阅读说明：本技术 一种基于边缘计算的同步时钟维持系统与方法 (Synchronous clock maintaining system and method based on edge calculation ) 是由 周一飞 李锐超 程志炯 刘丽娜 屈鸣 李方硕 李林欢 王伟 龙海莲 于 2020-12-10 设计创作，主要内容包括：本发明公开了一种基于边缘计算的同步时钟维持系统与方法,该系统通过综合晶体老化特性曲线和影响晶振频率误差的多环境因素(温度、湿度和压强等)进行BP神经网络训练,实时更新预测函数模型,提高晶振频率修正精度；采用深度边缘计算对晶振输出频率误差进行实时分析学习,在丢失GPS信号后通过预测函数模型得到晶振频率调节字,及时修正晶振输出频率以实现时钟维持；集成晶振内外部环境因素测量传感器,便于为深度边缘计算提供训练数据库。(The invention discloses a synchronous clock maintaining system and method based on edge calculation, the system carries out BP neural network training by integrating a crystal aging characteristic curve and multiple environmental factors (temperature, humidity, pressure and the like) influencing crystal oscillator frequency error, updates a prediction function model in real time and improves crystal oscillator frequency correction precision; real-time analysis and learning are carried out on the crystal oscillator output frequency error by adopting depth edge calculation, a crystal oscillator frequency adjusting word is obtained through a prediction function model after GPS signals are lost, and the crystal oscillator output frequency is corrected in time to realize clock maintenance; and the internal and external environmental factor measuring sensors of the crystal oscillator are integrated, so that a training database is provided for depth edge calculation conveniently.)

1. A synchronous clock maintenance system based on edge calculation is characterized by comprising a GPS receiving module (1), a filtering module (2), a time interval measuring module (3), a depth edge calculating module (4), an integrated environment measuring module (5), a crystal oscillator frequency predicting module (6), a signal conditioning circuit (7) and a phase-locked loop frequency division module (9);

the GPS receiving module (1) is used for receiving a time signal sent by a satellite, analyzing the time signal and outputting a GPS second pulse signal for synchronous time service;

the filtering module (2) is used for receiving the GPS pulse-per-second signal, filtering the GPS pulse-per-second signal and outputting a filtering signal;

the phase-locked loop frequency division module (9) is used for receiving the frequency output by the crystal oscillator (8) and dividing the frequency to obtain a crystal oscillator second pulse signal;

the time interval measuring module (3) is used for receiving the filtering signal and the crystal oscillator second pulse signal in real time, calculating a time interval error between rising edges of the filtering signal and the crystal oscillator second pulse signal and feeding back the time interval error to the depth edge calculating module (4);

the integrated environment measuring module (5) is used for collecting environment data in real time and sending the environment data to the depth edge calculating module (4);

the depth edge calculation module (4) is used for acquiring a crystal aging characteristic curve, the time interval error and the environmental data in real time and inputting the acquired crystal aging characteristic curve, the time interval error and the environmental data into a BP (back propagation) neural network for training to obtain a prediction function model; performing edge calculation analysis on the crystal aging characteristic curve, the time interval error and the environmental data acquired in real time through the prediction function model, and outputting crystal oscillator frequency adjusting word information;

the crystal oscillator frequency prediction module (6) is used for receiving the crystal oscillator frequency adjusting word information fed back by the depth edge calculation module (4), predicting the crystal oscillator feedback control information at the next moment and sending the crystal oscillator feedback control information to the signal conditioning circuit (7);

and the signal conditioning circuit (7) is used for adjusting the output voltage and correcting the output frequency of the crystal oscillator at the next moment through the received crystal oscillator feedback control information so as to realize clock maintenance.

2. An edge computation based synchronous clock maintenance system according to claim 1, characterized in that the depth edge computation module (4) comprises a filtering unit (41) and a BP neural network unit (42):

the filtering unit (41) is used for acquiring the environmental data sent by the integrated environment measuring module (5) in real time, filtering the environmental data through a Kalman filtering algorithm to obtain processed data, and sending the processed data to the BP neural network unit (42);

the BP neural network unit (42) is used for taking the crystal aging characteristic curve, the time interval error and the processing data which are acquired in real time as original learning data, and inputting the original learning data into the BP neural network for learning to obtain a prediction function model; and outputting crystal oscillator frequency adjusting word information through the prediction function model.

3. The system of claim 2, wherein the filtering the environment data through the kalman filter algorithm to obtain the processed data comprises:

calculating an estimated value of the current state quantity according to a forward estimation state variable equation, and calculating an estimated value of the current error covariance according to a forward estimation error covariance formula;

and updating the estimation value of the current state quantity and the estimation value of the current error covariance through Kalman gain to obtain processing data.

4. The system of claim 2, wherein the inputting of the raw learning data into a BP neural network for learning to obtain a prediction function model comprises:

inputting the original learning data into an input layer of the BP neural network, wherein the input layer converts the original learning data into input data of a hidden layer through an input layer function and inputs the input data of the hidden layer into the hidden layer;

the hidden layer processes the input data received by the hidden layer through a hidden layer activation function to obtain output data of the hidden layer;

and acquiring input data of an output layer based on the output data of the hidden layer, inputting the input data of the output layer into an output layer activation function, and acquiring a prediction function model.

5. The system according to claim 1, wherein the input layer function model is specifically:wherein, Net_in(j) Representing input data corresponding to the jth neuron in the hidden layer, w_ijRepresents the weight value, x, between the ith neuron of the input layer and the jth neuron of the hidden layer_iRepresenting the raw learning data corresponding to the ith neuron of the input layer.

6. The system according to claim 6, wherein the hidden layer activation function is specifically: net_out(j)＝f(Net_in(j) Net) among them_out(j) And f (-) represents the output data corresponding to the jth neuron in the hidden layer, and f (-) represents the hidden layer activation function.

7. The system according to claim 6, wherein the obtaining input data of an output layer based on the output data of the hidden layer comprises:

processing the output data of the hidden layer through an output layer function to obtain the input data of an output layer; the output layer transfer functionWherein, O_in(k) Representing input data corresponding to the kth neuron in the output layer, w_jkRepresenting the weight, Net, between the jth neuron of the hidden layer and the kth neuron of the output layer_out(j) And representing output data corresponding to the jth neuron in the hidden layer.

8. An edge-computing-based synchronous clock maintenance system according to claim 1, characterized in that said synchronous clock maintenance means further comprises a crystal oscillator (8); the crystal oscillator (8) is a transistor oscillator and is used as a clock source to send a time signal when the GPS signal is lost.

9. An edge computing based synchronized clock maintenance system according to claim 8, characterized in that said environmental data comprises temperature, humidity and pressure inside and outside the crystal (8).

10. A method for maintaining a synchronous clock based on edge calculation is characterized by comprising the following steps:

receiving a time signal sent by a satellite, analyzing the time signal and outputting a GPS second pulse signal;

filtering the GPS pulse-per-second signal and outputting a filtered signal;

receiving the filtering signal and a crystal oscillator second pulse signal in real time, and calculating a time interval error between rising edges of the filtering signal and the crystal oscillator second pulse signal;

judging whether a phase-locked loop frequency division module receives a filtering signal or not, and if so, carrying out frequency division on the filtering signal to obtain a crystal oscillator second pulse signal;

if the filtering signal is received, performing edge calculation analysis on a crystal aging characteristic curve, environment data and the time interval error which are acquired in real time through a prediction function model, and outputting crystal oscillator frequency adjusting word information;

and predicting crystal oscillator feedback control information at the next moment based on the crystal oscillator frequency adjusting word information, and adjusting output voltage based on the crystal oscillator feedback control information to correct the crystal oscillator output frequency at the next moment so as to realize clock maintenance.

Technical Field

The invention relates to the technical field of power system monitoring and control, in particular to a synchronous clock maintaining system and method based on edge calculation.

Background

With the access of large-scale power electronic devices to the power grid, new challenges are brought to monitoring and control of the power system. The accuracy of signal time service is an important prerequisite for detection and control of the power system, so how to ensure the accuracy of synchronous time service becomes an important problem. In recent years, the problem of synchronous time service of voltage and current is effectively solved by the wide-area measurement method based on satellite time service, but random errors exist in satellite time service signals, and the requirement of long-term accurate maintenance of clock precision cannot be met after the satellite time service signals are lost. The crystal oscillator is an important component for maintaining a time service clock, has an important function of providing a clock source after losing a time service signal of a main station, and mostly adopts a constant-temperature crystal oscillator in order to ensure high stability of a clock system, but the constant-temperature crystal oscillator is easily influenced by factors such as external temperature, pressure intensity, self-aging and the like, is easy to generate frequency deviation in the operation process, and is difficult to maintain the characteristic of stable output frequency for a long time. Therefore, in the field of synchronous clock maintenance, it is necessary to improve the accuracy and stability of the output frequency of the oven controlled crystal oscillator by an appropriate method.

The clock maintaining technology of the existing constant-temperature crystal oscillator mostly depends on expert experience to correct the output of the crystal oscillator, and the mode needs a large amount of actual tests to observe and count the error of the crystal oscillator, so that the design cost is increased and the design period is prolonged. In addition, according to the current research situation at home and abroad, only one of the factors such as temperature, pressure and crystal aging is usually considered for correction, so that the stability and accuracy of the constant-temperature crystal oscillator cannot be comprehensively adjusted, and the time service accuracy of the master station and the clock maintenance after the clock signal of the master station is lost are influenced.

Disclosure of Invention

The invention aims to solve the technical problems that the existing method for correcting the crystal oscillator output mostly depends on expert experience and only considers one factor, the stability and the accuracy of the constant-temperature crystal oscillator cannot be comprehensively adjusted, the time service precision of a master station is low, and the clock maintaining effect after clock signals of the master station are lost is poor. Therefore, the invention provides a synchronous clock maintaining system and method based on edge calculation, which comprehensively consider factors such as temperature, humidity, pressure, crystal aging and the like, train the synchronous clock maintaining system through a BP neural network algorithm to update a prediction function model in real time, correct a crystal oscillator clock source in real time through a feedback crystal oscillator frequency adjusting word, ensure clock maintenance after a master station clock signal is lost, prolong the crystal oscillator maintaining stable time and improve the accuracy of the clock source.

The invention is realized by the following technical scheme:

a synchronous clock maintaining system based on edge calculation comprises a GPS receiving module, a filtering module, a time interval measuring module, a depth edge calculating module, an integrated environment measuring module, a crystal oscillator frequency predicting module, a signal conditioning circuit and a phase-locked loop frequency dividing module;

the GPS receiving module is used for receiving a time signal sent by a satellite, analyzing the time signal and outputting a GPS second pulse signal for synchronous time service;

the filtering module is used for receiving the GPS pulse-per-second signal, filtering the GPS pulse-per-second signal and outputting a filtering signal;

the phase-locked loop frequency division module is used for receiving the frequency output by the crystal oscillator and dividing the frequency to obtain a second pulse signal of the crystal oscillator;

the time interval measuring module is used for receiving the filtering signal and the crystal oscillator second pulse signal in real time, calculating a time interval error between rising edges of the filtering signal and the crystal oscillator second pulse signal and feeding the time interval error back to the depth edge calculating module;

the integrated environment measuring module is used for collecting environment data in real time and sending the environment data to the depth edge calculating module;

the depth edge calculation module is used for acquiring a crystal aging characteristic curve, the time interval error and the environmental data in real time and inputting the acquired crystal aging characteristic curve, the time interval error and the environmental data into a BP neural network for training to obtain a prediction function model; performing edge calculation analysis on the crystal aging characteristic curve, the time interval error and the environmental data acquired in real time through the prediction function model, and outputting crystal oscillator frequency adjusting word information;

the crystal oscillator frequency prediction module is used for receiving the crystal oscillator frequency adjusting word information fed back by the depth edge calculation module, predicting the crystal oscillator feedback control information at the next moment and sending the crystal oscillator feedback control information to the signal conditioning circuit;

and the signal conditioning circuit is used for adjusting the output voltage and correcting the output frequency of the crystal oscillator at the next moment through the received crystal oscillator feedback control information so as to realize clock maintenance.

Further, the depth edge calculation module includes a filtering unit and a BP neural network unit:

the filtering unit is used for acquiring the environmental data sent by the integrated environmental measurement module in real time, filtering the environmental data through a Kalman filtering algorithm to obtain processed data and sending the processed data to the BP neural network unit;

the BP neural network unit is used for taking the crystal aging characteristic curve, the time interval error and the processing data which are acquired in real time as original learning data, and inputting the original learning data into the BP neural network for learning to obtain a prediction function model; and outputting crystal oscillator frequency adjusting word information through the prediction function model.

Further, the filtering the environment data through a kalman filtering algorithm to obtain processed data includes:

calculating an estimated value of the current state quantity according to a forward estimation state variable equation, and calculating an estimated value of the current error covariance according to a forward estimation error covariance formula;

and updating the estimation value of the current state quantity and the estimation value of the current error covariance through Kalman gain to obtain processing data.

Further, the inputting the original learning data into a BP neural network for learning to obtain a prediction function model includes:

inputting the original learning data into an input layer of the BP neural network, wherein the input layer converts the original learning data into input data of a hidden layer through an input layer function and inputs the input data of the hidden layer into the hidden layer;

the hidden layer processes the input data received by the hidden layer through a hidden layer activation function to obtain output data of the hidden layer;

and acquiring input data of an output layer based on the output data of the hidden layer, inputting the input data of the output layer into an output layer activation function, and acquiring a prediction function model.

Further, the input layer function model is specifically:wherein, Net_in(j) Representing input data corresponding to the jth neuron in the hidden layer, w_ijRepresents the weight value, x, between the ith neuron of the input layer and the jth neuron of the hidden layer_iRepresenting the raw learning data corresponding to the ith neuron of the input layer.

Further, the hidden layer activation function is specifically: net_out(j)＝f(Net_in(j) Net) among them_out(j) And f (-) represents the output data corresponding to the jth neuron in the hidden layer, and f (-) represents the hidden layer activation function.

Further, the obtaining input data of an output layer based on the output data of the hidden layer includes:

processing the output data of the hidden layer through an output layer function to obtain the input data of an output layer; the output layer transfer function, wherein,wherein, O_in(k) To representInput data, w, corresponding to the kth neuron in the output layer_jkRepresenting the weight, Net, between the jth neuron of the hidden layer and the kth neuron of the output layer_out(j) And representing output data corresponding to the jth neuron in the hidden layer.

Further, the synchronous clock maintaining device further comprises a crystal oscillator; the crystal oscillator is a transistor oscillator and is used as a clock source to send a time signal when the GPS signal is lost.

Further, the environmental data includes temperature, humidity and pressure inside and outside the crystal oscillator.

A synchronous clock maintenance method based on edge calculation comprises the following steps:

receiving a time signal sent by a satellite, analyzing the time signal and outputting a GPS second pulse signal; filtering the GPS pulse-per-second signal and outputting a filtered signal;

receiving the filtering signal and a crystal oscillator second pulse signal in real time, and calculating a time interval error between rising edges of the filtering signal and the crystal oscillator second pulse signal;

judging whether a phase-locked loop frequency division module receives a filtering signal or not, and if so, carrying out frequency division on the filtering signal to obtain a crystal oscillator second pulse signal;

if the filtering signal is received, performing edge calculation analysis on a crystal aging characteristic curve, environment data and the time interval error which are acquired in real time through a prediction function model, and outputting crystal oscillator frequency adjusting word information;

and predicting crystal oscillator feedback control information at the next moment based on the crystal oscillator frequency adjusting word information, and adjusting output voltage based on the crystal oscillator feedback control information to correct the crystal oscillator output frequency at the next moment so as to realize clock maintenance.

According to the synchronous clock maintaining system and method based on edge calculation, BP neural network training is carried out through a comprehensive crystal aging characteristic curve and multiple environmental factors (temperature, humidity, pressure and the like) influencing crystal oscillator frequency errors, a prediction function model is updated in real time, and crystal oscillator frequency correction precision is improved; real-time analysis and learning are carried out on the crystal oscillator output frequency error by adopting depth edge calculation, a crystal oscillator frequency adjusting word is obtained through a prediction function model after GPS signals are lost, and the crystal oscillator output frequency is corrected in time to realize clock maintenance; and the internal and external environmental factor measuring sensors of the crystal oscillator are integrated, so that a training database is provided for depth edge calculation conveniently.

Drawings

The accompanying drawings, which are included to provide a further understanding of the embodiments of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the principles of the invention. In the drawings:

FIG. 1 is a schematic block diagram of a synchronous clock maintenance system based on edge calculation according to the present invention.

FIG. 2 is a functional block diagram of the depth edge calculation module of FIG. 1.

Fig. 3 is a schematic diagram of BP neural network training.

FIG. 4 is a flowchart of a method for maintaining a synchronous clock based on edge calculation according to the present invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to examples and accompanying drawings, and the exemplary embodiments and descriptions thereof are only used for explaining the present invention and are not meant to limit the present invention.

Example 1

As shown in fig. 1-3, a synchronous clock maintaining system based on edge calculation includes a GPS receiving module 1, a filtering module 2, a time interval measuring module 3, a depth edge calculating module 4, an integrated environment measuring module 5, a crystal oscillator frequency predicting module 6, a signal conditioning circuit 7, and a phase-locked loop frequency dividing module 9.

And the GPS receiving module 1 is used for receiving the time signal sent by the satellite, analyzing the time signal and outputting a GPS second pulse signal for synchronous time service.

And the filtering module 2 is used for receiving the GPS second pulse signal, filtering the GPS second pulse signal and outputting a filtering signal.

Specifically, since the analyzed GPS second pulse signal usually contains an interference pulse, which affects the system time transfer accuracy and cannot be directly used, the waveform of the analyzed GPS second pulse signal needs to be shaped by the filter module 2 and input to the time interval measurement module 3.

And the phase-locked loop frequency division module 9 is used for receiving the frequency output by the crystal oscillator 8 and dividing the frequency to obtain a second pulse signal of the crystal oscillator.

And the time interval measuring module 3 is used for receiving the filtering signal and the crystal oscillator second pulse signal in real time, calculating a time interval error between rising edges of the filtering signal and the crystal oscillator second pulse signal and feeding the time interval error back to the depth edge calculating module 4.

And the integrated environment measuring module 5 is used for acquiring environment data in real time and sending the environment data to the depth edge calculating module 4.

The environmental data in this embodiment refers to data obtained by detecting the external, internal, and external temperatures, humidities, and pressures of the crystal oscillator by the temperature sensor, the humidity sensor, and the pressure sensor.

The depth edge calculation module 4 is used for acquiring a crystal aging characteristic curve, a time interval error and environmental data in real time and inputting the acquired data into a BP neural network for training to obtain a prediction function model; and performing edge calculation analysis on the crystal aging characteristic curve, the time interval error and the environmental data acquired in real time through a prediction function model, and outputting crystal oscillator frequency adjusting word information.

And the crystal oscillator frequency prediction module 6 is used for receiving the crystal oscillator frequency adjusting word information fed back by the depth edge calculation module 4, predicting the crystal oscillator feedback control information at the next moment and sending the crystal oscillator feedback control information to the signal conditioning circuit 7.

And the signal conditioning circuit 7 is used for adjusting the output voltage and correcting the output frequency of the crystal oscillator at the next moment through the received crystal oscillator feedback control information so as to maintain the clock.

Further, the synchronous clock maintaining device further comprises a crystal oscillator 8. The crystal oscillator 8 in this embodiment is a transistor oscillator, and is configured to send a time signal as a clock source when the GPS signal is lost.

Further, the environmental data includes temperature, humidity, and pressure inside and outside the crystal oscillator 8.

Further, the depth edge calculation module 4 includes a filtering unit 41 and a BP neural network unit 42:

and the filtering unit 41 is configured to obtain the environmental data sent by the integrated environment measurement module 5 in real time, perform filtering processing on the environmental data through a kalman filtering algorithm, obtain processed data, and send the processed data to the BP neural network unit 42.

The BP neural network unit 42 is configured to use the crystal aging characteristic curve, the time interval error, and the processing data obtained in real time as original learning data, and input the original learning data into the BP neural network for learning to obtain a prediction function model; and outputting crystal oscillator frequency adjusting word information through a prediction function model.

Further, filtering the environment data through a kalman filtering algorithm to obtain processed data, including:

and calculating the estimation value of the current state quantity according to a forward estimation state variable equation, and calculating the estimation value of the current error covariance according to a forward estimation error covariance formula. And updating the estimation value of the current state quantity and the estimation value of the current error covariance through Kalman gain to obtain processing data.

Specifically, the Kalman filtering algorithm first derives the state variable equation from the forward estimateCalculating the estimated value of the current state quantity and estimating the error covariance according to the forward directionCalculating an estimated value of the current error covariance; then passes the Kalman gainUpdating the estimation value of the current state quantity and the estimation value of the current error covariance to obtain the updated state quantityAnd updated error covarianceWherein the content of the first and second substances,andthe estimated values of the posterior states at the time points of (k-1) and k, respectively;an estimated value representing the prior state at the time k is obtained by the optimal estimation at the last time (k-1); p_K-1And P_kRespectively representing the posteriori estimated covariance of (k-1) and k time;representing the prior estimated covariance at time k; h is the state variable to measurement transformation matrix; z_kRepresenting the measured value; k_kRepresenting a filter gain matrix; a represents a state transition matrix; q represents process excitation noise covariance; r represents the measurement noise covariance; b denotes an input state transition matrix.

Combining the a priori estimates with the new measurements to construct an improved a posteriori estimate is achieved by the kalman gain equations, updated state quantities, and updated error covariances described above.

Further, inputting the original learning data into a BP neural network for learning to obtain a prediction function model, including:

and inputting the original learning data into an input layer of the BP neural network, wherein the input layer converts the original learning data into input data of a hidden layer through an input layer function and inputs the input data into the hidden layer.

And the hidden layer processes the input data received by the hidden layer through a hidden layer activation function to obtain the output data of the hidden layer.

And acquiring input data of the output layer based on the output data of the hidden layer, inputting the input data of the output layer into the output layer activation function, and acquiring a prediction function model.

Further, the input layer function model is specifically:wherein, Net_in(j) Representing input data corresponding to the jth neuron in the hidden layer, w_ijRepresents the weight value, x, between the ith neuron of the input layer and the jth neuron of the hidden layer_iRepresenting the raw learning data corresponding to the ith neuron of the input layer. M is the number of neurons in the input layer.

Further, the hidden layer activation function is specifically: net o_ut(j)＝f(Net_in(j) Net) among them_out(j) And f (-) represents the output data corresponding to the jth neuron in the hidden layer, and f (-) represents the hidden layer activation function.

Further, acquiring input data of the output layer based on the output data of the hidden layer includes:

processing the output data of the hidden layer through an output layer function to obtain the input data of the output layer; output layer transfer functionWherein, O_in(k) Representing input data corresponding to the kth neuron in the output layer, w_jkRepresenting the weight, Net, between the jth neuron of the hidden layer and the kth neuron of the output layer_out(j) And representing output data corresponding to the jth neuron in the hidden layer. Q is the number of neurons in the hidden layer, and the more the number of layers is, the higher the fitting precision is, but the problems of too low training speed and overfitting can be caused

Example 2

As shown in fig. 4, the present embodiment is different from embodiment 1 in that a method for maintaining a synchronous clock based on edge calculation includes:

and receiving a time signal sent by the satellite, analyzing the time signal and outputting a GPS second pulse signal.

And filtering the GPS pulse per second signal and outputting a filtered signal.

And receiving the filtering signal and the crystal oscillator second pulse signal in real time, and calculating the time interval error between the rising edges of the filtering signal and the crystal oscillator second pulse signal.

And judging whether the phase-locked loop frequency division module receives the filtering signal, and if so, carrying out frequency division on the filtering signal to obtain a crystal oscillator second pulse signal.

And if the filtering signal is received, performing edge calculation analysis on the crystal aging characteristic curve, the environmental data and the time interval error which are acquired in real time through a prediction function model, and outputting crystal oscillator frequency adjusting word information.

And predicting crystal oscillator feedback control information at the next moment based on the crystal oscillator frequency adjusting word information, and adjusting the output voltage based on the crystal oscillator feedback control information to correct the crystal oscillator output frequency at the next moment so as to realize clock maintenance.

It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions.

The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are merely exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.