阅读说明：本技术 注塑机料筒的温度pid控制器参数智能整定方法 (Intelligent setting method for temperature PID controller parameters of injection molding machine charging barrel ) 是由 任志刚 廖宁康 吴宗泽 王界兵 于 2021-08-23 设计创作，主要内容包括：本发明针对现有技术的局限性,提出了一种注塑机料筒的温度PID控制器参数智能整定方法,基于当前运行的特定注塑机料筒温度动态模型以及待优化的注塑件性能目标函数实现对PID参数的梯度信息的精确解析表达,并以表达出的参数梯度信息,利用梯度下降法来自动迭代求解PID最优参数值,可以取代或者辅助传统工人的手动反复调参,极大简化参数整定的过程,使得参数调整过程更加高效和智能化,达到理想的注塑机温度控制效果。本发明可以有效改进现有传统的人工PID参数整定方法,并且可以大规模应用于未来注塑机更精密的产品工业生产中,具有很好的现实生产意义。(The invention provides an intelligent setting method for parameters of a temperature PID controller of a charging barrel of an injection molding machine aiming at the limitations of the prior art, which is characterized in that the accurate analytic expression of the gradient information of a PID parameter is realized based on a currently operated specific charging barrel temperature dynamic model of the injection molding machine and a performance objective function of an injection molding piece to be optimized, the gradient information of the expressed parameter is used for automatically solving the optimal parameter value of the PID by iteration, the manual repeated parameter adjustment of the traditional workers can be replaced or assisted, the parameter setting process is greatly simplified, the parameter adjustment process is more efficient and intelligent, and the ideal temperature control effect of the injection molding machine is achieved. The invention can effectively improve the traditional manual PID parameter setting method, can be applied to the more precise product industrial production of the injection molding machine in the future in a large scale, and has good practical production significance.)

1. An intelligent parameter setting method for a temperature PID controller of a charging barrel of an injection molding machine is characterized by comprising the following steps:

s1, establishing a dynamic model of the target injection molding machine; establishing a temperature tracking objective function of the cavity of the injection molding machine according to production requirements;

s2, carrying out parameterization of proportion, integration and differentiation on the input signal of the temperature PID controller of the target injection molding machine;

s3, combining the parameterization result of the step S2, taking the temperature tracking objective function of the injection molding machine cavity as an optimization index, and taking the dynamic model as a constraint condition, and constructing an optimization problem of the parameters to be set of the temperature PID controller;

s4, solving gradient information of the injection molding machine cavity temperature tracking objective function about the parameters to be set of the temperature PID controller;

and S5, combining the gradient information, and applying a gradient descent method to carry out iterative optimization on the optimization problem to obtain the optimal value of the parameter to be set of the temperature PID controller.

2. The method for intelligently tuning PID controller parameters for temperature of a cylinder of an injection molding machine according to claim 1, wherein in the step S1, a dynamic model about a target injection molding machine is established by:

s11, acquiring a cavity temperature model of the charging barrel of the target injection molding machine;

s12, converting the cavity temperature model into a transfer function by using Laplace transform;

and S13, converting the transfer function into a form of a state equation by using a state space representation method to be used as a dynamic model of the target injection molding machine.

3. The method for intelligently tuning the parameters of the PID controller for the temperature of the injection molding machine cylinder according to claim 1, wherein the injection molding machine cavity temperature tracking objective function J is expressed by the following formula:

wherein, t_fRepresents a given time in the production request, r^dIndicating the target injection molding machine at a given time t_fOutput temperature desired value, x₁(t) represents a temperature output state value of the target injection molding machine.

4. The method for intelligently tuning the parameters of the PID controller for the temperature of the injection molding machine cylinder according to claim 3, wherein the parameterization result of the step S2 is expressed by the following formula:

u(t)＝K_p·(r^d-x₁(t))+K_i·x₃(t)-K_d·x₂(t)；

wherein u (t) represents the input signal of the temperature PID controller; k_p，K_i，K_dRespectively are proportional, integral and differential parameters to be set by a temperature PID controller of the target injection molding machine; x is the number of₂(t) is the temperature output state value x of the target injection molding machine₁(t) the derivative with respect to time t,temperature output tracking error e (t) r of injection molding machine^d-x₁(t)。

5. The method for intelligently tuning PID controller parameters for injection molding machine barrel temperature according to claim 4 wherein the optimization problem is expressed by the following formula:

x(0)＝0；

wherein:

state vector x ═ x₁(t)，x₂(t)，x₃(t)，x₄(t)]^T，k＝[K_p，K_i，K_d]^TRepresenting a parameter vector to be set; f (x (t | k), k) is the dynamic model.

6. The method for intelligently tuning PID controller parameters of injection molding machine cartridge temperature according to claim 5, wherein in step S4, gradient information of the injection molding machine cavity temperature tracking objective function with respect to the parameters to be set by the PID controller is obtained by the following formula

Wherein:

7. the PID controller parameter intelligent tuning method for temperature of injection molding machine barrel according to claim 5, wherein the iteration shape in step S5The formula is as follows: k is a radical of_t+1＝k_t+α·Δ_k；

Where α is the learning rate and Δ is the gradient vector

8. An intelligent parameter setting system for a temperature PID controller of a charging barrel of an injection molding machine is characterized by comprising a model and function establishing module (1), a parameterization module (2), an optimization problem establishing module (3), a gradient information obtaining module (4) and an iterative optimization searching module (5); the optimization problem building module (3) is respectively connected with the model and function building module (1) and the parameterization module (2), the gradient information acquisition module (4) is respectively connected with the model and function building module (1) and the parameterization module (2), and the iterative optimization searching module (5) is respectively connected with the optimization problem building module (3) and the gradient information acquisition module (4); wherein:

the model and function establishing module (1) is used for establishing a dynamic model related to a target injection molding machine; establishing a temperature tracking objective function of the cavity of the injection molding machine according to production requirements;

the parameterization module (2) is used for carrying out proportional, integral and differential parameterization on an input signal of a temperature PID controller of the target injection molding machine;

the optimization problem construction module (3) is used for constructing the optimization problem of the parameters to be set of the temperature PID controller by combining the parameterization result of the parameterization module (2) and taking the temperature tracking objective function of the cavity of the injection molding machine as an optimization index and the dynamic model as a constraint condition;

the gradient information acquisition module (4) is used for solving gradient information of a temperature tracking target function of the injection molding machine cavity about to-be-set parameters of the temperature PID controller;

the iterative optimization module (5) is used for combining the gradient information and applying a gradient descent method to carry out iterative optimization on the optimization problem to obtain the optimal value of the parameter to be set of the temperature PID controller.

9. A medium having a computer program stored thereon, characterized in that: the computer program when executed by a processor implements the steps of a method for intelligent tuning of PID controller parameters of the temperature of a cylinder of an injection molding machine according to any of claims 1 to 7.

10. A computer device, characterized by: comprising a medium, a processor and a computer program stored in said medium and executable by said processor, said computer program when executed by the processor implementing the steps of the method for intelligent tuning of PID controller parameters of the temperature of a cartridge of an injection molding machine according to any of claims 1 to 7.

Technical Field

The invention relates to the technical field of injection molding machine charging barrel cavity temperature control, in particular to an intelligent parameter setting method for a temperature PID controller of an injection molding machine charging barrel.

Background

The injection molding machine is an important carrier for ensuring the implementation of the whole precise injection molding process, the parameters such as the internal temperature of a cavity of the charging barrel, the injection speed and the like need to be precisely controlled in the operation process, and how to reasonably design the optimal parameter working interval of the controller is particularly important for the quality of the current injection molding finished product.

Currently, most internal temperature subsystems of a cavity of an injection molding machine still adopt a traditional proportional-integral-derivative (PID) controller to adjust the controller, wherein proportional (P) -integral (I) -derivative (D) parameters in the controller mainly depend on experience and knowledge of field workers, and manual trial and error parameter adjustment is performed on injection molding product specifications in different scenes; in the process, an operator is required to have a large amount of actual engineering experience knowledge and parameter adjustment tests, meanwhile, the difficulty of manual parameter adjustment is greatly increased due to the structural complexity based on different injection molding product parameters, the manual parameter adjustment process is complicated, time and labor are consumed, and the time cost of production is increased. Meanwhile, the manual parameter adjusting method often has the defects of large overshoot, large steady-state error and the like.

Chinese application patent publication No. CN11080914A, published as 2020.02.28: a parameter self-tuning temperature control method discloses a scheme for automatically learning parameters of proportional gain P, integral gain I and differential gain D according to different temperature control objects. This prior art technique has certain limitations.

Disclosure of Invention

Aiming at the limitation of the prior art, the invention provides an intelligent setting method for the parameters of a temperature PID controller of a charging barrel of an injection molding machine, and the technical scheme adopted by the invention is as follows:

an intelligent parameter setting method for a temperature PID controller of a charging barrel of an injection molding machine comprises the following steps:

s1, establishing a dynamic model of the target injection molding machine; establishing a temperature tracking objective function of the cavity of the injection molding machine according to production requirements;

s2, carrying out parameterization of proportion, integration and differentiation on the input signal of the temperature PID controller of the target injection molding machine;

s3, combining the parameterization result of the step S2, taking the temperature tracking objective function of the injection molding machine cavity as an optimization index, and taking the dynamic model as a constraint condition, and constructing an optimization problem of the parameters to be set of the temperature PID controller;

s4, solving gradient information of the injection molding machine cavity temperature tracking objective function about the parameters to be set of the temperature PID controller;

and S5, combining the gradient information, and applying a gradient descent method to carry out iterative optimization on the optimization problem to obtain the optimal value of the parameter to be set of the temperature PID controller.

Compared with the prior art, the method and the device have the advantages that the gradient information of the PID parameter is accurately analyzed and expressed based on the currently running specific injection molding machine charging barrel temperature dynamic model and the injection molding piece performance objective function to be optimized, the PID optimal parameter value is automatically solved in an iterative manner by using the gradient descent method according to the expressed parameter gradient information, the traditional manual repeated parameter adjustment of workers can be replaced or assisted, the parameter setting process is greatly simplified, the parameter adjustment process is more efficient and intelligent, and the ideal injection molding machine temperature control effect is achieved. The invention can effectively improve the traditional manual PID parameter setting method, can be applied to the more precise product industrial production of the injection molding machine in the future in a large scale, and has good practical production significance.

As a preferable scheme, in the step S1, the dynamic model of the target injection molding machine is established by:

s11, acquiring a cavity temperature model of the charging barrel of the target injection molding machine;

s12, converting the cavity temperature model into a transfer function by using Laplace transform;

and S13, converting the transfer function into a form of a state equation by using a state space representation method to be used as a dynamic model of the target injection molding machine.

As a preferred scheme, the injection molding machine cavity temperature tracking objective function J is expressed by the following formula:

wherein, t_fRepresents a given time in the production request, r^dIndicating the target injection molding machine at a given time t_fOutput temperature expectation ofValue, x₁(t) represents a temperature output state value of the target injection molding machine.

Further, the parameterization result of the step S2 is expressed by the following formula:

u(t)＝K_p·(r^d-x₁(t))+K_i·x₃(t)-K_d·x₂(t)；

wherein u (t) represents the input signal of the temperature PID controller; k_p，K_i，K_dRespectively are proportional, integral and differential parameters to be set by a temperature PID controller of the target injection molding machine; x is the number of₂(t) is the temperature output state value x of the target injection molding machine₁(t) the derivative with respect to time t,temperature output tracking error e (t) r of injection molding machine^d-x₁(t)。

Further, the optimization problem is expressed by the following formula:

x(0)＝0；

wherein:

state vector x ═ x₁(t)，x₂(t)，x₃(t)，x₄(t)]^T，

k＝[K_p，K_i，K_d]^TRepresenting a parameter vector to be set; f (x (t | k), k) is the dynamic model.

Further, in the step S4, the PID control of the injection molding machine cavity temperature tracking objective function with respect to the temperature is obtained by the following formulaGradient information of parameter to be set

Wherein:

further, the iteration in step S5 is in the form of: k is a radical of_t+1＝k_t+α·Δ_k；

Where α is the learning rate and Δ is the gradient vector

The present invention also provides the following:

an intelligent parameter setting system for a temperature PID controller of a charging barrel of an injection molding machine comprises a model and function establishing module, a parameterization module, an optimization problem establishing module, a gradient information obtaining module and an iterative optimization module; the optimization problem construction module is respectively connected with the model and function construction module and the parameterization module, the gradient information acquisition module is respectively connected with the model and function construction module and the parameterization module, and the iterative optimization searching module is respectively connected with the optimization problem construction module and the gradient information acquisition module; wherein:

the model and function establishing module is used for establishing a dynamic model related to the target injection molding machine; establishing a temperature tracking objective function of the cavity of the injection molding machine according to production requirements;

the parameterization module is used for carrying out parameterization of proportion, integration and differentiation on an input signal of a temperature PID controller of the target injection molding machine;

the optimization problem construction module is used for constructing an optimization problem of parameters to be set of the temperature PID controller by combining a parameterization result of the parameterization module, taking the temperature tracking objective function of the cavity of the injection molding machine as an optimization index and taking the dynamic model as a constraint condition;

the gradient information acquisition module is used for solving gradient information of a temperature tracking target function of the cavity of the injection molding machine about parameters to be set of the temperature PID controller;

and the iterative optimization searching module is used for combining the gradient information and applying a gradient descent method to iteratively search the optimization problem to obtain the optimal value of the parameter to be set of the temperature PID controller.

A medium having stored thereon a computer program which, when executed by a processor, implements the steps of the aforementioned method for intelligent tuning of PID controller parameters for the temperature of a cartridge of an injection molding machine.

A computer device comprising a medium, a processor and a computer program stored in said medium and executable by said processor, said computer program when executed by said processor implementing the steps of the aforementioned method for intelligent tuning of PID controller parameters for the temperature of a cartridge of an injection molding machine.

Drawings

FIG. 1 is a schematic flow chart of a method for intelligently tuning PID controller parameters of temperature of a charging barrel of an injection molding machine, which is provided by embodiment 1 of the invention;

fig. 2 is a schematic flowchart of step S1 provided in embodiment 1 of the present invention;

fig. 3 is a schematic diagram of an intelligent parameter setting system of a PID controller for temperature of a charging barrel of an injection molding machine, provided in embodiment 2 of the present invention.

Detailed Description

The drawings are for illustrative purposes only and are not to be construed as limiting the patent;

it should be understood that the embodiments described are only some embodiments of the present application, and not all embodiments. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments in the present application without any creative effort belong to the protection scope of the embodiments in the present application.

The terminology used in the embodiments of the present application is for the purpose of describing particular embodiments only and is not intended to be limiting of the embodiments of the present application. As used in the examples of this application and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.

When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the application, as detailed in the appended claims. In the description of the present application, it is to be understood that the terms "first," "second," "third," and the like are used solely to distinguish one from another and are not necessarily used to describe a particular order or sequence, nor are they to be construed as indicating or implying relative importance. The specific meaning of the above terms in the present application can be understood by those of ordinary skill in the art as appropriate.

Further, in the description of the present application, "a plurality" means two or more unless otherwise specified. "and/or" describes the association relationship of the associated objects, meaning that there may be three relationships, e.g., a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. The invention is further illustrated below with reference to the figures and examples.

In order to solve the limitation of the prior art, the present embodiment provides a technical solution, and the technical solution of the present invention is further described below with reference to the accompanying drawings and embodiments.

Example 1

Referring to fig. 1, a method for intelligently setting parameters of a PID controller for temperature of a cylinder of an injection molding machine includes the following steps:

s1, establishing a dynamic model of the target injection molding machine; establishing a temperature tracking objective function of the cavity of the injection molding machine according to production requirements;

s2, carrying out parameterization of proportion, integration and differentiation on the input signal of the temperature PID controller of the target injection molding machine;

s3, combining the parameterization result of the step S2, taking the temperature tracking objective function of the injection molding machine cavity as an optimization index, and taking the dynamic model as a constraint condition, and constructing an optimization problem of the parameters to be set of the temperature PID controller;

s4, solving gradient information of the injection molding machine cavity temperature tracking objective function about the parameters to be set of the temperature PID controller;

and S5, combining the gradient information, and applying a gradient descent method to carry out iterative optimization on the optimization problem to obtain the optimal value of the parameter to be set of the temperature PID controller.

Compared with the prior art, the method and the device have the advantages that the gradient information of the PID parameter is accurately analyzed and expressed based on the currently running specific injection molding machine charging barrel temperature dynamic model and the injection molding piece performance objective function to be optimized, the PID optimal parameter value is automatically solved in an iterative manner by using the gradient descent method according to the expressed parameter gradient information, the traditional manual repeated parameter adjustment of workers can be replaced or assisted, the parameter setting process is greatly simplified, the parameter adjustment process is more efficient and intelligent, and the ideal injection molding machine temperature control effect is achieved. The invention can effectively improve the traditional manual PID parameter setting method, can be applied to the more precise product industrial production of the injection molding machine in the future in a large scale, and has good practical production significance.

Specifically, in step S4, a sensitivity method may be used to solve gradient information of the injection molding machine cavity temperature tracking objective function with respect to the parameter to be set by the temperature PID controller.

Referring to fig. 2 as a preferred embodiment, in the step S1, a dynamic model about a target injection molding machine is established by the following steps:

s11, acquiring a cavity temperature model of the charging barrel of the target injection molding machine;

s12, converting the cavity temperature model into a transfer function by using Laplace transform;

and S13, converting the transfer function into a form of a state equation by using a state space representation method to be used as a dynamic model of the target injection molding machine.

Specifically, the corresponding cavity temperature model can be described according to the injection molding machine of a specific model of a specific manufacturer under the actual condition, the time-varying property of parameters can be considered in the modeling process, different disturbance categories and input nodes thereof can be determined according to different environmental scenes, and the dynamic constraint conditions of the model can be determined according to the physical upper and lower limits of the injection molding machine.

In an alternative embodiment, the cavity temperature model of the target injection molding machine barrel is described by the following second order differential equation:

wherein, y (t) is the temperature output signal of the charging barrel cavity of the injection molding machine, and u (t) is the input signal of the temperature PID controller of the target injection molding machine; tau, zeta and beta are the corresponding coefficients of the charging barrel system of the injection molding machine, and specific parameter setting can be carried out according to different injection molding machine system models.

The formula (i) is converted into the following transfer function through the step S12:

taking a taifu mechanical vertical high-speed disc injection molding machine with a model number of TFV4-85R2-SP as a specific example, tau, zeta and beta in the formula II can be specifically set to obtain the following specific second-order system transfer functions of the temperature control of the single-input single-output injection molding machine:

formula (c) will be converted in said step S13 into the form of the following equation of state:

wherein x is₁(t) represents a temperature output state value of the target injection molding machine; x is the number of₂(t) is the temperature output state value x of the target injection molding machine₁(t) derivative with time t.

As a preferred embodiment, the injection molding machine cavity temperature tracking objective function J is expressed by the following formula:

wherein, t_fRepresents a given time in the production request, r^dIndicating the target injection molding machine at a given time t_fOutput temperature desired value, x₁(t) represents a temperature output state value of the target injection molding machine.

Further, in the step S2, the input signal u (t) of the temperature PID controller may be represented by the following PID parameter form:

wherein, K_p，K_i，K_dRespectively are proportional, integral and differential parameters to be set by a temperature PID controller of the target injection molding machine; e (t) ═ r^d-x₁(t) outputting a tracking error for the temperature of the injection molding machine;

to be provided withThe following can be obtained by substituting a formula:

u(t)＝K_p·(r^d-x₁(t))+K_i·x₃(t)-K_d·x₂(t); formula (c)

On the basis of obtaining the above-mentioned parameterization result formula, further, by The injection molding machine cavity temperature tracking objective function J can be converted into:

meanwhile, the following conversion result can be further obtained by substituting the formula (sixty) into the equation of state (formula (iv):

the following optimization problem can thus be obtained:

x(0)＝0；

wherein:

state vector x ═ x₁(t)，x₂(t)，x₃(t)，x₄(t)]^T，k＝[K_p，K_i，K_d]^TRepresenting a parameter vector to be set; f (x (t | k), k) is the dynamic model, which can be in the shorthand form of formula ninu; the second constraint term x (0) ═ 0 indicates that all state variables have initial values of 0.

The optimization problem represented by equation r is a typical non-linear optimization problem with dynamically constrained equations, in that an optimal set of PID control gains K ═ K is found_p，K_i，K_d]^TMinimizing the objective function.

Further, in the step S4, the PID parameter variable K to be optimized [ K ] is calculated by introducing a sensitivity method to each state x (t) of the system_p，K_i，K_d]^TAnd establishing a sensitivity equation set according to the sensitivity information. To this end, the following parameter sensitivity variables may be defined:

namely, it isWherein the content of the first and second substances,is a 4 x 3 vector matrix with initial values of 0.

With the first constraint term in equation r, the state trajectory can be expressed as:

and successively solving the partial derivatives of k and t to obtain an extended differential equation set:

let the standard objective function, the state equation be of the form:

wherein, phi (x (t)_fI k), k) is a system end state performance index, L (x (t i k), k) is a tracking performance index, and the two satisfy the condition:

Φ(x(t_f|k)，k)＝0；

L(x(t|k)，k)＝[r(t)-x₁(t)]²；

and (3) solving partial derivatives of k on two sides of the equation to further obtain a gradient formula:

the model is a standard form of a gradient formula, the final-state performance index is omitted in the model, and a PID (proportion integration differentiation) parameter K [ K ] of an injection molding machine system output objective function J relative to the injection molding machine can be obtained_p，K_i，K_d]^TGradient information of,The following specific results:

wherein:

thus, the PID parameters K ═ K of the objective function J can be respectively obtained_p，K_i，K_d]^TThe gradient information of (a).

Further, the iteration form in step S5 is: k is a radical of_t+1＝k_t+α·Δ_k；

Where α is the learning rate and Δ is the gradient vectorAutomatic iterative optimization is carried out by using a gradient descent method, so that the target function is converged to a minimum value, and the optimal value corresponding to the minimum valueThe optimum parameters of the injection molding machine cavity temperature control system are obtained. The invention can effectively converge the initial parameter k to an optimal value and has stronger robustness.

Example 2

An intelligent parameter setting system for a temperature PID controller of a charging barrel of an injection molding machine is disclosed, and please refer to FIG. 3, and comprises a model and function establishing module 1, a parameterization module 2, an optimization problem establishing module 3, a gradient information obtaining module 4 and an iterative optimization searching module 5; the optimization problem building module 3 is respectively connected with the model and function building module 1 and the parameterization module 2, the gradient information acquisition module 4 is respectively connected with the model and function building module 1 and the parameterization module 2, and the iterative optimization module 5 is respectively connected with the optimization problem building module 3 and the gradient information acquisition module 4; wherein:

the model and function establishing module 1 is used for establishing a dynamic model related to a target injection molding machine; establishing a temperature tracking objective function of the cavity of the injection molding machine according to production requirements;

the parameterization module 2 is used for carrying out parameterization of proportion, integration and differentiation on an input signal of a temperature PID controller of the target injection molding machine;

the optimization problem construction module 3 is used for constructing an optimization problem of parameters to be set of the temperature PID controller by combining a parameterization result of the parameterization module 2, taking the temperature tracking objective function of the cavity of the injection molding machine as an optimization index and taking the dynamic model as a constraint condition;

the gradient information acquisition module 4 is used for solving gradient information of a temperature tracking target function of the injection molding machine cavity about parameters to be set of the temperature PID controller;

the iterative optimization module 5 is configured to perform iterative optimization on the optimization problem by using a gradient descent method in combination with the gradient information to obtain an optimal value of a parameter to be set of the temperature PID controller.

Example 3

A medium having stored thereon a computer program which, when executed by a processor, implements the steps of the method for intelligent tuning of PID controller parameters for temperature of a cartridge of an injection molding machine of embodiment 1.

Example 4

A computer apparatus comprising a medium, a processor, and a computer program stored in the medium and executable by the processor, the computer program when executed by the processor implementing the steps of the method for intelligent tuning of PID controller parameters for temperature of a cartridge of an injection molding machine of embodiment 1.

It should be understood that the above-described embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the claims of the present invention.