the main axis and intermediate axis data of multiple servo systems;

[0118] The S1 comprises the following steps:

[0119] S11, set the servo system set a={a1,a2,...,a i ,...,a a′}, a i represents the set i-th servo system, a′ represents the total number of set servo systems; then the hardware of each servo system in the servo system set is set to obtain the servo spindle hardware matrix as follows,

[0120]

[0121] in, They respectively represent the virtual spindle 1, spindle 2 and intermediate axis of the i-th servo system in the servo system; a 2ij represents the jth slave axis of the i-th servo system in the servo system, Indicates the total number of slave axes in each servo system of the servo system;

[0122] S12, setting a first operating state and a second operating state; the first operating state means that each slave axis in each servo system in the servo system set follows the virtual master axis 1 to run synchronously; the second operating state means that each slave axis in each servo system in the servo system set follows the master axis 2 to run synchronously;

[0123] Each servo system in the servo system set is set to the first operating state and the second operating state respectively; after the setting is completed, an encoder with the same precision is used to measure the rotation angle data of the virtual spindle 1, spindle 2 and intermediate shaft in each servo system in the servo system set to obtain the first rotation angle data matrix b1 and the second rotation angle data matrix b2; respectively as follows,

[0124]

[0125] Among them, b 1i1 , b 1i2 , b 1i3 respectively represent the rotation angle data of the virtual spindle 1, spindle 2 and intermediate shaft after the i-th servo system in the servo system set is set to the first operating state; b 2i1 , b 2i2 , b 2i3 respectively represent the rotation angle data of the virtual spindle 1, the spindle 2 and the intermediate shaft after the i-th servo system in the servo system set is set to the second operating state;

[0126] S2, collecting stability influencing parameter data and corresponding stability level data of multiple servo systems at multiple historical time points; then obtaining and optimizing the classification center data of each type of data in the collected stability influencing parameter data according to the collected stability level data, and obtaining the final classification center data matrix;

[0127] The S2 comprises the following steps:

[0128] S21, set the parameter type that affects the stability of the servo system operation, and obtain the parameter type set that affects the stability of the servo system operation. c i ′ represents the type of parameter that affects the stability of the servo system. Indicates the total number of parameter types that affect the stability of the servo system operation;

[0129] S22. Set multiple historical time points to obtain a set of historical time points represents the set i-th historical time point, d represents the total number of set historical time points; and then multiple servo system stability levels are set to obtain the stability level set d i ′ represents the set i-th stability level, The total number of stability levels set;

[0130] According to the historical time point set And the set of affected parameter types Acquisition servo system set a={a1,a2,...,a i ,...,a a′}, and the stability affecting parameter data and the corresponding stability level data of each servo system, and obtain the first stability affecting parameter data matrix set and stability level data matrix e; represents the stability influencing parameter data matrix at the i-th historical time point; and e are as follows,

[0131]

[0132] in, represents the kth stability influencing parameter data of the jth servo system at the ith historical time point, e ij represents the stability level data of the i-th servo system at the j-th historical time point;

[0133] S22, according to the stability level data matrix e, the first stability influencing parameter data matrix set The data is divided into two parts to obtain the classification matrix set of influencing parameter data. ei ′ represents the i-th influencing parameter data classification matrix divided as follows,

[0134]

[0135] Among them, e i ' jk represents the kth stability influencing parameter data of the jth servo system in the ith influencing parameter data classification matrix; represents the total number of stability influencing parameter data sets in the i-th influencing parameter data classification matrix;

[0136] S23, obtaining the influencing parameter data classification matrix set The optimal classification center data of each influencing parameter data classification matrix in the final classification center data matrix is ​​obtained as follows,

[0137]

[0138] in, Representing the jth stability influencing parameter data in the classification center data set of the i-th influencing parameter data classification matrix in the influencing parameter data classification matrix set;

[0139] The S23 comprises the following steps:

[0140] S231. Build the first pelican population f 1i represents the i-th pelican in the first pelican population, f1′ represents the size of the first pelican population; the maximum number of iterations of the first pelican population is set to The current number of iterations is They are respectively recorded as the first maximum number of iterations and the first current number of iterations; and the search space dimension of the first pelican population is set to

[0141] S232, multiple times from the influencing parameter data classification matrix set A row of data is randomly selected from each influencing parameter data classification matrix to form the initial position matrix of each pelican in the first pelican population, and the initial position matrix set is obtained. represents the initial position matrix of the i-th pelican in the first pelican population; as follows,

[0142]

[0143] in, express The kth stability influencing parameter data in the jth classification center data set;

[0144] S233, according to the initial position matrix set Construct the fitness function g of the first pelican population as follows:

[0145]

[0146] Among them, e i ' j′k represents the kth stability influencing parameter data of the j′th servo system in the i-th influencing parameter data classification matrix; represents the kth stability influencing parameter data in the i-th classification center data set in the initial position matrix of the i′th pelican in the first pelican population;

[0147] S234, start the iteration operation; before the iteration, set the first current iteration number of each iteration to 1; during each iteration, calculate the fitness value of each pelican in the first pelican population according to the fitness function g of the first pelican population to obtain a first fitness value set; take the maximum fitness in the first fitness value set as the first global optimal fitness and the corresponding pelican position as the first global optimal position;

[0148] Then, the position of each pelican in the first pelican population is updated according to the first global optimal position and the first global optimal fitness; after the update is completed, the first current iteration number is increased by 1 and the next round of iteration is entered;

[0149] S235, when When , the iteration is stopped and the first final global optimal position is output; the first final global optimal position is used as the final classification center data matrix

[0150] S3, constructing acceleration change equations of the intermediate shafts of multiple servo systems during acceleration and deceleration processes; and obtaining stability influencing parameter data of the multiple servo systems based on the constructed acceleration change equations of the multiple servo systems to obtain a second stability influencing parameter data matrix set;

[0151] The S3 comprises the following steps:

[0152] S31, in the first operating state, when the intermediate shaft of each servo system in the servo system is first accelerated and then decelerated;

[0153] Construct the acceleration change equation of each servo system in the servo system set during the acceleration process and the deceleration process, and obtain the first initial acceleration change equation set b1′={b1′1,b1′2,...,b1′ i ,...,b1′ a′} and the second initial acceleration change equation set b2′={b2′1,b2′2,...,b2′ i ,...,b2′ a′}; b1′ i and b2′ i They respectively represent the acceleration variation equations of the i-th servo system in the servo system set during the acceleration process and the deceleration process; they are as follows,

[0154]

[0155] In the formula, Respectively represent b1′ i and b2′ i The first-order parameter in ; is the independent variable, indicating time; c 1i 、c 2i Respectively represent b1′ i and b2′ i The bias parameters in ;

[0156] S32, the first initial acceleration change equation set b1′={b1′1,b1′2,...,b1′ i ,...,b1′ a′} and the second initial acceleration change equation set b2′={b2′1,b2′2,...,b2′ i ,...,b2′ a′} is applied to the servo system set a={a1,a2,...,a i ,...,a a′} in each servo system and start online spindle switching;

[0157] Select the time points of the spindle switching process to obtain the switching time point set g i ′ represents the i-th time point selected during the spindle switching process, Indicates the total number of time points selected from the spindle switching process; according to the influencing parameter type set And switch time point set Acquisition servo system set a={a1,a2,...,a i ,...,a a′}, and obtain the second stability influencing parameter data matrix set represents the stability influencing parameter data matrix at the i-th switching time point; as follows,

[0158]

[0159] in, represents the kth stability influencing parameter data of the jth servo system at the i-th switching time point;

[0160] S4, classifying the stability level of the servo system corresponding to the second stability influencing parameter data matrix set according to the final classification center data matrix to obtain a stability level data matrix; and then determining whether it is necessary to optimize the acceleration change equation of the servo system according to the stability level data matrix to obtain a first final acceleration change equation set and a second final acceleration change equation set;

[0161] The S4 comprises the following steps:

[0162] S41, respectively calculating the second stability influencing parameter data matrix set Each row of data in the final classification center data matrix The Euclidean distance of each row of data in the Euclidean distance matrix set is obtained. h i represents the Euclidean distance data matrix at the i-th switching time point; as follows,

[0163]

[0164] Among them, h ijk It means that at the i-th switching time point, the servo system set a={a1, a2, ..., a i ,...,a a′} from the stability influencing parameter data of the jth set of servo systems to the final classification center data matrix The Euclidean distance data between the k-th row of data;

[0165] S42, coordination stability level set The Euclidean distance matrix set The stability level data of the servo system corresponding to the minimum value of each row of data in each Euclidean distance matrix is ​​classified into the stability level corresponding to the classification center data, and the stability level data matrix h′ is obtained; as follows,

[0166]

[0167] Among them, h i ' jk It means that at the i-th switching time point, the servo system set a={a1, a2, ..., a i ,...,a a′}Stability level data of the jth servo system in;

[0168] S43, setting a stability level data threshold m1′; comparing each stability level data in the stability level data matrix h′ with the stability level data threshold m1′; when there is stability level data in the stability level data matrix h′ that is less than the stability level data threshold m1′, optimizing the first initial acceleration change equation and the second initial acceleration change equation of the servo system corresponding to the stability level data; after the optimization is completed, obtaining the first final acceleration change equation set and the second final acceleration change equation set; otherwise, there is no need to optimize the first initial acceleration change equation and the second initial acceleration change equation of the servo system corresponding to the stability level data;

[0169] In S43, optimizing the first initial acceleration change equation and the second initial acceleration change equation of the servo system corresponding to the stability level data includes the following steps:

[0170] S431, setting the first initial acceleration change equation to be optimized And the second initial acceleration change equation to be optimized They are as follows:

[0171]

[0172] In the formula, Respectively and The first-order parameter in; l1 and l2 represent and The bias parameters in ;

[0173] The first initial acceleration change equation to be optimized And the second initial acceleration change equation to be optimized Integrate the parameters in to get the initial parameter set

[0174] S432. Build a second pelican population f 2i represents the i-th pelican in the second pelican population, f2′ represents the size of the second pelican population; the maximum number of iterations of the second pelican population is set to The current number of iterations is Recorded as the second maximum number of iterations and the second current number of iterations respectively; and then setting the search space dimension of the second pelican population to 4;

[0175] S433: Based on the first initial parameter set The initial position of each pelican in the second pelican population is set by using sin chaotic mapping to obtain the first initial position matrix

[0176]

[0177] in, represent the components of the initial position of the i-th pelican in the second pelican population in the 1st, 2nd, 3rd, and 4th search space dimensions respectively;

[0178] The calculation formula of the sin chaotic map is as follows:

[0179]

[0180] In the formula, Respectively represent the result values ​​generated in the i-th round and the i+1-th round of iteration;

[0181] S434, constructing a fitness function matrix m of the second pelican population according to the stability level data threshold; as follows,

[0182]

[0183] In the formula, m′2 represents the first initial acceleration change equation to be optimized And the second initial acceleration change equation to be optimized The corresponding servo system stability level data;

[0184] S435, start iteration, before iteration, set the second current iteration number Set to 1; in each round of iteration, calculate the fitness value of each pelican in the second pelican population according to the fitness function matrix m of the second pelican population to obtain a second fitness value set; take the maximum fitness in the second fitness value set as the second global optimal fitness and the corresponding pelican position as the second global optimal position;

[0185] Then, the position of each pelican in the second pelican population is updated according to the second global optimal position and the second global optimal fitness; after the update is completed, the second current iteration number is increased by 1 and the next round of iteration is entered;

[0186] S436, when When , the iteration is stopped, and the second final global optimal position is output; the second final global optimal position is used as the final parameter set;

[0187] Substitute the final parameter set into the first initial acceleration change equation to be optimized And the second initial acceleration change equation to be optimized The first final acceleration change equation and the second final acceleration change equation are obtained; the first final acceleration change equation and the second final acceleration change equation are applied to the corresponding servo system to obtain an optimized servo system; the optimized servo system performs a spindle switching operation and switches the spindle according to the switching time point set. Calculate the stability level data of the optimized servo system at each switching time point to obtain an optimized stability level data set;

[0188] When each data in the optimized stability level data set is greater than or equal to the stability level data threshold m1′, the first final acceleration change equation and the second final acceleration change equation are substituted into the first initial acceleration change equation set b1′={b1′1, b1′2, ..., b1′ i ,...,b1′ a′} and the second set of initial acceleration change equations

[0189] b2′={b2′1,b2′2,...,b2′ i ,...,b2′ a′}, the first final acceleration change equation set and the second final acceleration change equation set are obtained; otherwise, return to S435 to continue iterating until each data in the optimized stability level data set is greater than or equal to the stability level data threshold m1′, and the first final acceleration change equation set and the second final acceleration change equation set are obtained;

[0190] S5, determining whether to apply the first final acceleration change equation set and the second final acceleration change equation set to the servo system of the spindle to be switched according to the rotation angle data of the spindle and the intermediate shaft of the servo system of the spindle to be switched and the spindle and intermediate shaft data of multiple servo systems;

[0191] The S5 comprises the following steps:

[0192] S51, setting a servo system of a spindle to be switched; using an encoder with the same precision to measure the rotation angle data of a virtual spindle 1, a spindle 2 and an intermediate shaft of the servo system of the spindle to be switched in a first operating state and a second operating state, to obtain a first spindle rotation angle data set and a second spindle rotation angle data set;

[0193] S52, calculating the Euclidean distance data between the first spindle rotation angle data set to be switched and the second spindle rotation angle data set to be switched and each row of data in the first rotation angle data matrix b1 and the second rotation angle data matrix b2, respectively, to obtain a first Euclidean distance data set and a second Euclidean distance data set;

[0194] S53, applying a first final acceleration change equation and a second final acceleration change equation of the servo system corresponding to the smallest Euclidean distance data set in the first Euclidean distance data set and the second Euclidean distance data set to the servo system of the spindle to be switched;

[0195] 4. Execute position offset; set the position to the actual position of virtual spindle 1; position offset = position output after planning and executing the absolute positioning motion curve; mode = cycle mode; cycle = cycle of virtual spindle 1;

[0196] 5. Generate the actual execution data of the intermediate axis; Position = output position after executing position offset; Speed ​​= set speed of spindle 2 minus speed output set in the planned execution of absolute positioning motion curve; Acceleration = set speed of spindle 2 - acceleration output set in the planned execution of absolute positioning motion curve; Torque = set torque of spindle 2; Cycle period = cycle period of spindle 2; Mode = cycle period mode; Axis = intermediate axis;

[0197] 6. The switch is complete.

[0198] Embodiment 2

[0199] This embodiment discloses an online operation switching spindle control system based on big data, and the system can implement the method of the above embodiment, including a servo system spindle data acquisition module, a first stability influencing parameter acquisition module, a classification center data optimization module, an initial acceleration change equation construction module, a second stability influencing parameter acquisition module, a classification module, an equation determination optimization module and a switching application determination module;

[0200] The servo system spindle data acquisition module is used to collect spindle and intermediate shaft data of multiple servo systems to obtain a first rotation angle data matrix and a second rotation angle data matrix;

[0201] The first stability influencing parameter acquisition module is used to acquire stability influencing parameter data and corresponding stability grade data of multiple servo systems at multiple historical time points to obtain a first stability influencing parameter data matrix set and a stability grade data matrix;

[0202] The classification center data optimization module is used to obtain and optimize the classification center data of each type of data in the first stability influencing parameter data matrix according to the stability level data matrix to obtain a final classification center data matrix;

[0203] The initial acceleration change equation construction module is used to construct acceleration change equations of multiple servo system intermediate shafts during acceleration and deceleration to obtain a first initial acceleration change equation set and a second initial acceleration change equation set;

[0204] The second stability influencing parameter acquisition module is used to acquire stability influencing parameter data of multiple servo systems based on the first initial acceleration change equation set and the second initial acceleration change equation set to obtain a second stability influencing parameter data matrix set;

[0205] The classification module is used to classify the stability level of the servo system corresponding to the second stability influencing parameter data matrix set according to the final classification center data matrix to obtain a stability level data matrix;

[0206] The equation determination optimization module is used to determine whether the acceleration change equation of the servo system needs to be optimized according to the stability level data matrix, so as to obtain a first final acceleration change equation set and a second final acceleration change equation set;

[0207] The switching application determination module is used to determine whether to apply the first final acceleration change equation set and the second final acceleration change equation set to the servo system of the main shaft to be switched according to the rotation angle data of the main shaft and the intermediate shaft of the servo system of the main shaft to be switched.

[0208] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0209] The preferred embodiments of the invention disclosed above are only used to help explain the invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific implementation methods described. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the invention, so that those skilled in the art can understand and use the invention well.

Claims

1. A method for controlling the online operation switching spindle based on big data, characterized in that: The following steps are involved: S1, collecting the main axis and intermediate axis data of multiple servo systems; S2, collecting stability influencing parameter data and corresponding stability level data of multiple servo systems at multiple historical time points; then obtaining and optimizing the classification center data of each type of data in the collected stability influencing parameter data according to the collected stability level data, and obtaining the final classification center data matrix; S3, constructing acceleration change equations of the intermediate shafts of multiple servo systems during acceleration and deceleration processes; and obtaining stability influencing parameter data of the multiple servo systems based on the constructed acceleration change equations of the multiple servo systems to obtain a second stability influencing parameter data matrix set; S4, classifying the stability level of the servo system corresponding to the second stability influencing parameter data matrix set according to the final classification center data matrix to obtain a stability level data matrix; and then determining whether it is necessary to optimize the acceleration change equation of the servo system according to the stability level data matrix to obtain a first final acceleration change equation set and a second final acceleration change equation set; S5. Determine whether to apply the first final acceleration change equation set and the second final acceleration change equation set to the servo system of the spindle to be switched based on the rotation angle data of the spindle and the intermediate shaft of the servo system of the spindle to be switched and the spindle and intermediate shaft data of multiple servo systems.

2. According to the big data-based online operation switching spindle control method of claim 1, it is characterized in that: The S1 comprises the following steps: S11, setting a servo system set; and then setting the hardware of each servo system in the servo system set to obtain a servo spindle hardware matrix; S12, measuring the servo system, collecting the rotation angle data of the virtual spindle 1, spindle 2 and intermediate shaft in each servo system, and obtaining a first rotation angle data matrix and a second rotation angle data matrix.

3. The method for controlling the online operation switching of the main axis based on big data according to claim 2 is characterized in that: The S2 comprises the following steps: S21, setting the parameter type that affects the running stability of the servo system to obtain an influencing parameter type set; S22, setting a plurality of historical time points to obtain a historical time point set; then setting a plurality of servo system stability levels to obtain a stability level set; collecting stability influencing parameter data of the servo system set and corresponding stability level data according to the historical time point set and the influencing parameter type set to obtain a first stability influencing parameter data matrix set and a stability level data matrix; S22, dividing the first stability influencing parameter data matrix set according to the stability grade data matrix to obtain an influencing parameter data classification matrix set; S23, obtaining the optimal classification center data of the influencing parameter data classification matrix set to obtain a final classification center data matrix.

4. The method for controlling the online operation switching of the main axis based on big data according to claim 3 is characterized in that: The S23 comprises the following steps: S231, construct a first pelican population; set the maximum number of iterations of the first pelican population to The current number of iterations is S232, randomly selecting a row of data from each influencing parameter data classification matrix in the influencing parameter data classification matrix set for multiple times to form an initial position matrix for the first pelican population, to obtain an initial position matrix set; S233, constructing a fitness function of the first pelican population according to the initial position matrix set; S234, start an iterative operation; in each round of iteration, update the position of each pelican in the first pelican population according to the fitness function g of the first pelican population; S235, when When , stop the iteration and output the first final global optimal position; use the first final global optimal position as the final classification center data matrix; otherwise, continue the iteration.

5. The method for controlling the online operation switching of the main axis based on big data according to claim 4 is characterized in that: The S3 comprises the following steps: S31, constructing acceleration change equations of each servo system in the servo system set during acceleration and deceleration to obtain a first initial acceleration change equation set and a second initial acceleration change equation set; S32. Apply the first set of initial acceleration change equations and the second set of initial acceleration change equations to each servo system; select time points for the spindle switching process to obtain a switching time point set; collect stability influencing parameter data of each servo system according to the influencing parameter type set and the switching time point set to obtain a second stability influencing parameter data matrix set.

6. The method for controlling the online operation switching spindle based on big data according to claim 5 is characterized in that: The S4 comprises the following steps: S41, respectively calculating the Euclidean distance from each row of data in the second stability influencing parameter data matrix set to each row of data in the final classification center data matrix to obtain a Euclidean distance matrix set; S42, classifying the stability level data of the servo system corresponding to the minimum value of each row of data in each Euclidean distance matrix in the Euclidean distance matrix set into the stability level corresponding to the classification center data in accordance with the stability level set, to obtain a stability level data matrix; S43, setting a stability level data threshold; comparing each stability level data in the stability level data matrix with the stability level data threshold; when there is stability level data in the stability level data matrix that is less than the stability level data threshold, optimizing the first initial acceleration change equation and the second initial acceleration change equation of the servo system corresponding to the stability level data; after the optimization is completed, obtaining the first final acceleration change equation set and the second final acceleration change equation set; otherwise, there is no need to optimize the first initial acceleration change equation and the second initial acceleration change equation of the servo system corresponding to the stability level data.

7. The method for controlling the online operation switching of the main axis based on big data according to claim 6 is characterized in that: In S43, optimizing the first initial acceleration change equation and the second initial acceleration change equation of the servo system corresponding to the stability level data includes the following steps: S431, setting a first initial acceleration change equation to be optimized and a second initial acceleration change equation to be optimized; integrating parameters in the first initial acceleration change equation to be optimized and the second initial acceleration change equation to be optimized to obtain an initial parameter set; S432, construct a second pelican population; set the maximum number of iterations of the second pelican population to The current number of iterations is Denoted as the second maximum number of iterations and the second current number of iterations respectively; S433, setting the initial position of the second pelican population according to the first initial parameter set and using a sin chaotic mapping method to obtain a first initial position matrix; S434, constructing a fitness function matrix of the second pelican population according to the stability level data threshold; S435, start iteration; in each round of iteration, update the position of each pelican in the second pelican population according to the fitness function matrix m of the second pelican population; S436, when When , stop iteration and output the second final global optimal position; otherwise, continue iteration; take the second final global optimal position as the final parameter set; substitute the final parameter set into the first initial acceleration change equation to be optimized and the second initial acceleration change equation to be optimized to obtain the first final acceleration change equation and the second final acceleration change equation; apply the first final acceleration change equation and the second final acceleration change equation to the corresponding servo system to obtain the optimized servo system; calculate the stability level data of the optimized servo system at each switching time point according to the switching time point set to obtain the optimized stability level data set; when each data in the optimized stability level data set is greater than or equal to the stability level data threshold, substitute the first final acceleration change equation and the second final acceleration change equation into the first initial acceleration change equation set and the second initial acceleration change equation set to obtain the first final acceleration change equation set and the second final acceleration change equation set; otherwise, return to S435 to continue iteration until each data in the optimized stability level data set is greater than or equal to the stability level data threshold, and the first final acceleration change equation set and the second final acceleration change equation set are obtained.

8. The method for controlling the online operation switching of the main axis based on big data according to claim 7 is characterized in that: The S5 comprises the following steps: S51, setting a servo system of a spindle to be switched; measuring the rotation angle data of a virtual spindle 1, a spindle 2 and an intermediate shaft of the servo system of the spindle to be switched, and obtaining a first spindle rotation angle data set and a second spindle rotation angle data set; S52, calculating the Euclidean distance data between the first spindle rotation angle data set to be switched and the second spindle rotation angle data set to be switched and each row of data in the first rotation angle data matrix and the second rotation angle data matrix, respectively, to obtain a first Euclidean distance data set and a second Euclidean distance data set; S53: Apply the first final acceleration change equation and the second final acceleration change equation of the servo system corresponding to the smallest Euclidean distance data in the first Euclidean distance data set and the second Euclidean distance data set to the servo system of the spindle to be switched.

9. A system for implementing the online operation switching spindle control method based on big data as described in any one of claims 1 to 8.

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